@Article{info:doi/10.2196/23205,
author="Farsi, Deema",
title="Social Media and Health Care, Part I: Literature Review of Social Media Use by Health Care Providers",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e23205",
keywords="social media",
keywords="social networking",
keywords="internet",
keywords="health care",
keywords="COVID-19",
keywords="research activity",
keywords="medical education",
keywords="telemedicine",
keywords="mobile phone",
abstract="Background: As the world continues to advance technologically, social media (SM) is becoming an essential part of billions of people's lives worldwide and is affecting almost every industry imaginable. As the world is becoming more digitally oriented, the health care industry is increasingly visualizing SM as an important channel for health care promotion, employment, recruiting new patients, marketing for health care providers (HCPs), building a better brand name, etc. HCPs are bound to ethical principles toward their colleagues, patients, and the public in the digital world as much as in the real world. Objective: This review aims to shed light on SM use worldwide and to discuss how it has been used as an essential tool in the health care industry from the perspective of HCPs. Methods: A literature review was conducted between March and April 2020 using MEDLINE, PubMed, Google Scholar, and Web of Science for all English-language medical studies that were published since 2007 and discussed SM use in any form for health care. Studies that were not in English, whose full text was not accessible, or that investigated patients' perspectives were excluded from this part, as were reviews pertaining to ethical and legal considerations in SM use. Results: The initial search yielded 83 studies. More studies were included from article references, and a total of 158 studies were reviewed. SM uses were best categorized as health promotion, career development or practice promotion, recruitment, professional networking or destressing, medical education, telemedicine, scientific research, influencing health behavior, and public health care issues. Conclusions: Multidimensional health care, including the pairing of health care with SM and other forms of communication, has been shown to be very successful. Striking the right balance between digital and traditional health care is important. ",
doi="10.2196/23205",
url="https://www.jmir.org/2021/4/e23205",
url="http://www.ncbi.nlm.nih.gov/pubmed/33664014"
}


@Article{info:doi/10.2196/27275,
author="Borges do Nascimento, J{\'u}nior Israel
and Marcolino, Soriano Milena
and Abdulazeem, Mohamed Hebatullah
and Weerasekara, Ishanka
and Azzopardi-Muscat, Natasha
and Gon{\c{c}}alves, Andr{\'e} Marcos
and Novillo-Ortiz, David",
title="Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e27275",
keywords="public health",
keywords="big data",
keywords="health status",
keywords="evidence-based medicine",
keywords="big data analytics",
keywords="secondary data analysis",
keywords="machine learning",
keywords="systematic review",
keywords="overview",
keywords="World Health Organization",
abstract="Background: Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective: The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2--related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health. Methods: Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results: The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. ``Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease'' and ``suicide mortality rate'' were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as ``critically low'' for 25 reviews, as ``low'' for 7 reviews, and as ``moderate'' for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions: Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=214048 ",
doi="10.2196/27275",
url="https://www.jmir.org/2021/4/e27275",
url="http://www.ncbi.nlm.nih.gov/pubmed/33847586"
}


@Article{info:doi/10.2196/22477,
author="Meiksin, Rebecca
and Melendez-Torres, J. G.
and Falconer, Jane
and Witzel, Charles T.
and Weatherburn, Peter
and Bonell, Chris",
title="eHealth Interventions to Address Sexual Health, Substance Use, and Mental Health Among Men Who Have Sex With Men: Systematic Review and Synthesis of Process Evaluations",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="23",
volume="23",
number="4",
pages="e22477",
keywords="eHealth",
keywords="digital health",
keywords="men who have sex with men",
keywords="sexual health",
keywords="HIV",
keywords="STI",
keywords="substance use",
keywords="mental health",
keywords="systematic review",
keywords="process evaluation",
abstract="Background: Men who have sex with men (MSM) face disproportionate risks concerning HIV and other sexually transmitted infections, substance use, and mental health. These outcomes constitute an interacting syndemic among MSM; interventions addressing all 3 together could have multiplicative effects. eHealth interventions can be accessed privately, and evidence from general populations suggests these can effectively address all 3 health outcomes. However, it is unclear how useable, accessible, or acceptable eHealth interventions are for MSM and what factors affect this. Objective: We undertook a systematic review of eHealth interventions addressing sexual risk, substance use, and common mental illnesses among MSM and synthesized evidence from process evaluations. Methods: We searched 19 databases, 3 trials registers, OpenGrey, and Google, and supplemented this by reference checks and requests to experts. Eligible reports were those that discussed eHealth interventions offering ongoing support to MSM aiming to prevent sexual risk, substance use, anxiety or depression; and assessed how intervention delivery or receipt varied with characteristics of interventions, providers, participants, or context. Reviewers screened citations on titles, abstracts, and then full text. Reviewers assessed quality of eligible studies, and extracted data on intervention, study characteristics, and process evaluation findings. The analysis used thematic synthesis. Results: A total of 12 reports, addressing 10 studies of 8 interventions, were eligible for process synthesis. Most addressed sexual risk alone or with other outcomes. Studies were assessed as medium and high reliability (reflecting the trustworthiness of overall findings) but tended to lack depth and breadth in terms of the process issues explored. Intervention acceptability was enhanced by ease of use; privacy protection; use of diverse media; opportunities for self-reflection and to gain knowledge and skills; and content that was clear, interactive, tailored, reflective of MSM's experiences, and affirming of sexual-minority identity. Technical issues and interventions that were too long detracted from acceptability. Some evidence suggested that acceptability varied by race or ethnicity and educational level; findings on variation by socioeconomic status were mixed. No studies explored how intervention delivery or receipt varied by provider characteristics. Conclusions: Findings suggest that eHealth interventions targeting sexual risk, substance use, and mental health are acceptable for MSM across sociodemographic groups. We identified the factors shaping MSM's receipt of such interventions, highlighting the importance of tailored content reflecting MSM's experiences and of language affirming sexual-minority identities. Intervention developers can draw on these findings to increase the usability and acceptability of integrated eHealth interventions to address the syndemic of sexual risk, substance use, and mental ill health among MSM. Evaluators of these interventions can draw on our findings to plan evaluations that explore the factors shaping usability and acceptability. ",
doi="10.2196/22477",
url="https://www.jmir.org/2021/4/e22477",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890855"
}


@Article{info:doi/10.2196/24982,
author="Eberle, Claudia
and Loehnert, Maxine
and Stichling, Stefanie",
title="Clinical Effectiveness of Different Technologies for Diabetes in Pregnancy: Systematic Literature Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e24982",
keywords="diabetes technologies",
keywords="diabetes management",
keywords="pregnancy",
keywords="digital health",
keywords="eHealth",
keywords="systematic review",
abstract="Background: Hyperglycemia in pregnancy occurs worldwide and is closely associated with health issues in women and their offspring, such as pregnancy and birth complications, respectively, as well as comorbidities, such as metabolic and cardiovascular diseases. To optimize the management of diabetic pregnancies, sustainable strategies are urgently needed. Investigation of constantly evolving technologies for diabetes that help to manage pregnancy and health is required. Objective: We aimed to conduct a systematic review to assess the clinical effectiveness of technologies for diabetes in pregnancy. Methods: Relevant databases including MEDLINE (PubMed), Cochrane Library, Embase, CINAHL, and Web of Science Core Collection were searched in September 2020 for clinical studies (2008-2020). Findings were organized by type of diabetes, type of technology, and outcomes (glycemic control, pregnancy- and birth-related outcomes, and neonatal outcomes). Study quality was assessed using Effective Public Health Practice Project criteria. Results: We identified 15 randomized controlled trials, 3 randomized crossover trials, 2 cohort studies, and 2 controlled clinical trials. Overall, 9 studies focused on type 1 diabetes, 0 studies focused on gestational diabetes, and 3 studies focused on both type 1 diabetes and type 2 diabetes. We found that 9 studies were strong quality, 11 were moderate quality, and 2 were weak quality. Technologies for diabetes seemed to have particularly positive effects on glycemic control in all types of diabetes, shown by some strong and moderate quality studies. Positive trends in pregnancy-related, birth-related, and neonatal outcomes were observed. Conclusions: Technologies have the potential to effectively improve the management of diabetes during pregnancy. Further research on the clinical effectiveness of these technologies is needed, especially in pregnant women with type 2 diabetes. ",
doi="10.2196/24982",
url="https://www.jmir.org/2021/4/e24982",
url="http://www.ncbi.nlm.nih.gov/pubmed/33908894"
}


@Article{info:doi/10.2196/22215,
author="Gaspar, Martins Andr{\'e}a G.
and Lap{\~a}o, Velez Lu{\'i}s",
title="eHealth for Addressing Balance Disorders in the Elderly: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e22215",
keywords="balance disorders",
keywords="falls",
keywords="elderly",
keywords="eHealth",
keywords="telemedicine",
abstract="Background: The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective: The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods: A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results: Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions: eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed. ",
doi="10.2196/22215",
url="https://www.jmir.org/2021/4/e22215",
url="http://www.ncbi.nlm.nih.gov/pubmed/33908890"
}


@Article{info:doi/10.2196/26877,
author="Ruhi, Umar
and Chugh, Ritesh",
title="Utility, Value, and Benefits of Contemporary Personal Health Records: Integrative Review and Conceptual Synthesis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e26877",
keywords="electronic personal health records",
keywords="PHR",
keywords="functionality synopsis",
keywords="value analysis",
keywords="consumer health informatics",
abstract="Background: Contemporary personal health record (PHR) technologies offer a useful platform for individuals to maintain a lifelong record of personally reported and clinically sourced data from various points of medical care. Objective: This paper presents an integrative review and synthesis of the extant literature on PHRs. This review draws upon multiple lenses of analysis and deliberates value perspectives of PHRs at the product, consumer, and industry levels. Methods: Academic databases were searched using multiple keywords related to PHRs for the years 2001-2020. Three research questions were formulated and used as selection criteria in our review of the extant literature relevant to our study. Results: We offer a high-level functional utility model of PHR features and functions. We also conceptualize a consumer value framework of PHRs, highlighting the applications of these technologies across various health care delivery activities. Finally, we provide a summary of the benefits of PHRs for various health care constituents, including consumers, providers, payors, and public health agencies. Conclusions: PHR products offer a myriad of content-, connectivity-, and collaboration-based features and functions for their users. Although consumers benefit from the tools provided by PHR technologies, their overall value extends across the constituents of the health care delivery chain. Despite advances in technology, our literature review identifies a shortfall in the research addressing consumer value enabled by PHR tools. In addition to scholars and researchers, our literature review and proposed framework may be especially helpful for value analysis committees in the health care sector that are commissioned for the appraisal of innovative health information technologies such as PHRs. ",
doi="10.2196/26877",
url="https://www.jmir.org/2021/4/e26877",
url="http://www.ncbi.nlm.nih.gov/pubmed/33866308"
}


@Article{info:doi/10.2196/26939,
author="McCall, C. Hugh
and Hadjistavropoulos, D. Heather
and Sundstr{\"o}m, Francis Christopher Richard",
title="Exploring the Role of Persuasive Design in Unguided Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Among Adults: Systematic Review, Meta-analysis, and Meta-regression",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e26939",
keywords="ICBT",
keywords="internet",
keywords="depression",
keywords="anxiety",
keywords="persuasive design",
keywords="eHealth",
abstract="Background: Internet-delivered cognitive behavioral therapy (ICBT) is an effective treatment that can overcome barriers to mental health care. Various research groups have suggested that unguided ICBT (ie, ICBT without therapist support) and other eHealth interventions can be designed to enhance user engagement and thus outcomes. The persuasive systems design framework captures most design recommendations for eHealth interventions, but there is little empirical evidence that persuasive design is related to clinical outcomes in unguided ICBT. Objective: This study aims to provide an updated meta-analysis of randomized controlled trials of unguided ICBT for depression and anxiety, describe the frequency with which various persuasive design principles are used in such interventions, and use meta-regression to explore whether a greater number of persuasive design elements predicts efficacy in unguided ICBT for depression and anxiety. Methods: We conducted a systematic review of 5 databases to identify randomized controlled trials of unguided ICBT for depression and anxiety. We conducted separate random effects meta-analyses and separate meta-regressions for depression and anxiety interventions. Each meta-regression included 2 steps. The first step included, as a predictor, whether each intervention was transdiagnostic. For the meta-regression of ICBT for depression, the first step also included the type of control condition. The number of persuasive design principles identified for each intervention was added as a predictor in the second step to reveal the additional variance in effect sizes explained by persuasive design. Results: Of the 4471 articles we identified in our search, 46 (1.03\%) were eligible for inclusion in our analyses. Our meta-analyses showed effect sizes (Hedges g) ranging from 0.22 to 0.31 for depression interventions, depending on the measures taken to account for bias in the results. We found a mean effect size of 0.45 (95\% CI 0.33-0.56) for anxiety interventions, with no evidence that the results were inflated by bias. Included interventions were identified as using between 1 and 13 persuasive design principles, with an average of 4.95 (SD 2.85). The meta-regressions showed that a greater number of persuasive design principles predicted greater efficacy in ICBT for depression (R2 change=0.27; B=0.04; P=.02) but not anxiety (R2 change=0.05; B=0.03; P=.17). Conclusions: These findings show wide variability in the use of persuasive design in unguided ICBT for depression and anxiety and provide preliminary support for the proposition that more persuasively designed interventions are more efficacious, at least in the treatment of depression. Further research is needed to clarify the role of persuasive design in ICBT. ",
doi="10.2196/26939",
url="https://www.jmir.org/2021/4/e26939",
url="http://www.ncbi.nlm.nih.gov/pubmed/33913811"
}


@Article{info:doi/10.2196/25140,
author="Cliffe, Bethany
and Tingley, Jessica
and Greenhalgh, Isobel
and Stallard, Paul",
title="mHealth Interventions for Self-Harm: Scoping Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e25140",
keywords="mHealth",
keywords="self-harm",
keywords="digital interventions",
keywords="self-injury",
keywords="NSSI",
keywords="mobile phone",
abstract="Background: Self-harm is a growing issue with increasing prevalence rates; however, individuals who self-harm do not often receive treatment. Mobile health (mHealth) interventions are a possible solution to some of the barriers that individuals face when seeking support, and they have also been found to be effective in improving mental health. Thus far, reviews of mHealth interventions for self-harm have been limited by study type. Therefore, we determined that a broader scoping review will provide a more exhaustive understanding of mHealth interventions for self-harm. Objective: This scoping review aims to identify mHealth interventions for self-harm within the literature, understand the types and features of interventions that have been developed and evaluated, highlight research findings around mHealth interventions for self-harm, and determine what outcomes are typically used to assess the efficacy of interventions. Methods: A search was conducted using Embase, PubMed, PsycINFO, PsycEXTRA, Web of Science, and the Cochrane Library. Studies were included if they described an mHealth intervention designed to have a direct (ie, if the intervention was designed for self-harm or for people who self-harm) or indirect (ie, if self-harm was measured as an outcome) treatment effect and if the paper was available in English. There were no exclusion criteria based on the study design. Results: A total of 36 papers were included in the review, and most of them were randomized controlled trials published within the last 4 years. The interventions were mostly smartphone apps and calling or texting services, with 62\% (21/34) having underlying therapeutic models to inform the intervention content. They were generally shown to be promising and appealing, but only 5 were widely available for use. Outcomes focused on a reduction of self-harm and suicidality, mood, and the users' experiences of the intervention. Samples were typically nondiverse, and there was limited variety in the study designs and in the measurements of self-harm recovery. Conclusions: Promising and appealing mHealth interventions have been developed but are not widely available. Research could benefit from greater diversity as well as a broader and more nuanced understanding of recovery from self-harm. ",
doi="10.2196/25140",
url="https://www.jmir.org/2021/4/e25140",
url="http://www.ncbi.nlm.nih.gov/pubmed/33929329"
}


@Article{info:doi/10.2196/26699,
author="M{\"o}nninghoff, Annette
and Kramer, Niklas Jan
and Hess, Jan Alexander
and Ismailova, Kamila
and Teepe, W. Gisbert
and Tudor Car, Lorainne
and M{\"u}ller-Riemenschneider, Falk
and Kowatsch, Tobias",
title="Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e26699",
keywords="mHealth",
keywords="physical activity",
keywords="systematic review",
keywords="meta-analysis",
keywords="mobile phone",
abstract="Background: Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood. Objective: The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators. Methods: We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ?6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool. Results: Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99\% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95\% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95\% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95\% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95\% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95\% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95\% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95\% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95\% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95\% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3\% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence. Conclusions: mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects. ",
doi="10.2196/26699",
url="https://www.jmir.org/2021/4/e26699",
url="http://www.ncbi.nlm.nih.gov/pubmed/33811021"
}


@Article{info:doi/10.2196/23914,
author="Kazevman, Gill
and Mercado, Marck
and Hulme, Jennifer
and Somers, Andrea",
title="Prescribing Phones to Address Health Equity Needs in the COVID-19 Era: The PHONE-CONNECT Program",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="6",
volume="23",
number="4",
pages="e23914",
keywords="digital health equity",
keywords="health inequity",
keywords="digital determinants of health",
keywords="emergency medicine",
keywords="COVID-19",
keywords="public health",
keywords="health policy",
keywords="primary care",
keywords="cell phone",
doi="10.2196/23914",
url="https://www.jmir.org/2021/4/e23914",
url="http://www.ncbi.nlm.nih.gov/pubmed/33760753"
}


@Article{info:doi/10.2196/21726,
author="Davies, R. Alisha
and Honeyman, Matthew
and Gann, Bob",
title="Addressing the Digital Inverse Care Law in the Time of COVID-19: Potential for Digital Technology to Exacerbate or Mitigate Health Inequalities",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="7",
volume="23",
number="4",
pages="e21726",
keywords="COVID-19",
keywords="digital divide",
keywords="digital exclusion",
keywords="digital health",
keywords="health inequality",
keywords="population health",
doi="10.2196/21726",
url="https://www.jmir.org/2021/4/e21726",
url="http://www.ncbi.nlm.nih.gov/pubmed/33735096"
}


@Article{info:doi/10.2196/23635,
author="Li, Yaning
and Ye, Hongqiang
and Ye, Fan
and Liu, Yunsong
and Lv, Longwei
and Zhang, Ping
and Zhang, Xiao
and Zhou, Yongsheng",
title="The Current Situation and Future Prospects of Simulators in Dental Education",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="8",
volume="23",
number="4",
pages="e23635",
keywords="dental simulator",
keywords="dental education",
keywords="virtual reality",
doi="10.2196/23635",
url="https://www.jmir.org/2021/4/e23635",
url="http://www.ncbi.nlm.nih.gov/pubmed/33830059"
}


@Article{info:doi/10.2196/24179,
author="Steinkamp, Jackson
and Kantrowitz, Jacob
and Sharma, Abhinav
and Bala, Wasif",
title="Beyond Notes: Why It Is Time to Abandon an Outdated Documentation Paradigm",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e24179",
keywords="electronic medical records",
keywords="health informatics",
keywords="information chaos",
keywords="medical documentation",
keywords="clinicians",
keywords="medical notes",
keywords="electronic medical notes",
keywords="medical team",
doi="10.2196/24179",
url="https://www.jmir.org/2021/4/e24179",
url="http://www.ncbi.nlm.nih.gov/pubmed/33877053"
}


@Article{info:doi/10.2196/25916,
author="Logan, E. Deirdre
and Simons, E. Laura
and Caruso, J. Thomas
and Gold, I. Jeffrey
and Greenleaf, Walter
and Griffin, Anya
and King, D. Christopher
and Menendez, Maria
and Olbrecht, A. Vanessa
and Rodriguez, Samuel
and Silvia, Megan
and Stinson, N. Jennifer
and Wang, Ellen
and Williams, E. Sara
and Wilson, Luke",
title="Leveraging Virtual Reality and Augmented Reality to Combat Chronic Pain in Youth: Position Paper From the Interdisciplinary Network on Virtual and Augmented Technologies for Pain Management",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e25916",
keywords="virtual reality",
keywords="pediatric",
keywords="chronic pain",
abstract="Background: Virtual reality (VR) and augmented reality (AR) interventions are emerging as promising tools in the treatment of pediatric chronic pain conditions. However, in this young field, there is little consensus to guide the process of engaging in the development and evaluation of targeted VR-based interventions. Objective: The INOVATE-Pain (Interdisciplinary Network on Virtual and Augmented Technologies for Pain management) consortium aims to advance the field of VR for pediatric chronic pain rehabilitation by providing guidance for best practices in the design, evaluation, and dissemination of VR-based interventions targeting this population. Methods: An interdisciplinary meeting of 16 academics, clinicians, industry partners, and philanthropy partners was held in January 2020. Results: Reviewing the state of the field, the consortium identified important directions for research-driven innovation in VR and AR clinical care, highlighted key opportunities and challenges facing the field, and established a consensus on best methodological practices to adopt in future efforts to advance the research and practice of VR and AR in pediatric pain. The consortium also identified important next steps to undertake to continue to advance the work in this promising new area of digital health pain interventions. Conclusions: To realize the promise of this realm of innovation, key ingredients for success include productive partnerships among industry, academic, and clinical stakeholders; a uniform set of outcome domains and measures for standardized evaluation; and widespread access to the latest opportunities, tools, and resources. The INOVATE-Pain collaborative hopes to promote the creation, rigorous yet efficient evaluation, and dissemination of innovative VR-based interventions to reduce pain and improve quality of life for children. ",
doi="10.2196/25916",
url="https://www.jmir.org/2021/4/e25916",
url="http://www.ncbi.nlm.nih.gov/pubmed/33667177"
}


@Article{info:doi/10.2196/25502,
author="Lalande, Kathleen
and Greenman, S. Paul
and Bouchard, Karen
and Johnson, M. Susan
and Tulloch, Heather",
title="The Healing Hearts Together Randomized Controlled Trial and the COVID-19 Pandemic: A Tutorial for Transitioning From an In-Person to a Web-Based Intervention",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="6",
volume="23",
number="4",
pages="e25502",
keywords="web-based intervention",
keywords="internet-based intervention",
keywords="randomized controlled trial",
keywords="COVID-19",
keywords="research",
keywords="tutorial",
keywords="digital medicine",
keywords="behavioral medicine",
keywords="telehealth",
keywords="telemedicine",
keywords="cardiovascular rehabilitation",
doi="10.2196/25502",
url="https://www.jmir.org/2021/4/e25502",
url="http://www.ncbi.nlm.nih.gov/pubmed/33729984"
}


@Article{info:doi/10.2196/19439,
author="Wadensten, Towe
and Nystr{\"o}m, Emma
and Franz{\'e}n, Karin
and Lindam, Anna
and Wasteson, Elisabet
and Samuelsson, Eva",
title="A Mobile App for Self-management of Urgency and Mixed Urinary Incontinence in Women: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e19439",
keywords="eHealth",
keywords="mHealth",
keywords="urinary incontinence",
keywords="urgency urinary incontinence",
keywords="mixed urinary incontinence",
keywords="self-management",
keywords="mobile app",
keywords="smartphone app",
keywords="women",
abstract="Background: Many women experience urgency (UUI) and mixed (MUI) urinary incontinence but commonly hesitate to seek care. Treatment access and self-management for these conditions can be supported through eHealth approaches. Objective: This study aimed to investigate the efficacy of the mobile app T{\"a}t II for self-management of UUI and MUI in women. Methods: This randomized controlled trial included women ?18 years old with UUI or MUI and ?2 leakages per week. Those with red-flag symptoms were excluded. Participants were recruited via analog and digital advertisements and screened for initial selection through a web-based questionnaire. Data were collected using another questionnaire and a 2-day bladder diary. A telephone interview confirmed the symptom diagnosis. Participants were randomized (1:1) to receive access to a treatment app (including pelvic floor muscle training, bladder training, psychoeducation, lifestyle advice, tailored advice, exercise log, reinforcement messages, and reminders) or an information app (control group), with no external treatment guidance provided. The primary outcome was incontinence symptoms at the 15-week follow-up, measured using the International Consultation on Incontinence Questionnaire (ICIQ)?Urinary Incontinence Short Form (ICIQ-UI SF). Urgency symptoms were assessed using the ICIQ?Overactive Bladder Module (ICIQ-OAB) and quality of life using the ICIQ?Lower Urinary Tract Symptoms Quality of Life Module (ICIQ-LUTSqol). Incontinence episode frequency (IEF) was calculated per bladder diary entries. Improvement was measured using the Patient's Global Impression of Improvement. All outcomes were self-reported. Cure was defined as no leakages per the bladder diary. Intention-to-treat analysis was performed. Results: Between April 2017 and March 2018, 123 women (mean age 58.3, SD 9.6 years) were randomized to the treatment (n=60, 2 lost to follow-up) or information (n=63) group. Of these, 35 (28\%) women had UUI, and 88 (72\%) had MUI. Mean ICIQ-UI SF score at follow-up was lower in the treatment group than in the information group (estimated difference ?3.1, 95\% CI ?4.8 to ?1.3). The estimated between-group difference was ?1.8 (95\% CI ?2.8 to ?0.99) for mean ICIQ-OAB score and ?6.3 (95\% CI ?10.5 to ?2.1) for the mean ICIQ-LUTSqol score at follow-up. IEF reduction from baseline to follow-up was greater in the treatment group (?10.5, IQR ?17.5 to ?3.5) than in the information group (P<.001). Improvement was reported by 87\% (52/60) of treatment group participants and by 30\% (19/63) of information group participants. The cure rate was 32\% in the treatment group, and 6\% in the information group (odds ratio 5.4, 95\% CI 1.9-15.6; P=.002). About 67\% (40/60) of the treatment group participants used the app more than thrice a week. Conclusions: The treatment app was effective for improving urgency and mixed incontinence in women. When self-management is appropriate, this app may be a good alternative to pharmacological treatment or other conservative management, thus increasing access to care. Trial Registration: ClinicalTrials.gov NCT03097549; https://clinicaltrials.gov/ct2/show/NCT03097549 ",
doi="10.2196/19439",
url="https://www.jmir.org/2021/4/e19439",
url="http://www.ncbi.nlm.nih.gov/pubmed/33818395"
}


@Article{info:doi/10.2196/24260,
author="Bailey, Eleanor
and Robinson, Jo
and Alvarez-Jimenez, Mario
and Nedeljkovic, Maja
and Valentine, Lee
and Bendall, Sarah
and D'Alfonso, Simon
and Gilbertson, Tamsyn
and McKechnie, Ben
and Rice, Simon",
title="Moderated Online Social Therapy for Young People With Active Suicidal Ideation: Qualitative Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e24260",
keywords="suicide",
keywords="youth",
keywords="social media",
keywords="internet-based intervention",
abstract="Background: Web-based interventions are a promising approach to support youth at risk of suicide, and those incorporating peer-to-peer social networking may have the added potential to target interpersonal states of perceived burdensomeness and thwarted belongingness. Owing to feasibility and safety concerns, including fear of contagion, this had not been tested until recently. In 2018, we conducted a pilot evaluation to test the feasibility, safety, and acceptability of a Moderated Online Social Therapy intervention, called Affinity, with a sample of young people with active suicidal ideation. Objective: The aim of this study is to report qualitative data collected from study participants regarding their experience of the web-based social network and the consequent safety features. Methods: Affinity is a closed website incorporating 3 key components: therapeutic content delivered via comics, peer-to-peer social networking, and moderation by peers and clinicians. Semistructured interviews were conducted with 17 young people who participated in the pilot study after 8 weeks of exposure to the intervention. Interview data from 2 young people who did not use Affinity were excluded from the analysis. The interviews were analyzed using thematic analysis, with the frequency of responses characterized using the consensual qualitative research method. The results are reported in accordance with the Consolidated Criteria for Reporting Qualitative Research checklist. Results: A total of 4 overarching themes were identified: a safe and supportive environment, the importance of mutual experiences, difficulty engaging and connecting, and the pros and cons of banning discussions about suicide. Interestingly, although Affinity was perceived to be safe and free of judgment, concerns about negative evaluation and triggering others were significant barriers to posting on the social network. Participants generally supported the banning of conversations about suicide, although for some this was perceived to reinforce stigma or was associated with frustration and distress. Conclusions: The results not only support the safety and potential therapeutic benefit of the social networking aspect of Affinity but also highlight several implementation challenges. There is a need to carefully balance the need for stringent safety and design features while ensuring that the potential for therapeutic benefit is maximized. ",
doi="10.2196/24260",
url="https://www.jmir.org/2021/4/e24260",
url="http://www.ncbi.nlm.nih.gov/pubmed/33818392"
}


@Article{info:doi/10.2196/21466,
author="Biliunaite, Ieva
and Kazlauskas, Evaldas
and Sanderman, Robbert
and Truskauskaite-Kuneviciene, Inga
and Dumarkaite, Austeja
and Andersson, Gerhard",
title="Internet-Based Cognitive Behavioral Therapy for Informal Caregivers: Randomized Controlled Pilot Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="7",
volume="23",
number="4",
pages="e21466",
keywords="caregiver burden",
keywords="informal caregivers",
keywords="internet intervention",
keywords="cognitive behavioral therapy",
keywords="eHealth",
keywords="mHealth",
abstract="Background: Caregiving for a family member can result in reduced well-being for the caregiver. Internet-delivered cognitive behavioral therapy (ICBT) may be one way to support this population. This is especially the case for caregivers in countries with limited resources, but high demand for psychological services. Objective: In this study we evaluated the effects of a therapist-guided 8-week-long ICBT intervention for informal caregivers. Methods: In total, 63 participants were recruited online and randomized either to the intervention or to the wait-list control group. The main study outcome was the Caregiver Burden Inventory (CBI). Secondary outcomes included measures of caregiver depression, anxiety, stress, and quality of life. Results: Moderate between-group effect sizes were observed for the CBI measure, in favor of the intervention group, with a Cohen d=--0.70 for the intention-to-treat analysis. Analyses of the subscales of the CBI showed significant reductions on the subscales of Development and Physical Health. Moderate reductions were found for depression and anxiety scores as indicated by the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scores. Large between-group effects were observed for reduction in stress and increase in quality of life as indicated by the Perceived Stress Scale-14 (PSS-14), The Brunnsviken Brief Quality of Life Scale (BBQ), and The World Health Organization-Five Well-Being Index (WHO-5). In addition, participants experienced little to no difficulty in using the program and were mostly satisfied with the intervention's platform and the choice of content. Conclusions: This is the first internet intervention study for informal caregivers in Lithuania. The results suggest that therapist-guided ICBT can be effective in reducing caregiver burden, anxiety, depression, stress, and improving quality of life. Trial Registration: ClinicalTrials.gov NCT04052724; https://clinicaltrials.gov/ct2/show/NCT04052724 ",
doi="10.2196/21466",
url="https://www.jmir.org/2021/4/e21466",
url="http://www.ncbi.nlm.nih.gov/pubmed/33825687"
}


