@Article{info:doi/10.2196/67950,
author="Guo, Rongrong
and Zhang, Jiwen
and Yang, Fangyu
and Wu, Ying",
title="Efficacy of an Intelligent and Integrated Older Adult Care Model on Quality of Life Among Home-Dwelling Older Adults: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="21",
volume="27",
pages="e67950",
keywords="efficacy",
keywords="home care",
keywords="integrated care",
keywords="intelligent",
keywords="elderly people",
keywords="quality of life",
keywords="mobile phone",
abstract="Background: Integrated care models enhanced by the clinical decision support system offer innovative approaches to managing the growing global burden of older adult care. However, their efficacy remains uncertain. Objective: This study aimed to evaluate the efficacy of an intelligent and integrated older adult care model, termed the SMART (Sensors and scales [receptor], a Mobile phone autonomous response system [central nervous system in the spinal cord], a Remote cloud management center [central nervous system in the brain], and a Total care system [effector]) system, in improving the quality of life (QOL) for home-dwelling older adults. Methods: In this stratified randomized controlled trial, we consecutively recruited older adults aged 65 years or older from November 1, 2020, to December 31, 2020. Eligible participants were randomly allocated 1:1 to either the SMART group, receiving routine discharge instructions and personalized integrated care interventions across 11 domains (decreased or lost self-care ability, falls, delirium, dysphagia, incontinence, constipation, urinary retention, cognitive decline, depression, impaired skin integrity, and common diseases) generated by the SMART system, or the usual care group, receiving only routine discharge instructions. The intervention lasted for 3 months. The primary end point was the percent change in QOL from baseline to the 3-month follow-up, assessed using the World Health Organization Quality of Life Instrument - Older Adults Module. Secondary end points included functional status at the 3-month follow-up and percent changes in health self-management ability, social support, and confidence in avoiding falling from baseline to the 3-month follow-up. Data were analyzed following the intention-to-treat principle, using covariance or logistic regression models, as appropriate. Subgroup and sensitivity analyses were conducted to assess result consistency and robustness. Results: In total, 94 participants were recruited, with 48 assigned to the SMART group. The personalized and integrated care by the SMART system significantly improved the QOL among the older adults, with an estimated intervention difference of 11.97\% (95\% CI 7.2\%-16.74\%, P<.001), and social support and health self-management ability as well, with estimated intervention differences of 6.75\% (95\% CI 3.19\%-10.3\%, P<.001) and 4.95\% (95\% CI 0.11\%-10\%, P=.003), respectively, while insignificantly improving in the Modified Falls Efficacy Scale score. Similarly, the SMART system had a 66\% reduction in instrumental activities of daily living disability (odds ratio [OR] 0.34, 95\% CI 0.11-0.83, P=.02). However, the SMART system did not significantly affect activities of daily living disability or the Modified Falls Efficacy Scale score. The subgroup and sensitivity analyses confirmed the robustness of the findings. Conclusions: The personalized and integrated older adult care by the SMART system demonstrated significant efficacy in improving QOL, health self-management ability, and social support, while reducing instrumental activities of daily living disability among home-dwelling older adults. Trial Registration: Chinese Clinical Trial Registry ChiCTR-IOR-17010368; https://tinyurl.com/2zax24xr ",
doi="10.2196/67950",
url="https://www.jmir.org/2025/1/e67950"
}


@Article{info:doi/10.2196/58377,
author="Alhumaid, Khadija
and Ayoubi, Kevin
and Khalifa, Maha
and Salloum, Said",
title="Factors Determining Acceptance of Internet of Things in Medical Education: Mixed Methods Study",
journal="JMIR Hum Factors",
year="2025",
month="Apr",
day="10",
volume="12",
pages="e58377",
keywords="collaborative learning",
keywords="student",
keywords="college",
keywords="university",
keywords="education",
keywords="Internet of Things",
keywords="IoT",
keywords="technology acceptance model",
keywords="technology optimism",
keywords="TAM",
keywords="experience",
keywords="attitude",
keywords="opinion",
keywords="perception",
keywords="perspective",
keywords="acceptance",
keywords="adoption",
keywords="survey",
keywords="questionnaire",
keywords="ANN",
keywords="deep learning",
keywords="structural equation modeling",
keywords="neural network",
keywords="intent",
keywords="use",
keywords="medical education",
keywords="artificial neural network",
keywords="technology innovation",
abstract="Background: The global increase in the Internet of Things (IoT) adoption has sparked interest in its application within the educational sector, particularly in colleges and universities. Previous studies have often focused on individual attitudes toward IoT without considering a multiperspective approach and have overlooked the impact of IoT on the technology acceptance model outside the educational domain. Objective: This study aims to bridge the research gap by investigating the factors influencing IoT adoption in educational settings, thereby enhancing the understanding of collaborative learning through technology. It seeks to elucidate how IoT can facilitate learning processes and technology acceptance among college and university students in the United Arab Emirates. Methods: A questionnaire was distributed to students across various colleges and universities in the United Arab Emirates, garnering 463 participants. The data collected were analyzed using a hybrid approach that integrates structural equation modeling (SEM) and artificial neural network (ANN), along with importance-performance map analysis to evaluate the significance and performance of each factor affecting IoT adoption. Results: The study, involving 463 participants, identifies 2 primary levels at which factors influence the intention to adopt IoT technologies. Initial influences include technology optimism (TOP), innovation, and learning motivation, crucial for application engagement. Advanced influences stem from technology acceptance model constructs, particularly perceived ease of use (PE) and perceived usefulness (PU), which directly enhance adoption intentions. Detailed statistical analysis using partial least squares--SEM reveals significant relationships: TOP and innovativeness impact PE ($\beta$=.412, P=.04; $\beta$=.608, P=.002, respectively), and PU significantly influences TOP ($\beta$=.381, P=.04), innovativeness ($\beta$=.557, P=.003), and learning motivation ($\beta$=.752, P<.001). These results support our hypotheses (H1, H2, H3, H4, and H5). Further, the intention to use IoT is significantly affected by PE and usefulness ($\beta$=.619, P<.001; $\beta$=.598, P<.001, respectively). ANN modeling enhances these findings, showing superior predictive power (R2=89.7\%) compared to partial least squares--SEM (R2=86.3\%), indicating a more effective identification of nonlinear associations. Importance-performance map analysis corroborates these results, demonstrating the importance and performance of PU as most critical, followed by technology innovativeness and optimism, in shaping behavioral intentions to use IoT. Conclusions: This research contributes methodologically by leveraging deep ANN architecture to explore nonlinear relationships among factors influencing IoT adoption in education. The study underscores the importance of both intrinsic motivational factors and perceived technological attributes in fostering IoT adoption, offering insights for educational institutions considering IoT integration into their learning environments. ",
doi="10.2196/58377",
url="https://humanfactors.jmir.org/2025/1/e58377"
}


@Article{info:doi/10.2196/66273,
author="Gyrard, Amelie
and Abedian, Somayeh
and Gribbon, Philip
and Manias, George
and van Nuland, Rick
and Zatloukal, Kurt
and Nicolae, Emilia Irina
and Danciu, Gabriel
and Nechifor, Septimiu
and Marti-Bonmati, Luis
and Mallol, Pedro
and Dalmiani, Stefano
and Autexier, Serge
and Jendrossek, Mario
and Avramidis, Ioannis
and Garcia Alvarez, Eva
and Holub, Petr
and Blanquer, Ignacio
and Boden, Anna
and Hussein, Rada",
title="Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="24",
volume="27",
pages="e66273",
keywords="artificial intelligence",
keywords="cancer",
keywords="European Health Data Space",
keywords="health care standards",
keywords="interoperability",
keywords="AI",
keywords="health data",
keywords="cancer use cases",
keywords="IoT",
keywords="Internet of Things",
keywords="primary data",
keywords="diagnosis",
keywords="prognosis",
keywords="decision-making",
doi="10.2196/66273",
url="https://www.jmir.org/2025/1/e66273",
url="http://www.ncbi.nlm.nih.gov/pubmed/40126534"
}