@Article{info:doi/10.2196/22399,
author="Seljelid, Berit
and Varsi, Cecilie
and Solberg Nes, Lise
and {\O}ystese, Astrid Kristin
and B{\o}r{\o}sund, Elin",
title="A Digital Patient-Provider Communication Intervention (InvolveMe): Qualitative Study on the Implementation Preparation Based on Identified Facilitators and Barriers",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="8",
volume="23",
number="4",
pages="e22399",
keywords="eHealth",
keywords="digital communication",
keywords="secure messages",
keywords="digital symptom assessment",
keywords="implementation",
keywords="tailoring",
keywords="Consolidated Framework for Implementation Research",
keywords="CFIR",
keywords="facilitators",
keywords="barriers",
keywords="stakeholders",
abstract="Background: Chronic health conditions are affecting an increasing number of individuals, who experience various symptoms that decrease their quality of life. Digital communication interventions that enable patients to report their symptoms have been shown to positively impact chronic disease management by improving access to care, patient-provider communication, clinical outcomes, and health-related quality of life. These interventions have the potential to prepare patients and health care providers (HCPs) before visits and improve patient-provider communication. Despite the recent rapid development and increasing number of digital communication interventions that have shown positive research results, barriers to realizing the benefits offered through these types of interventions still exist. Objective: The aim of this study is to prepare for the implementation of a digital patient-provider communication intervention in the daily workflow at 2 outpatient clinics by identifying potential determinants of implementation using the Consolidated Framework for Implementation Research (CFIR) to tailor the use of digital communication intervention to the intended context and identify key aspects for an implementation plan. Methods: A combination of focus groups, workshops, and project steering committee meetings was conducted with HCPs (n=14) and patients (n=2) from 2 outpatient clinics at a university hospital. The CFIR was used to guide data collection and analysis. Transcripts, written minutes, and notes were analyzed and coded into 5 CFIR domains using thematic analysis. Results: Data were examined and analyzed into 18 CFIR constructs relevant to the study purpose. On the basis of the identified determinants, important intervention tailoring includes adjustments to the digital features and adjustments to fit the clinical workflow and a decision to conduct a future pilot study. Furthermore, it was decided to provide the intervention to patients as early as possible in their disease trajectory, with tailored information about its use. Key aspects for the implementation plan encompassed maintaining the identified engagement and positive attitude, involving key stakeholders in the implementation process, and providing the needed support and training. Conclusions: This study offers insight into the involvement of stakeholders in the tailoring and implementation planning of a digital communication intervention in clinical practice. Stakeholder involvement in the identification of implementation facilitators and barriers can contribute to the tailoring of digital communication interventions and how they are used and can also inform systematic and targeted implementation planning. ",
doi="10.2196/22399",
url="https://www.jmir.org/2021/4/e22399",
url="http://www.ncbi.nlm.nih.gov/pubmed/33830063"
}


@Article{info:doi/10.2196/24639,
author="Acharya, Chathur
and Sehrawat, S. Tejasav
and McGuire, B. Deborah
and Shaw, Jawaid
and Fagan, Andrew
and McGeorge, Sara
and Olofson, Amy
and White, B. Melanie
and Gavis, Edith
and Kamath, S. Patrick
and Bergstrom, Lori
and Bajaj, Singh Jasmohan",
title="Perspectives of Inpatients With Cirrhosis and Caregivers on Using Health Information Technology: Cross-sectional Multicenter Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e24639",
keywords="hepatic encephalopathy",
keywords="cirrhosis",
keywords="outcomes",
keywords="acceptance",
keywords="PatientBuddy",
keywords="ascites",
keywords="readmissions",
keywords="hepatic",
keywords="encephalopathy",
abstract="Background: Health information technology (IT) interventions to decrease readmissions for cirrhosis may be limited by patient-associated factors. Objective: The aim of this study was to determine perspectives regarding adoption versus refusal of health IT interventions among patient-caregiver dyads. Methods: Inpatients with cirrhosis and their caregivers were approached to participate in a randomized health IT intervention trial requiring daily contact with research teams via the Patient Buddy app. This app focuses on ascites, medications, and hepatic encephalopathy over 30 days. Regression analyses for characteristics associated with acceptance were performed. For those who declined, a semistructured interview was performed with themes focused on caregivers, protocol, transport/logistics, technology demands, and privacy. Results: A total of 349 patient-caregiver dyads were approached (191 from Virginia Commonwealth University, 56 from Richmond Veterans Affairs Medical Center, and 102 from Mayo Clinic), 87 of which (25\%) agreed to participate. On regression, dyads agreeing included a male patient (odds ratio [OR] 2.08, P=.01), gastrointestinal bleeding (OR 2.3, P=.006), or hepatic encephalopathy admission (OR 2.0, P=.01), whereas opioid use (OR 0.46, P=.03) and alcohol-related etiology (OR 0.54, P=.02) were associated with refusal. Race, study site, and other admission reasons did not contribute to refusing participation. Among the 262 dyads who declined randomization, caregiver reluctance (43\%), perceived burden (31\%), technology-related issues (14\%), transportation/logistics (10\%), and others (4\%), but not privacy, were highlighted as major concerns. Conclusions: Patients with cirrhosis admitted with hepatic encephalopathy and gastrointestinal bleeding without opioid use or alcohol-related etiologies were more likely to participate in a health IT intervention focused on preventing readmissions. Caregiver and study burden but not privacy were major reasons to decline participation. Reducing perceived patient-caregiver burden and improving communication may improve participation. Trial Registration: ClinicalTrials.gov NCT03564626; https://www.clinicaltrials.gov/ct2/show/NCT03564626 ",
doi="10.2196/24639",
url="https://www.jmir.org/2021/4/e24639",
url="http://www.ncbi.nlm.nih.gov/pubmed/33744844"
}


@Article{info:doi/10.2196/24690,
author="Xu, Ran
and Cavallo, David",
title="Social Network Analysis of the Effects of a Social Media--Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e24690",
keywords="weight loss intervention",
keywords="social media intervention",
keywords="electronic health",
keywords="social network analysis",
abstract="Background: Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective: The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media--based weight loss intervention. Methods: We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants' interaction networks over the 12-week intervention period and linked participants' network characteristics (eg, in-degree, out-degree, network constraint) to participants' changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results: In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss ($\beta$=--.94, P=.04); receiving comments positively predicted changes in self-efficacy ($\beta$=7.81, P=.009), and the degree to which one's network neighbors are tightly connected with each other weakly predicted changes in perceived social support ($\beta$=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss ($\beta$=--.89, P=.02). Conclusions: Our analyses using data from this pilot study linked participants' network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants' psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials. ",
doi="10.2196/24690",
url="https://www.jmir.org/2021/4/e24690",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835033"
}


@Article{info:doi/10.2196/19743,
author="Tindall, Lucy
and Toner, Paul
and Mikocka-Walus, Antonina
and Wright, Barry",
title="Perceptions of and Opinions on a Computerized Behavioral Activation Program for the Treatment of Depression in Young People: Thematic Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e19743",
keywords="depression",
keywords="qualitative",
keywords="thematic analysis",
keywords="young people",
keywords="health care professionals",
keywords="computerized therapies",
abstract="Background: Depression is one of the leading causes of illness and disability in young people, with approximately 20\% having experienced a depressive episode by the age of 18 years. Behavioral activation (BA), a National Institute for Health and Care Excellence--recommended treatment for adults with depression, has shown preliminary support for its use with young people. BA may have the potential to be adapted and delivered in a computerized format to address the barriers often associated with young people accessing support. Despite the benefits of adopting computerized therapy delivery, the limited effectiveness of some programs has been attributed to a failure to tailor interventions to patients and practices. Therefore, while developing new treatments, it is important that target users be involved in the intervention design. Objective: This qualitative study aims to explore the views and preferences of young people and health care professionals regarding the development of a new computerized BA therapy for young people with low mood or depression, to ensure that the therapy was suitable for the target user. Methods: Semistructured focus groups and individual interviews were conducted with young people (those with experience in accessing support and those without) and health care professionals regarding the development of a new computerized BA therapy for young people with low mood or depression. The data were analyzed using thematic analysis. Results: A total of 27 individuals, comprising both health care professionals and young people, participated in this study. Vital information pertaining to the important components of a new therapy, including its presentation, delivery, and content, was collected. Conclusions: Variations in perspectives highlighted the need to adopt a systemic approach in therapy development by considering the opinions of young people with and without experience in accessing mental health support and health care professionals. ",
doi="10.2196/19743",
url="https://www.jmir.org/2021/4/e19743",
url="http://www.ncbi.nlm.nih.gov/pubmed/33847594"
}


@Article{info:doi/10.2196/25219,
author="Mansor, Elliza
and Ahmad, Norliza
and Raj, Diana
and Mohd Zulkefli, Afiah Nor
and Mohd Shariff, Zalilah",
title="Predictors of Parental Barriers to Reduce Excessive Child Screen Time Among Parents of Under-Five Children in Selangor, Malaysia: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e25219",
keywords="child",
keywords="self-efficacy",
keywords="screen time",
keywords="Malaysia",
keywords="parent-child relations",
keywords="public sector",
keywords="children",
keywords="screen",
keywords="parental",
abstract="Background: Globally, there is an increasing prevalence of excessive screen time exposure among young children, including in Malaysia. Parents are advised to limit this exposure, but there are barriers for many of them to follow this recommendation. To date, there is a lack of research on the factors that cause these parental barriers. Objective: This study aimed to determine the parental barrier toward the reduction of excessive child screen time and its predictors among parents of children aged younger than 5 years in the Petaling District, Selangor, Malaysia. Methods: A cross-sectional study was conducted from April 2019 to June 2020 among 789 parent-child dyads attending child health clinics in the Petaling District. Validated self-administered questionnaires were used to capture information on sociodemographic, parental, child-related, and environmental factors and parental barriers. Stratified sampling with probability proportionate to size was employed. Data were analyzed using SPSS Statistics version 25 (IBM Corp). Descriptive analysis and bivariable analysis were performed before multiple linear regression was used to identify predictors of parental barriers. Results: The overall mean score of parental barriers was 3.51 (SD 0.83), indicating that the average numbers of barriers experienced by parents were more than 3. The multivariable analysis showed that the predictors of parental barriers included monthly household income (adjusted $\beta$=--.03, 95\% CI --0.05 to --0.02), parents who worked in public sectors (adjusted $\beta$=.18, 95\% CI 0.06 to 0.29), positive parental attitude on screens (adjusted $\beta$=.68, 95\% CI 0.58 to 0.79), low parent self-efficacy to influence child's physical activity (adjusted $\beta$=--.32, 95\% CI --0.43 to --0.20), and child screen time (adjusted $\beta$=.04, 95\% CI 0.02 to 0.06). Conclusions: The strongest predictor of parental barriers to reduce excessive child screen time was the positive parental attitude on screen time which could contribute to their abilities to limit child screen time. Thus, future intervention strategies should aim to foster correct parental attitudes toward screen time activities among young children. ",
doi="10.2196/25219",
url="https://www.jmir.org/2021/4/e25219",
url="http://www.ncbi.nlm.nih.gov/pubmed/33847590"
}


@Article{info:doi/10.2196/23487,
author="Morriss, Richard
and Kaylor-Hughes, Catherine
and Rawsthorne, Matthew
and Coulson, Neil
and Simpson, Sandra
and Guo, Boliang
and James, Marilyn
and Lathe, James
and Moran, Paul
and Tata, J. Laila
and Williams, Laura",
title="A Direct-to-Public Peer Support Program (Big White Wall) Versus Web-Based Information to Aid the Self-management of Depression and Anxiety: Results and Challenges of an Automated Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="23",
volume="23",
number="4",
pages="e23487",
keywords="peer support",
keywords="digital mental health",
keywords="depression",
keywords="anxiety",
keywords="population reach",
keywords="productivity",
keywords="mobile phone",
abstract="Background: Effective help for depression and anxiety reaches a small proportion of people who might benefit from it. The scale of the problem suggests the need for effective, safe web-based public health services delivered directly to the public. One model, the Big White Wall (BWW), offers peer support at low cost. As these interventions are delivered digitally, we tested whether a randomized controlled trial (RCT) intervention could also be fully delivered and evaluated digitally. Objective: This study aims to determine the reach, feasibility, acceptability, baseline costs, and outcomes of a public health campaign for an automated RCT of the BWW, providing digital peer support and information, compared with a standard website used by the National Health Service Moodzone (MZ), to people with probable mild-to-moderate depression and anxiety disorder. The primary outcome was the change in self-rated well-being at 6 weeks, measured using the Warwick-Edinburgh Mental Well-Being Scale. Methods: An 18-month campaign was conducted across Nottinghamshire, the United Kingdom (target population 914,000) to advertise the trial directly to the public through general marketing, web-based and social media sources, health services, other public services, and third-sector groups. The population reach of this campaign was examined by the number of people accessing the study website and self-registering to the study. A pragmatic, parallel-group, single-blind RCT was then conducted using a fully automated trial website in which eligible participants were randomized to receive either 6 months of access to BWW or signposted to MZ. Those eligible for participation were aged >16 years with probable mild-to-moderate depression or anxiety disorders. Results: Of 6483 visitors to the study website, 1510 (23.29\%) were eligible. Overall, 790 of 1510 (52.32\%) visitors participated. Of 790 visitors, 397 (50.3\%) were randomized to BWW and 393 (49.7\%) to MZ. Their mean age was 38 (SD 13.8) years, 81.0\% (640/790) were female, 93.4\% (738/790) were White, and 47.4\% (271/572) had no contact with health services in the previous 3 months. We estimated 3-month productivity losses of {\textsterling}1001.01 (95\% CI 868.75-1133.27; US \$1380.79; 95\% CI 1198.35-1563.23) per person for those employed. Only 16.6\% (131/790) participants completed the primary outcome assessment. There were no differences in the primary or secondary outcomes between the 2 groups. Conclusions: Most participants reached and those eligible for this trial of digital interventions were White women not in recent contact with health services and whose productivity losses represent a significant annual societal burden. A fully automated RCT recruiting directly from the public failed to recruit and retain sufficient participants to test the clinical effectiveness of this digital intervention, primarily because it did not personally engage participants and explain how these unfamiliar interventions might benefit them. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 12673428; https://www.isrctn.com/ISRCTN12673428 International Registered Report Identifier (IRRID): RR2-10.2196/resprot.8061 ",
doi="10.2196/23487",
url="https://www.jmir.org/2021/4/e23487",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890858"
}


@Article{info:doi/10.2196/25480,
author="Laine, Anna
and Anttila, Minna
and Hirvonen, Heli
and V{\"a}lim{\"a}ki, Maritta",
title="Feasibility of a Web-Based Psychoeducation Course and Experiences of Caregivers Living With a Person With Schizophrenia Spectrum Disorder: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="23",
volume="23",
number="4",
pages="e25480",
keywords="caregiver",
keywords="informal caregiver",
keywords="internet",
keywords="mental health",
keywords="psychoeducation",
keywords="schizophrenia",
keywords="mobile phone",
abstract="Background: Schizophrenia is a severe mental illness that burdens both patients and caregivers. Objective: The aim of this study is to examine the feasibility of a web-based psychoeducation course targeted at caregivers of persons with schizophrenia spectrum disorders (SSDs) and to describe their experiences of living with a person with SSD based on the material caregivers produced during the web-based course. Methods: A convergent, parallel, mixed methods study design was used. First, caregivers' engagement in the course was evaluated quantitatively. Second, the overview of the course feedback was evaluated using quantitative and qualitative methods. Third, the experiences of being a caregiver to a person with SSD were analyzed qualitatively with the thematic analysis of the writings caregivers produced during the web-based course. Results: A total of 30 caregivers participated in the study and a web-based psychoeducation course. Less than two-thirds (18/30, 60\%) completed the course. Content was most often logged for the first module, Orientation (3465 log-ins), and the lowest number of log-ins was recorded for the Daily life module (1061 log-ins). Feedback on the course varied; over half (10/17, 59\%) of the caregivers considered the content to be very good or good, about half (9/17, 53\%) considered the website layout to be good, only 6\% (1/17) felt that the usability of the website was poor, and no one felt that it was very poor. From the reported experiences of being a caregiver to a person with SSD, 3 themes were formed: the caregiver's own well-being, relationship with the person with SSD, and experience of health care services. Conclusions: The web-based psychoeducation course for caregivers living with a person with SSD seems to be especially suitable for those who have little experience as a caregiver. In the future, more planning and the consideration of aspects related to the needs of specific target groups, course content, practical arrangements, and scheduling should be taken into account. In addition, although caregivers can improve their own well-being in different ways, they need regular support and cooperation from health care professionals. ",
doi="10.2196/25480",
url="https://www.jmir.org/2021/4/e25480",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890862"
}


@Article{info:doi/10.2196/25333,
author="Coorey, Genevieve
and Peiris, David
and Scaria, Anish
and Mulley, John
and Neubeck, Lis
and Hafiz, Nashid
and Redfern, Julie",
title="An Internet-Based Intervention for Cardiovascular Disease Management Integrated With Primary Care Electronic Health Records: Mixed Methods Evaluation of Implementation Fidelity and User Engagement",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e25333",
keywords="eHealth",
keywords="electronic health record",
keywords="web-based intervention",
keywords="implementation fidelity",
keywords="user engagement",
keywords="mixed methods",
keywords="cardiovascular disease",
keywords="primary health care",
keywords="mobile phone",
abstract="Background: Growing evidence supports the benefits of eHealth interventions to increase patient engagement and improve outcomes for a range of conditions. However, ineffective program delivery and usage attrition limit exposure to these interventions and may reduce their effectiveness. Objective: This study aims to evaluate the delivery fidelity of an eHealth intervention, describe use patterns, compare outcomes between low and high users, and identify mediating factors on intervention delivery and receipt. Methods: This is a mixed methods study of an internet-based intervention being evaluated for effectiveness in a randomized controlled trial (RCT). The intervention comprised medication and cardiovascular disease (CVD) risk data uploaded from the primary care electronic health record (EHR); interactive, personalized CVD risk score estimation; goal setting and self-monitoring; an interactive social forum; and optional receipt of heart health messages. Fidelity was assessed over 12 months. Trial outcomes were compared between low and high users. Data sources included program delivery records, web log data, trial data, and thematic analysis of communication records. Results: Most participants in the intervention group (451/486, 93\%) had an initial training session conducted by telephone (413/447, 92.4\% of participants trained), with a mean duration of 44 minutes (range 10-90 minutes). Staff conducted 98.45\% (1776/1804) of the expected follow-ups, mostly by telephone or email. Of the 451 participants who commenced log-ins, 46.8\% (211) were categorized as low users (defined as at least one log-in in 3 or fewer months of follow-up), 40.4\% (182) were categorized as high users (at least one log-in in more than 3 months of follow-up), and 12.8\% (58) were nonadopters (no log-ins after their training session). The mean log-in frequency was 3-4 per month in ongoing users. There was no significant difference between the groups in the primary trial outcome of adherence to guideline-recommended medications (P=.44). In unadjusted analyses, high users had significantly greater eHealth literacy scores (P=.003) and were more likely to meet recommended weekly targets for fruit (P=.03) and fish (P=.004) servings; however, the adjusted findings were not significant. Interactive screen use was highest for goal tracking and lowest for the chat forum. Screens with EHR-derived data held only an early interest for most users. Fidelity measures (reach, content, dose delivered, and dose received) were influenced by the facilitation strategies used by staff, invisible qualities of staff-participant communication, and participants' responsiveness to intervention attributes. Conclusions: A multifeature internet-based intervention was delivered with high fidelity to the RCT protocol and was regularly used by 40.4\% (182/451) of users over 12 months. Higher log-in frequency as an indicator of greater intervention exposure was not associated with statistically significant improvements in eHealth literacy scores, lifestyle changes, or clinical outcomes. Attributes of the intervention and individualized support influenced initial and ongoing use. ",
doi="10.2196/25333",
url="https://www.jmir.org/2021/4/e25333",
url="http://www.ncbi.nlm.nih.gov/pubmed/33900204"
}


@Article{info:doi/10.2196/24861,
author="Brussoni, Mariana
and Han, S. Christina
and Lin, Yingyi
and Jacob, John
and Pike, Ian
and Bundy, Anita
and Faulkner, Guy
and Gardy, Jennifer
and Fisher, Brian
and M{\^a}sse, Louise",
title="A Web-Based and In-Person Risk Reframing Intervention to Influence Mothers' Tolerance for, and Parenting Practices Associated With, Children's Outdoor Risky Play: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="27",
volume="23",
number="4",
pages="e24861",
keywords="outdoor play",
keywords="mothering",
keywords="independent mobility",
keywords="physical activity",
keywords="risk perception",
keywords="risky play",
keywords="risk reframing",
abstract="Background: Outdoor risky play, such as climbing, racing, and independent exploration, is an important part of childhood and is associated with various positive physical, mental, and developmental outcomes for children. Parental attitudes and fears, particularly mothers', are a major deterrent to children's opportunities for outdoor risky play. Objective: The aim of this study was to evaluate the efficacy of 2 versions of an intervention to reframe mothers' perceptions of risk and change parenting behaviors: a web-based intervention or an in-person workshop, compared with the control condition. Methods: The Go Play Outside! randomized controlled trial was conducted in Canada from 2017 to 2018. Participants were recruited through social media, snowball sampling, and community notices. Mothers of children aged 6-12 years were self-assessed through eligibility questions, and those eligible and consented to participate in the study were randomized into a fully automated web-based intervention, the in-person workshop, or the control condition. The intervention was underpinned by social cognitive theory, incorporating behavior change techniques. Participants progressed through a series of self-reflection exercises and developed a goal for change. Control participants received the Position Statement on Active Outdoor Play. The primary outcome was increase in tolerance of risky play and the secondary outcome was goal attainment. Data were collected online via REDCap at baseline, 1 week, and 3 months after the intervention. Randomization was conducted using sealed envelope. Allocations were concealed to researchers at assignment and data analysis. We conducted mediation analyses to examine whether the intervention influenced elements of social cognitive theory, as hypothesized. Results: A total of 451 mothers were randomized and completed baseline sociodemographic assessments: 150 in the web-based intervention, 153 in the in-person workshop, and 148 in the control condition. Among these, a total of 351 mothers completed the intervention. At 1 week after the intervention, 113, 85, and 135 mothers completed assessments for each condition, respectively, and at 3 months after the intervention, 105, 84, and 123 completed the assessments, respectively. Compared with mothers in the control condition, mothers in the web-based intervention had significantly higher tolerance of risky play at 1 week (P=.004) and 3 months after the intervention (P=.007); and mothers in the in-person workshop had significantly higher tolerance of risky play at 1 week after the intervention (P=.02). No other significant outcomes were found. None of the potential mediators were found to significantly mediate the outcomes. Conclusions: The trial demonstrates that the web-based intervention was effective in increasing mothers' tolerance for risk in play. Trial Registration: ClinicalTrials.gov NCT03374683; https://clinicaltrials.gov/ct2/show/NCT03374683 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-018-2552-4 ",
doi="10.2196/24861",
url="https://www.jmir.org/2021/4/e24861",
url="http://www.ncbi.nlm.nih.gov/pubmed/33904820"
}


@Article{info:doi/10.2196/25672,
author="Marklund, Sarah
and Tistad, Malin
and Lundell, Sara
and {\"O}strand, Lina
and S{\"o}rlin, Ann
and Bostr{\"o}m, Carina
and Wadell, Karin
and Nyberg, Andre",
title="Experiences and Factors Affecting Usage of an eHealth Tool for Self-Management Among People With Chronic Obstructive Pulmonary Disease: Qualitative Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e25672",
keywords="COPD",
keywords="qualitative content analysis",
keywords="eHealth",
keywords="self-management",
keywords="primary care",
keywords="chronic disease",
abstract="Background: Self-management strategies are regarded as highly prioritized in chronic obstructive pulmonary disease (COPD) treatment guidelines. However, individual and structural barriers lead to a staggering amount of people with COPD that are not offered support for such strategies, and new approaches are urgently needed to circumvent these barriers. A promising way of delivering health services such as support for self-management strategies is the use of eHealth tools. However, there is a lack of knowledge about the usage of, and factors affecting the use of, eHealth tools over time in people with COPD. Objective: This study aimed, among people with COPD, to explore and describe the experiences of an eHealth tool over time and factors that might affect usage. Methods: The eHealth tool included information on evidence-based self-management treatment for people with COPD, including texts, pictures, videos as well as interactive components such as a step registration function with automatized feedback. In addition to the latter, automated notifications of new content and pedometers were used as triggers to increase usage. After having access to the tool for 3 months, 16 individuals (12 women) with COPD were individually interviewed. At 12 months' access to the tool, 7 (5 women) of the previous 16 individuals accepted a second individual interview. Data were analyzed using qualitative content analysis. User frequency was considered in the analysis, and participants were divided into users and nonusers/seldom users depending on the number of logins and minutes of usage per month. Results: Three main categories, namely, ambiguous impact, basic conditions for usage, and approaching capability emerged from the analysis, which, together with their subcategories, reflect the participants' experiences of using the eHealth tool. Nonusers/seldom users (median 1.5 logins and 1.78 minutes spent on the site per month) reported low motivation, a higher need for technical support, a negative view about the disease and self-management, and had problematic health literacy as measured by the Communicative and Critical Health Literacy Scale (median [range] 154 [5-2102]). Users (median 10 logins and 43 minutes per month) felt comfortable with information technology (IT) tools, had a positive view on triggers, and had sufficient health literacy (median [range] 5 [5-1400]). Benefits including behavior changes were mainly expressed after 12 months had passed and mainly among users. Conclusions: Findings of this study indicate that the level of motivation, comfortability with IT tools, and the level of health literacy seem to affect usage of an eHealth tool over time. Besides, regarding behavioral changes, gaining benefits from the eHealth tool seems reserved for the users and specifically after 12 months, thus suggesting that eHealth tools can be suitable media for supporting COPD-specific self-management skills, although not for everyone or at all times. These novel findings are of importance when designing new eHealth tools as well as when deciding on whether or not an eHealth tool might be appropriate to use if the goal is to support self-management among people with COPD. Trial Registration: ClinicalTrials.gov NCT02696187; https://clinicaltrials.gov/ct2/show/NCT02696187 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-016851 ",
doi="10.2196/25672",
url="https://www.jmir.org/2021/4/e25672",
url="http://www.ncbi.nlm.nih.gov/pubmed/33929327"
}


@Article{info:doi/10.2196/27463,
author="Baumgartner, Christian
and Schaub, Patrick Michael
and Wenger, Andreas
and Malischnig, Doris
and Augsburger, Mareike
and Walter, Marc
and Berger, Thomas
and Stark, Lars
and Ebert, Daniel David
and Keough, T. Matthew
and Haug, Severin",
title="CANreduce 2.0 Adherence-Focused Guidance for Internet Self-Help Among Cannabis Users: Three-Arm Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e27463",
keywords="cannabis",
keywords="common mental disorders",
keywords="adherence",
keywords="social presence",
keywords="internet",
keywords="cognitive behavioral therapy",
keywords="motivational interviewing",
keywords="therapy",
keywords="mental health",
keywords="mental disorder",
keywords="guidance",
keywords="self-help",
keywords="drug abuse",
keywords="randomized controlled trial",
abstract="Background: Despite increasing demand for treatment among cannabis users in many countries, most users are not in treatment. Internet-based self-help offers an alternative for those hesitant to seek face-to-face therapy, though low effectiveness and adherence issues often arise. Objective: Through adherence-focused guidance enhancement, we aimed to increase adherence to and the effectiveness of internet-based self-help among cannabis users. Methods: From July 2016 to May 2019, cannabis users (n=775; male: 406/575, 70.6\%, female: 169/575, 29.4\%; age: mean 28.3 years) not in treatment were recruited from the general population and were randomly assigned to (1) an adherence-focused guidance enhancement internet-based self-help intervention with social presence, (2) a similar intervention with an impersonal service team, and (3) access to internet as usual. Controls who were placed on a waiting list for the full intervention after 3 months underwent an assessment and had access to internet as usual. The primary outcome measurement was cannabis-use days over the preceding 30 days. Secondary outcomes included cannabis-dependence severity, changes in common mental disorder symptoms, and intervention adherence. Differences between the study arms in primary and secondary continuous outcome variables at baseline, posttreatment, and follow-up were tested using pooled linear models. Results: All groups exhibited reduced cannabis-use days after 3 months (social presence: --8.2 days; service team: --9.8 days; internet as usual: --4.2 days). The participants in the service team group (P=.01, d=.60) reported significantly fewer cannabis-use days than those in the internet as usual group; the reduction of cannabis use in the social presence group was not significant (P=.07, d=.40). There was no significant difference between the 2 intervention groups regarding cannabis-use reduction. The service team group also exhibited superior improvements in cannabis-use disorder, cannabis-dependence severity, and general anxiety symptoms after 3 months to those in the internet as usual group. Conclusions: The adherence-focused guidance enhancement internet-based self-help intervention with an impersonal service team significantly reduced cannabis use, cannabis-use disorder, dependence severity, and general anxiety symptoms. Trial Registration: ISRCTN Registry ISRCTN11086185; http://www.isrctn.com/ISRCTN11086185 ",
doi="10.2196/27463",
url="https://www.jmir.org/2021/4/e27463",
url="http://www.ncbi.nlm.nih.gov/pubmed/33929333"
}