@Article{info:doi/10.2196/56692,
author="Chien, Shuo-Chen
and Yen, Chia-Ming
and Chang, Yu-Hung
and Chen, Ying-Erh
and Liu, Chia-Chun
and Hsiao, Yu-Ping
and Yang, Ping-Yen
and Lin, Hong-Ming
and Yang, Tsung-En
and Lu, Xing-Hua
and Wu, I-Chien
and Hsu, Chih-Cheng
and Chiou, Hung-Yi
and Chung, Ren-Hua",
title="Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="4",
volume="27",
pages="e56692",
keywords="bibliometric analysis",
keywords="Google Trends",
keywords="content analysis",
keywords="long-term care",
keywords="older adults",
keywords="artificial intelligence",
keywords="Internet of Things",
keywords="edge intelligence",
abstract="Background: The global aging population poses critical challenges for long-term care (LTC), including workforce shortages, escalating health care costs, and increasing demand for high-quality care. Integrating artificial intelligence (AI), the Internet of Things (IoT), and edge intelligence (EI) offers transformative potential to enhance care quality, improve safety, and streamline operations. However, existing research lacks a comprehensive analysis that synthesizes academic trends, public interest, and deeper insights regarding these technologies. Objective: This study aims to provide a holistic overview of AI, IoT, and EI applications in LTC for older adults through a comprehensive bibliometric analysis, public interest insights from Google Trends, and content analysis of the top-cited research papers. Methods: Bibliometric analysis was conducted using data from Web of Science, PubMed, and Scopus to identify key themes and trends in the field, while Google Trends was used to assess public interest. A content analysis of the top 1\% of most-cited papers provided deeper insights into practical applications. Results: A total of 6378 papers published between 2014 and 2023 were analyzed. The bibliometric analysis revealed that the United States, China, and Canada are leading contributors, with strong thematic overlaps in areas such as dementia care, machine learning, and wearable health monitoring technologies. High correlations were found between academic and public interest, in key topics such as ``long-term care'' ($\tau$=0.89, P<.001) and ``caregiver'' ($\tau$=0.72, P=.004). The content analysis demonstrated that social robots, particularly PARO, significantly improved mood and reduced agitation in patients with dementia. However, limitations, including small sample sizes, short study durations, and a narrow focus on dementia care, were noted. Conclusions: AI, IoT, and EI collectively form a powerful ecosystem in LTC settings, addressing different aspects of care for older adults. Our study suggests that increased international collaboration and the integration of emerging themes such as ``rehabilitation,'' ``stroke,'' and ``mHealth'' are necessary to meet the evolving care needs of this population. Additionally, incorporating high-interest keywords such as ``machine learning,'' ``smart home,'' and ``caregiver'' can enhance discoverability and relevance for both academic and public audiences. Future research should focus on expanding sample sizes, conducting long-term multicenter trials, and exploring broader health conditions beyond dementia, such as frailty and depression. ",
doi="10.2196/56692",
url="https://www.jmir.org/2025/1/e56692",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053718"
}


@Article{info:doi/10.2196/54470,
author="Seth, Mattias
and Jalo, Hoor
and H{\"o}gstedt, {\AA}sa
and Medin, Otto
and Sj{\"o}qvist, Arne Bengt
and Candefjord, Stefan",
title="Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="23",
volume="27",
pages="e54470",
keywords="Internet of Medical Things",
keywords="enabling technologies",
keywords="standards",
keywords="cross-domain interoperability",
keywords="scoping review",
keywords="technology",
keywords="medical emergency",
keywords="internet",
keywords="prehospital care",
keywords="gerontology",
keywords="global population",
keywords="chronic disease",
keywords="multimorbidity",
keywords="health care system",
keywords="home-based care",
keywords="innovation",
keywords="digital health",
keywords="health informatics",
keywords="telehealth",
keywords="artificial intelligence",
abstract="Background: The aging global population and the rising prevalence of chronic disease and multimorbidity have strained health care systems, driving the need for expanded health care resources. Transitioning to home-based care (HBC) may offer a sustainable solution, supported by technological innovations such as Internet of Medical Things (IoMT) platforms. However, the full potential of IoMT platforms to streamline health care delivery is often limited by interoperability challenges that hinder communication and pose risks to patient safety. Gaining more knowledge about addressing higher levels of interoperability issues is essential to unlock the full potential of IoMT platforms. Objective: This scoping review aims to summarize best practices and technologies to overcome interoperability issues in IoMT platform development for prehospital care and HBC. Methods: This review adheres to a protocol published in 2022. Our literature search followed a dual search strategy and was conducted up to August 2023 across 6 electronic databases: IEEE Xplore, PubMed, Scopus, ACM Digital Library, Sage Journals, and ScienceDirect. After the title, abstract, and full-text screening performed by 2 reviewers, 158 articles were selected for inclusion. To answer our 2 research questions, we used 2 models defined in the protocol: a 6-level interoperability model and a 5-level IoMT reference model. Data extraction and synthesis were conducted through thematic analysis using Dedoose. The findings, including commonly used technologies and standards, are presented through narrative descriptions and graphical representations. Results: The primary technologies and standards reported for interoperable IoMT platforms in prehospital care and HBC included cloud computing (19/30, 63\%), representational state transfer application programming interfaces (REST APIs; 17/30, 57\%), Wi-Fi (17/30, 57\%), gateways (15/30, 50\%), and JSON (14/30, 47\%). Message queuing telemetry transport (MQTT; 7/30, 23\%) and WebSocket (7/30, 23\%) were commonly used for real-time emergency alerts, while fog and edge computing were often combined with cloud computing for enhanced processing power and reduced latencies. By contrast, technologies associated with higher interoperability levels, such as blockchain (2/30, 7\%), Kubernetes (3/30, 10\%), and openEHR (2/30, 7\%), were less frequently reported, indicating a focus on lower level of interoperability in most of the included studies (17/30, 57\%). Conclusions: IoMT platforms that support higher levels of interoperability have the potential to deliver personalized patient care, enhance overall patient experience, enable early disease detection, and minimize time delays. However, our findings highlight a prevailing emphasis on lower levels of interoperability within the IoMT research community. While blockchain, microservices, Docker, and openEHR are described as suitable solutions in the literature, these technologies seem to be seldom used in IoMT platforms for prehospital care and HBC. Recognizing the evident benefit of cross-domain interoperability, we advocate a stronger focus on collaborative initiatives and technologies to achieve higher levels of interoperability. International Registered Report Identifier (IRRID): RR2-10.2196/40243 ",
doi="10.2196/54470",
url="https://www.jmir.org/2025/1/e54470"
}


@Article{info:doi/10.2196/59921,
author="Lu, Wei
and Silvera-Tawil, David
and Yoon, Hwan-Jin
and Higgins, Liesel
and Zhang, Qing
and Karunanithi, Mohanraj
and Bomke, Julia
and Byrnes, Joshua
and Hewitt, Jennifer
and Smallbon, Vanessa
and Freyne, Jill
and Prabhu, Deepa
and Varnfield, Marlien",
title="Impact of the Smarter Safer Homes Solution on Quality of Life and Health Outcomes in Older People Living in Their Own Homes: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="22",
volume="27",
pages="e59921",
keywords="randomized controlled trial",
keywords="digital health",
keywords="eHealth",
keywords="smart home",
keywords="sensor",
keywords="health monitoring",
keywords="home monitoring",
keywords="aged care",
keywords="aging in place",
keywords="older adult",
keywords="quality of life",
abstract="Background: An increasingly aging population, accompanied by a shortage of residential aged care homes and workforce and consumer feedback, has driven a growing interest in enabling older people to age in place through home-based care. In this context, smart home technologies for remote health monitoring have gained popularity for supporting older people to live in their own homes. Objective: This study aims to investigate the impact of smart home monitoring on multiple outcomes, including quality of life, activities of daily living, and depressive symptoms among older people living in their own homes over a 12-month period. Methods: We conducted an open-label, parallel-group randomized controlled trial. The control group continued to receive their existing care from aged care service providers. Meanwhile, the intervention group, in addition to receiving their usual aged care services, had their activities of daily living monitored using a smart home platform. Surveys including the Adult Social Care Outcomes Toolkit (ASCOT), EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Katz Index of Independence in Activities of Daily Living (Katz ADL), Lawton Instrumental Activities of Daily Living Scale (IADL), and Geriatric Depression Scale (GDS) were conducted at baseline and 6 and 12 months from baseline. Linear mixed-effects models were used to compare the difference between the intervention and control groups, with the ASCOT as the primary outcome measure. Results: Data from 130 participants were used in the analysis, with no significant differences in baseline characteristics between the control group (n=61) and the intervention group (n=69). In comparison to the control group, the intervention group had a higher ASCOT score at the 6-month assessment (mean difference 0.045, 95\% CI 0.001 to 0.089; Cohen d=0.377). However, this difference did not persist at the 12-month assessment (mean difference 0.031, 95\% CI --0.014 to 0.076; Cohen d=0.259). There were no significant differences in EQ-5D-5L, Katz ADL, IADL, and GDS observed between the intervention and control groups at the 6-month and 12-month assessments. Conclusions: The study demonstrates that smart home monitoring can improve social care--related quality of life for older people living in their own homes. However, the improvement was not sustained over the long term. The lack of statistically significant findings and diminished long-term improvements may be attributed to the influence of the COVID-19 pandemic during the later stage of the trial. Further research with a larger sample size is needed to evaluate the effect of smart home monitoring on broader quality-of-life measures. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em International Registered Report Identifier (IRRID): RR2-10.2196/31970 ",
doi="10.2196/59921",
url="https://www.jmir.org/2025/1/e59921"
}