@Article{info:doi/10.2196/27503,
author="Kirszenblat, Romain
and Edouard, Paul",
title="Validation of the Withings ScanWatch as a Wrist-Worn Reflective Pulse Oximeter: Prospective Interventional Clinical Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e27503",
keywords="connected watch",
keywords="COPD",
keywords="COVID-19",
keywords="neural network",
keywords="pulse oxygen saturation",
keywords="reflective pulse oximeter",
keywords="sleep apnea syndrome",
keywords="SpO2",
keywords="Withings ScanWatch",
keywords="wearable",
keywords="respiratory",
keywords="oxygen",
keywords="respiratory disease",
keywords="oximeter",
keywords="validation",
keywords="accuracy",
keywords="safety",
abstract="Background: A decrease in the level of pulse oxygen saturation as measured by pulse oximetry (SpO2) is an indicator of hypoxemia that may occur in various respiratory diseases, such as chronic obstructive pulmonary disease (COPD), sleep apnea syndrome, and COVID-19. Currently, no mass-market wrist-worn SpO2 monitor meets the medical standards for pulse oximeters. Objective: The main objective of this monocentric and prospective clinical study with single-blind analysis was to test and validate the accuracy of the reflective pulse oximeter function of the Withings ScanWatch to measure SpO2 levels at different stages of hypoxia. The secondary objective was to confirm the safety of this device when used as intended. Methods: To achieve these objectives, we included 14 healthy participants aged 23-39 years in the study, and we induced several stable plateaus of arterial oxygen saturation (SaO2) ranging from 100\%-70\% to mimic nonhypoxic conditions and then mild, moderate, and severe hypoxic conditions. We measured the SpO2 level with a Withings ScanWatch on each participant's wrist and the SaO2 from blood samples with a co-oximeter, the ABL90 hemoximeter (Radiometer Medical ApS). Results: After removal of the inconclusive measurements, we obtained 275 and 244 conclusive measurements with the two ScanWatches on the participants' right and left wrists, respectively, evenly distributed among the 3 predetermined SpO2 groups: SpO2?80\%, 80\%<SpO2?90\%, and 90\%<SpO2. We found a strong association and a high level of agreement between the measurements collected from the devices, with high Pearson correlation coefficients of r=0.944 and r=0.954 on the correlation plots, low Pearson correlation coefficients of r=0.083 (P=.17) and r=0.23 (P=.001) on Bland-Altman plots, biases of 0.98\% (95\% CI 0.65-1.32) and 1.56\% (95\% CI 1.24-1.87), and root mean square errors of 2.97\% and 3.00\% from the participants' right and left hands, respectively. Conclusions: In conclusion, the Withings ScanWatch is able to measure SpO2 levels with adequate accuracy at a clinical grade. No undesirable effects or adverse events were reported during the study. Trial Registration: ClinicalTrials.gov NCT04380389; http://clinicaltrials.gov/ct2/show/NCT04380389 ",
doi="10.2196/27503",
url="https://www.jmir.org/2021/4/e27503",
url="http://www.ncbi.nlm.nih.gov/pubmed/33857011"
}


@Article{info:doi/10.2196/25468,
author="Siste, Kristiana
and Hanafi, Enjeline
and Sen, Thung Lee
and Wahjoepramono, Perdana Petra Octavian
and Kurniawan, Andree
and Yudistiro, Ryan",
title="Potential Correlates of Internet Gaming Disorder Among Indonesian Medical Students: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e25468",
keywords="internet gaming disorder",
keywords="medical students",
keywords="psychopathology",
keywords="temperament",
keywords="risk factors",
abstract="Background: Internet gaming disorder has been a controversial topic for nearly a decade. Although internet addiction has been studied in medical students, there is a paucity of evidence regarding internet gaming disorder. Previous studies in Indonesia explored only the prevalence rate and characteristics. Objective: This study aimed to determine the prevalence rate of internet gaming disorder and correlations between internet gaming disorder, temperament, and psychopathology among Indonesian medical students. Methods: A cross-sectional study was performed from August 2019 to September 2019 using total and convenience sampling at a private university and a public university, respectively. The study variables were measured using the Indonesian version of the 10-item Internet Gaming Disorder Test, the Temperament and Character Inventory, and the Symptoms Checklist 90. Chi-square and logistic regression analyses were conducted to examine the relationships between demographic factors, temperament, psychopathology, and the presence of internet gaming disorder. Results: Among the 639 respondents, the prevalence rate of internet gaming disorder was 2.03\% (n=13), with a mean age of 20.23 (SD 0.13) years and an average gaming duration of 19.0 (SD 0.96) hours/week. Up to 71.2\% respondents played using their mobile phones, and respondents with internet gaming disorder reported experiencing all psychopathologies assessed, except phobic anxiety. Bivariate analysis demonstrated that internet gaming disorder was associated with gender, gaming duration, gaming community affiliation, and 9 out of 10 domains of psychopathology. In a logistic regression model, internet gaming disorder was correlated with weekly gaming hours ?20 hours (odds ratio [OR] 4.21, 95\% CI 1.08-16.38, P=.04). Conclusions: These findings suggest that the prevalence of internet gaming disorder among medical students in Jakarta, Indonesia is similar to that in other populations of Asian countries. The predisposing factor for internet gaming disorder was weekly gaming duration, while other demographic, temperament, and psychopathology variables acted as probable moderators. Strategies should, therefore, be developed and integrated into medical curriculum to screen and aid individuals with these predisposing factors. ",
doi="10.2196/25468",
url="https://www.jmir.org/2021/4/e25468",
url="http://www.ncbi.nlm.nih.gov/pubmed/33871379"
}


@Article{info:doi/10.2196/25773,
author="Strudwick, Gillian
and McLay, David
and Lo, Brian
and Shin, Danielle Hwayeon
and Currie, Leanne
and Thomson, Nicole
and Maillet, {\'E}ric
and Strong, Vanessa
and Miller, Alanna
and Shen, Nelson
and Campbell, Janis",
title="Development of a Resource Guide to Support the Engagement of Mental Health Providers and Patients With Digital Health Tools: Multimethod Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e25773",
keywords="digital health",
keywords="mental health",
keywords="psychiatry",
keywords="COVID-19",
keywords="nursing informatics",
keywords="health informatics",
abstract="Background: As mental illness continues to affect 1 in 5 individuals, and the need for support has increased during the COVID-19 pandemic, the promise of digital mental health tools remains largely unrealized due to a lack of uptake by patients and providers. Currently, most efforts on supporting the uptake of digital mental health tools remain fragmented across organizations and geography. There is a critical need to synthesize these efforts in order to provide a coordinated strategy of supporting the adoption of digital mental health tools. Objective: The specific aim of this project is to develop a web-based resource document to support the engagement of mental health providers and patients in the use of digital mental health tools. Methods: The web-based resource was developed using a multimethod approach. A grey literature review was conducted in 2019 to identify relevant toolkits that are available in the public domain. This was supplemented with an environmental scan where individuals with expertise in the development, acquisition, implementation, and evaluation of digital mental health tools were invited to contribute additional tools or documents not identified in the grey literature search. An engagement workshop was held with stakeholders to explore how the resource document should be developed and delivered. These findings were collectively used to develop the final iteration of the resource document. Results: Based on a gray literature review and environmental scan with 27 experts, 25 resources were identified and included in the resource guide. These resources were developed for patients and providers by organizations from 5 countries. An engagement workshop was held with 14 stakeholders, and barriers related to cultural sensitivity, sustainability, and accessibility of the toolkit were identified. The final iteration of the resource document was developed by the research team using findings from the gray literature review, environmental scan, and engagement workshop. The contents of the 45-page resource guide are directed at mental health care providers, administrators, and patients (inclusive of families and caregivers). Conclusions: The use of a multimethod approach led to the development of a resource guide that builds on existing evidence on digital mental health tools and was co-designed with stakeholders and end-users. The resource guide is now publicly available online for free and is being promoted through digital health and mental health websites. Future work should explore how this document can be integrated into clinical care delivery and pathways. ",
doi="10.2196/25773",
url="https://www.jmir.org/2021/4/e25773",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885374"
}


@Article{info:doi/10.2196/25279,
author="Linton, Anthony Myles-Jay
and Jelbert, Sarah
and Kidger, Judi
and Morris, Richard
and Biddle, Lucy
and Hood, Bruce",
title="Investigating the Use of Electronic Well-being Diaries Completed Within a Psychoeducation Program for University Students: Longitudinal Text Analysis Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e25279",
keywords="psychoeducation",
keywords="diary",
keywords="students",
keywords="text analysis",
keywords="wellbeing",
keywords="science of happiness",
keywords="university",
keywords="emotional tone",
keywords="e-mental health",
keywords="mobile phone",
abstract="Background: Psychoeducation has the potential to support students experiencing distress and help meet the demand for support; however, there is a need to understand how these programs are experienced. Web-based diaries are a useful activity for psychoeducation because of their therapeutic benefits, ability to capture naturalistic data relevant to well-being, and appropriateness for text analysis methods. Objective: This study aims to examine how university students use electronic diaries within a psychoeducation program designed to enhance mental well-being. Methods: The Science of Happiness course was administered to 154 undergraduate students in a university setting (the United Kingdom). Diaries were collected from the students for 9 weeks. Baseline well-being data were collected using the Short Warwick-Edinburgh Mental Wellbeing Scale (SWEMWBS). The percentage of negative and positive emotion words used in diaries (emotional tone) and use of words from five life domains (social, work, money, health, and leisure) were calculated using the Linguistic Inquiry and Word Count 2015 software. Random effects (generalized least squares) regression models were estimated to examine whether time, diary characteristics, demographics, and baseline well-being predict the emotional tone of diaries. Results: A total of 149 students participated in the diary study, producing 1124 individual diary entries. Compliance with the diary task peaked in week 1 (n=1041, 92.62\%) and was at its lowest in week 3 (n=807, 71.81\%). Compared with week 1, diaries were significantly more positive in their emotional tone during week 5 (mean difference 23.90, 95\% CI 16.89-30.90) and week 6 (mean difference 26.62, 95\% CI 19.35-33.88) when students were tasked with writing about gratitude and their strengths. Across weeks, moderate and high baseline SWEMWBS scores were associated with a higher percentage of positive emotion words used in diaries (increases compared with students scoring low in SWEMWBS were 5.03, 95\% CI 0.08-9.98 and 7.48, 95\% CI 1.84-13.12, respectively). At week 1, the diaries of students with the highest levels of baseline well-being (82.92, 95\% CI 73.08-92.76) were more emotionally positive on average than the diaries of students with the lowest levels of baseline well-being (59.38, 95\% CI 51.02-67.73). Diaries largely focused on the use of social words. The emotional tone of diary entries was positively related to the use of leisure (3.56, 95\% CI 2.28-4.85) and social words (0.74, 95\% CI 0.21-1.27), and inversely related to the use of health words (?1.96, 95\% CI ?3.70 to ?0.22). Conclusions: We found evidence for short-term task-specific spikes in the emotional positivity of web-based diary entries and recommend future studies examine the possibility of long-term impacts on the writing and well-being of students. With student well-being strategies in mind, universities should value and encourage leisure and social activities. ",
doi="10.2196/25279",
url="https://www.jmir.org/2021/4/e25279",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885373"
}


@Article{info:doi/10.2196/26960,
author="Chu, Cherry
and Cram, Peter
and Pang, Andrea
and Stamenova, Vess
and Tadrous, Mina
and Bhatia, Sacha R.",
title="Rural Telemedicine Use Before and During the COVID-19 Pandemic: Repeated Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e26960",
keywords="chronic disease",
keywords="chronic illness",
keywords="COVID-19",
keywords="health care",
keywords="health services",
keywords="older adults",
keywords="remote",
keywords="rural",
keywords="pandemic",
keywords="population",
keywords="telemedicine",
keywords="virtual care",
abstract="Background: The COVID-19 pandemic has led to a notable increase in telemedicine adoption. However, the impact of the pandemic on telemedicine use at a population level in rural and remote settings remains unclear. Objective: This study aimed to evaluate changes in the rate of telemedicine use among rural populations and identify patient characteristics associated with telemedicine use prior to and during the pandemic. Methods: We conducted a repeated cross-sectional study on all monthly and quarterly rural telemedicine visits from January 2012 to June 2020, using administrative data from Ontario, Canada. We compared the changes in telemedicine use among residents of rural and urban regions of Ontario prior to and during the pandemic. Results: Before the pandemic, telemedicine use was steadily low in 2012-2019 for both rural and urban populations but slightly higher overall for rural patients (11 visits per 1000 patients vs 7 visits per 1000 patients in December 2019, P<.001). The rate of telemedicine visits among rural patients significantly increased to 147 visits per 1000 patients in June 2020. A similar but steeper increase (P=.15) was observed among urban patients (220 visits per 1000 urban patients). Telemedicine use increased across all age groups, with the highest rates reported among older adults aged ?65 years (77 visits per 100 patients in 2020). The proportions of patients with at least 1 telemedicine visit were similar across the adult age groups (n=82,246/290,401, 28.3\% for patients aged 18-49 years, n=79,339/290,401, 27.3\% for patients aged 50-64 years, and n=80,833/290,401, 27.8\% for patients aged 65-79 years), but lower among younger patients <18 years (n=23,699/290,401, 8.2\%) and older patients ?80 years (n=24,284/290,401, 8.4\%) in 2020 (P<.001). There were more female users than male users of telemedicine (n=158,643/290,401, 54.6\% vs n=131,758/290,401, 45.4\%, respectively, in 2020; P<.001). There was a significantly higher proportion of telemedicine users residing in relatively less rural than in more rural regions (n=261,814/290,401, 90.2\% vs n=28,587/290,401, 9.8\%, respectively, in 2020; P<.001). Conclusions: Telemedicine adoption increased in rural and remote areas during the COVID-19 pandemic, but its use increased in urban and less rural populations. Future studies should investigate the potential barriers to telemedicine use among rural patients and the impact of rural telemedicine on patient health care utilization and outcomes. ",
doi="10.2196/26960",
url="https://www.jmir.org/2021/4/e26960",
url="http://www.ncbi.nlm.nih.gov/pubmed/33769942"
}


@Article{info:doi/10.2196/24552,
author="Parise, Martina
and Tartaglione, Linda
and Cutruzzol{\`a}, Antonio
and Maiorino, Ida Maria
and Esposito, Katherine
and Pitocco, Dario
and Gnasso, Agostino
and Irace, Concetta",
title="Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="6",
volume="23",
number="4",
pages="e24552",
keywords="chronic disease",
keywords="COVID-19",
keywords="diabetes",
keywords="effectiveness",
keywords="management",
keywords="technology",
keywords="teleassistance",
keywords="telehealth",
keywords="telemedicine",
keywords="time in range",
keywords="type 1 diabetes",
abstract="Background: Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. Objective: During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. Methods: This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. Results: Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16\% vs n=155/166, 93.4\% and n=9/43, 21\% vs n=128/166, 77.1\%, respectively; P<.001) compared to patients who completed the study. TIR significantly increased from the first (62\%, SD 18\%) to the second (65\%, SD 16\%) virtual visit (P=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55\%, SD 17\% and follow-up TIR=66\%, SD 13\%; P=.01) than among those using CGM, and in those with a baseline GMI of ?7.5\% (n=46; baseline TIR=45\%, SD 15\% and follow-up TIR=53\%, SD 18\%; P<.001) than in those with a GMI of <7.5\% (n=120; baseline TIR=68\%, SD 15\% and follow-up TIR=69\%, SD 15\%; P=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95\% CI were 5 (95\% CI 0.7-8.0) (P=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. Conclusions: These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible. ",
doi="10.2196/24552",
url="https://www.jmir.org/2021/4/e24552",
url="http://www.ncbi.nlm.nih.gov/pubmed/33769945"
}


@Article{info:doi/10.2196/18799,
author="Ducrot, Pauline
and Montagni, Ilaria
and Nguyen Thanh, Viet
and Serry, Anne-Juliette
and Richard, Jean-Baptiste",
title="Evolution of Online Health-Related Information Seeking in France From 2010 to 2017: Results From Nationally Representative Surveys",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e18799",
keywords="internet",
keywords="health information",
keywords="information-seeking behavior",
keywords="eHealth literacy",
abstract="Background: Given the rapid ongoing progression of the internet and increase in health information available from disparate online sources, it is important to understand how these changes impact online health information-seeking behavior of the population and the way of managing one's health. Objective: This paper aims at describing the evolution of internet use as a source of health information between 2010 and 2017, as well as the characteristics of online health information seekers, topics of interest, sources of information, and trust in retrieved information and potential impact on behavior. Methods: Data from the French nationally representative surveys Health Barometers were used (N=4141 in 2010, 4811 in 2014, and 6255 in 2017). Evolutions over time were assessed using chi-square tests. Associations with sociodemographic characteristics and health status were evaluated using logistic regression models. Results: The use of the internet as a source of health information rose between 2010 and 2014 (from 37.3\% to 67.9\%, P<.001) but decreased significantly in 2017 (60.3\%, P<.001). Overall, the profile of health information seekers compared with nonseekers did not change over time. They were more likely to be women, to be younger, to have a higher educational level, to have a higher household income, and to be executives. Between 2014 and 2017, the proportion of those who did not pay attention to the source of information significantly increased to reach 39.7\% (P<.001). In 2017 as in 2014, general health-related websites remained the first source of information (38.6\%) while institutional websites were the third source (8.1\%). Most information seekers trusted the information found online in 2010 (more than 80\%), with a slight decrease between 2014 and 2017 (P=.048). Among individual characteristics, trust in the information was the main determinant of the way of managing one's health (odds ratio 4.06, 95\% CI 3.26-5.06). Conclusions: After a rapid growth in the internet use for seeking health information in the 2010 to 2014 period, a decrease was recorded in 2017, in parallel with a decrease in trust in the quality and reliability of information found online. These findings underline the need for public health authorities to increase citizens' eHealth literacy and to provide alternative trustworthy sources combining the popularity and accessibility of general health information websites. ",
doi="10.2196/18799",
url="https://www.jmir.org/2021/4/e18799",
url="http://www.ncbi.nlm.nih.gov/pubmed/33851927"
}


@Article{info:doi/10.2196/27121,
author="Banholzer, Nicolas
and Feuerriegel, Stefan
and Fleisch, Elgar
and Bauer, Friedrich Georg
and Kowatsch, Tobias",
title="Computer Mouse Movements as an Indicator of Work Stress: Longitudinal Observational Field Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="2",
volume="23",
number="4",
pages="e27121",
keywords="work stress",
keywords="psychological stress",
keywords="stress indicator",
keywords="computer mouse movements",
keywords="human-computer interactions",
abstract="Background: Work stress affects individual health and well-being. These negative effects could be mitigated through regular monitoring of employees' stress. Such monitoring becomes even more important as the digital transformation of the economy implies profound changes in working conditions. Objective: The goal of this study was to investigate the association between computer mouse movements and work stress in the field. Methods: We hypothesized that stress is associated with a speed-accuracy trade-off in computer mouse movements. To test this hypothesis, we conducted a longitudinal field study at a large business organization, where computer mouse movements from regular work activities were monitored over 7 weeks; the study included 70 subjects and 1829 observations. A Bayesian regression model was used to estimate whether self-reported acute work stress was associated with a speed-accuracy trade-off in computer mouse movements. Results: There was a negative association between stress and the two-way interaction term of mouse speed and accuracy (mean ?0.32, 95\% highest posterior density interval ?0.58 to ?0.08), which means that stress was associated with a speed-accuracy trade-off. The estimated association was not sensitive to different processing of the data and remained negative after controlling for the demographics, health, and personality traits of subjects. Conclusions: Self-reported acute stress is associated with computer mouse movements, specifically in the form of a speed-accuracy trade-off. This finding suggests that the regular analysis of computer mouse movements could indicate work stress. ",
doi="10.2196/27121",
url="https://www.jmir.org/2021/4/e27121",
url="http://www.ncbi.nlm.nih.gov/pubmed/33632675"
}


@Article{info:doi/10.2196/20954,
author="Liu, Sam
and Perdew, Megan
and Lithopoulos, Alexander
and Rhodes, E. Ryan",
title="The Feasibility of Using Instagram Data to Predict Exercise Identity and Physical Activity Levels: Cross-sectional Observational Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e20954",
keywords="social media",
keywords="exercise identity",
keywords="physical activity",
keywords="physical fitness",
abstract="Background: Exercise identity is an important predictor for regular physical activity (PA). There is a lack of research on the potential mechanisms or antecedents of identity development. Theories of exercise identity have proposed that investment, commitment and self-referential (eg, I am an exerciser) statements, and social activation (comparison, support) may be crucial to identity development. Social media may be a potential mechanism to shape identity. Objective: The objectives of this study were to (1) explore whether participants were willing to share their Instagram data with researchers to predict their lifestyle behaviors; (2) examine whether PA-related Instagram uses (ie, the percentage of PA-related Instagram posts, fitness-related followings, and the number of likes received on PA-related posts) were positively associated with exercise identity; and (3) evaluate whether exercise identity mediates the relationship between PA-related Instagram use and weekly PA minutes. Methods: Participants (18-30 years old) were asked to complete a questionnaire to evaluate their current levels of exercise identity and PA. Participants' Instagram data for the past 12 months before the completion of the questionnaire were extracted and analyzed with their permission. Instagram posts related to PA in the 12 months before their assessment, the number of likes received for each PA-related post, and verified fitness- or PA-related followings by the participants were extracted and analyzed. Pearson correlation analyses were used to evaluate the relationship among exercise identity, PA, and Instagram uses. We conducted mediation analyses using the PROCESS macro modeling tool to examine whether exercise identity mediated the relationship between Instagram use variables and PA. Descriptive statistical analyses were used to compare the number of willing participants versus those who were not willing to share their Instagram data. Results: Of the 76 participants recruited to participate, 54\% (n=41) shared their Instagram data. The percentage of PA-related Instagram posts (r=0.38; P=.01) and fitness-related Instagram followings (r=0.39; P=.01) were significantly associated with exercise identity. The average number of ``likes'' received (r=0.05, P=.75) was not significantly associated with exercise identity. Exercise identity significantly influenced the relationship between Instagram usage metrics (ie, the percentage of PA-related Instagram posts [P=.01] and verified fitness-related Instagram accounts [P=.01]) and PA level. Exercise identity did not significantly influence the relationship between the average number of ``likes'' received for the PA-related Instagram posts and PA level. Conclusions: Our results suggest that an increase in PA-related Instagram posts and fitness-related followings were associated with a greater sense of exercise identity. Higher exercise identity led to higher PA levels. Exercise identity significantly influenced the relationship between PA-related Instagram posts (P=.01) and fitness-related followings on PA levels (P=.01). These results suggest that Instagram may influence a person's exercise identity and PA levels. Future intervention studies are warranted. ",
doi="10.2196/20954",
url="https://www.jmir.org/2021/4/e20954",
url="http://www.ncbi.nlm.nih.gov/pubmed/33871380"
}


@Article{info:doi/10.2196/24237,
author="Brons, Annette
and de Schipper, Antoine
and Mironcika, Svetlana
and Toussaint, Huub
and Schouten, Ben
and Bakkes, Sander
and Kr{\"o}se, Ben",
title="Assessing Children's Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e24237",
keywords="motor development",
keywords="fine motor function",
keywords="gamification",
keywords="playful",
keywords="motor skill assessment",
keywords="Movement ABC (MABC)",
keywords="machine learning",
keywords="motor function",
keywords="motor skills",
keywords="toys",
keywords="children",
keywords="game",
keywords="movement assessment",
abstract="Background: Approximately 5\%-10\% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called ``Futuro Cube.'' The game ``roadrunner'' focused on speed while the game ``maze'' focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used $\alpha$=.05. Results: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046). Conclusions: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty. ",
doi="10.2196/24237",
url="https://www.jmir.org/2021/4/e24237",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885371"
}


@Article{info:doi/10.2196/19187,
author="Holyoke, Paul
and Yogaratnam, Karthika
and Kalles, Elizabeth",
title="Web-Based Smartphone Algorithm for Calculating Blood Pressure From Photoplethysmography Remotely in a General Adult Population: Validation Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="23",
volume="23",
number="4",
pages="e19187",
keywords="blood pressure measurement",
keywords="remote monitoring",
keywords="hypertension",
abstract="Background: Outside of a clinical setting, oscillometric devices make remote monitoring of blood pressure and virtual care more convenient and feasible. HeartBeat Technologies Ltd developed a novel approach to measuring blood pressure remotely after an initial blood pressure reading by a nurse using the conventional measurement method. Using a finger pulse oximeter, a photoplethysmogram wave is transmitted by Bluetooth to a smartphone or tablet. A smartphone app (MediBeat) transmits the photoplethysmogram to a server for analysis by a proprietary algorithm---the person's current blood pressure is sent back to the smartphone and to the individual's health care provider. Objective: This study sought to determine whether the HeartBeat algorithm calculates blood pressure as accurately as required by the European Society of Hypertension International Protocol revision 2010 (ESH-IP2) for validation of blood pressure measuring devices. Methods: ESH-IP2 requirements, modified to conform to a more recent international consensus statement, were followed. The ESH-IP2 establishes strict guidelines for the conduct and reporting of any validation of any device to measure blood pressure, including using the standard manual blood pressure instrument as a comparator and specific required accuracy levels for low, medium, and high ranges of blood pressure readings. The consensus statement requires a greater number of study participants for each of the blood pressure ranges. The validation of the accuracy of the algorithm was conducted with a Contec CMS50EW pulse oximeter and a Samsung Galaxy XCover 4 smartphone. Results: The differences between the HeartBeat-calculated and the manually measured blood pressures of 62 study participants did not meet the ESH-IP2 standards for accuracy for either systolic or diastolic blood pressure measurements. There was no discernible pattern in the inaccuracies of the HeartBeat-calculated measurements. Conclusions: The October 4, 2019 version of the HeartBeat algorithm, implemented in combination with the MediBeat app, a pulse oximeter, and an Android smartphone, was not sufficiently accurate for use in a general adult population. Trial Registration: ClinicalTrials.gov NCT04082819; http://clinicaltrials.gov/ct2/show/NCT04082819 ",
doi="10.2196/19187",
url="https://www.jmir.org/2021/4/e19187",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890856"
}


@Article{info:doi/10.2196/27069,
author="Tsai, Wen-Hsun
and Wu, Yi-Syuan
and Cheng, Chia-Shiang
and Kuo, Ming-Hao
and Chang, Yu-Tien
and Hu, Fu-Kang
and Sun, Chien-An
and Chang, Chi-Wen
and Chan, Ta-Chien
and Chen, Chao-Wen
and Lee, Chia-Cheng
and Chu, Chi-Ming",
title="A Technology Acceptance Model for Deploying Masks to Combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-Based Cross-sectional Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="21",
volume="23",
number="4",
pages="e27069",
keywords="personal health record",
keywords="electronic medical record",
keywords="my health bank",
keywords="technology acceptance model",
keywords="structural equation model",
keywords="electronic health record",
keywords="COVID-19",
keywords="protection",
keywords="survey",
keywords="model",
keywords="intention",
keywords="usage",
keywords="literacy",
keywords="privacy",
keywords="security",
abstract="Background: The successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. Objective: This study aims to understand people's intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. Methods: From March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from ``strongly disagree'' (1 point) to ``strongly agree'' (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. Results: A total of 350 valid questionnaire responses were collected (female: 219/350, 62.6\%; age: 21-30 years: 238/350, 68.0\%; university-level education: 228/350, 65.1\%; occupation as student: 195/350, 56.6\%; average monthly income <NT \$30,000 [<US \$1054.89]: 230/350, 65.7\%; residence in northern Taiwan: 236/350, 67.4\%; and health status perceived as ``good'': 171/350, 48.9\%). Five indicators, including chi-square test (X2310=2.63), goodness-of-fit index (0.85), adjusted goodness-of-fit index (0.81), comparative fit index (0.91), and root mean square error of approximation (0.07), were calculated. The results indicated a good fit. Further analysis indicated that the most important factor affecting respondents' behavioral intention to use MHB was their attitude toward use (0.78), followed by perceived ease of use (0.65), perceived usefulness (0.41), health literacy (0.10), and privacy and security (0.07). Conclusions: From the perspective of the populace, this study explored the factors affecting the use of MHB and constructed an interpretation model with a strong goodness of fit. The results of our analysis are consistent with the technology acceptance model. Through the diverse value-added services of MHB, Taiwan's experience in pandemic prevention with smart technology can facilitate future responses to unknown, emerging infectious diseases. ",
doi="10.2196/27069",
url="https://www.jmir.org/2021/4/e27069",
url="http://www.ncbi.nlm.nih.gov/pubmed/33819168"
}