@Article{info:doi/10.2196/57622,
author="Probst, Freya
and Rees, Jessica
and Aslam, Zayna
and Mexia, Nikitia
and Molteni, Erika
and Matcham, Faith
and Antonelli, Michela
and Tinker, Anthea
and Shi, Yu
and Ourselin, Sebastien
and Liu, Wei",
title="Evaluating a Smart Textile Loneliness Monitoring System for Older People: Co-Design and Qualitative Focus Group Study",
journal="JMIR Aging",
year="2024",
month="Dec",
day="17",
volume="7",
pages="e57622",
keywords="loneliness",
keywords="smart textiles",
keywords="wearable technology",
keywords="health monitoring",
keywords="older people",
keywords="co-design",
keywords="design requirement",
keywords="mobile phone",
abstract="Background: Previous studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development. The use of these technologies and infrastructural questions have been insufficiently addressed. Systems generally consist of sensors or software installed in smartphones or homes. However, no studies have attempted to use smart textiles, which are fabrics with integrated electronics. Objective: This study aims to understand the design requirements for a smart textile loneliness monitoring system from the perspectives of older people and stakeholders. Methods: We conducted co-design workshops with 5 users and 6 stakeholders to determine the design requirements for smart textile loneliness monitoring systems. We derived a preliminary product concept of the smart wearable and furniture system. Digital and physical models and a use case were evaluated in a focus group study with older people and stakeholders (n=7). Results: The results provided insights for designing systems that use smart textiles to monitor loneliness in older people and widen their use. The findings informed the general system, wearables and furniture, materials, sensor positioning, washing, sensor synchronization devices, charging, intervention, and installation and maintenance requirements. This study provided the first insight from a human-centered perspective into smart textile loneliness monitoring systems for older people. Conclusions: We recommend more research on the intervention that links to the monitored loneliness in a way that addresses different needs to ensure its usefulness and value to people. Future systems must also reflect on questions of identification of system users and the available infrastructure and life circumstances of people. We further found requirements that included user cooperation, compatibility with other worn medical devices, and long-term durability. ",
doi="10.2196/57622",
url="https://aging.jmir.org/2024/1/e57622",
url="http://www.ncbi.nlm.nih.gov/pubmed/39688889"
}


@Article{info:doi/10.2196/60261,
author="Hossein Motlagh, Naser
and Zuniga, Agustin
and Thi Nguyen, Ngoc
and Flores, Huber
and Wang, Jiangtao
and Tarkoma, Sasu
and Prosperi, Mattia
and Helal, Sumi
and Nurmi, Petteri",
title="Population Digital Health: Continuous Health Monitoring and Profiling at Scale",
journal="Online J Public Health Inform",
year="2024",
month="Nov",
day="20",
volume="16",
pages="e60261",
keywords="digital health",
keywords="population health",
keywords="modeling, health data",
keywords="health monitoring",
keywords="monitoring",
keywords="wearable devices",
keywords="wearables",
keywords="machine learning",
keywords="networking infrastructure",
keywords="cost-effectiveness",
keywords="device",
keywords="sensor",
keywords="PDH",
keywords="equity",
doi="10.2196/60261",
url="https://ojphi.jmir.org/2024/1/e60261"
}


@Article{info:doi/10.2196/64806,
author="C Areias, Anabela
and G Moulder, Robert
and Molinos, Maria
and Janela, Dora
and Bento, Virg{\'i}lio
and Moreira, Carolina
and Yanamadala, Vijay
and P Cohen, Steven
and Dias Correia, Fernando
and Costa, Fab{\'i}ola",
title="Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool",
journal="JMIR Med Inform",
year="2024",
month="Nov",
day="19",
volume="12",
pages="e64806",
keywords="telerehabilitation",
keywords="predictive modeling",
keywords="personalized medicine",
keywords="rehabilitation",
keywords="clinical decision support",
keywords="machine learning",
keywords="artificial intelligence",
abstract="Background: Low back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment. Objective: This study aims to develop an AI tool that continuously assists physical therapists in predicting an individual's potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments. Methods: Data collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values. Results: At each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95\% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95\% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates. Conclusions: This study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP. Trial Registration: ClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685 ",
doi="10.2196/64806",
url="https://medinform.jmir.org/2024/1/e64806"
}


@Article{info:doi/10.2196/53447,
author="Chung, Jane
and Pretzer-Aboff, Ingrid
and Parsons, Pamela
and Falls, Katherine
and Bulut, Eyuphan",
title="Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study",
journal="JMIR Res Protoc",
year="2024",
month="Nov",
day="12",
volume="13",
pages="e53447",
keywords="Wi-Fi sensing",
keywords="dementia",
keywords="mild cognitive impairment",
keywords="older adults",
keywords="health disparities",
keywords="in-home activities",
keywords="mobility",
keywords="machine learning",
abstract="Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information--based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing--based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults' Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447 ",
doi="10.2196/53447",
url="https://www.researchprotocols.org/2024/1/e53447"
}


@Article{info:doi/10.2196/50461,
author="Kallio, Johanna
and Kinnula, Atte
and M{\"a}kel{\"a}, Satu-Marja
and J{\"a}rvinen, Sari
and R{\"a}s{\"a}nen, Pauli
and Hosio, Simo
and Bordallo L{\'o}pez, Miguel",
title="Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="31",
volume="26",
pages="e50461",
keywords="field trial",
keywords="behavioral research",
keywords="sensor data",
keywords="machine learning",
keywords="pervasive technology",
keywords="stakeholder engagement",
keywords="qualitative coding",
keywords="mobile phone",
abstract="Background: Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments. Objective: This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings. Methods: We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success. Results: Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes. Conclusions: Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions. ",
doi="10.2196/50461",
url="https://www.jmir.org/2024/1/e50461"
}


@Article{info:doi/10.2196/54210,
author="Timon, M. Claire
and Heffernan, Emma
and Kilcullen, Sophia
and Hopper, Louise
and Lee, Hyowon
and Gallagher, Pamela
and Smeaton, F. Alan
and Moran, Kieran
and Hussey, Pamela
and Murphy, Catriona",
title="Developing Independent Living Support for Older Adults Using Internet of Things and AI-Based Systems: Co-Design Study",
journal="JMIR Aging",
year="2024",
month="Oct",
day="24",
volume="7",
pages="e54210",
keywords="independent living",
keywords="gerontology",
keywords="geriatric",
keywords="older adult",
keywords="elderly",
keywords="aging",
keywords="Internet of Things",
keywords="IoT",
keywords="wearable electronic device",
keywords="medical device",
keywords="daily living activities",
keywords="quality of life",
keywords="QoL",
keywords="artificial intelligence",
keywords="AI",
keywords="algorithm",
keywords="predictive model",
keywords="predictive analytics",
keywords="predictive system",
keywords="practical model",
abstract="Background: The number of older people with unmet health care and support needs is increasing substantially due to the challenges facing health care systems worldwide. There are potentially great benefits to using the Internet of Things coupled with artificial intelligence to support independent living and the measurement of health risks, thus improving quality of life for the older adult population. Taking a co-design approach has the potential to ensure that these technological solutions are developed to address specific user needs and requirements. Objective: The aim of this study was to investigate stakeholders' perceptions of independent living and technology solutions, identify stakeholders' suggestions on how technology could assist older adults to live independently, and explore the acceptability and usefulness of a prototype Internet of Things solution called the NEX system to support independent living for an older adult population. Methods: The development of the NEX system was carried out in 3 key phases with a strong focus on diverse stakeholder involvement. The initial predesign exploratory phase recruited 17 stakeholders, including older adults and family caregivers, using fictitious personas and scenarios to explore initial perceptions of independent living and technology solutions. The subsequent co-design and testing phase expanded this to include a comprehensive web-based survey completed by 380 stakeholders, encompassing older adults, family caregivers, health care professionals, and home care support staff. This phase also included prototype testing at home by 7 older adults to assess technology needs, requirements, and the initial acceptability of the system. Finally, in the postdesign phase, workshops were held between academic and industry partners to analyze data collected from the earlier stages and to discuss recommendations for the future development of the system. Results: The predesign phase revealed 3 broad themes: loneliness and technology, aging and technology, and adopting and using technology. The co-design phase highlighted key areas where technology could assist older adults to live independently: home security, falls and loneliness, remote monitoring by family members, and communication with clients. Prototype testing revealed that the acceptability aspects of the prototype varied across technology types. Ambient sensors and voice-activated assistants were described as the most acceptable technology by participants. Last, the postdesign analysis process highlighted that ambient sensors have the potential for automatic detection of activities of daily living, resulting in key recommendations for future developments and deployments in this area. Conclusions: This study demonstrates the significance of incorporating diverse stakeholder perspectives in developing solutions that support independent living. Additionally, it emphasizes the advantages of prototype testing in home environments, offering crucial insights into the real-world experiences of users interacting with technological solutions. International Registered Report Identifier (IRRID): RR2-10.2196/35277 ",
doi="10.2196/54210",
url="https://aging.jmir.org/2024/1/e54210"
}