@Article{info:doi/10.2196/23866,
author="Fareed, Naleef
and Jonnalagadda, Pallavi
and MacEwan, R. Sarah
and Di Tosto, Gennaro
and Scarborough, Seth
and Huerta, R. Timothy
and McAlearney, Scheck Ann",
title="Differential Effects of Outpatient Portal User Status on Inpatient Portal Use: Observational Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e23866",
keywords="patient portals",
keywords="technology experience",
keywords="health information technology",
keywords="technology acceptance",
keywords="technology use",
keywords="log files",
keywords="mobile phone",
abstract="Background: The decision to use patient portals can be influenced by multiple factors, including individuals' perceptions of the tool, which are based on both their personal skills and experiences. Prior experience with one type of portal may make individuals more comfortable with using newer portal technologies. Experienced outpatient portal users in particular may have confidence in their ability to use inpatient portals that have similar functionality. In practice, the use of both outpatient and inpatient portal technologies can provide patients with continuity of access to their health information across care settings, but the influence of one type of portal use on the use of other portals has not been studied. Objective: This study aims to understand how patients' use of an inpatient portal is influenced by outpatient portal use. Methods: This study included patients from an academic medical center who were provided access to an inpatient portal during their hospital stays between 2016 and 2018 (N=1571). We analyzed inpatient portal log files to investigate how inpatient portal use varied by using 3 categories of outpatient portal users: prior users, new users, and nonusers. Results: Compared with prior users (695/1571, 44.24\%) of an outpatient portal, new users (214/1571, 13.62\%) had higher use of a select set of inpatient portal functions (messaging function: incidence rate ratio [IRR] 1.33, 95\% CI 1.06-1.67; function that provides access to the outpatient portal through the inpatient portal: IRR 1.34, 95\% CI 1.13-1.58). Nonusers (662/1571, 42.14\%), compared with prior users, had lower overall inpatient portal use (all active functions: IRR 0.68, 95\% CI 0.60-0.78) and lower use of specific functions, which included the function to review vitals and laboratory results (IRR 0.51, 95\% CI 0.36-0.73) and the function to access the outpatient portal (IRR 0.53, 95\% CI 0.45-0.62). In comparison with prior users, nonusers also had lower odds of being comprehensive users (defined as using 8 or more unique portal functions; odds ratio [OR] 0.57, 95\% CI 0.45-0.73) or composite users (defined as comprehensive users who initiated a 75th or greater percentile of portal sessions) of the inpatient portal (OR 0.42, 95\% CI 0.29-0.60). Conclusions: Patients' use of an inpatient portal during their hospital stay appeared to be influenced by a combination of factors, including prior outpatient portal use. For new users, hospitalization itself, a major event that can motivate behavioral changes, may have influenced portal use. In contrast, nonusers might have lower self-efficacy in their ability to use technology to manage their health, contributing to their lower portal use. Understanding the relationship between the use of outpatient and inpatient portals can help direct targeted implementation strategies that encourage individuals to use these tools to better manage their health across care settings. ",
doi="10.2196/23866",
url="https://www.jmir.org/2021/4/e23866",
url="http://www.ncbi.nlm.nih.gov/pubmed/33929328"
}


@Article{info:doi/10.2196/26232,
author="Mannheim, Ittay
and Wouters, M. Eveline J.
and van Boekel, C. Leonieke
and van Zaalen, Yvonne",
title="Attitudes of Health Care Professionals Toward Older Adults' Abilities to Use Digital Technology: Questionnaire Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="21",
volume="23",
number="4",
pages="e26232",
keywords="ageism",
keywords="attitudes",
keywords="stereotype activation",
keywords="digital technology",
abstract="Background: Digital technologies (DTs) for older adults focus mainly on health care and are considered to have the potential to improve the well-being of older adults. However, adoption rates of these DTs are considered low. Although previous research has investigated possible reasons for adoption and acceptance of DT, age-based stereotypes (eg, those held by health care professionals) toward the abilities of older adults to use DTs have yet to be considered as possible barriers to adoption. Objective: The aim of this study was to investigate the influencing role of ageism in the context of health care professionals attitudes toward older adults' abilities to use health care DT. A further goal was to examine if social comparison and stereotype activation affect and moderate this association. Methods: A new measurement to assess health care professionals' attitudes toward older adults using technology (ATOAUT-10) was developed and used in 2 studies. Study 1 involved the development of the ATOAUT-10 scale using a principal component analysis and further examined health care professionals' attitudes toward the use of health care DTs and correlations with ageism. Study 2 further explored the correlation between ageism and ATOAUT in an experimental design with health care professionals. Results: In study 1, physiotherapists (N=97) rated older adults as young as 50 years as less able to use health care DT compared to younger adults (P<.001). A multiple regression analysis revealed that higher levels of ageism, beyond other predictors, were predictive of more negative ATOAUT, ($\beta$=.36; t=3.73; P<.001). In study 2, the salience of age was manipulated. Health care professionals (N=93) were randomly assigned to rate the abilities of a young or old person to use health care DT. Old age salience moderated the correlation between ageism and ATOAUT (R2=0.19; F6,85=3.35; P=.005), such that higher levels of ageism correlated with more negative ATOAUT in the old age salient condition, but not the young condition. Stereotype activation accounted for health care professionals' attitudes more than did the experience of working with older patients or the professionals' age. Conclusions: Negative and ageist attitudes of health care professionals can potentially affect how older adults are viewed in relation to DT and consequently might influence actual use and adoption of technology-based treatment. Future studies should broaden the validation of the ATOAUT-10 scale on more diverse samples and focus on the discriminatory aspect of ageism and self-ageism of older adults. This study calls for a focus on ageism as a determinant of adoption of DT. ",
doi="10.2196/26232",
url="https://www.jmir.org/2021/4/e26232",
url="http://www.ncbi.nlm.nih.gov/pubmed/33881408"
}


@Article{info:doi/10.2196/25872,
author="Jones, E. Sarah
and Campbell, K. Penny
and Kimp, J. Alexander
and Bennell, Kim
and Foster, E. Nadine
and Russell, Trevor
and Hinman, S. Rana",
title="Evaluation of a Novel e-Learning Program for Physiotherapists to Manage Knee Osteoarthritis via Telehealth: Qualitative Study Nested in the PEAK (Physiotherapy Exercise and Physical Activity for Knee Osteoarthritis) Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e25872",
keywords="osteoarthritis",
keywords="knee",
keywords="physiotherapy",
keywords="exercise",
keywords="e-learning",
keywords="qualitative",
keywords="telehealth",
keywords="pain",
keywords="education",
abstract="Background: The delivery of physiotherapy via telehealth could provide more equitable access to services for patients. Videoconference-based telehealth has been shown to be an effective and acceptable mode of service delivery for exercise-based interventions for chronic knee pain; however, specific training in telehealth is required for physiotherapists to effectively and consistently deliver care using telehealth. The development and evaluation of training programs to upskill health care professionals in the management of osteoarthritis (OA) has also been identified as an important priority to improve OA care delivery. Objective: This study aims to explore physiotherapists' experiences with and perceptions of an e-learning program about best practice knee OA management (focused on a structured program of education, exercise, and physical activity) that includes telehealth delivery via videoconferencing. Methods: We conducted a qualitative study using individual semistructured telephone interviews, nested within the Physiotherapy Exercise and Physical Activity for Knee Osteoarthritis randomized controlled trial, referred to as the PEAK trial. A total of 15 Australian physiotherapists from metropolitan and regional private practices were interviewed following the completion of an e-learning program. The PEAK trial e-learning program involved self-directed learning modules, a mock video consultation with a researcher (simulated patient), and 4 audited practice video consultations with pilot patients with chronic knee pain. Interviews were audio recorded and transcribed verbatim. Data were thematically analyzed. Results: A total of five themes (with associated subthemes) were identified: the experience of self-directed e-learning (physiotherapists were more familiar with in-person learning; however, they valued the comprehensive, self-paced web-based modules. Unwieldy technological features could be frustrating); practice makes perfect (physiotherapists benefited from the mock consultation with the researcher and practice sessions with pilot patients alongside individualized performance feedback, resulting in confidence and preparedness to implement new skills); the telehealth journey (although inexperienced with telehealth before training, physiotherapists were confident and able to deliver remote care following training; however, they still experienced some technological challenges); the whole package (the combination of self-directed learning modules, mock consultation, and practice consultations with pilot patients was felt to be an effective learning approach, and patient information booklets supported the training package); and impact on broader clinical practice (training consolidated and refined existing OA management skills and enabled a switch to telehealth when the COVID-19 pandemic affected in-person clinical care). Conclusions: Findings provide evidence for the perceived effectiveness and acceptability of an e-learning program to train physiotherapists (in the context of a clinical trial) on best practice knee OA management, including telehealth delivery via videoconferencing. The implementation of e-learning programs to upskill physiotherapists in telehealth appears to be warranted, given the increasing adoption of telehealth service models for the delivery of clinical care. ",
doi="10.2196/25872",
url="https://www.jmir.org/2021/4/e25872",
url="http://www.ncbi.nlm.nih.gov/pubmed/33929326"
}


@Article{info:doi/10.2196/19875,
author="Mamede, Andr{\'e}
and Noordzij, Gera
and Jongerling, Joran
and Snijders, Merlijn
and Schop-Etman, Astrid
and Denktas, Semiha",
title="Combining Web-Based Gamification and Physical Nudges With an App (MoveMore) to Promote Walking Breaks and Reduce Sedentary Behavior of Office Workers: Field Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="12",
volume="23",
number="4",
pages="e19875",
keywords="internet",
keywords="eHealth",
keywords="mHealth",
keywords="mobile phone",
keywords="lifestyle",
keywords="obesity",
keywords="social network",
keywords="multilevel analysis",
keywords="physical exercise",
abstract="Background: Sedentary behavior (SB) and lack of physical activity (PA) have been associated with poorer health outcomes and are increasingly prevalent in individuals working in sedentary occupations such as office jobs. Gamification and nudges have attracted attention as promising strategies to promote changes in health behavior. However, most effectiveness studies thus far lacked active controls, and few studies have tested interventions combining these strategies. Objective: This study investigates the effectiveness of combining a gamified digital app with physical nudges to increase PA and reduce SB in Dutch office workers. Methods: Employees in the municipality of Rotterdam (N=298) from two office locations were randomized at the location level to either a 10-week intervention, combining a 5-week gamification phase encompassing a gamified digital app with social support features and a 5-week physical nudges phase, or to an active control (ie, basic digital app with self-monitoring and goal setting). The primary outcome was the daily step count, objectively measured via accelerometers. Secondary outcomes were self-reported PA and SB measured at baseline and at 5, 10, and 14 weeks. Mixed effects models were used to analyze the effects of the intervention on the outcome measures. Results: A total of 78.5\% (234/298) of participants completed the study and provided accelerometer data, whereas 36.9\% (110/298) participants completed the self-report measures at 14 weeks. In the gamification phase, step count data were missing for 13.5\% (473/3492) of observations in the control and 11.4\% (445/3888) in the intervention condition; however, these percentages increased to 39.6\% (1154/2910) and 59.6\% (1932/3492) at follow-up, respectively. During the gamification phase, intervention participants increased their number of daily steps by 634 (95\% CI 154.2-1113.8; P=.01) more than participants in the control group, after controlling for relevant factors. Improvements were not sustained during the physical nudges phase (P=.76) or follow-up (P=.88). Conclusions: A digital intervention with gamification and social support features significantly increased the step count of office workers compared with an active control. Physical nudges in the workplace were insufficient to promote the maintenance of behavioral changes achieved in the gamification phase. Future research should explore the long-term effectiveness of similar gamified digital interventions. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 49129401; https://www.isrctn.com/ISRCTN14881571 ",
doi="10.2196/19875",
url="https://www.jmir.org/2021/4/e19875",
url="http://www.ncbi.nlm.nih.gov/pubmed/33843593"
}


@Article{info:doi/10.2196/23948,
author="Chen, Yuanfang
and Ouyang, Liu
and Bao, S. Forrest
and Li, Qian
and Han, Lei
and Zhang, Hengdong
and Zhu, Baoli
and Ge, Yaorong
and Robinson, Patrick
and Xu, Ming
and Liu, Jie
and Chen, Shi",
title="A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="7",
volume="23",
number="4",
pages="e23948",
keywords="COVID-19",
keywords="clinical type",
keywords="multimodality",
keywords="classification",
keywords="machine learning",
keywords="diagnosis",
keywords="prediction",
keywords="reliable",
keywords="decision support",
abstract="Background: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results: Using clinical and laboratory results independently as input, the random forest models achieved >90\% and >95\% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97\% predictive accuracy. Conclusions: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority. ",
doi="10.2196/23948",
url="https://www.jmir.org/2021/4/e23948",
url="http://www.ncbi.nlm.nih.gov/pubmed/33714935"
}


@Article{info:doi/10.2196/16651,
author="Austrian, Jonathan
and Mendoza, Felicia
and Szerencsy, Adam
and Fenelon, Lucille
and Horwitz, I. Leora
and Jones, Simon
and Kuznetsova, Masha
and Mann, M. Devin",
title="Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e16651",
keywords="AB testing",
keywords="randomized controlled trials",
keywords="clinical decision support",
keywords="clinical informatics",
keywords="usability",
keywords="alert fatigue",
abstract="Background: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. Objective: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. Methods: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. Results: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid ({\textasciitilde}6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. Conclusions: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. Trial Registration: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191 ",
doi="10.2196/16651",
url="https://www.jmir.org/2021/4/e16651",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835035"
}


@Article{info:doi/10.2196/24153,
author="Luo, Gang
and Nau, L. Claudia
and Crawford, W. William
and Schatz, Michael
and Zeiger, S. Robert
and Koebnick, Corinna",
title="Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e24153",
keywords="asthma",
keywords="forecasting",
keywords="patient care management",
keywords="machine learning",
abstract="Background: Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. Objective: The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits. Methods: Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70\% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018. Results: Our method explained the forecasting results for 97.57\% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months. Conclusions: For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC. International Registered Report Identifier (IRRID): RR2-10.2196/resprot.5039 ",
doi="10.2196/24153",
url="https://www.jmir.org/2021/4/e24153",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856359"
}


@Article{info:doi/10.2196/25657,
author="Tremoulet, D. Patrice
and Shah, D. Priyanka
and Acosta, A. Alisha
and Grant, W. Christian
and Kurtz, T. Jon
and Mounas, Peter
and Kirchhoff, Michael
and Wade, Elizabeth",
title="Usability of Electronic Health Record--Generated Discharge Summaries: Heuristic Evaluation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e25657",
keywords="discharge summary",
keywords="usability",
keywords="electronic health record (EHR)",
keywords="care coordination",
keywords="elderly patients",
keywords="patient safety",
keywords="heuristic evaluation",
keywords="human factors",
abstract="Background: Obtaining accurate clinical information about recent acute care visits is extremely important for outpatient providers. However, documents used to communicate this information are often difficult to use. This puts patients at risk of adverse events. Elderly patients who are seen by more providers and have more care transitions are especially vulnerable. Objective: This study aimed to (1) identify the information about elderly patients' recent acute care visits needed to coordinate their care, (2) use this information to assess discharge summaries, and (3) provide recommendations to help improve the quality of electronic health record (EHR)--generated discharge summaries, thereby increasing patient safety. Methods: A literature review, clinician interviews, and a survey of outpatient providers were used to identify and categorize data needed to coordinate care for recently discharged elderly patients. Based upon those data, 2 guidelines for creating useful discharge summaries were created. The new guidelines, along with 17 previously developed medical documentation usability heuristics, were applied to assess 4 simulated elderly patient discharge summaries. Results: The initial research effort yielded a list of 29 items that should always be included in elderly patient discharge summaries and a list of 7 ``helpful, but not always necessary'' items. Evaluation of 4 deidentified elderly patient discharge summaries revealed that none of the documents contained all 36 necessary items; between 14 and 18 were missing. The documents each had several other issues, and they differed significantly in organization, layout, and formatting. Conclusions: Variations in content and structure of discharge summaries in the United States make them unnecessarily difficult to use. Standardization would benefit both patients, by lowering the risk of care transition--related adverse events, and outpatient providers, by helping reduce frustration that can contribute to burnout. In the short term, acute care providers can help improve the quality of their discharge summaries by working with EHR vendors to follow recommendations based upon this study. Meanwhile, additional human factors work should determine the most effective way to organize and present information in discharge summaries, to facilitate effective standardization. ",
doi="10.2196/25657",
url="https://www.jmir.org/2021/4/e25657",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856353"
}


@Article{info:doi/10.2196/24120,
author="Kim, Kipyo
and Yang, Hyeonsik
and Yi, Jinyeong
and Son, Hyung-Eun
and Ryu, Ji-Young
and Kim, Chul Yong
and Jeong, Cheol Jong
and Chin, Jun Ho
and Na, Young Ki
and Chae, Dong-Wan
and Han, Seok Seung
and Kim, Sejoong",
title="Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e24120",
keywords="acute kidney injury",
keywords="recurrent neural network",
keywords="prediction model",
keywords="external validation",
keywords="internal validation",
keywords="kidney",
keywords="neural networks",
abstract="Background: Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. Objective: We aimed to present an externally validated recurrent neural network (RNN)--based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support. Methods: Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots. Results: We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels. Conclusions: We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI. ",
doi="10.2196/24120",
url="https://www.jmir.org/2021/4/e24120",
url="http://www.ncbi.nlm.nih.gov/pubmed/33861200"
}


@Article{info:doi/10.2196/22796,
author="Tong, Yao
and Messinger, I. Amanda
and Wilcox, B. Adam
and Mooney, D. Sean
and Davidson, H. Giana
and Suri, Pradeep
and Luo, Gang",
title="Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e22796",
keywords="asthma",
keywords="forecasting",
keywords="machine learning",
keywords="patient care management",
keywords="risk factors",
abstract="Background: Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare. Objective: This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system. Methods: All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months. Results: Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10\% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6\% (13,268/14,644), a sensitivity of 70.2\% (153/218), and a specificity of 90.91\% (13,115/14,426). Conclusions: Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes. International Registered Report Identifier (IRRID): RR2-10.2196/resprot.5039 ",
doi="10.2196/22796",
url="https://www.jmir.org/2021/4/e22796",
url="http://www.ncbi.nlm.nih.gov/pubmed/33861206"
}


@Article{info:doi/10.2196/24996,
author="Lv, Haichen
and Yang, Xiaolei
and Wang, Bingyi
and Wang, Shaobo
and Du, Xiaoyan
and Tan, Qian
and Hao, Zhujing
and Liu, Ying
and Yan, Jun
and Xia, Yunlong",
title="Machine Learning--Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e24996",
keywords="heart failure",
keywords="machine learning",
keywords="predictive modeling",
keywords="mortality",
keywords="positive inotropic agents",
keywords="readmission",
abstract="Background: With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. Objective: Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. Methods: For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. Results: Of the 13,602 patients with HF enrolled in the study, 537 (3.95\%) died within 1 year and 2779 patients (20.43\%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 $\mu$g/L), followed by percentage of lymphocytes (<14.688\%) and neutrophil count (4.870{\texttimes}109/L). Conclusions: ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making. ",
doi="10.2196/24996",
url="https://www.jmir.org/2021/4/e24996",
url="http://www.ncbi.nlm.nih.gov/pubmed/33871375"
}


@Article{info:doi/10.2196/24716,
author="Mahalingaiah, Shruthi
and Cheng, Jojo J.
and Winter, R. Michael
and Rodriguez, Erika
and Fruh, Victoria
and Williams, Anna
and Nguyen, MyMy
and Madhavan, Rashmi
and Karanja, Pascaline
and MacRae, Jill
and Konanki, Charan Sai
and Lane, J. Kevin
and Aschengrau, Ann",
title="Multimodal Recruitment to Study Ovulation and Menstruation Health: Internet-Based Survey Pilot Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e24716",
keywords="polycystic ovary syndrome",
keywords="PCOS",
keywords="menstrual cycle",
keywords="multimodal recruitment strategy",
keywords="epidemiology",
keywords="recruitment",
keywords="pilot",
keywords="strategy",
keywords="women",
keywords="feasibility",
keywords="online survey",
keywords="ovulation",
keywords="menstrual",
abstract="Background: Multimodal recruitment strategies are a novel way to increase diversity in research populations. However, these methods have not been previously applied to understanding the prevalence of menstrual disorders such as polycystic ovary syndrome. Objective: The purpose of this study was to test the feasibility of recruiting a diverse cohort to complete a web-based survey on ovulation and menstruation health. Methods: We conducted the Ovulation and Menstruation Health Pilot Study using a REDCap web-based survey platform. We recruited 200 women from a clinical population, a community fair, and the internet. Results: We recruited 438 women over 29 weeks between September 2017 and March 2018. After consent and eligibility determination, 345 enrolled, 278 started (clinic: n=43; community fair: n=61; internet: n=174), and 247 completed (clinic: n=28; community fair: n=60; internet: n=159) the survey. Among all participants, the median age was 25.0 (SD 6.0) years, mean BMI was 26.1 kg/m2 (SD 6.6), 79.7\% (216/271) had a college degree or higher, and 14.6\% (37/254) reported a physician diagnosis of polycystic ovary syndrome. Race and ethnicity distributions were 64.7\% (176/272) White, 11.8\% (32/272) Black/African American, 7.7\% (21/272) Latina/Hispanic, and 5.9\% (16/272) Asian individuals; 9.9\% (27/272) reported more than one race or ethnicity. The highest enrollment of Black/African American individuals was in clinic (17/42, 40.5\%) compared to 1.6\% (1/61) in the community fair and 8.3\% (14/169) using the internet. Survey completion rates were highest among those who were recruited from the internet (159/174, 91.4\%) and community fairs (60/61, 98.4\%) compared to those recruited in clinic (28/43, 65.1\%). Conclusions: Multimodal recruitment achieved target recruitment in a short time period and established a racially diverse cohort to study ovulation and menstruation health. There were greater enrollment and completion rates among those recruited via the internet and community fair. ",
doi="10.2196/24716",
url="https://www.jmir.org/2021/4/e24716",
url="http://www.ncbi.nlm.nih.gov/pubmed/33861203"
}


@Article{info:doi/10.2196/25323,
author="Chaiken, Rebecca Sarina
and Han, Lisa
and Darney, G. Blair
and Han, Leo",
title="Factors Associated With Perceived Trust of False Abortion Websites: Cross-sectional Online Survey",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e25323",
keywords="abortion",
keywords="website trust",
keywords="internet use",
keywords="reproductive health",
keywords="misinformation",
abstract="Background: Most patients use the internet to search for health information. While there is a vast repository of searchable information online, much of the content is unregulated and therefore potentially incorrect, conflicting, or confusing. Abortion information online is particularly prone to being inaccurate as antichoice websites publish purposefully misleading information in formats that appear as neutral resources. To understand how antichoice websites appear neutral, we need to understand the specific website features of antichoice websites that impart an impression of trustworthiness. Objective: We sought to identify the characteristics of false or misleading abortion websites that make these websites appear trustworthy to the public. Methods: We conducted a cross-sectional study using Amazon's Mechanical Turk platform. We used validated questionnaires to ask participants to rate 11 antichoice websites and one neutral website identified by experts, focusing on website content, creators, and design. We collected sociodemographic data and participant views on abortion. We used a composite measure of ``mean overall trust'' as our primary outcome. Using correlation matrices, we determined which website characteristics were most associated with mean overall trust. Finally, we used linear regression to identify participant characteristics associated with overall trust. Results: Our analytic sample included 498 participants aged from 22 to 70 years, and 50.1\% (247/493) identified as female. Across 11 antichoice websites, creator confidence (``I believe that the creators of this website are honest and trustworthy'') had the highest correlation coefficient (strongest relationship) with mean overall trust (coefficient=0.70). Professional appearance (coefficient=0.59), look and feel (coefficient=0.59), perception that the information is created by experts (coefficient=0.59), association with a trustworthy organization (coefficient=0.58), valued features and functionalities (coefficient=0.54), and interactive capabilities (coefficient=0.52) all demonstrated strong relationships with mean overall trust. At the individual level, prochoice leaning was associated with higher overall trust of the neutral website (B=?0.43, 95\% CI ?0.87 to 0.01) and lower mean overall trust of the antichoice websites (B=0.52, 95\% CI 0.05 to 0.99). Conclusions: The mean overall trust of antichoice websites is most associated with design characteristics and perceived trustworthiness of website creators. Those who believe that access to abortion should be limited are more likely to have higher mean overall trust for antichoice websites. ",
doi="10.2196/25323",
url="https://www.jmir.org/2021/4/e25323",
url="http://www.ncbi.nlm.nih.gov/pubmed/33871378"
}


@Article{info:doi/10.2196/20996,
author="Jagesar, R. Raj
and Vorstman, A. Jacob
and Kas, J. Martien",
title="Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="7",
volume="23",
number="4",
pages="e20996",
keywords="digital phenotyping",
keywords="mobile behavioral monitoring",
keywords="passive behavioral monitoring",
keywords="smartphone-based behavioral monitoring",
keywords="research data management",
keywords="psychoinformatics",
keywords="mobile phone",
abstract="Background: Digital phenotyping, the measurement of human behavioral phenotypes using personal devices, is rapidly gaining popularity. Novel initiatives, ranging from software prototypes to user-ready research platforms, are innovating the field of biomedical research and health care apps. One example is the BEHAPP project, which offers a fully managed digital phenotyping platform as a service. The innovative potential of digital phenotyping strategies resides among others in their capacity to objectively capture measurable and quantitative components of human behavior, such as diurnal rhythm, movement patterns, and communication, in a real-world setting. The rapid development of this field underscores the importance of reliability and safety of the platforms on which these novel tools are operated. Large-scale studies and regulated research spaces (eg, the pharmaceutical industry) have strict requirements for the software-based solutions they use. Security and sustainability are key to ensuring continuity and trust. However, the majority of behavioral monitoring initiatives have not originated primarily in these regulated research spaces, which may be why these components have been somewhat overlooked, impeding the further development and implementation of such platforms in a secure and sustainable way. Objective: This study aims to provide a primer on the requirements and operational guidelines for the development and operation of a secure behavioral monitoring platform. Methods: We draw from disciplines such as privacy law, information, and computer science to identify a set of requirements and operational guidelines focused on security and sustainability. Taken together, the requirements and guidelines form the foundation of the design and implementation of the BEHAPP behavioral monitoring platform. Results: We present the base BEHAPP data collection and analysis flow and explain how the various concepts from security and sustainability are addressed in the design. Conclusions: Digital phenotyping initiatives are steadily maturing. This study helps the field and surrounding stakeholders to reflect upon and progress toward secure and sustainable operation of digital phenotyping--driven research. ",
doi="10.2196/20996",
url="https://www.jmir.org/2021/4/e20996",
url="http://www.ncbi.nlm.nih.gov/pubmed/33825695"
}


@Article{info:doi/10.2196/22042,
author="Ovalle, Anaelia
and Goldstein, Orpaz
and Kachuee, Mohammad
and Wu, C. Elizabeth S.
and Hong, Chenglin
and Holloway, W. Ian
and Sarrafzadeh, Majid",
title="Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e22042",
keywords="online social networks",
keywords="machine learning",
keywords="behavioral intervention",
keywords="data mining",
keywords="msm",
keywords="public health",
abstract="Background: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets. Objective: This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men. Methods: The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages. Results: Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6\%, 85.9\%, and 85.3\%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control. Conclusions: To our knowledge, our study is the first empirical evaluation of a social media--based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media--based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media--based just-in-time adaptive intervention to target substance use and HIV risk behavior. ",
doi="10.2196/22042",
url="https://www.jmir.org/2021/4/e22042",
url="http://www.ncbi.nlm.nih.gov/pubmed/33900200"
}


@Article{info:doi/10.2196/25358,
author="Renfrew, Elise Melanie
and Morton, Peter Darren
and Northcote, Maria
and Morton, Kyle Jason
and Hinze, Scott Jason
and Przybylko, Geraldine",
title="Participant Perceptions of Facilitators and Barriers to Adherence in a Digital Mental Health Intervention for a Nonclinical Cohort: Content Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e25358",
keywords="web-based mental health",
keywords="health promotion",
keywords="eHealth",
keywords="adherence",
keywords="participant perceptions",
keywords="mobile phone",
abstract="Background: Digital mental health promotion interventions (MHPIs) present a scalable opportunity to attenuate the risk of mental health distress among nonclinical cohorts. However, adherence is frequently suboptimal, and little is known about participants' perspectives concerning facilitators and barriers to adherence in community-based settings. Objective: This study aimed to examine participants' perceptions of facilitators and barriers to adherence in a web- and mobile app--based MHPI for a nonclinical cohort. Methods: This qualitative study used inductive, reflexive thematic analysis to explore free-text responses in a postintervention evaluation of a 10-week digital MHPI. The intervention was administered using a web and mobile app from September to December 2018. Participants (N=320) were Australian and New Zealand members of a faith-based organization who self-selected into the study, owned a mobile phone with messaging capability, had an email address and internet access, were fluent in English, provided informed consent, and gave permission for their data to be used for research. The postintervention questionnaire elicited participants' perceptions of facilitators and barriers to adherence during the intervention period. Results: Key factors that facilitated adherence were engaging content, time availability and management, ease of accessibility, easy or enjoyable practical challenges, high perceived value, and personal motivation to complete the intervention. The primary perceived barrier to adherence was the participants' lack of time. Other barriers included completing and recording practical activities, length of video content, technical difficulties, and a combination of personal factors. Conclusions: Time scarcity was the foremost issue for the nonclinical cohort engaged in this digital MHPI. Program developers should streamline digital interventions to minimize the time investment for participants. This may include condensed content, optimization of intuitive web and app design, simplified recording of activities, and greater participant autonomy in choosing optional features. Nonetheless, participants identified a multiplicity of other interindividual factors that facilitated or inhibited adherence. ",
doi="10.2196/25358",
url="https://www.jmir.org/2021/4/e25358",
url="http://www.ncbi.nlm.nih.gov/pubmed/33851925"
}