@Article{info:doi/10.2196/51259,
author="Rashid, Zulqarnain
and Folarin, A. Amos
and Zhang, Yuezhou
and Ranjan, Yatharth
and Conde, Pauline
and Sankesara, Heet
and Sun, Shaoxiong
and Stewart, Callum
and Laiou, Petroula
and Dobson, B. Richard J.",
title="Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform",
journal="JMIR Ment Health",
year="2024",
month="Oct",
day="23",
volume="11",
pages="e51259",
keywords="digital biomarkers",
keywords="mHealth",
keywords="mobile apps",
keywords="Internet of Things",
keywords="remote data collection",
keywords="wearables",
keywords="real-time monitoring",
keywords="platform",
keywords="biomarkers",
keywords="wearable",
keywords="smartphone",
keywords="data collection",
keywords="open-source platform",
keywords="RADAR-base",
keywords="phenotyping",
keywords="mobile phone",
keywords="IoT",
abstract="Background: The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Objective: Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. Methods: We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. Results: The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. Conclusions: RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management. ",
doi="10.2196/51259",
url="https://mental.jmir.org/2024/1/e51259"
}


@Article{info:doi/10.2196/50141,
author="Sollender, E. Grace
and Jiang, Tommy
and Finkelshtein, Ilana
and Osadchiy, Vadim
and Zheng, H. Michael
and Mills, N. Jesse
and Singer, S. Jennifer
and Eleswarapu, V. Sriram",
title="Understanding Pediatric Experiences With Symptomatic Varicoceles: Mixed Methods Study of an Online Varicocele Community",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="10",
volume="8",
pages="e50141",
keywords="adolescents",
keywords="online support",
keywords="online forums",
keywords="peer support",
keywords="natural language processing",
abstract="Background: Varicoceles affect up to 30\% of postpubertal adolescent males. Studying this population remains difficult due to this topic's sensitive nature. Using the popularity of social media in this cohort and natural language processing (NLP) techniques, our aim was to identify perceptions of adolescent males on an internet varicocele forum to inform how physicians may better evaluate and counsel this pediatric population. Objective: We aimed to characterize themes of discussion and specific concerns expressed by adolescents using a mixed methods approach involving quantitative NLP and qualitative annotation of an online varicocele community. Methods: We extracted posts from the Reddit community ``r/varicocele'' (5100 members) with criteria of discussant age ?21 years and word count >20. We used qualitative thematic analysis and the validated constant comparative method, as well as an NLP technique called the meaning extraction method with principal component analysis (MEM/PCA), to identify discussion themes. Two investigators independently interrogated 150 randomly selected posts to further characterize content based on NLP-identified themes and calculated the Kaiser-Meyer-Olkin (KMO) statistic and the Bartlett test. Both quantitative and qualitative approaches were then compared to identify key themes of discussion. Results: A total of 1103 posts met eligibility criteria from July 2015 to June 2022. Among the 150 randomly selected posts, MEM/PCA and qualitative thematic analysis separately revealed key themes: an overview of varicocele (40/150, 27\%), management (29/150, 19\%), postprocedural experience (28/150, 19\%), seeking community (26/150, 17\%) and second opinions after visiting a physician (27/150, 18\%). Quantitative analysis also identified ``hypogonadism'' and ``semen analysis'' as concerns when discussing their condition. The KMO statistic was >0.60 and the Bartlett test was <0.01, indicating the appropriateness of MEM/PCA. The mean age was 17.5 (SD 2.2; range 14-21) years, and there were trends toward higher-grade (40/45, 89\% had a grade of ?2) and left-sided varicoceles. Urologists were the topic of over 50\% (53/82) of discussions among discussants, and varicocelectomy remained the intervention receiving the most interest. A total of 60\% (90/150) of discussants described symptomatic varicoceles, with 62 of 90 reporting pain, 24 of 90 reporting hypogonadism symptoms, and 45 of 90 reporting aesthetics as the primary concern. Conclusions: We applied a mixed methods approach to identify uncensored concerns of adolescents with varicoceles. Both qualitative and quantitative approaches identified that adolescents often turned to social media as an adjunct to doctors' visits and to seek peer support. This population prioritized symptom control, with an emphasis on pain, aesthetics, sexual function, and hypogonadism. These data highlight how each adolescent may approach varicoceles uniquely, informing urologists how to better interface with this pediatric population. Additionally, these data may highlight the key drivers of decision-making when electing for procedural management of varicoceles. ",
doi="10.2196/50141",
url="https://formative.jmir.org/2024/1/e50141"
}