@Article{info:doi/10.2196/23882,
author="Gulick, Victoria
and Graves, Daniel
and Ames, Shannon
and Krishnamani, Parimala Pavitra",
title="Effect of a Virtual Reality--Enhanced Exercise and Education Intervention on Patient Engagement and Learning in Cardiac Rehabilitation: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e23882",
keywords="virtual reality",
keywords="VR",
keywords="cardiac rehabilitation",
keywords="patient experience",
keywords="patient education",
keywords="outpatient therapy",
keywords="exercise",
abstract="Background: Cardiac rehabilitation (CR) is clinically proven to reduce morbidity and mortality; however, many eligible patients do not enroll in treatment. Furthermore, many enrolled patients do not complete their full course of treatment. This is greatly influenced by socioeconomic factors but is also because of patients' lack of understanding of the importance of their care and a lack of motivation to maintain attendance. Objective: This study aims to explore the potential benefits of virtual reality (VR) walking trails within CR treatment, specifically with regard to patient knowledge retention, satisfaction with treatment, and the overall attendance of treatment sessions. Methods: New CR patients were enrolled and randomized on a rolling basis to either the control group or intervention group. Intervention patients completed their time on the treadmill with VR walking trails, which included audio-recorded education, whereas control patients completed the standard of care therapy. Both groups were assisted by nursing staff for all treatment sessions. Primary outcomes were determined by assessing 6-minute walk test improvement. In addition, secondary outcomes of patients' cardiac knowledge and satisfaction were assessed via a computer-based questionnaire; patient adherence to the recommended number of sessions was also monitored. Cardiac knowledge assessment included a prerehabilitation education quiz, and the same quiz was repeated at patients' final visit and again at the 2-month follow-up. The satisfaction questionnaire was completed at the final visit. Results: Between January 2018 and May 2019, 72 patients were enrolled---41 in the intervention group and 31 in the control group. On the basis of the results of the prerehabilitation and postrehabilitation 6-minute walk test, no significant differences were observed between the intervention and control groups (P=.64). No statistical differences were observed between groups in terms of education (P=.86) or satisfaction (P=.32) at any time point. The control group had statistically more favorable rates of attendance, as determined by the risk group comparison (P=.02) and the comparison of the rates for completing the minimum number of sessions (P=.046), but no correlation was observed between the study group and reasons for ending treatment. Conclusions: Although no improvements were seen in the VR intervention group over the control group, it is worth noting that limitations in the study design may have influenced these outcomes, not the medium itself. Furthermore, the qualitative information suggests that patients may have indeed enjoyed their experience with VR, even though quantitative satisfaction data did not capture this. Further considerations for how and when VR should be applied to CR are suggested in this paper. Trial Registration: ClinicalTrials.gov NCT03945201; https://clinicaltrials.gov/ct2/show/NCT03945201 ",
doi="10.2196/23882",
url="https://www.jmir.org/2021/4/e23882",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856355"
}


@Article{info:doi/10.2196/25504,
author="Gold, I. Jeffrey
and Annick, T. Erin
and Lane, S. Arianna
and Ho, Katherine
and Marty, T. Ryan
and Espinoza, C. Juan",
title="``Doc McStuffins: Doctor for a Day'' Virtual Reality (DocVR) for Pediatric Preoperative Anxiety and Satisfaction: Pediatric Medical Technology Feasibility Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e25504",
keywords="virtual reality (VR)",
keywords="pediatric",
keywords="anxiety",
keywords="preoperative",
keywords="satisfaction",
keywords="Doc McStuffins",
abstract="Background: Preoperative anxiety is a common occurrence among children and is associated with a host of maladaptive postoperative behaviors. Consequently, increased attention has been placed on interventions to reduce preoperative anxiety and its associated outcomes. Child Life preparation prior to surgery includes evidence-based practices such as age-appropriate distraction and therapeutic play. Virtual reality (VR) is a promising addition to the Child Life toolbox to address anxiety prior to surgery. The current study evaluates the implementation and feasibility of a VR experience, ``Doc McStuffins: Doctor for a Day Virtual Reality Experience'' (DocVR), developed by Disney Junior in collaboration with Children's Hospital Los Angeles, to target pediatric preoperative anxiety. Objective: The primary aim of this study was to examine the feasibility and efficacy of DocVR for preoperative anxiety. A secondary aim was to improve patient, caregiver, and health care provider satisfaction with the preoperative experience. Methods: In this study, 51 patients (age 6-14 years) scheduled for surgery in the ambulatory surgery center and the main operating room at Children's Hospital Los Angeles were approached to participate in Disney's DocVR experience. The patients played the DocVR experience for an average of 18 minutes (3-55 minutes). Irrespective of surgical procedure, patients and their families were eligible, as long as they had no known marked cognitive or visual impairments that would interfere with completing the survey and engaging in the DocVR experience. Results: Patients who tried the DocVR experience (n=51) responded overwhelmingly positively to both the VR technology and to the game itself. Patients experienced a statistically significant decrease in anxiety following DocVR game play (Z=--3.26, P=.001). On the Facial Affective Scale, the percentage of patients who chose the face with the most positive facial expression to represent their affect increased from 23\% (12/51) pre-VR to 49\% (25/47) post-VR. Furthermore, 97\% (38/39) of patients reported feeling more comfortable at the hospital, and 74\% (28/38) reported feeling less scared at the hospital after playing the game. The game was enjoyed by 94\% (46/49) of patients, and 88\% (30/34) of patients reported feeling both ``Interested'' and ``Involved'' in the game. Conclusions: DocVR is a feasible and beneficial VR experience to relieve pediatric preoperative anxiety and improve satisfaction in the preoperative area. The VR experience resulted in a decrease in overall anxiety and an increase in overall positive affect during the preoperative time. Patients also responded positively to the game, confirming their interest in the content and affirming the quality of the DocVR experience. The positive response to the game indicates that DocVR has the potential to make the overall preoperative experience less anxiety-producing and more comfortable, which leads to improved patient satisfaction. Naturally, improved patient outcomes lead to improved caregiver and health care provider satisfaction. ",
doi="10.2196/25504",
url="https://www.jmir.org/2021/4/e25504",
url="http://www.ncbi.nlm.nih.gov/pubmed/33730687"
}


@Article{info:doi/10.2196/24577,
author="Guo, Ziqiu
and Zhao, Zhi Sheng
and Guo, Ningyuan
and Wu, Yongda
and Weng, Xue
and Wong, Yuen-Ha Janet
and Lam, Hing Tai
and Wang, Ping Man",
title="Socioeconomic Disparities in eHealth Literacy and Preventive Behaviors During the COVID-19 Pandemic in Hong Kong: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e24577",
keywords="COVID-19",
keywords="eHealth literacy",
keywords="preventive behaviors",
keywords="socioeconomic disparities",
keywords="web-based information seeking",
abstract="Background: eHealth literacy can potentially facilitate web-based information seeking and taking informed measures. Objective: This study aimed to evaluate socioeconomic disparities in eHealth literacy and seeking of web-based information on COVID-19, and their associations with COVID-19 preventive behaviors. Methods: The COVID-19 Health Information Survey (CoVHIns), using telephonic (n=500) and web-based surveys (n=1001), was conducted among adults in Hong Kong in April 2020. The Chinese eHealth literacy scale (eHEALS; score range 8-40) was used to measure eHealth literacy. COVID-19 preventive behaviors included wearing surgical masks, wearing fabric masks, washing hands, social distancing, and adding water or bleach to the household drainage system. Adjusted beta coefficients and the slope indices of inequality for the eHEALS score by socioeconomic status, adjusted odds ratios (aORs) for seeking of web-based information on COVID-19 by socioeconomic status, and aORs for the high adherence to preventive behaviors by the eHEALS score and seeking of web-based information on COVID-19 were calculated. Results: The mean eHEALS score was 26.10 (SD 7.70). Age was inversely associated with the eHEALS score, but education and personal income were positively associated with the eHEALS score and seeking of web-based information on COVID-19 (for all, P for trend<.05). Participants who sought web-based information on COVID-19 showed high adherence to the practice of wearing surgical masks (aOR 1.56, 95\% CI 1.15-2.13), washing hands (aOR 1.33, 95\% CI 1.05-1.71), social distancing (aOR 1.48, 95\% CI 1.14-1.93), and adding water or bleach to the household drainage system (aOR 1.67, 95\% CI 1.28-2.18). Those with the highest eHEALS score displayed high adherence to the practice of wearing surgical masks (aOR 3.84, 95\% CI 1.63-9.05), washing hands (aOR 4.14, 95\% CI 2.46-6.96), social distancing (aOR 2.25, 95\% CI 1.39-3.65), and adding water or bleach to the household drainage system (aOR 1.94, 95\% CI 1.19-3.16), compared to those with the lowest eHEALS score. Conclusions: Chinese adults with a higher socioeconomic status had higher eHealth literacy and sought more web-based information on COVID-19; both these factors were associated with a high adherence to the guidelines for preventive behaviors during the COVID-19 pandemic. ",
doi="10.2196/24577",
url="https://www.jmir.org/2021/4/e24577",
url="http://www.ncbi.nlm.nih.gov/pubmed/33784240"
}


@Article{info:doi/10.2196/24360,
author="Slovis, Heritier Benjamin
and Riggio, M. Jeffrey
and Girondo, Melanie
and Martino, Cara
and Babula, Bracken
and Roke, M. Lindsey
and Kairys, C. John",
title="Reduction in Hospital System Opioid Prescribing for Acute Pain Through Default Prescription Preference Settings: Pre--Post Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e24360",
keywords="informatics",
keywords="electronic health record",
keywords="opioids",
keywords="prescriptions",
keywords="oxycodone",
abstract="Background: The United States is in an opioid epidemic. Passive decision support in the electronic health record (EHR) through opioid prescription presets may aid in curbing opioid dependence. Objective: The objective of this study is to determine whether modification of opioid prescribing presets in the EHR could change prescribing patterns for an entire hospital system. Methods: We performed a quasi-experimental retrospective pre--post analysis of a 24-month period before and after modifications to our EHR's opioid prescription presets to match Centers for Disease Control and Prevention guidelines. We included all opioid prescriptions prescribed at our institution for nonchronic pain. Our modifications to the EHR include (1) making duration of treatment for an opioid prescription mandatory, (2) adding a quick button for 3 days' duration while removing others, and (3) setting the default quantity of all oral opioid formulations to 10 tablets. We examined the quantity in tablets, duration in days, and proportion of prescriptions greater than 90 morphine milligram equivalents/day for our hospital system, and compared these values before and after our intervention for effect. Results: There were 78,246 prescriptions included in our study written on 30,975 unique patients. There was a significant reduction for all opioid prescriptions pre versus post in (1) the overall median quantity of tablets dispensed (54 [IQR 40-120] vs 42 [IQR 18-90]; P<.001), (2) median duration of treatment (10.5 days [IQR 5.0-30] vs 7.5 days [IQR 3.0-30]; P<.001), and (3) proportion of prescriptions greater than 90 morphine milligram equivalents/day (27.46\% [10,704/38,976; 95\% CI 27.02\%-27.91\%] vs 22.86\% [8979/39,270; 95\% CI 22.45\%-23.28\%]; P<.001). Conclusions: Modifications of opioid prescribing presets in the EHR can improve prescribing practice patterns. Reducing duration and quantity of opioid prescriptions could reduce the risk of dependence and overdose. ",
doi="10.2196/24360",
url="https://www.jmir.org/2021/4/e24360",
url="http://www.ncbi.nlm.nih.gov/pubmed/33851922"
}


@Article{info:doi/10.2196/23961,
author="Beauvais, Bradley
and Kruse, Scott Clemens
and Fulton, Lawrence
and Shanmugam, Ramalingam
and Ramamonjiarivelo, Zo
and Brooks, Matthew",
title="Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e23961",
keywords="electronic health records",
keywords="medical informatics",
keywords="hospitals",
keywords="delivery of health care",
keywords="financial management",
keywords="quality of health care",
keywords="treatment outcome",
abstract="Background: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. Objective: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. Methods: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value--Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. Results: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6\%; $\beta$=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3\%; $\beta$=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3\%; $\beta$=?.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9\%; Cerner: $\beta$=.0330, SE 0.0135, P=.01; Epic: $\beta$=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: $\beta$=.0388, SE 0.0122, P=.002; Cerner: $\beta$=.0283, SE 0.0124, P=.02; Meditech: $\beta$=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2\%). Conclusions: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery. ",
doi="10.2196/23961",
url="https://www.jmir.org/2021/4/e23961",
url="http://www.ncbi.nlm.nih.gov/pubmed/33851924"
}


@Article{info:doi/10.2196/21622,
author="Hamaya, Rikuta
and Fukuda, Hiroshi
and Takebayashi, Masaki
and Mori, Masaki
and Matsushima, Ryuji
and Nakano, Ken
and Miyake, Kuniaki
and Tani, Yoshiaki
and Yokokawa, Hirohide",
title="Effects of an mHealth App (Kencom) With Integrated Functions for Healthy Lifestyles on Physical Activity Levels and Cardiovascular Risk Biomarkers: Observational Study of 12,602 Users",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e21622",
keywords="mHealth",
keywords="app",
keywords="cardiovascular disease",
keywords="physical activity",
keywords="smartphone",
keywords="mobile phone",
abstract="Background: Mobile health (mHealth) apps are considered to be potentially powerful tools for improving lifestyles and preventing cardiovascular disease (CVD), although only few have undergone large, well-designed epidemiological research. ``kencom'' is a novel mHealth app with integrated functions for healthy lifestyles such as monitoring daily health/step data, providing tailored health information, or facilitating physical activity through group-based game events. The app is linked to large-scale Japanese insurance claims databases and annual health check-up databases, thus comprising a large longitudinal cohort. Objective: We aimed to assess the effects of kencom on physical activity levels and CVD risk factors such as obesity, hypertension, dyslipidemia, and diabetes mellitus in a large population in Japan. Methods: Daily step count, annual health check-up data, and insurance claim data of the kencom users were integrated within the kencom system. Step analysis was conducted by comparing the 1-year average daily step count before and after kencom registration. In the CVD risk analysis, changes in CVD biomarkers following kencom registration were evaluated among the users grouped into the quintile according to their change in step count. Results: A total of 12,602 kencom users were included for the step analysis and 5473 for the CVD risk analysis. The participants were generally healthy and their mean age was 44.1 (SD 10.2) years. The daily step count significantly increased following kencom registration by a mean of 510 steps/day (P<.001). In particular, participation in ``Arukatsu'' events held twice a year within the app was associated with a remarkable increase in step counts. In the CVD risk analysis, the users of the highest quintile in daily step change had, compared with those of the lowest quartile, a significant reduction in weight (--0.92 kg, P<.001), low-density lipoprotein cholesterol (--2.78 mg/dL, P=.004), hemoglobin A1c (HbA1c; --0.04\%, P=.004), and increase in high-density lipoprotein cholesterol (+1.91 mg/dL, P<.001) after adjustment of confounders. Conclusions: The framework of kencom successfully integrated the Japanese health data from multiple data sources to generate a large, longitudinal data set. The use of the kencom app was significantly associated with enhanced physical activity, which might lead to weight loss and improvement in lipid profile. ",
doi="10.2196/21622",
url="https://www.jmir.org/2021/4/e21622",
url="http://www.ncbi.nlm.nih.gov/pubmed/33900203"
}


@Article{info:doi/10.2196/17127,
author="Zhang, Chao
and Lakens, Dani{\"e}l
and IJsselsteijn, A. Wijnand",
title="Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e17127",
keywords="behavior change",
keywords="health behavior",
keywords="digital health intervention",
keywords="decision-making",
keywords="learning",
keywords="self-control",
keywords="habits",
keywords="theoretical framework",
doi="10.2196/17127",
url="https://www.jmir.org/2021/4/e17127",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835036"
}


@Article{info:doi/10.2196/22394,
author="Castaldo, Rossana
and Cavaliere, Carlo
and Soricelli, Andrea
and Salvatore, Marco
and Pecchia, Leandro
and Franzese, Monica",
title="Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="1",
volume="23",
number="4",
pages="e22394",
keywords="prostate cancer",
keywords="machine learning",
keywords="systematic review",
keywords="meta-analysis",
keywords="diagnosis",
keywords="imaging",
keywords="radiomics",
keywords="genomics",
keywords="clinical",
keywords="biomarkers",
abstract="Background: Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. Objective: This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies--version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. Results: In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. Conclusions: The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers. ",
doi="10.2196/22394",
url="https://www.jmir.org/2021/4/e22394",
url="http://www.ncbi.nlm.nih.gov/pubmed/33792552"
}


@Article{info:doi/10.2196/25167,
author="Bang, Seok Chang
and Lim, Hyun
and Jeong, Min Hae
and Hwang, Hyeon Sung",
title="Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e25167",
keywords="convolutional neural network",
keywords="deep learning",
keywords="automated deep learning",
keywords="endoscopy",
keywords="gastric neoplasms",
keywords="neural network",
keywords="deep learning model",
keywords="artificial intelligence",
abstract="Background: In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3\%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective: The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms. Additionally, endoscopist--artificial intelligence interactions were explored. Methods: The same 2899 endoscopic images that were employed to establish the previous model were used. A prospective multicenter validation using 206 and 1597 novel images was conducted. The primary outcome was external test accuracy. Neuro-T, Create ML Image Classifier, and AutoML Vision were used in establishing the models. Three doctors with different levels of endoscopy expertise were asked to classify the invasion depth of gastric neoplasms for each image without AutoDL support, with faulty AutoDL support, and with best performance AutoDL support in sequence. Results: The Neuro-T--based model reached 89.3\% (95\% CI 85.1\%-93.5\%) external test accuracy. For the model establishment time, Create ML Image Classifier showed the fastest time of 13 minutes while reaching 82.0\% (95\% CI 76.8\%-87.2\%) external test accuracy. While the expert endoscopist's decisions were not influenced by AutoDL, the faulty AutoDL misled the endoscopy trainee and the general physician. However, this was corrected by the support of the best performance AutoDL model. The trainee gained the most benefit from the AutoDL support. Conclusions: AutoDL is deemed useful for the on-site establishment of customized deep learning models. An inexperienced endoscopist with at least a certain level of expertise can benefit from AutoDL support. ",
doi="10.2196/25167",
url="https://www.jmir.org/2021/4/e25167",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856356"
}


@Article{info:doi/10.2196/25053,
author="Bang, Seok Chang
and Ahn, Yong Ji
and Kim, Jie-Hyun
and Kim, Young-Il
and Choi, Ju Il
and Shin, Geon Woon",
title="Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e25053",
keywords="early gastric cancer",
keywords="artificial intelligence",
keywords="machine learning",
keywords="endoscopic submucosal dissection",
keywords="undifferentiated",
keywords="gastric cancer",
keywords="endoscopy",
keywords="dissection",
abstract="Background: Undifferentiated type of early gastric cancer (U-EGC) is included among the expanded indications of endoscopic submucosal dissection (ESD); however, the rate of curative resection remains unsatisfactory. Endoscopists predict the probability of curative resection by considering the size and shape of the lesion and whether ulcers are present or not. The location of the lesion, indicating the likely technical difficulty, is also considered. Objective: The aim of this study was to establish machine learning (ML) models to better predict the possibility of curative resection in U-EGC prior to ESD. Methods: A nationwide cohort of 2703 U-EGCs treated by ESD or surgery were adopted for the training and internal validation cohorts. Separately, an independent data set of the Korean ESD registry (n=275) and an Asan medical center data set (n=127) treated by ESD were chosen for external validation. Eighteen ML classifiers were selected to establish prediction models of curative resection with the following variables: age; sex; location, size, and shape of the lesion; and whether ulcers were present or not. Results: Among the 18 models, the extreme gradient boosting classifier showed the best performance (internal validation accuracy 93.4\%, 95\% CI 90.4\%-96.4\%; precision 92.6\%, 95\% CI 89.5\%-95.7\%; recall 99.0\%, 95\% CI 97.8\%-99.9\%; and F1 score 95.7\%, 95\% CI 93.3\%-98.1\%). Attempts at external validation showed substantial accuracy (first external validation 81.5\%, 95\% CI 76.9\%-86.1\% and second external validation 89.8\%, 95\% CI 84.5\%-95.1\%). Lesion size was the most important feature in each explainable artificial intelligence analysis. Conclusions: We established an ML model capable of accurately predicting the curative resection of U-EGC before ESD by considering the morphological and ecological characteristics of the lesions. ",
doi="10.2196/25053",
url="https://www.jmir.org/2021/4/e25053",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856358"
}


@Article{info:doi/10.2196/25759,
author="Yin, Jiamin
and Ngiam, Yuan Kee
and Teo, Hai Hock",
title="Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e25759",
keywords="artificial intelligence",
keywords="machine learning",
keywords="deep learning",
keywords="system implementation",
keywords="clinical practice",
keywords="review",
abstract="Background: Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. Objective: The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice. Methods: We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings. Results: We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation. Conclusions: This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology. ",
doi="10.2196/25759",
url="https://www.jmir.org/2021/4/e25759",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885365"
}


@Article{info:doi/10.2196/27468,
author="Ghaderzadeh, Mustafa
and Asadi, Farkhondeh
and Jafari, Ramezan
and Bashash, Davood
and Abolghasemi, Hassan
and Aria, Mehrad",
title="Deep Convolutional Neural Network--Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e27468",
keywords="artificial intelligence",
keywords="classification",
keywords="computer-aided detection",
keywords="computed tomography scan",
keywords="convolutional neural network",
keywords="coronavirus",
keywords="COVID-19",
keywords="deep learning",
keywords="machine learning",
keywords="machine vision",
keywords="model",
keywords="pandemic",
abstract="Background: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. Objective: Machine vision--based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)--based algorithm. Methods: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. Results: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. Conclusions: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non--COVID-19 ones without any error in the application phase. Overall, the proposed deep learning--based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources. ",
doi="10.2196/27468",
url="https://www.jmir.org/2021/4/e27468",
url="http://www.ncbi.nlm.nih.gov/pubmed/33848973"
}


@Article{info:doi/10.2196/25757,
author="Cheng, Xiaolu
and Lin, Shuo-Yu
and Wang, Kevin
and Hong, Alicia Y.
and Zhao, Xiaoquan
and Gress, Dustin
and Wojtusiak, Janusz
and Cheskin, J. Lawrence
and Xue, Hong",
title="Healthfulness Assessment of Recipes Shared on Pinterest: Natural Language Processing and Content Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e25757",
keywords="healthfulness assessment",
keywords="recipes on Pinterest",
keywords="social networks",
keywords="natural language processing",
abstract="Background: Although Pinterest has become a popular platform for distributing influential information that shapes users' behaviors, the role of recipes pinned on Pinterest in these behaviors is not well understood. Objective: This study aims to explore the patterns of food ingredients and the nutritional content of recipes posted on Pinterest and to examine the factors associated with recipes that engage more users. Methods: Data were collected from Pinterest between June 28 and July 12, 2020 (207 recipes and 2818 comments). All samples were collected via 2 new user accounts with no search history. A codebook was developed with a raw agreement rate of 0.97 across all variables. Content analysis and natural language processing sentiment analysis techniques were employed. Results: Recipes using seafood or vegetables as the main ingredient had, on average, fewer calories and less sodium, sugar, and cholesterol than meat- or poultry-based recipes. For recipes using meat as the main ingredient, more than half of the energy was obtained from fat (277/490, 56.6\%). Although the most followed pinners tended to post recipes containing more poultry or seafood and less meat, recipes with higher fat content or providing more calories per serving were more popular, having more shared photos or videos and comments. The natural language processing--based sentiment analysis suggested that Pinterest users weighted taste more heavily than complexity (225/2818, 8.0\%) and health (84/2828, 2.9\%). Conclusions: Although popular pinners tended to post recipes with more seafood or poultry or vegetables and less meat, recipes with higher fat and sugar content were more user-engaging, with more photo or video shares and comments. Data on Pinterest behaviors can inform the development and implementation of nutrition health interventions to promote healthy recipe sharing on social media platforms. ",
doi="10.2196/25757",
url="https://www.jmir.org/2021/4/e25757",
url="http://www.ncbi.nlm.nih.gov/pubmed/33877052"
}


@Article{info:doi/10.2196/25228,
author="Karbownik, Seweryn Micha?
and Horne, Robert
and Paul, Ewelina
and Kowalczyk, Edward
and Szemraj, Janusz",
title="Determinants of Knowledge About Dietary Supplements Among Polish Internet Users: Nationwide Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="21",
volume="23",
number="4",
pages="e25228",
keywords="dietary supplements",
keywords="knowledge",
keywords="beliefs",
keywords="advertising",
keywords="education",
keywords="statistical model",
keywords="health services",
keywords="Poland",
keywords="online social networking",
abstract="Background: An accurate understanding of dietary supplements (DS) is a prerequisite for informed decisions regarding their intake. However, there is a need for studies on this understanding among the public based on validated research tools. Objective: This study aims to assess the knowledge about DS among Polish internet users with no medical education and to identify its determinants and design an appropriate predictive model. Methods: The study protocol was prospectively registered with a statistical analysis plan. Polish users of a web-based health service and a social networking service were administered a survey consisting of the recently developed questionnaire on knowledge about DS, the questionnaire on trust in advertising DS, the beliefs about medicines questionnaire, and several other health-related single-item measures and sociodemographic questions. The results were subjected to general linear modeling. Results: A total of 6273 participants were included. Of the 17 yes or no questions in the questionnaire of knowledge about DS, the mean number of correct responses was 9.0 (95\% CI 8.9-9.1). Health service users performed worse than social networking users by 2.3 points (95\% CI 2.1-2.5) in an analysis adjusted for potential confounders. Internet users had fewer true beliefs about DS if they presented higher trust in their advertising (adjusted $\beta$=?.37; 95\% CI ?.39 to ?.34), used DS (adjusted $\beta$=?.14; 95\% CI ?.17 to ?.12), experienced their positive effect (adjusted $\beta$=?.16; 95\% CI ?.18 to ?.13), were older or younger than 35 years (adjusted $\beta$=?.14; 95\% CI ?.17 to ?.12), expressed interest in the topic of DS (adjusted $\beta$=?.10; 95\% CI ?.13 to ?.08), reported getting information about the products from friends (adjusted $\beta$=?.13; 95\% CI ?.15 to ?.11), and believed that medicines are harmful (adjusted $\beta$=?.12; 95\% CI ?.15 to ?.10). The proposed 5-predictor model could explain 31.2\% of the variance in knowledge about DS. The model appeared resistant to overfitting and was able to forecast most of the observed associations. Conclusions: Polish internet users with no medical education exhibit some false beliefs regarding DS. Trusting the advertising of DS appears to conflict with knowledge about them. There is an urgent need for effective web-based educational campaigns on DS and the promotion of advertising literacy. After the proposed predictive model is externally validated, it may help identify the least informed target audience. ",
doi="10.2196/25228",
url="https://www.jmir.org/2021/4/e25228",
url="http://www.ncbi.nlm.nih.gov/pubmed/33658173"
}


@Article{info:doi/10.2196/25160,
author="Ze{\v c}evi{\'c}, Mila
and Mijatovi{\'c}, Dejan
and Kos Kokli{\v c}, Mateja
and ?abkar, Vesna
and Gidakovi{\'c}, Petar",
title="User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e25160",
keywords="diet-tracking apps",
keywords="mobile apps",
keywords="user reviews",
keywords="topic modeling",
keywords="n-grams",
keywords="mHealth",
keywords="nutrition",
keywords="diet",
keywords="well-being",
abstract="Background: The availability and use of mobile apps in health and nutrition management are increasing. Ease of access and user friendliness make diet-tracking apps an important ally in their users' efforts to lose and manage weight. To foster motivation for long-term use and to achieve goals, it is necessary to better understand users' opinions and needs for dietary self-monitoring. Objective: The aim of this study was to identify the key topics and issues that users highlight in their reviews of diet-tracking apps on Google Play Store. Identifying the topics that users frequently mention in their reviews of these apps, along with the user ratings for each of these apps, allowed us to identify areas where further improvement of the apps could facilitate app use, and support users' weight loss and intake management efforts. Methods: We collected 72,084 user reviews from Google Play Store for 15 diet-tracking apps that allow users to track and count calories. After a series of text processing operations, two text-mining techniques (topic modeling and topical n-grams) were applied to the corpus of user reviews of diet-tracking apps. Results: Using the topic modeling technique, 11 separate topics were extracted from the pool of user reviews. Most of the users providing feedback were generally satisfied with the apps they use (average rating of 4.4 out of 5 for the 15 apps). Most topics referred to the positive evaluation of the apps and their functions. Negatively rated topics mostly referred to app charges and technical difficulties encountered. We identified the positive and negative topic trigrams (3-word combinations) among the most frequently mentioned topics. Usability and functionality (tracking options) of apps were rated positively on average. Negative ratings were associated with trigrams related to adding new foods, technical issues, and app charges. Conclusions: Motivating users to use an app over time could help them better achieve their nutrition goals. Although user reviews generally showed positive opinions and ratings of the apps, developers should pay more attention to users' technical problems and inform users about expected payments, along with their refund and cancellation policies, to increase user loyalty. ",
doi="10.2196/25160",
url="https://www.jmir.org/2021/4/e25160",
url="http://www.ncbi.nlm.nih.gov/pubmed/33885375"
}


@Article{info:doi/10.2196/22432,
author="Hui, Yan Chi
and McKinstry, Brian
and Fulton, Olivia
and Buchner, Mark
and Pinnock, Hilary",
title="Patients' and Clinicians' Visions of a Future Internet-of-Things System to Support Asthma Self-Management: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e22432",
keywords="asthma",
keywords="supported self-management",
keywords="telehealth",
keywords="mobile application",
keywords="internet-of-things",
abstract="Background: Supported self-management for asthma reduces acute attacks and improves control. The internet of things could connect patients to health care providers, community services, and their living environments to provide overarching support for self-management. Objective: We aimed to identify patients' and clinicians' preferences for a future internet-of-things system and explore their visions of its potential to support holistic self-management. Methods: In an exploratory sequential mixed methods study, we recruited patients from volunteer databases and charities' social media. We purposively sampled participants to interview them about their vision of the design and utility of the internet of things as a future strategy for supporting self-management. Respondents who were not invited to participate in the interviews were invited to complete a web-based questionnaire to prioritize the features suggested by the interviewees. Clinicians were recruited from professional networks. Interviews were transcribed and analyzed thematically using PRISMS self-management taxonomy. Results: We interviewed 12 patients and 12 clinicians in the United Kingdom, and 140 patients completed the web-based questionnaires. Patients expressed mostly wanting a system to log their asthma control status automatically; provide real-time advice to help them learn about their asthma, identify and avoid triggers, and adjust their treatment. Peak flow (33/140, 23.6\%), environmental (pollen, humidity, air temperature) (33/140, 23.6\%), and asthma symptoms (25/140, 17.9\%) were the specific data types that patient most wanted. Information about asthma and text or email access to clinical advice provided a feeling of safety for patients. Clinicians wanted automated objective data about the patients' condition that they could access during consultations. The potential reduction in face-to-face consultations was appreciated by clinicians which they perceived could potentially save patients' travel time and health service resources. Lifestyle logs of fitness regimes or weight control were valued by some patients but were of less interest to clinicians. Conclusions: An automated internet-of-things system that requires minimal input from the user and provides timely advice in line with an asthma action plan agreed by the patient with their clinician was preferred by most respondents. Links to asthma information and the ability to connect with clinicians by text or email were perceived by patients as features that would provide a sense of safety. Further studies are needed to evaluate the usability and effectiveness of internet-of-things systems in routine clinical practice. ",
doi="10.2196/22432",
url="https://www.jmir.org/2021/4/e22432",
url="http://www.ncbi.nlm.nih.gov/pubmed/33847592"
}


@Article{info:doi/10.2196/25094,
author="Fang, Andrew Hao Sen
and Tan, Hwee Teng
and Tan, Cheryl Yan Fang
and Tan, Marcus Chun Jin",
title="Blockchain Personal Health Records: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e25094",
keywords="blockchain",
keywords="personal health records",
keywords="electronic health records",
keywords="distributed ledger",
keywords="systematic review",
abstract="Background: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing. Objective: This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs. Methods: Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form. Results: A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage. Conclusions: This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space. ",
doi="10.2196/25094",
url="https://www.jmir.org/2021/4/e25094",
url="http://www.ncbi.nlm.nih.gov/pubmed/33847591"
}


@Article{info:doi/10.2196/21082,
author="Garga, Shirali
and Thomas, Meryl
and Bhatia, Ashneet
and Sullivan, Aidan
and John-Leader, Franklin
and Pit, Sabrina",
title="Geosocial Networking Dating App Usage and Risky Sexual Behavior in Young Adults Attending a Music Festival: Cross-sectional Questionnaire Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e21082",
keywords="sexual health",
keywords="mobile apps",
keywords="young adults",
keywords="music festival",
abstract="Background: Despite the prevalent use of geosocial networking dating apps (GNDAs), there is limited research on their impact on sexual health outcomes among young music festivals attendees. Objective: This study aims to explore the use of GNDAs and risky sexual behaviors of young adults attending a music festival. Methods: The music festival attendees (N=862) completed a cross-sectional questionnaire study encompassing demographics, dating app use, and risky sexual behaviors in the past year. Associations between these variables were estimated using bivariate and multivariate logistic regression analyses. Results: Of the respondents, 51.9\% (448/862) had used GNDAs in the previous year. Compared with people who had 1 partner, people who had 2-5 sexual partners in the previous year had almost 7 times the odds of using dating apps (odds ratio [OR] 6.581, 95\% CI 4.643-9.328) and those who had more than 5 partners had 14 times the odds of using dating apps (OR 14.294, 95\% CI 8.92-22.906). Condom users were more likely to be app users (P<.001), as were those who relied on emergency Plan B (P=.002), but people using hormonal contraception were less likely to use dating apps (P=.004). After adjusting for sexual orientation and relationship status, those having casual sex had 3.096 (95\% CI 2.225-4.307; P<.001) times the odds of using dating apps and those having multiple sexual partners had 3.943 (95\% CI 2.782-5.588; P<.001) times the odds of using dating apps. Similarly, after adjusting for sexual orientation, relationship status, and number of sexual partners, people who had no discussions before having sex about sexually transmitted infections (STIs) or boundaries were more likely to use dating apps (OR 1.755, 95\% CI 1.232-2.500; P=.002). Those who perceived the risk of having sex without contraception to be very high had 2.486 (95\% CI 2.213-5.096; P=.01) times the odds of using dating apps than those who perceived no risk. Compared with those who perceived no risk, people who thought that the risk of having multiple sexual partners was low to high had 1.871 (95\% CI 1.024-3.418; P=.04) times the odds of using dating apps. A significant number of app users (389/440, 88.4\%) indicated that GNDAs should promote safe sex. Conclusions: This study identified that festival goers engaging in certain high-risk sexual behaviors, including casual sex, having multiple sexual partners, and having sex without discussion about STI status and boundaries, are more likely to use dating apps. Festival goers who perceived sex without any form of contraception, having sex while drunk, and having multiple sexual partners as risky were more likely to be app users. Policy makers and GNDA developers should acknowledge the vulnerability of their users to adverse sexual health outcomes and use GNDAs as a platform to promote risk-reduction practices. ",
doi="10.2196/21082",
url="https://www.jmir.org/2021/4/e21082",
url="http://www.ncbi.nlm.nih.gov/pubmed/33856354"
}


@Article{info:doi/10.2196/25817,
author="Jiang, Xuehan
and Xie, Hong
and Tang, Rui
and Du, Yanmei
and Li, Tao
and Gao, Jinsheng
and Xu, Xiuping
and Jiang, Siqi
and Zhao, Tingting
and Zhao, Wei
and Sun, Xingzhi
and Hu, Gang
and Wu, Dejun
and Xie, Guotong",
title="Characteristics of Online Health Care Services From China's Largest Online Medical Platform:  Cross-sectional Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e25817",
keywords="eHealth",
keywords="internet hospital",
keywords="China",
keywords="online health care services",
keywords="mHealth",
keywords="COVID-19",
keywords="digital health",
keywords="app",
keywords="online consultation",
keywords="user experience",
abstract="Background: Internet hospitals in China are in great demand due to limited and unevenly distributed health care resources, lack of family doctors, increased burdens of chronic diseases, and rapid growth of the aged population. The COVID-19 epidemic catalyzed the expansion of online health care services. In recent years, internet hospitals have been rapidly developed. Ping An Good Doctor is the largest, national online medical entry point in China and is a widely used platform providing online health care services. Objective: This study aims to give a comprehensive description of the characteristics of the online consultations and inquisitions in Ping An Good Doctor. The analyses tried to answer the following questions: (1) What are the characteristics of the consultations in Ping An Good Doctor in terms of department and disease profiles? (2) Who uses the online health services most frequently? and (3) How is the user experience of the online consultations of Ping An Good Doctor? Methods: A total of 35.3 million consultations and inquisitions over the course of 1 year were analyzed with respect to the distributions of departments and diseases, user profiles, and consulting behaviors. Results: The geographical distribution of the usage of Ping An Good Doctor showed that Shandong (18.4\%), Yunnan (15.6\%), Shaanxi (7.2\%), and Guangdong (5.5\%) were the provinces that used it the most; they accounted for 46.6\% of the total consultations and inquisitions. In terms of department distribution, we found that gynecology and obstetrics (19.2\%), dermatology (17.0\%), and pediatrics (14.4\%) were the top three departments in Ping An Good Doctor. The disease distribution analysis showed that, except for nondisease-specific consultations, acute upper respiratory infection (AURI) (4.1\%), pregnancy (2.8\%), and dermatitis (2.4\%) were the most frequently consulted diseases. In terms of user profiles, females (60.4\%) from 19 to 35 years of age were most likely to seek consultations online, in general. The user behavior analyses showed that the peak times of day for online consultations occurred at 10 AM, 3 PM, and 9 PM. Regarding user experience, 93.0\% of users gave full marks following their consultations. For some disease-related health problems, such as AURI, dermatitis, and eczema, the feedback scores were above average. Conclusions: The prevalence of internet hospitals, such as Ping An Good Doctor, illustrated the great demand for online health care services that can go beyond geographical limitations. Our analyses showed that nondisease-specific issues and moderate health problems were much more frequently consulted about than severe clinical conditions. This indicated that internet hospitals played the role of the family doctor, which helped to relieve the stress placed on offline hospitals and facilitated people's lives. In addition, good user experiences, especially regarding disease-related inquisitions, suggested that online health services can help solve health problems. With support from the government and acceptance by the public, online health care services could develop at a fast pace and greatly benefit people's daily lives. ",
doi="10.2196/25817",
url="https://www.jmir.org/2021/4/e25817",
url="http://www.ncbi.nlm.nih.gov/pubmed/33729985"
}


@Article{info:doi/10.2196/28216,
author="Jiang, Ying
and Koh, Ling Karen Wei
and Ramachandran, Joann Hadassah
and Tay, Kian Yee
and Wu, Xi Vivien
and Shorey, Shefaly
and Wang, Wenru",
title="Patients' Experiences of a Nurse-Led, Home-Based Heart Failure Self-management Program: Findings From a Qualitative Process Evaluation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="27",
volume="23",
number="4",
pages="e28216",
keywords="self-care",
keywords="psychosocial educational",
keywords="nurse-led",
keywords="mHealth",
keywords="self-management",
keywords="heart failure",
keywords="process evaluation",
keywords="nursing",
keywords="mobile phone",
abstract="Background: Heart failure (HF) is a major public health problem that places a significant disease burden on society. Self-care is important in the management of HF because it averts disease progression and reduces the number of hospitalizations. Effective nursing interventions promote HF self-care. Objective: This study aims to explore participants' perspectives on a nurse-led, home-based heart failure self-management program (HOM-HEMP) in a randomized controlled trial conducted in Singapore to gain insight into the effectiveness of the study intervention. Methods: A descriptive, qualitative approach was used. English- or Chinese-speaking participants from the intervention arms were recruited through a purposive sampling method from January 2019 to July 2019. Individual, face-to-face, semistructured interviews were conducted with 11 participants. All interviews were audio recorded and transcribed verbatim, with the participant identifiers omitted to ensure confidentiality. The thematic analysis approach was used to identify, analyze, and report patterns (themes) within the data. Results: A total of six themes emerged from the process evaluation interviews and were categorized according to the Donabedian structure-process-outcome framework as intervention structure, intervention process, and intervention outcome. These six themes were manageability of the intervention, areas for improvement, benefits of visiting, personal accountability in self-care, empowered with knowledge and skills in self-care after the intervention, and increased self-efficacy in cardiac care. Conclusions: The findings of the process evaluation provided additional information on participants' perceptions and experiences with the HOM-HEMP intervention. Although a home visit may be perceived as resource intensive, it remains to be the preferred way of engagement for most patients. Nurses play an important role in promoting HF self-care. The process of interaction with the patient can be an important process for empowering self-care behavior changes. ",
doi="10.2196/28216",
url="https://www.jmir.org/2021/4/e28216",
url="http://www.ncbi.nlm.nih.gov/pubmed/33904823"
}


@Article{info:doi/10.2196/25379,
author="Muric, Goran
and Lerman, Kristina
and Ferrara, Emilio",
title="Gender Disparity in the Authorship of Biomedical Research Publications During the COVID-19 Pandemic: Retrospective Observational Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="12",
volume="23",
number="4",
pages="e25379",
keywords="science of science",
keywords="gender disparities",
keywords="research evaluation",
keywords="COVID-19",
abstract="Background: Gender imbalances in academia have been evident historically and persist today. For the past 60 years, we have witnessed the increase of participation of women in biomedical disciplines, showing that the gender gap is shrinking. However, preliminary evidence suggests that women, including female researchers, are disproportionately affected by the COVID-19 pandemic in terms of unequal distribution of childcare, elderly care, and other kinds of domestic and emotional labor. Sudden lockdowns and abrupt shifts in daily routines have had disproportionate consequences on their productivity, which is reflected by a sudden drop in research output in biomedical research, consequently affecting the number of female authors of scientific publications. Objective: The objective of this study is to test the hypothesis that the COVID-19 pandemic has had a disproportionate adverse effect on the productivity of female researchers in the biomedical field in terms of authorship of scientific publications. Methods: This is a retrospective observational bibliometric study. We investigated the proportion of male and female researchers who published scientific papers during the COVID-19 pandemic, using bibliometric data from biomedical preprint servers and selected Springer-Nature journals. We used the ordinary least squares regression model to estimate the expected proportions over time by correcting for temporal trends. We also used a set of statistical methods, such as the Kolmogorov-Smirnov test and regression discontinuity design, to test the validity of the results. Results: A total of 78,950 papers from the bioRxiv and medRxiv repositories and from 62 selected Springer-Nature journals by 346,354 unique authors were analyzed. The acquired data set consisted of papers that were published between January 1, 2019, and August 2, 2020.  The proportion of female first authors publishing in the biomedical field during the pandemic dropped by 9.1\%, on average, across disciplines (expected arithmetic mean yest=0.39; observed arithmetic mean y=0.35; standard error of the estimate, Sest=0.007; standard error of the observation, $\sigma$x=0.004). The impact was particularly pronounced for papers related to COVID-19 research, where the proportion of female scientists in the first author position dropped by 28\% (yest=0.39; y=0.28; Sest=0.007; $\sigma$x=0.007). When looking at the last authors, the proportion of women dropped by 7.9\%, on average (yest=0.25; y=0.23; Sest=0.005; $\sigma$x=0.003), while the proportion of women writing about COVID-19 as the last author decreased by 18.8\% (yest=0.25; y=0.21; Sest=0.005; $\sigma$x=0.007). Further, by geocoding authors' affiliations, we showed that the gender disparities became even more apparent when disaggregated by country, up to 35\% in some cases. Conclusions: Our findings document a decrease in the number of publications by female authors in the biomedical field during the global pandemic. This effect was particularly pronounced for papers related to COVID-19, indicating that women are producing fewer publications related to COVID-19 research. This sudden increase in the gender gap was persistent across the 10 countries with the highest number of researchers. These results should be used to inform the scientific community of this worrying trend in COVID-19 research and the disproportionate effect that the pandemic has had on female academics. ",
doi="10.2196/25379",
url="https://www.jmir.org/2021/4/e25379",
url="http://www.ncbi.nlm.nih.gov/pubmed/33735097"
}


@Article{info:doi/10.2196/24656,
author="Chatterjee, Ayan
and Prinz, Andreas
and Gerdes, Martin
and Martinez, Santiago",
title="An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e24656",
keywords="activity",
keywords="nutrition",
keywords="sensor",
keywords="questionnaire",
keywords="SSN",
keywords="ontology",
keywords="SNOMED CT",
keywords="eCoach",
keywords="personalized",
keywords="recommendation",
keywords="automated",
keywords="CDSS",
keywords="healthy lifestyle",
keywords="interoperability",
keywords="eHealth",
keywords="goal setting",
keywords="semantics",
keywords="simulation",
keywords="proposition",
abstract="Background: Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This ``proof-of-concept'' study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. Objective: The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. Methods: We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of ``Semantic Sensor Network Ontology'' and ``Systematized Nomenclature of Medicine---Clinical Terms'' to develop our proposed eHealth ontology. The ontology has been created using Prot{\'e}g{\'e} (version 5.x). We have used the Java-based ``Jena Framework'' (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the ``HermiT 1.4.3.x'' ontology reasoner available in Prot{\'e}g{\'e} 5.x. Results: The proposed ontology has been implemented for the study case ``obesity.'' However, it can be extended further to other lifestyle diseases. ``UiA eHealth Ontology'' has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with ``Owl Viz,'' and the formal representation has been used to infer a participant's health status using the ``HermiT'' reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. Conclusions: This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation. ",
doi="10.2196/24656",
url="https://www.jmir.org/2021/4/e24656",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835031"
}


@Article{info:doi/10.2196/26261,
author="Seong, Donghyeong
and Jung, Sungwon
and Bae, Sungchul
and Chung, Jongsuk
and Son, Dae-Soon
and Yi, Byoung-Kee",
title="Fast Healthcare Interoperability Resources (FHIR)--Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e26261",
keywords="FHIR",
keywords="clinical NGS genomic testing",
keywords="clinical massive parallel sequencing",
keywords="quality control",
keywords="genomic reporting",
abstract="Background: Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. Objective: The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)--based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. Methods: We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. Results: Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. Conclusions: This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data. ",
doi="10.2196/26261",
url="https://www.jmir.org/2021/4/e26261",
url="http://www.ncbi.nlm.nih.gov/pubmed/33908889"
}


@Article{info:doi/10.2196/29452,
author="Witchel, J. Harry
and Thompson, A. Georgina
and Jones, I. Christopher
and Westling, I. Carina E.
and Romero, Juan
and Nicotra, Alessia
and Maag, Bruno
and Critchley, D. Hugo",
title="Correction: Spelling Errors and Shouting Capitalization Lead to Additive Penalties to Trustworthiness of Online Health Information: Randomized Experiment With Laypersons",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e29452",
doi="10.2196/29452",
url="https://www.jmir.org/2021/4/e29452",
url="http://www.ncbi.nlm.nih.gov/pubmed/33848255"
}


@Article{info:doi/10.2196/29629,
author="Zhang, Chao
and Lakens, Dani{\"e}l
and IJsselsteijn, A. Wijnand",
title="Correction: Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e29629",
doi="10.2196/29629",
url="https://www.jmir.org/2021/4/e29629",
url="http://www.ncbi.nlm.nih.gov/pubmed/33857012"
}


@Article{info:doi/10.2196/29598,
author="Chrzanowski, J?drzej
and So?ek, Julia
and Fendler, Wojciech
and Jemielniak, Dariusz",
title="Correction: Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="15",
volume="23",
number="4",
pages="e29598",
doi="10.2196/29598",
url="https://www.jmir.org/2021/4/e29598",
url="http://www.ncbi.nlm.nih.gov/pubmed/33857009"
}


@Article{info:doi/10.2196/29790,
author="Siste, Kristiana
and Hanafi, Enjeline
and Sen, Thung Lee
and Wahjoepramono, Perdana Petra Octavian
and Kurniawan, Andree
and Yudistiro, Ryan",
title="Correction: Potential Correlates of Internet Gaming Disorder Among Indonesian Medical Students: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="21",
volume="23",
number="4",
pages="e29790",
doi="10.2196/29790",
url="https://www.jmir.org/2021/4/e29790",
url="http://www.ncbi.nlm.nih.gov/pubmed/33882024"
}


@Article{info:doi/10.2196/29877,
author="He, Ying
and Aliyu, Aliyu
and Evans, Mark
and Luo, Cunjin",
title="Correction: Health Care Cybersecurity Challenges and Solutions Under the Climate of COVID-19: Scoping Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e29877",
doi="10.2196/29877",
url="https://www.jmir.org/2021/4/e29877",
url="http://www.ncbi.nlm.nih.gov/pubmed/33909589"
}


@Article{info:doi/10.2196/29069,
author="Evanoff, A. Bradley
and Strickland, R. Jaime
and Dale, Marie Ann
and Hayibor, Lisa
and Page, Emily
and Duncan, G. Jennifer
and Kannampallil, Thomas
and Gray, L. Diana",
title="Correction: Work-Related and Personal Factors Associated With Mental Well-Being During the COVID-19 Response: Survey of Health Care and Other Workers",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e29069",
doi="10.2196/29069",
url="https://www.jmir.org/2021/4/e29069",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835934"
}


@Article{info:doi/10.2196/26518,
author="Zhou, Xinyu
and Song, Yi
and Jiang, Hao
and Wang, Qian
and Qu, Zhiqiang
and Zhou, Xiaoyu
and Jit, Mark
and Hou, Zhiyuan
and Lin, Leesa",
title="Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e26518",
keywords="COVID-19",
keywords="engagement",
keywords="latent Dirichlet allocation",
keywords="public response",
keywords="sentiment",
keywords="social media",
keywords="topic modeling",
abstract="Background: COVID-19 cases resurged worldwide in the second half of 2020. Not much is known about the changes in public responses to containment measures from the initial outbreak to resurgence. Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence. Objective: This study aimed to assess and compare public responses to containment measures during the initial outbreak and resurgence of COVID-19 in China. Methods: We curated all COVID-19--related posts from Sina Weibo (China's version of Twitter) during the initial outbreak and resurgence of COVID-19 in Beijing, China. With a Python script, we constructed subsets of Weibo posts focusing on 3 containment measures: lockdown, the test-trace-isolate strategy, and suspension of gatherings. The Baidu open-source sentiment analysis model and latent Dirichlet allocation topic modeling, a widely used machine learning algorithm, were used to assess public engagement, sentiments, and frequently discussed topics on each containment measure. Results: A total of 8,985,221 Weibo posts were curated. In China, the containment measures evolved from a complete lockdown for the general population during the initial outbreak to a more targeted response strategy for high-risk populations during COVID-19 resurgence. Between the initial outbreak and resurgence, the average daily proportion of Weibo posts with negative sentiments decreased from 57\% to 47\% for the lockdown, 56\% to 51\% for the test-trace-isolate strategy, and 55\% to 48\% for the suspension of gatherings. Among the top 3 frequently discussed topics on lockdown measures, discussions on containment measures accounted for approximately 32\% in both periods, but those on the second-most frequently discussed topic shifted from the expression of negative emotions (11\%) to its impacts on daily life or work (26\%). The public expressed a high level of panic (21\%) during the initial outbreak but almost no panic (1\%) during resurgence. The more targeted test-trace-isolate measure received the most support (60\%) among all 3 containment measures in the initial outbreak, and its support rate approached 90\% during resurgence. Conclusions: Compared to the initial outbreak, the public expressed less engagement and less negative sentiments on containment measures and were more supportive toward containment measures during resurgence. Targeted test-trace-isolate strategies were more acceptable to the public. Our results indicate that when COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk populations should be promoted to balance pandemic control and its impact on daily life and the economy. ",
doi="10.2196/26518",
url="https://www.jmir.org/2021/4/e26518",
url="http://www.ncbi.nlm.nih.gov/pubmed/33750739"
}


@Article{info:doi/10.2196/26627,
author="Hussain, Amir
and Tahir, Ahsen
and Hussain, Zain
and Sheikh, Zakariya
and Gogate, Mandar
and Dashtipour, Kia
and Ali, Azhar
and Sheikh, Aziz",
title="Artificial Intelligence--Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="5",
volume="23",
number="4",
pages="e26627",
keywords="artificial intelligence",
keywords="COVID-19",
keywords="deep learning",
keywords="Facebook",
keywords="health informatics",
keywords="natural language processing",
keywords="public health",
keywords="sentiment analysis",
keywords="social media",
keywords="Twitter",
keywords="infodemiology",
keywords="vaccination",
abstract="Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence--based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning--based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58\%, 22\%, and 17\% in the United Kingdom, compared to 56\%, 24\%, and 18\% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence--enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake. ",
doi="10.2196/26627",
url="https://www.jmir.org/2021/4/e26627",
url="http://www.ncbi.nlm.nih.gov/pubmed/33724919"
}


@Article{info:doi/10.2196/25852,
author="Oh, Bumjo
and Hwangbo, Suhyun
and Jung, Taeyeong
and Min, Kyungha
and Lee, Chanhee
and Apio, Catherine
and Lee, Hyejin
and Lee, Seungyeoun
and Moon, Kyong Min
and Kim, Shin-Woo
and Park, Taesung",
title="Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e25852",
keywords="clinical decision support system",
keywords="clinical characteristics",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="prognostic tool",
keywords="severity",
abstract="Background: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. Objective: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. Methods: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. Results: Out of a total of 5601 patients, 4455 (79.5\%), 330 (5.9\%), 512 (9.1\%), and 301 (5.4\%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. Conclusions: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission. ",
doi="10.2196/25852",
url="https://www.jmir.org/2021/4/e25852",
url="http://www.ncbi.nlm.nih.gov/pubmed/33822738"
}


@Article{info:doi/10.2196/24389,
author="Shapiro, B. Mark
and Karim, Fazle
and Muscioni, Guido
and Augustine, Saju Abel",
title="Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="7",
volume="23",
number="4",
pages="e24389",
keywords="compartmental models",
keywords="COVID-19",
keywords="decision-making",
keywords="estimate",
keywords="infection rate",
keywords="infectious disease",
keywords="modeling",
keywords="pandemic",
keywords="prediction",
keywords="reproduction number",
keywords="SARS-CoV-2",
keywords="United States",
abstract="Background: The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual. Objective: We propose a simple method for estimating the time-varying infection rate and the Rt. Methods: We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels. Results: The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6\% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2\% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt. Conclusions: The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures. ",
doi="10.2196/24389",
url="https://www.jmir.org/2021/4/e24389",
url="http://www.ncbi.nlm.nih.gov/pubmed/33755577"
}


@Article{info:doi/10.2196/27293,
author="Viana dos Santos Santana, {\'I}ris
and CM da Silveira, Andressa
and Sobrinho, {\'A}lvaro
and Chaves e Silva, Lenardo
and Dias da Silva, Leandro
and Santos, S. Danilo F.
and Gurj{\~a}o, C. Edmar
and Perkusich, Angelo",
title="Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="8",
volume="23",
number="4",
pages="e27293",
keywords="COVID-19",
keywords="test prioritization",
keywords="classification models",
keywords="medical diagnosis",
abstract="Background: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective: The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods: Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results: Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions: The DT classification model can effectively (with a mean accuracy?89.12\%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing. ",
doi="10.2196/27293",
url="https://www.jmir.org/2021/4/e27293",
url="http://www.ncbi.nlm.nih.gov/pubmed/33750734"
}


@Article{info:doi/10.2196/26645,
author="Kwok, On Kin
and Wei, In Wan
and Huang, Ying
and Kam, Man Kai
and Chan, Yang Emily Ying
and Riley, Steven
and Chan, Henry Ho Hin
and Hui, Cheong David Shu
and Wong, Shan Samuel Yeung
and Yeoh, Kiong Eng",
title="Evolving Epidemiological Characteristics of COVID-19 in Hong Kong From January to August 2020: Retrospective Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e26645",
keywords="SARS-CoV-2",
keywords="COVID-19",
keywords="evolving epidemiology",
keywords="containment delay",
keywords="serial interval",
keywords="Hong Kong",
keywords="epidemiology",
keywords="public health",
keywords="transmission",
keywords="China",
keywords="intervention",
keywords="case study",
abstract="Background: COVID-19 has plagued the globe, with multiple SARS-CoV-2 clusters hinting at its evolving epidemiology. Since the disease course is governed by important epidemiological parameters, including containment delays (time between symptom onset and mandatory isolation) and serial intervals (time between symptom onsets of infector-infectee pairs), understanding their temporal changes helps to guide interventions. Objective: This study aims to characterize the epidemiology of the first two epidemic waves of COVID-19 in Hong Kong by doing the following: (1) estimating the containment delays, serial intervals, effective reproductive number (Rt), and proportion of asymptomatic cases; (2) identifying factors associated with the temporal changes of the containment delays and serial intervals; and (3) depicting COVID-19 transmission by age assortativity and types of social settings. Methods: We retrieved the official case series and the Apple mobility data of Hong Kong from January-August 2020. The empirical containment delays and serial intervals were fitted to theoretical distributions, and factors associated with their temporal changes were quantified in terms of percentage contribution (the percentage change in the predicted outcome from multivariable regression models relative to a predefined comparator). Rt was estimated with the best fitted distribution for serial intervals. Results: The two epidemic waves were characterized by imported cases and clusters of local cases, respectively. Rt peaked at 2.39 (wave 1) and 3.04 (wave 2). The proportion of asymptomatic cases decreased from 34.9\% (0-9 years) to 12.9\% (?80 years). Log-normal distribution best fitted the 1574 containment delays (mean 5.18 [SD 3.04] days) and the 558 serial intervals (17 negative; mean 4.74 [SD 4.24] days). Containment delays decreased with involvement in a cluster (percentage contribution: 10.08\%-20.73\%) and case detection in the public health care sector (percentage contribution: 27.56\%, 95\% CI 22.52\%-32.33\%). Serial intervals decreased over time (6.70 days in wave 1 versus 4.35 days in wave 2) and with tertiary transmission or beyond (percentage contribution: --50.75\% to --17.31\%), but were lengthened by mobility (percentage contribution: 0.83\%). Transmission within the same age band was high (18.1\%). Households (69.9\%) and social settings (20.3\%) were where transmission commonly occurred. Conclusions: First, the factors associated with reduced containment delays suggested government-enacted interventions were useful for achieving outbreak control and should be further encouraged. Second, the shorter serial intervals associated with the composite mobility index calls for empirical surveys to disentangle the role of different contact dimensions in disease transmission. Third, the presymptomatic transmission and asymptomatic cases underscore the importance of remaining vigilant about COVID-19. Fourth, the time-varying epidemiological parameters suggest the need to incorporate their temporal variations when depicting the epidemic trajectory. Fifth, the high proportion of transmission events occurring within the same age group supports the ban on gatherings outside of households, and underscores the need for residence-centered preventive measures. ",
doi="10.2196/26645",
url="https://www.jmir.org/2021/4/e26645",
url="http://www.ncbi.nlm.nih.gov/pubmed/33750740"
}


@Article{info:doi/10.2196/24053,
author="Mo, Han Phoenix Kit
and Fong, In Vivian Wai
and Song, Bo
and Di, Jiangli
and Wang, Qian
and Wang, Linhong",
title="Association of Perceived Threat, Negative Emotions, and Self-Efficacy With Mental Health and Personal Protective Behavior Among Chinese Pregnant Women During the COVID-19 Pandemic: Cross-sectional Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="12",
volume="23",
number="4",
pages="e24053",
keywords="COVID-19",
keywords="pregnant women",
keywords="depression",
keywords="anxiety",
keywords="self-efficacy",
keywords="mental health",
keywords="survey",
keywords="threat",
keywords="emotion",
abstract="Background: COVID-19 is an emerging infectious disease that has created health care challenges worldwide. Pregnant women are particularly affected by this disease. Objective: The aims of this study are to assess the levels of perceived threat (susceptibility, severity, impact), negative emotions (fear, worry), and self-efficacy of pregnant women in China related to COVID-19 and to examine their associations with mental health (depression and anxiety) and personal protective behavior (wearing a face mask). Methods: A total of 4087 pregnant women from China completed a cross-sectional web-based survey between March 3 and 10, 2020. Results: The prevalence of probable depression and anxiety was 48.7\% (1989/4087) and 33.0\% (1347/4087), respectively; 23.8\% participants (974/4087) reported always wearing a face mask when going out. Of the 4087 participants, 32.1\% (1313) and 36.4\% (1490) perceived themselves or their family members to be susceptible to COVID-19 infection, respectively; 3216-3518 (78.7\%-86.1\%) agreed the disease would have various severe consequences. Additionally, 2275 of the 4087 participants (55.7\%) showed self-efficacy in protecting themselves from contracting COVID-19, and 2232 (54.6\%) showed efficacy in protecting their family members; 1303 (31.9\%) reported a high level of fear of the disease, and 2780-3056 (68.0\%-74.8\%) expressed worry about various aspects of COVID-19. The results of the multivariate multinominal logistic regression analyses showed that perceived severity, perceived impact, fear, and worry were risk factors for probable depression and anxiety, while self-efficacy was a protective factor. The results of the multivariate logistic regression analysis showed that perceived susceptibility was associated with always wearing a face mask. Conclusions: Chinese pregnant women showed high levels of mental distress but low levels of personal protective behavior during the COVID-19 pandemic. Interventions are needed to promote the mental health and health behavior of pregnant women during the pandemic. ",
doi="10.2196/24053",
url="https://www.jmir.org/2021/4/e24053",
url="http://www.ncbi.nlm.nih.gov/pubmed/33729983"
}


@Article{info:doi/10.2196/26331,
author="Chrzanowski, J?drzej
and So?ek, Julia
and Fendler, Wojciech
and Jemielniak, Dariusz",
title="Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="12",
volume="23",
number="4",
pages="e26331",
keywords="COVID-19",
keywords="pandemic",
keywords="media",
keywords="Wikipedia",
keywords="internet",
keywords="online health information",
keywords="information seeking",
keywords="interest",
keywords="retrospective",
keywords="surveillance",
keywords="infodemiology",
keywords="infoveillance",
abstract="Background: In the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population's altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions. Objective: We aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic. Methods: We performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic. Results: We observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19--related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns. Conclusions: The analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles. ",
doi="10.2196/26331",
url="https://www.jmir.org/2021/4/e26331",
url="http://www.ncbi.nlm.nih.gov/pubmed/33667176"
}


@Article{info:doi/10.2196/26874,
author="Griffith, Janessa
and Marani, Husayn
and Monkman, Helen",
title="COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e26874",
keywords="vaccine hesitancy",
keywords="vaccine",
keywords="COVID-19",
keywords="immunization",
keywords="Twitter",
keywords="infodemiology",
keywords="infoveillance",
keywords="social media",
keywords="behavioral science",
keywords="behavior",
keywords="Canada",
keywords="content analysis",
keywords="framework",
keywords="hesitancy",
abstract="Background: With the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75\% of people in Canada plan to receive one of the vaccines. Objective: The purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine. Methods: We screened 3915 tweets from public Twitter profiles in Canada by using the search words ``vaccine'' and ``COVID.'' The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework. Results: Overall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes: concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack of legal liability from vaccine companies. This study also examined mistrust toward the medical industry not due to hesitancy, but due to the legacy of communities marginalized by health care institutions. These themes were categorized into the following five Theoretical Domains Framework constructs: knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion. Conclusions: With the World Health Organization stating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines. ",
doi="10.2196/26874",
url="https://www.jmir.org/2021/4/e26874",
url="http://www.ncbi.nlm.nih.gov/pubmed/33769946"
}


@Article{info:doi/10.2196/27045,
author="Zeng, Chengbo
and Zhang, Jiajia
and Li, Zhenlong
and Sun, Xiaowen
and Olatosi, Bankole
and Weissman, Sharon
and Li, Xiaoming",
title="Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="13",
volume="23",
number="4",
pages="e27045",
keywords="COVID-19",
keywords="mobility",
keywords="incidence",
keywords="South Carolina",
abstract="Background: Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective: The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results: Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7\%, 90.9\%, and 81.6\% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3\%-74.5\%. Conclusions: Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences. ",
doi="10.2196/27045",
url="https://www.jmir.org/2021/4/e27045",
url="http://www.ncbi.nlm.nih.gov/pubmed/33784239"
}


@Article{info:doi/10.2196/26211,
author="Dom{\'i}nguez-Olmedo, L. Juan
and Gragera-Mart{\'i}nez, {\'A}lvaro
and Mata, Jacinto
and Pach{\'o}n {\'A}lvarez, Victoria",
title="Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e26211",
keywords="COVID-19",
keywords="electronic health record",
keywords="machine learning",
keywords="mortality",
keywords="prediction",
abstract="Background: The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain's health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. Objective: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. Methods: After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. Results: After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95\% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95\% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95\% CI 0.92-0.95) for accuracy, 0.77 (95\% CI 0.72-0.83) for the F-score, 0.93 (95\% CI 0.89-0.98) for sensitivity, and 0.91 (95\% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. Conclusions: Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality. ",
doi="10.2196/26211",
url="https://www.jmir.org/2021/4/e26211",
url="http://www.ncbi.nlm.nih.gov/pubmed/33793407"
}


@Article{info:doi/10.2196/26994,
author="Sorkin, H. Dara
and Janio, A. Emily
and Eikey, V. Elizabeth
and Schneider, Margaret
and Davis, Katelyn
and Schueller, M. Stephen
and Stadnick, A. Nicole
and Zheng, Kai
and Neary, Martha
and Safani, David
and Mukamel, B. Dana",
title="Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="16",
volume="23",
number="4",
pages="e26994",
keywords="COVID-19",
keywords="digital technologies",
keywords="mHealth",
keywords="mental health",
keywords="anxiety",
keywords="depression",
keywords="MTurk",
keywords="e-mental health",
keywords="digital health",
keywords="distress",
keywords="self-management",
abstract="Background: Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically. Objective: The objective of this study was to systematically examine whether there was a COVID-19 pandemic--related increase in the self-reported use of digital mental health tools and other technologies to manage mental health. Methods: We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one's mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects. Results: Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95\% CI 1.27-3.35), but not anxiety (OR 1.21, 95\% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95\% CI 1.12-1.27 and OR 1.12, 95\% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95\% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95\% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95\% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95\% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95\% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types. Conclusions: Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy. ",
doi="10.2196/26994",
url="https://www.jmir.org/2021/4/e26994",
url="http://www.ncbi.nlm.nih.gov/pubmed/33822737"
}


@Article{info:doi/10.2196/27060,
author="Chung, Heewon
and Ko, Hoon
and Kang, Seong Wu
and Kim, Won Kyung
and Lee, Hooseok
and Park, Chul
and Song, Hyun-Ok
and Choi, Tae-Young
and Seo, Ho Jae
and Lee, Jinseok",
title="Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e27060",
keywords="COVID-19",
keywords="artificial intelligence",
keywords="blood samples",
keywords="mortality prediction",
abstract="Background: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. Objective: The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. Methods: We developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC). Results: We found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2\%), specificity (90.4\%), accuracy (90.4\%), balanced accuracy (90.3\%), and AUC (0.96). Conclusions: Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance. ",
doi="10.2196/27060",
url="https://www.jmir.org/2021/4/e27060",
url="http://www.ncbi.nlm.nih.gov/pubmed/33764883"
}


@Article{info:doi/10.2196/24191,
author="K{\"o}nig, Alexandra
and Riviere, Kevin
and Linz, Nicklas
and Lindsay, Hali
and Elbaum, Julia
and Fabre, Roxane
and Derreumaux, Alexandre
and Robert, Philippe",
title="Measuring Stress in Health Professionals Over the Phone Using Automatic Speech Analysis During the COVID-19 Pandemic: Observational Pilot Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="19",
volume="23",
number="4",
pages="e24191",
keywords="stress detection",
keywords="speech",
keywords="voice analysis",
keywords="COVID-19",
keywords="phone monitoring",
keywords="computer linguistics",
abstract="Background: During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior. Objective: This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants' speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks. Methods: Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed. Results: Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31). Conclusions: Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety. ",
doi="10.2196/24191",
url="https://www.jmir.org/2021/4/e24191",
url="http://www.ncbi.nlm.nih.gov/pubmed/33739930"
}


@Article{info:doi/10.2196/23311,
author="Wang, Zhicheng
and Lin, Leesa
and Guo, Yan
and Xiong, Huayi
and Tang, Kun",
title="The Uncounted Casualties of a Hidden COVID-19 Epidemic in China: Cross-sectional Study on Deaths Related to Overwork",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e23311",
keywords="nonpharmaceutical interventions",
keywords="on-duty deaths",
keywords="COVID-19",
keywords="overwork death",
keywords="crowdsourced data",
keywords="intervention",
keywords="mortality",
keywords="casualty",
keywords="cross-sectional",
keywords="overwork",
keywords="stress",
abstract="Background: During the COVID-19 response, nonclinical essential workers usually worked overtime and experienced significant work stress, which subsequently increased their risk of mortality due to cardiovascular diseases, stroke, and pre-existing conditions. Deaths on duty, including deaths due to overwork, during the COVID-19 response were usually reported on web-based platforms for public recognition and solidarity. Although no official statistics are available for these casualties, a list of on-duty deaths has been made publicly available on the web by crowdsourcing. Objective: This study aims to understand the trends and characteristics of deaths related to overwork among the frontline nonclinical essential workers participating in nonpharmaceutical interventions during the first wave of COVID-19 in China. Methods: Based on a web-based crowdsourced list of deaths on duty during the first wave of the COVID-19 response in China, we manually verified all overwork-related death records against the full-text web reports from credible sources. After excluding deaths caused by COVID-19 infection and accidents, a total of 340 deaths related to overwork among nonclinical essential workers were attributed to combatting the COVID-19 crisis. We coded the key characteristics of the deceased workers, including sex, age at death, location, causes of death, date of incidence, date of death, containment duties, working area, and occupation. The temporal and spatial correlations between deaths from overwork and COVID-19 cases in China were also examined using Pearson correlation coefficient. Results: From January 20 to April 26, 2020, at least 340 nonclinical frontline workers in China were reported to have died as a result of overwork while combatting COVID-19. The weekly overwork mortality was positively correlated with weekly COVID-19 cases (r=0.79, P<.001). Two-thirds of deceased workers (230/340, 67.6\%) were under 55 years old, and two major causes of deaths related to overwork were cardiovascular diseases (138/340, 40.6\%) and cerebrovascular diseases (73/340, 21.5\%). Outside of Hubei province, there were almost 2.5 times as many deaths caused by COVID-19--related overwork (308/340, 90.6\%) than by COVID-19 itself (n=120). Conclusions: The high number of deaths related to overwork among nonclinical essential workers at the frontline of the COVID-19 epidemic is alarming. Policies for occupational health protection against work hazards should therefore be prioritized and enforced. ",
doi="10.2196/23311",
url="https://www.jmir.org/2021/4/e23311",
url="http://www.ncbi.nlm.nih.gov/pubmed/33822735"
}


@Article{info:doi/10.2196/23488,
author="Resnicow, Ken
and Bacon, Elizabeth
and Yang, Penny
and Hawley, Sarah
and Van Horn, Lee M.
and An, Lawrence",
title="Novel Predictors of COVID-19 Protective Behaviors Among US Adults: Cross-sectional Survey",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e23488",
keywords="COVID-19",
keywords="protective behavior",
keywords="psychological predictors",
keywords="reactance",
keywords="conspiracy beliefs",
keywords="public health",
keywords="health communication",
keywords="communication",
keywords="protection",
keywords="behavior",
keywords="psychology",
abstract="Background: A central component of the public health strategy to control the COVID-19 pandemic involves encouraging mask wearing and social distancing to protect individuals from acquiring and transmitting the virus. Objective: This study aims to understand the psychological factors that drive adoption or rejection of these protective behaviors, which can inform public health interventions to control the pandemic. Methods: We conducted an online survey of a representative sample of 1074 US adults and assessed three novel potential predictors of COVID-19 behaviors: trait reactance, COVID-19 conspiracy beliefs, and COVID-19 apocalypse beliefs. Key outcomes (dependent variables) included an index of COVID-19 protective behaviors, the number of trips taken from the home, and COVID-19 knowledge. Results: In bivariate analyses, all three predictors were significantly correlated in the hypothesized direction with the three COVID-19 outcomes. Specifically, each predictor was negatively (P<.01) correlated with the COVID-19 protective behaviors index and COVID-19 knowledge score, and positively correlated with trips taken from home per week (more of which was considered higher risk). COVID-19 protective behaviors and COVID-19 knowledge were significantly lower in the top median compared to the bottom median for all three predictors. In general, these findings remained significant after adjusting for all novel predictors plus age, gender, income, education, race, political party, and religiosity. Self-identified Republicans (vs other political affiliations) reported the highest values for each of the novel predictors. Conclusions: This study can inform the development of health communication interventions to encourage the adoption of COVID-19 protective behaviors. Interestingly, we found that higher scores of all three novel predictors were associated with lower COVID-19 knowledge, suggesting that lack of an accurate understanding of the virus may be driving some of these attitudes; although, it is also possible that these attributes may interfere with one's willingness or ability to seek and absorb accurate health information. These individuals may be particularly immune to accepting new information and yielding their beliefs. Health communication professionals may apply lessons learned from countering similar beliefs around climate change and vaccine hesitancy. Messages designed for individuals prone to reactance may be more effective if they minimize controlling language and emphasize the individual's independence in adopting these behavioral recommendations. Messaging for those who possess conspiracy beliefs should similarly not assume that providing evidence contrary to these beliefs will alone alter behavior. Other communication techniques such as rolling with resistance, a strategy used in motivational interviewing, may be helpful. Messaging for those with apocalyptic beliefs may require using religious leaders as the message source and using scripture that would support the adoption of COVID-19 protection behaviors. ",
doi="10.2196/23488",
url="https://www.jmir.org/2021/4/e23488",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835930"
}


@Article{info:doi/10.2196/24586,
author="Furstrand, Dorthe
and Pihl, Andreas
and Orbe, Bayram Elif
and Kingod, Natasja
and S{\o}ndergaard, Jens",
title="``Ask a Doctor About Coronavirus'': How Physicians on Social Media Can Provide Valid Health Information During a Pandemic",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e24586",
keywords="COVID-19",
keywords="coronavirus",
keywords="digital health literacy",
keywords="eHealth literacy",
keywords="Facebook",
keywords="framework",
keywords="health information",
keywords="health literacy",
keywords="health promotion",
keywords="infodemic",
keywords="infodemiology",
keywords="mental health",
keywords="misinformation",
keywords="pandemic",
keywords="patient-physician relationship",
keywords="public health",
keywords="social media",
keywords="trust",
keywords="web-based community",
doi="10.2196/24586",
url="https://www.jmir.org/2021/4/e24586",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835935"
}


@Article{info:doi/10.2196/21747,
author="He, Ying
and Aliyu, Aliyu
and Evans, Mark
and Luo, Cunjin",
title="Health Care Cybersecurity Challenges and Solutions Under the Climate of COVID-19: Scoping Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="20",
volume="23",
number="4",
pages="e21747",
keywords="health care",
keywords="security incidents",
keywords="root causes",
keywords="cybersecurity challenges",
keywords="cybersecurity solutions",
keywords="COVID-19",
keywords="pandemics",
abstract="Background: COVID-19 has challenged the resilience of the health care information system, which has affected our ability to achieve the global goal of health and well-being. The pandemic has resulted in a number of recent cyberattacks on hospitals, pharmaceutical companies, the US Department of Health and Human Services, the World Health Organization and its partners, and others. Objective: The aim of this review was to identify key cybersecurity challenges, solutions adapted by the health sector, and areas of improvement needed to counteract the recent increases in cyberattacks (eg, phishing campaigns and ransomware attacks), which have been used by attackers to exploit vulnerabilities in technology and people introduced through changes to working practices in response to the COVID-19 pandemic. Methods: A scoping review was conducted by searching two major scientific databases (PubMed and Scopus) using the search formula ``(covid OR healthcare) AND cybersecurity.'' Reports, news articles, and industry white papers were also included if they were related directly to previously published works, or if they were the only available sources at the time of writing. Only articles in English published in the last decade were included (ie, 2011-2020) in order to focus on current issues, challenges, and solutions. Results: We identified 9 main challenges in cybersecurity, 11 key solutions that health care organizations adapted to address these challenges, and 4 key areas that need to be strengthened in terms of cybersecurity capacity in the health sector. We also found that the most prominent and significant methods of cyberattacks that occurred during the pandemic were related to phishing, ransomware, distributed denial-of-service attacks, and malware. Conclusions: ?This scoping review identified the most impactful methods of cyberattacks that targeted the health sector during the COVID-19 pandemic, as well as the challenges in cybersecurity, solutions, and areas in need of improvement. We provided useful insights to the health sector on cybersecurity issues during the COVID-19 pandemic as well as other epidemics or pandemics that may materialize in the future. ",
doi="10.2196/21747",
url="https://www.jmir.org/2021/4/e21747",
url="http://www.ncbi.nlm.nih.gov/pubmed/33764885"
}


@Article{info:doi/10.2196/24369,
author="Wang, Menghua
and Liao, Banghua
and Jian, Zhongyu
and Jin, Xi
and Xiang, Liyuan
and Yuan, Chi
and Li, Hong
and Wang, Kunjie",
title="Participation in Virtual Urology Conferences During the COVID-19 Pandemic: Cross-sectional Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="21",
volume="23",
number="4",
pages="e24369",
keywords="virtual conference",
keywords="COVID-19",
keywords="survey",
abstract="Background: Due to the influence of the COVID-19 pandemic, conventional face-to-face academic conferences have been restricted, and many of these conferences have moved onto the internet. Objective: The aim of this study was to investigate the virtual conferences in the field of urology during the COVID-19 pandemic and provide suggestions for better organization of such conferences. Methods: A cross-sectional survey was conducted from May 30 to June 15, 2020, in China. Our team designed a 23-item questionnaire to investigate the conferences attended by urologists during the COVID-19 pandemic. SPSS 22.0 (IBM Corporation) was applied to analyze the data collected. Results: A total of 330 Chinese urologists participated in our survey, and the response rate was 89.7\% (330/368). Among the participants, 40.9\% (135/330) were associate chief physicians. The proportion of participants who took part in conventional face-to-face academic conferences decreased from 92.7\% (306/330) before the COVID-19 pandemic to 22.1\% (73/330) during the pandemic (P<.001). In contrast, the proportion of urologists who took part in virtual conferences increased from 69.4\% (229/330) to 90\% (297/330) (P<.001). Most urologists (70.7\%, 210/297) chose to participate in the virtual conferences at home and thought that a meeting length of 1-2 hours was most appropriate. Among the urologists, 73.7\% (219/297) reported that their participation in the virtual conferences went smoothly, while the remaining respondents reported that they had experienced lags in video and audio streaming during the virtual conferences. When comparing conventional face-to-face conferences with virtual conferences, 70.7\% (210/297) of the respondents thought that both conference formats were acceptable, while 17.9\% (53/297) preferred virtual conferences and 11.5\% (34/297) preferred conventional face-to-face meetings. Conclusions: Virtual conferences are increasing in popularity during the COVID-19 pandemic; however, many aspects of these conferences could be improved for better organization. ",
doi="10.2196/24369",
url="https://www.jmir.org/2021/4/e24369",
url="http://www.ncbi.nlm.nih.gov/pubmed/33844635"
}


@Article{info:doi/10.2196/24964,
author="Patel, I. Darpan
and Gamez, Yazmin
and Shah, Lajja
and Patel, Jaini",
title="Decline of Psychological Health Following the Designation of COVID-19 as a Pandemic: Descriptive Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e24964",
keywords="anxiety",
keywords="COVID-19",
keywords="descriptive study",
keywords="mental health",
keywords="pandemic",
keywords="physical health",
keywords="quality of life",
keywords="stress",
abstract="Background: COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, and as of this writing, Texas, United States, has reported >675,000 cases with over 14,000 deaths. Many of the preventive measures implemented during the pandemic can increase sedentary lifestyles, which can lead to the development of chronic diseases, including obesity, among the general population and cause serious threats to people's physical health and overall quality of life. Individuals with pre-existing comorbidities are at an increased risk of COVID-19 and may hence have higher levels of stress. Objective: This study aimed to investigate the relationship between physical activity levels and mental health status on an individual level and to compare them between those with and those without comorbidities in a cohort of Texas residents, before and after COVID-19 was declared a pandemic. Methods: An electronic survey was disseminated throughout various regions of Texas. In total, 160 individuals were asked questions about their demographic characteristics, time spent on daily physical activities, and daily mental health status before and after COVID-19 was declared a pandemic. Frequency distributions and descriptive statistics were analyzed. Results: Overall, 94 (58\%) participants reported having ?1 medical condition, and 31 (13.1\%) had >3 medical conditions. Physical activity levels among participants with ?1 pre-existing comorbidity drastically---but not significantly---decreased, as evident from a 10\% increase in sedentary lifestyles after COVID-19 was declared a pandemic. On the contrary, we observed a 9\% increase in the number of individuals without a pre-existing comorbidity who reported 30-60 min of physical activity per week. There was a 2-fold increase in the number of participants reporting more frequent feelings of nervousness, too much worry, trouble relaxing, and the fear of something awful happening after the pandemic. More specifically, individuals with pre-existing medical conditions reported, on average, a 10\% higher incidence of feelings of stress, anxiety, and sadness compared to their healthy counterparts after COVID-19 was declared a pandemic. Conclusions: Stressful life conditions and chronic comorbidities are risk factors that can affect mental health and reduce the ability to perform activities of daily life. Therefore, when implementing pandemic protocols, municipalities should consider providing mental health support to their citizens to protect them from this rather inconspicuous adverse effect. ",
doi="10.2196/24964",
url="https://www.jmir.org/2021/4/e24964",
url="http://www.ncbi.nlm.nih.gov/pubmed/33793408"
}


@Article{info:doi/10.2196/26459,
author="Koyama, Sachiko
and Ueha, Rumi
and Kondo, Kenji",
title="Loss of Smell and Taste in Patients With Suspected COVID-19: Analyses of Patients' Reports on Social Media",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e26459",
keywords="COVID-19",
keywords="anosmia",
keywords="ageusia",
keywords="free reports on social media",
keywords="symptomatic",
keywords="asymptomatic",
keywords="recovery of senses",
keywords="symptom",
keywords="social media",
keywords="smell",
keywords="taste",
keywords="senses",
keywords="patient-reported",
keywords="benefit",
keywords="limit",
keywords="diagnosis",
abstract="Background: The year 2020 was the year of the global COVID-19 pandemic. The severity of the situation has become so substantial that many or even most of the patients with mild to moderate symptoms had to self-isolate without specific medical treatments or even without being tested for COVID-19. Many patients joined internet membership groups to exchange information and support each other. Objective: Our goal is to determine the benefits and limits of using social media to understand the symptoms of patients with suspected COVID-19 with mild to moderate symptoms and, in particular, their symptoms of anosmia (loss of the sense of smell) and ageusia (loss of the sense of taste). The voluntary reports on an internet website of a membership group will be the platform of the analyses. Methods: Posts and comments of members of an internet group known as COVID-19 Smell and Taste Loss, founded on March 24, 2020, to support patients with suspected COVID-19 were collected and analyzed daily. Demographic data were collected using the software mechanism called Group Insights on the membership group website. Results: Membership groups on social media have become rare sources of support for patients with suspected COVID-19 with mild to moderate symptoms. These groups provided mental support to their members and became resources for information on COVID-19 tests and medicines or supplements. However, the membership was voluntary, and often the members leave without notification. It is hard to be precise from the free voluntary reports. The number of women in the group (6995/9227, 75.38\% as of October 12, 2020) was about three times more than men (2272/9227, 24.62\% as of October 12, 2020), and the peak age of members was between 20-40 years in both men and women. Patients who were asymptomatic other than the senses comprised 14.93\% (53/355) of the total patients. Recovery of the senses was higher in the patients who were asymptomatic besides having anosmia and ageusia. Most (112/123, 91.06\%) patients experienced other symptoms first and then lost their senses, on average, 4.2 days later. Patients without other symptoms tended to recover earlier (P=.02). Patients with anosmia and ageusia occasionally reported distorted smell and taste (parosmia and dysgeusia) as well as experiencing or perceiving the smell and taste without the sources of the smell or taste (phantosmia and phantogeusia). Conclusions: Our analysis of the social media database of suspected COVID-19 patients' voices demonstrated that, although accurate diagnosis of patients is not always obtained with social media--based analyses, it may be a useful tool to collect a large amount of data on symptoms and the clinical course of worldwide rapidly growing infectious diseases. ",
doi="10.2196/26459",
url="https://www.jmir.org/2021/4/e26459",
url="http://www.ncbi.nlm.nih.gov/pubmed/33788699"
}


@Article{info:doi/10.2196/27214,
author="Szilagyi, Istvan-Szilard
and Ullrich, Torsten
and Lang-Illievich, Kordula
and Klivinyi, Christoph
and Schittek, Alexander Gregor
and Simonis, Holger
and Bornemann-Cimenti, Helmar",
title="Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e27214",
keywords="COVID-19",
keywords="data mining",
keywords="Google Trends",
keywords="incidence",
keywords="internet",
keywords="interest",
keywords="pain",
keywords="research",
keywords="trend",
abstract="Background: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. Objective: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. Methods: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann--Whitney U tests. Results: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. Conclusions: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups. ",
doi="10.2196/27214",
url="https://www.jmir.org/2021/4/e27214",
url="http://www.ncbi.nlm.nih.gov/pubmed/33844638"
}


@Article{info:doi/10.2196/27832,
author="Han, J. Paul K.
and Scharnetzki, Elizabeth
and Scherer, M. Aaron
and Thorpe, Alistair
and Lary, Christine
and Waterston, B. Leo
and Fagerlin, Angela
and Dieckmann, F. Nathan",
title="Communicating Scientific Uncertainty About the COVID-19 Pandemic: Online Experimental Study of an Uncertainty-Normalizing Strategy",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="22",
volume="23",
number="4",
pages="e27832",
keywords="uncertainty",
keywords="communication",
keywords="ambiguity",
keywords="vaccination",
keywords="COVID-19",
abstract="Background: Communicating scientific uncertainty about public health threats such as COVID-19 is an ethically desirable task endorsed by expert guidelines on crisis communication.  However, the communication of scientific uncertainty is challenging because of its potential to promote ambiguity aversion---a well-described syndrome of negative psychological responses consisting of heightened risk perceptions, emotional distress, and decision avoidance.  Communication strategies that can inform the public about scientific uncertainty while mitigating ambiguity aversion are a critical unmet need. Objective: This study aimed to evaluate whether an ``uncertainty-normalizing'' communication strategy---aimed at reinforcing the expected nature of scientific uncertainty about the COVID-19 pandemic---can reduce ambiguity aversion, and to compare its effectiveness to conventional public communication strategies aimed at promoting hope and prosocial values. Methods: In an online factorial experiment conducted from May to June 2020, a national sample of 1497 US adults read one of five versions of an informational message describing the nature, transmission, prevention, and treatment of COVID-19; the versions varied in level of expressed scientific uncertainty and supplemental focus (ie, uncertainty-normalizing, hope-promoting, and prosocial).  Participants then completed measures of cognitive, emotional, and behavioral manifestations of ambiguity aversion (ie, perceived likelihood of getting COVID-19, COVID-19 worry, and intentions for COVID-19 risk-reducing behaviors and vaccination).  Analyses assessed (1) the extent to which communicating uncertainty produced ambiguity-averse psychological responses; (2) the comparative effectiveness of uncertainty-normalizing, hope-promoting, and prosocial communication strategies in reducing ambiguity-averse responses; and (3) potential moderators of the effects of alternative uncertainty communication strategies. Results: The communication of scientific uncertainty about the COVID-19 pandemic increased perceived likelihood of getting COVID-19 and worry about COVID-19, consistent with ambiguity aversion.  However, it did not affect intentions for risk-reducing behaviors or vaccination.  The uncertainty-normalizing strategy reduced these aversive effects of communicating scientific uncertainty, resulting in levels of both perceived likelihood of getting COVID-19 and worry about COVID-19 that did not differ from the control message that did not communicate uncertainty.  In contrast, the hope-promoting and prosocial strategies did not decrease ambiguity-averse responses to scientific uncertainty.  Age and political affiliation, respectively, moderated the effects of uncertainty communication strategies on intentions for COVID-19 risk-reducing behaviors and worry about COVID-19. Conclusions: Communicating scientific uncertainty about the COVID-19 pandemic produces ambiguity-averse cognitive and emotional, but not behavioral, responses among the general public, and an uncertainty-normalizing communication strategy reduces these responses.  Normalizing uncertainty may be an effective strategy for mitigating ambiguity aversion in crisis communication efforts.  More research is needed to test uncertainty-normalizing communication strategies and to elucidate the factors that moderate their effectiveness. ",
doi="10.2196/27832",
url="https://www.jmir.org/2021/4/e27832",
url="http://www.ncbi.nlm.nih.gov/pubmed/33769947"
}


@Article{info:doi/10.2196/26628,
author="Yeung, YS Arnold
and Roewer-Despres, Francois
and Rosella, Laura
and Rudzicz, Frank",
title="Machine Learning--Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="23",
volume="23",
number="4",
pages="e26628",
keywords="COVID-19",
keywords="machine learning",
keywords="nonpharmaceutical interventions",
keywords="cultural dimensions",
keywords="random forest",
keywords="AdaBoost",
keywords="forecast",
keywords="informatics",
keywords="epidemiology",
keywords="artificial intelligence",
abstract="Background: National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects. Objective: The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth---the percentage change in total cumulative cases---across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms. Methods: We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non--time series machine learning models in predicting confirmed infection growth. We used three validation methods---in-distribution, out-of-distribution, and country-based cross-validation---for the evaluation, each of which was applicable to a different use case of the models. Results: Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case. Conclusions: This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections. ",
doi="10.2196/26628",
url="https://www.jmir.org/2021/4/e26628",
url="http://www.ncbi.nlm.nih.gov/pubmed/33844636"
}


@Article{info:doi/10.2196/25215,
author="Romer, Daniel
and Jamieson, Hall Kathleen",
title="Patterns of Media Use, Strength of Belief in COVID-19 Conspiracy Theories, and the Prevention of COVID-19 From March to July 2020 in the United States: Survey Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="27",
volume="23",
number="4",
pages="e25215",
keywords="COVID-19",
keywords="conspiracy beliefs",
keywords="social media",
keywords="print news media",
keywords="broadcast news media",
keywords="conservative media",
keywords="vaccination",
keywords="mask wearing",
keywords="belief",
keywords="misinformation",
keywords="infodemic",
keywords="United States",
keywords="intention",
keywords="prevention",
abstract="Background: Holding conspiracy beliefs regarding the COVID-19 pandemic in the United States has been associated with reductions in both actions to prevent the spread of the infection (eg, mask wearing) and intentions to accept a vaccine when one becomes available. Patterns of media use have also been associated with acceptance of COVID-19 conspiracy beliefs. Here we ask whether the type of media on which a person relies increased, decreased, or had no additional effect on that person's COVID-19 conspiracy beliefs over a 4-month period. Objective: We used panel data to explore whether use of conservative and social media in the United States, which were previously found to be positively related to holding conspiracy beliefs about the origins and prevention of COVID-19, were associated with a net increase in the strength of those beliefs from March to July of 2020. We also asked whether mainstream news sources, which were previously found to be negatively related to belief in pandemic-related conspiracies, were associated with a net decrease in the strength of such beliefs over the study period. Additionally, we asked whether subsequent changes in pandemic conspiracy beliefs related to the use of media were also related to subsequent mask wearing and vaccination intentions. Methods: A survey that we conducted with a national US probability sample in March of 2020 and again in July with the same 840 respondents assessed belief in pandemic-related conspiracies, use of various types of media information sources, actions taken to prevent the spread of the disease and intentions to vaccinate, and various demographic characteristics. Change across the two waves was analyzed using path analytic techniques. Results: We found that conservative media use predicted an increase in conspiracy beliefs ($\beta$=.17, 99\% CI .10-.25) and that reliance on mainstream print predicted a decrease in their belief ($\beta$=--.08, 99\% CI --.14 to --.02). Although many social media platforms reported downgrading or removing false or misleading content, ongoing use of such platforms by respondents predicted growth in conspiracy beliefs as well ($\beta$=.072, 99\% CI .018-.123). Importantly, conspiracy belief changes related to media use between the two waves of the study were associated with the uptake of mask wearing and changes in vaccination intentions in July. Unlike other media, use of mainstream broadcast television predicted greater mask wearing ($\beta$=.17, 99\% CI .09-.26) and vaccination intention ($\beta$=.08, 95\% CI .02-.14), independent of conspiracy beliefs. Conclusions: The findings point to the need for greater efforts on the part of commentators, reporters, and guests on conservative media to report verifiable information about the pandemic. The results also suggest that social media platforms need to be more aggressive in downgrading, blocking, and counteracting claims about COVID-19 vaccines, claims about mask wearing, and conspiracy beliefs that have been judged problematic by public health authorities. ",
doi="10.2196/25215",
url="https://www.jmir.org/2021/4/e25215",
url="http://www.ncbi.nlm.nih.gov/pubmed/33857008"
}


@Article{info:doi/10.2196/26075,
author="Patr{\'i}cio, Andr{\'e}
and Costa, S. Rafael
and Henriques, Rui",
title="Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e26075",
keywords="COVID-19",
keywords="machine learning",
keywords="intensive care admissions",
keywords="respiratory assistance",
keywords="predictive models",
keywords="data modeling",
keywords="clinical informatics",
abstract="Background: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. Objective: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. Methods: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. Results: For the target cohort, 75\% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60\% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50\% precision. Conclusions: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end. ",
doi="10.2196/26075",
url="https://www.jmir.org/2021/4/e26075",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835931"
}


@Article{info:doi/10.2196/23446,
author="Adly, Sedky Aya
and Adly, Sedky Mahmoud
and Adly, Sedky Afnan",
title="Telemanagement of Home-Isolated COVID-19 Patients Using Oxygen Therapy With Noninvasive Positive Pressure Ventilation and Physical Therapy Techniques: Randomized Clinical Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e23446",
keywords="telemedicine",
keywords="oxygen therapy",
keywords="noninvasive positive airway pressure",
keywords="BiPAP",
keywords="osteopathic medicine",
keywords="physical therapy",
keywords="SARS-CoV-2",
keywords="COVID-19",
keywords="teletherapy",
keywords="telemanagement",
abstract="Background: With the growing stress on hospitals caused by the COVID-19 pandemic, the need for home-based solutions has become a necessity to support these overwhelmed hospitals. Objective: The goal of this study was to compare two nonpharmacological respiratory treatment methods for home-isolated COVID-19 patients using a newly developed telemanagement health care system. Methods: In this single-blinded randomized clinical trial, 60 patients with stage 1 pneumonia caused by SARS-CoV-2 infection were treated. Group A (n=30) received oxygen therapy with bilevel positive airway pressure (BiPAP) ventilation, and Group B (n=30) received osteopathic manipulative respiratory and physical therapy techniques. Arterial blood gases of PaO2 and PaCO2, pH, vital signs (ie, temperature, respiratory rate, oxygen saturation, heart rate, and blood pressure), and chest computed tomography scans were used for follow-up and for assessment of the course and duration of recovery. Results: Analysis of the results showed a significant difference between the two groups (P<.05), with Group A showing shorter recovery periods than Group B (mean 14.9, SD 1.7 days, and mean 23.9, SD 2.3 days, respectively). Significant differences were also observed between baseline and final readings in all of the outcome measures in both groups (P<.05). Regarding posttreatment satisfaction with our proposed telemanagement health care system, positive responses were given by most of the patients in both groups. Conclusions: It was found that home-based oxygen therapy with BiPAP can be a more effective prophylactic treatment approach than osteopathic manipulative respiratory and physical therapy techniques, as it can impede exacerbation of early-stage COVID-19 pneumonia. Telemanagement health care systems are promising methods to help in the pandemic-related shortage of hospital beds, as they showed reasonable effectiveness and reliability in the monitoring and management of patients with early-stage COVID-19 pneumonia. Trial Registration: ClinicalTrials.gov NCT04368923; https://clinicaltrials.gov/ct2/show/NCT04368923 ",
doi="10.2196/23446",
url="https://www.jmir.org/2021/4/e23446",
url="http://www.ncbi.nlm.nih.gov/pubmed/33819166"
}


@Article{info:doi/10.2196/26558,
author="Xu, Stanley
and Glenn, Sungching
and Sy, Lina
and Qian, Lei
and Hong, Vennis
and Ryan, S. Denison
and Jacobsen, Steven",
title="Impact of the COVID-19 Pandemic on Health Care Utilization in a Large Integrated Health Care System: Retrospective Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e26558",
keywords="cohort",
keywords="COVID-19",
keywords="difference-in-difference analysis",
keywords="health care utilization",
keywords="health care worker",
keywords="impact",
keywords="knowledge",
keywords="pandemic",
keywords="policy",
keywords="retrospective",
keywords="telehealth",
keywords="telemedicine",
keywords="usage",
keywords="utilization",
abstract="Background: The COVID-19 pandemic has caused an abrupt reduction in the use of in-person health care, accompanied by a corresponding surge in the use of telehealth services. However, the extent and nature of changes in health care utilization during the pandemic may differ by care setting. Knowledge of the impact of the pandemic on health care utilization is important to health care organizations and policy makers. Objective: The aims of this study are (1) to evaluate changes in in-person health care utilization and telehealth visits during the COVID-19 pandemic and (2) to assess the difference in changes in health care utilization between the pandemic year 2020 and the prepandemic year 2019. Methods: We retrospectively assembled a cohort consisting of members of a large integrated health care organization, who were enrolled between January 6 and November 2, 2019 (prepandemic year), and between January 5 and October 31, 2020 (pandemic year). The rates of visits were calculated weekly for four settings: inpatient, emergency department (ED), outpatient, and telehealth. Using Poisson models, we assessed the impact of the pandemic on health care utilization during the early days of the pandemic and conducted difference-in-deference (DID) analyses to measure the changes in health care utilization, adjusting for the trend of health care utilization in the prepandemic year. Results: In the early days of the pandemic, we observed significant reductions in inpatient, ED, and outpatient utilization (by 30.2\%, 37.0\%, and 80.9\%, respectively). By contrast, there was a 4-fold increase in telehealth visits between weeks 8 (February 23) and 12 (March 22) in 2020. DID analyses revealed that after adjusting for prepandemic secular trends, the reductions in inpatient, ED, and outpatient visit rates in the early days of the pandemic were 1.6, 8.9, and 367.2 visits per 100 person-years (P<.001), respectively, while the increase in telehealth visits was 272.9 visits per 100 person-years (P<.001). Further analyses suggested that the increase in telehealth visits offset the reduction in outpatient visits by week 26 (June 28, 2020). Conclusions: In-person health care utilization decreased drastically during the early period of the pandemic, but there was a corresponding increase in telehealth visits during the same period. By end-June 2020, the combined outpatient and telehealth visits had recovered to prepandemic levels. ",
doi="10.2196/26558",
url="https://www.jmir.org/2021/4/e26558",
url="http://www.ncbi.nlm.nih.gov/pubmed/33882020"
}


@Article{info:doi/10.2196/26940,
author="Yu, Fengyun
and Geldsetzer, Pascal
and Meierkord, Anne
and Yang, Juntao
and Chen, Qiushi
and Jiao, Lirui
and Abou-Arraj, E. Nadeem
and Pan, An
and Wang, Chen
and B{\"a}rnighausen, Till
and Chen, Simiao",
title="Knowledge About COVID-19 Among Adults in China: Cross-sectional Online Survey",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e26940",
keywords="COVID-19",
keywords="knowledge",
keywords="perception",
keywords="risk",
keywords="public health",
keywords="China",
keywords="cross-sectional",
keywords="survey",
abstract="Background: A detailed understanding of the public's knowledge and perceptions of COVID-19 could inform governments' public health actions in response to the pandemic. Objective: The aim of this study was to determine the knowledge and perceptions of COVID-19 among adults in China and its variation among provinces and by sociodemographic characteristics. Methods: Between May 8 and June 8, 2020, we conducted a cross-sectional online survey among adults in China who were registered with the private survey company KuRunData. We set a target sample size of 10,000 adults, aiming to sample 300-360 adults from each province in China. Participants were asked 25 questions that tested their knowledge about COVID-19, including measures to prevent infection, common symptoms, and recommended care-seeking behavior. We disaggregated responses by age; sex; education; province; household income; rural--urban residency; and whether or not a participant had a family member, friend, or acquaintance who they know to have been infected with SARS-CoV-2. All analyses used survey sampling weights. Results: There were 5079 men and 4921 women who completed the questionnaire and were included in the analysis. Out of 25 knowledge questions, participants answered a mean and median of 21.4 (95\% CI 21.3-21.4) and 22 (IQR 20-23) questions correctly, respectively. A total of 83.4\% (95\% CI 82.7\%-84.1\%) of participants answered four-fifths or more of the questions correctly. For at least one of four ineffective prevention measures (using a hand dryer, regular nasal irrigation, gargling mouthwash, and taking antibiotics), 68.9\% (95\% CI 68.0\%-69.8\%) of participants answered that it was an effective method to prevent a SARS-CoV-2 infection. Although knowledge overall was similar across provinces, the percent of participants who answered the question on recommended care-seeking behavior correctly varied from 47.0\% (95\% CI 41.4\%-52.7\%) in Tibet to 87.5\% (95\% CI 84.1\%-91.0\%) in Beijing. Within provinces, participants who were male, were middle-aged, were residing in urban areas, and had higher household income tended to answer a higher proportion of the knowledge questions correctly. Conclusions: This online study of individuals across China suggests that the majority of the population has good knowledge of COVID-19. However, a substantial proportion still holds misconceptions or incorrect beliefs about prevention methods and recommended health care--seeking behaviors, especially in rural areas and some less wealthy provinces in Western China. This study can inform the development of tailored public health policies and promotion campaigns by identifying knowledge areas for which misconceptions are comparatively common and provinces that have relatively low knowledge. ",
doi="10.2196/26940",
url="https://www.jmir.org/2021/4/e26940",
url="http://www.ncbi.nlm.nih.gov/pubmed/33844637"
}


@Article{info:doi/10.2196/25987,
author="Legler, Sean
and Diehl, Matthew
and Hilliard, Brian
and Olson, Andrew
and Markowitz, Rebecca
and Tignanelli, Christopher
and Melton, B. Genevieve
and Broccard, Alain
and Kirsch, Jonathan
and Usher, Michael",
title="Evaluation of an Intrahospital Telemedicine Program for Patients Admitted With COVID-19: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e25987",
keywords="telemedicine",
keywords="hospital medicine",
keywords="COVID-19",
keywords="telehealth",
keywords="hospital",
keywords="mixed methods",
keywords="evaluation",
keywords="impact",
keywords="exposure",
keywords="risk",
keywords="communication",
abstract="Background: The increasing incidence of COVID-19 infection has challenged health care systems to increase capacity while conserving personal protective equipment (PPE) supplies and minimizing nosocomial spread. Telemedicine shows promise to address these challenges but lacks comprehensive evaluation in the inpatient environment. Objective: The aim of this study is to evaluate an intrahospital telemedicine program (virtual care), along with its impact on exposure risk and communication. Methods: We conducted a natural experiment of virtual care on patients admitted for COVID-19. The primary exposure variable was documented use of virtual care. Patient characteristics, PPE use rates, and their association with virtual care use were assessed. In parallel, we conducted surveys with patients and clinicians to capture satisfaction with virtual care along the domains of communication, medical treatment, and exposure risk. Results: Of 137 total patients in our primary analysis, 43 patients used virtual care. In total, there were 82 inpatient days of use and 401 inpatient days without use. Hospital utilization and illness severity were similar in patients who opted in versus opted out. Virtual care was associated with a significant reduction in PPE use and physical exam rate. Surveys of 41 patients and clinicians showed high rates of recommendation for further use, and subjective improvements in communication. However, providers and patients expressed limitations in usability, medical assessment, and empathetic communication. Conclusions: In this pilot natural experiment, only a subset of patients used inpatient virtual care. When used, virtual care was associated with reductions in PPE use, reductions in exposure risk, and patient and provider satisfaction. ",
doi="10.2196/25987",
url="https://www.jmir.org/2021/4/e25987",
url="http://www.ncbi.nlm.nih.gov/pubmed/33872187"
}


@Article{info:doi/10.2196/28973,
author="Eibensteiner, Fabian
and Ritschl, Valentin
and Nawaz, A. Faisal
and Fazel, S. Sajjad
and Tsagkaris, Christos
and Kulnik, Tino Stefan
and Crutzen, Rik
and Klager, Elisabeth
and V{\"o}lkl-Kernstock, Sabine
and Schaden, Eva
and Kletecka-Pulker, Maria
and Willschke, Harald
and Atanasov, G. Atanas",
title="People's Willingness to Vaccinate Against COVID-19 Despite Their Safety Concerns: Twitter Poll Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="29",
volume="23",
number="4",
pages="e28973",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="vaccine",
keywords="vaccination",
keywords="Twitter",
keywords="survey",
keywords="vaccination willingness",
keywords="vaccination hesitancy",
keywords="coronavirus",
keywords="vaccine confidence",
keywords="willingness",
keywords="hesitancy",
keywords="social media",
keywords="safety",
keywords="concern",
keywords="public health",
keywords="opinion",
keywords="perception",
abstract="Background: On January 30, 2020, the World Health Organization's Emergency Committee declared the rapid, worldwide spread of COVID-19 a global health emergency. Since then, tireless efforts have been made to mitigate the spread of the disease and its impact, and these efforts have mostly relied on nonpharmaceutical interventions. By December 2020, the safety and efficacy of the first COVID-19 vaccines were demonstrated. The large social media platform Twitter has been used by medical researchers for the analysis of important public health topics, such as the public's perception on antibiotic use and misuse and human papillomavirus vaccination. The analysis of Twitter-generated data can be further facilitated by using Twitter's built-in, anonymous polling tool to gain insight into public health issues and obtain rapid feedback on an international scale. During the fast-paced course of the COVID-19 pandemic, the Twitter polling system has provided a viable method for gaining rapid, large-scale, international public health insights on highly relevant and timely SARS-CoV-2--related topics. Objective: The purpose of this study was to understand the public's perception on the safety and acceptance of COVID-19 vaccines in real time by using Twitter polls. Methods: We developed 2 Twitter polls to explore the public's views on available COVID-19 vaccines. The surveys were pinned to the Digital Health and Patient Safety Platform Twitter timeline for 1 week in mid-February 2021, and Twitter users and influencers were asked to participate in and retweet the polls to reach the largest possible audience. Results: The adequacy of COVID-19 vaccine safety (ie, the safety of currently available vaccines; poll 1) was agreed upon by 1579 out of 3439 (45.9\%) Twitter users. In contrast, almost as many Twitter users (1434/3439, 41.7\%) were unsure about the safety of COVID-19 vaccines. Only 5.2\% (179/3439) of Twitter users rated the available COVID-19 vaccines as generally unsafe. Poll 2, which addressed the question of whether users would undergo vaccination, was answered affirmatively by 82.8\% (2862/3457) of Twitter users, and only 8\% (277/3457) categorically rejected vaccination at the time of polling. Conclusions: In contrast to the perceived high level of uncertainty about the safety of the available COVID-19 vaccines, we observed an elevated willingness to undergo vaccination among our study sample. Since people's perceptions and views are strongly influenced by social media, the snapshots provided by these media platforms represent a static image of a moving target. Thus, the results of this study need to be followed up by long-term surveys to maintain their validity. This is especially relevant due to the circumstances of the fast-paced pandemic and the need to not miss sudden rises in the incidence of vaccine hesitancy, which may have detrimental effects on the pandemic's course. ",
doi="10.2196/28973",
url="https://www.jmir.org/2021/4/e28973",
url="http://www.ncbi.nlm.nih.gov/pubmed/33872185"
}


@Article{info:doi/10.2196/24316,
author="Zheng, Yingfeng
and Wang, Wei
and Zhong, Yuxin
and Wu, Fengchun
and Zhu, Zhuoting
and Tham, Yih-Chung
and Lamoureux, Ecosse
and Xiao, Liang
and Zhu, Erta
and Liu, Haoning
and Jin, Ling
and Liang, Linyi
and Luo, Lixia
and He, Mingguang
and Morgan, Ian
and Congdon, Nathan
and Liu, Yizhi",
title="A Peer-to-Peer Live-Streaming Intervention for Children During COVID-19 Homeschooling to Promote Physical Activity and Reduce Anxiety and Eye Strain: Cluster Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e24316",
keywords="homeschooling",
keywords="children",
keywords="anxiety, digital eye strain",
keywords="peer to peer",
keywords="live streaming",
keywords="digital health",
keywords="intervention",
keywords="health information",
keywords="physical activity",
keywords="COVID-19",
keywords="online learning",
keywords="behavior",
keywords="app",
keywords="mobile phone",
abstract="Background: The COVID-19 pandemic has led to worldwide school closures, with millions of children confined to online learning at home. As a result, children may be susceptible to anxiety and digital eye strain, highlighting a need for population interventions. Objective: The objective of our study was to investigate whether a digital behavior change intervention aimed at promoting physical activity could reduce children's anxiety and digital eye strain while undergoing prolonged homeschooling during the COVID-19 pandemic. Methods: In this cluster randomized controlled trial, homeschooled grade 7 students at 12 middle schools in southern China were recruited through local schools and randomly assigned by the school to receive (1:1 allocation): (1) health education information promoting exercise and ocular relaxation, and access to a digital behavior change intervention, with live streaming and peer sharing of promoted activities (intervention), or (2) health education information only (control). The primary outcome was change in self-reported anxiety score. Secondary outcomes included change in self-reported eye strain and sleep quality. Results: On March 16, 2020, 1009 children were evaluated, and 954 (94.5\%) eligible children of consenting families were included in the intention-to-treat analysis. Children in the intervention (n=485, 6 schools) and control (n=469, 6 schools) groups were aged 13.5 (SD 0.5) years, and 52.3\% (n=499) were male. The assigned interventions were completed by 896 children (intervention: n=467, 96.3\%; control: n=429, 91.5\%). The 2-week change in square-root--transformed self-reported anxiety scores was greater in the intervention (--0.23, 95\% CI --0.27 to --0.20) vs control group (0.12, 95\% CI 0.09-0.16; unadjusted difference --0.36, 95\% CI --0.63 to --0.08; P=.02). There was a significant reduction in square-root--transformed eye strain in the intervention group (--0.08, 95\% CI --0.10 to 0.06) compared to controls (0.07, 95\% CI 0.05-0.09; difference --0.15, 95\% CI --0.26 to --0.03; P=.02). Change in sleep quality was similar between the two groups. Conclusions: This digital behavior change intervention reduced children's anxiety and eye strain during COVID-19--associated online schooling. Trial Registration: ClinicalTrials.gov NCT04309097; http://clinicaltrials.gov/ct2/show/NCT04309097 ",
doi="10.2196/24316",
url="https://www.jmir.org/2021/4/e24316",
url="http://www.ncbi.nlm.nih.gov/pubmed/33882021"
}


@Article{info:doi/10.2196/27341,
author="Adikari, Achini
and Nawaratne, Rashmika
and De Silva, Daswin
and Ranasinghe, Sajani
and Alahakoon, Oshadi
and Alahakoon, Damminda",
title="Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="30",
volume="23",
number="4",
pages="e27341",
keywords="COVID-19",
keywords="pandemic",
keywords="lockdown",
keywords="human emotions",
keywords="affective computing",
keywords="human-centric artificial intelligence",
keywords="artificial intelligence",
keywords="AI",
keywords="machine learning",
keywords="natural language processing",
keywords="language modeling",
keywords="infodemiology",
keywords="infoveillance",
abstract="Background: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises. ",
doi="10.2196/27341",
url="https://www.jmir.org/2021/4/e27341",
url="http://www.ncbi.nlm.nih.gov/pubmed/33819167"
}


@Article{info:doi/10.2196/27667,
author="Yamada, Yasunori
and Shinkawa, Kaoru
and Kobayashi, Masatomo
and Takagi, Hironobu
and Nemoto, Miyuki
and Nemoto, Kiyotaka
and Arai, Tetsuaki",
title="Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="8",
volume="23",
number="4",
pages="e27667",
keywords="cognitive impairment",
keywords="smart speaker",
keywords="speech analysis",
keywords="accident",
keywords="prevention",
keywords="older adults",
keywords="prediction",
keywords="risk",
keywords="assistant",
abstract="Background: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data---neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)---from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7\% accuracy, which was 6.7\% higher than that using cognitive assessment data, and could achieve up to 88.3\% accuracy when the model used both types of data. Conclusions: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function. ",
doi="10.2196/27667",
url="https://www.jmir.org/2021/4/e27667",
url="http://www.ncbi.nlm.nih.gov/pubmed/33830066"
}


@Article{info:doi/10.2196/25312,
author="Ermolina, Alena
and Tiberius, Victor",
title="Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="9",
volume="23",
number="4",
pages="e25312",
keywords="Delphi study",
keywords="medical informatics",
keywords="voice-controlled intelligent personal assistants",
keywords="internet of things",
keywords="smart devices",
abstract="Background: Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence--powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide range of applications in health care, covering off-clinic education, health monitoring, and communication. However, conflicting factors, such as patient safety and privacy concerns, make it difficult to foresee the further development of VIPAs in health care. Objective: This study aimed to develop a plausible scenario for the further development of VIPAs in health care to support decision making regarding the procurement of VIPAs in health care organizations. Methods: We conducted a two-stage Delphi study with an internationally recruited panel consisting of voice assistant experts, medical professionals, and representatives of academia, governmental health authorities, and nonprofit health associations having expertise with voice technology. Twenty projections were formulated and evaluated by the panelists. Descriptive statistics were used to derive the desired scenario. Results: The panelists expect VIPAs to be able to provide solid medical advice based on patients' personal health information and to have human-like conversations. However, in the short term, voice assistants might neither provide frustration-free user experience nor outperform or replace humans in health care. With a high level of consensus, the experts agreed with the potential of VIPAs to support elderly people and be widely used as anamnesis, informational, self-therapy, and communication tools by patients and health care professionals. Although users' and governments' privacy concerns are not expected to decrease in the near future, the panelists believe that strict regulations capable of preventing VIPAs from providing medical help services will not be imposed. Conclusions: According to the surveyed experts, VIPAs will show notable technological development and gain more user trust in the near future, resulting in widespread application in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff. ",
doi="10.2196/25312",
url="https://www.jmir.org/2021/4/e25312",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835032"
}


@Article{info:doi/10.2196/25493,
author="Brandberg, Helge
and Sundberg, Johan Carl
and Spaak, Jonas
and Koch, Sabine
and Zakim, David
and Kahan, Thomas",
title="Use of Self-Reported Computerized Medical History Taking for Acute Chest Pain in the Emergency Department -- the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS): Prospective Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="27",
volume="23",
number="4",
pages="e25493",
keywords="chest pain",
keywords="computerized history taking",
keywords="coronary artery disease",
keywords="eHealth",
keywords="emergency department",
keywords="health informatics",
keywords="medical history",
keywords="risk management",
abstract="Background: Chest pain is one of the most common chief complaints in emergency departments (EDs). Collecting an adequate medical history is challenging but essential in order to use recommended risk scores such as the HEART score (based on history, electrocardiogram, age, risk factors, and troponin). Self-reported computerized history taking (CHT) is a novel method to collect structured medical history data directly from the patient through a digital device. CHT is rarely used in clinical practice, and there is a lack of evidence for utility in an acute setting. Objective: This substudy of the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS) aimed to evaluate whether patients with acute chest pain can interact effectively with CHT in the ED. Methods: Prospective cohort study on self-reported medical histories collected from acute chest pain patients using a CHT program on a tablet. Clinically stable patients aged 18 years and older with a chief complaint of chest pain, fluency in Swedish, and a nondiagnostic electrocardiogram or serum markers for acute coronary syndrome were eligible for inclusion. Patients unable to carry out an interview with CHT (eg, inadequate eyesight, confusion or agitation) were excluded. Effectiveness was assessed as the proportion of patients completing the interview and the time required in order to collect a medical history sufficient for cardiovascular risk stratification according to HEART score. Results: During 2017-2018, 500 participants were consecutively enrolled. The age and sex distribution (mean 54.3, SD 17.0 years; 213/500, 42.6\% women) was similar to that of the general chest pain population (mean 57.5, SD 19.2 years; 49.6\% women). Common reasons for noninclusion were language issues (182/1000, 18.2\%), fatigue (158/1000, 15.8\%), and inability to use a tablet (152/1000, 15.2\%). Sufficient data to calculate HEART score were collected in 70.4\% (352/500) of the patients. Key modules for chief complaint, cardiovascular history, and respiratory history were completed by 408 (81.6\%), 339 (67.8\%), and 291 (58.2\%) of the 500 participants, respectively, while 148 (29.6\%) completed the entire interview (in all 14 modules). Factors associated with completeness were age 18-69 years (all key modules: Ps<.001), male sex (cardiovascular: P=.04), active workers (all key modules: Ps<.005), not arriving by ambulance (chief complaint: P=.03; cardiovascular: P=.045), and ongoing chest pain (complete interview: P=.002). The median time to collect HEART score data was 23 (IQR 18-31) minutes and to complete an interview was 64 (IQR 53-77) minutes. The main reasons for discontinuing the interview prior to completion (n=352) were discharge from the ED (101, 28.7\%) and tiredness (95, 27.0\%). Conclusions: A majority of patients with acute chest pain can interact effectively with CHT on a tablet in the ED to provide sufficient data for risk stratification with a well-established risk score. The utility was somewhat lower in patients 70 years and older, in patients arriving by ambulance, and in patients without ongoing chest pain. Further studies are warranted to assess whether CHT can contribute to improved management and prognosis in this large patient group. Trial Registration: ClinicalTrials.gov NCT03439449; https://clinicaltrials.gov/ct2/show/NCT03439449 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-031871 ",
doi="10.2196/25493",
url="https://www.jmir.org/2021/4/e25493",
url="http://www.ncbi.nlm.nih.gov/pubmed/33904821"
}