@Article{info:doi/10.2196/58380,
author="Wen, Ming-Huan
and Chen, Po-Yin
and Lin, Shirling
and Lien, Ching-Wen
and Tu, Sheng-Hsiang
and Chueh, Ching-Yi
and Wu, Ying-Fang
and Tan Cheng Kian, Kelvin
and Hsu, Yeh-Liang
and Bai, Dorothy",
title="Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="3",
volume="26",
pages="e58380",
keywords="patient safety",
keywords="falls",
keywords="fall prevention",
keywords="fall risk",
keywords="sensors",
keywords="Internet of Things",
keywords="bed-exit alert",
keywords="motion-sensing mattress system",
keywords="care quality",
keywords="quality improvement",
keywords="ubiquitous health",
keywords="mHealth",
abstract="Background: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. Objective: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. Methods: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. Results: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2\% (n=8) in the traditional patient care system ward and 0.1\% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88\% (odds ratio 0.12, 95\% CI 0.01-0.97; P=.047). Conclusions: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety. ",
doi="10.2196/58380",
url="https://www.jmir.org/2024/1/e58380",
url="http://www.ncbi.nlm.nih.gov/pubmed/39361417"
}


@Article{info:doi/10.2196/53711,
author="Lim, Sachiko
and Johannesson, Paul",
title="An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study",
journal="JMIR Form Res",
year="2024",
month="Sep",
day="26",
volume="8",
pages="e53711",
keywords="infectious disease",
keywords="ontology",
keywords="IoT",
keywords="infectious disease surveillance",
keywords="patient monitoring",
keywords="infectious disease management",
keywords="risk analysis",
keywords="early warning",
keywords="data integration",
keywords="semantic interoperability",
keywords="public health",
abstract="Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner. ",
doi="10.2196/53711",
url="https://formative.jmir.org/2024/1/e53711",
url="http://www.ncbi.nlm.nih.gov/pubmed/39325530"
}


@Article{info:doi/10.2196/50043,
author="Pulantara, Wayan I.
and Wang, Yuhan
and Burke, E. Lora
and Sereika, M. Susan
and Bizhanova, Zhadyra
and Kariuki, K. Jacob
and Cheng, Jessica
and Beatrice, Britney
and Loar, India
and Cedillo, Maribel
and Conroy, B. Molly
and Parmanto, Bambang",
title="Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Aug",
day="7",
volume="12",
pages="e50043",
keywords="integrated system",
keywords="IoT integration",
keywords="wearable",
keywords="mHealth Fitbit",
keywords="Nokia",
keywords="clinical trial management",
keywords="research study management",
keywords="study tracking",
keywords="remote assessment",
keywords="tracking",
keywords="Fitbit",
keywords="wearable devices",
keywords="device",
keywords="management",
keywords="data analysis",
keywords="behavioral",
keywords="data collection",
keywords="Internet of Things",
keywords="IoT",
keywords="mHealth",
keywords="mobile health",
doi="10.2196/50043",
url="https://mhealth.jmir.org/2024/1/e50043"
}


@Article{info:doi/10.2196/55842,
author="Song, Sunmi
and Seo, YoungBin
and Hwang, SeoYeon
and Kim, Hae-Young
and Kim, Junesun",
title="Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Jun",
day="17",
volume="12",
pages="e55842",
keywords="depression",
keywords="monitoring system",
keywords="IoT",
keywords="AI",
keywords="wearable device",
keywords="digital mental health phenotyping",
keywords="living lab",
keywords="senior care",
keywords="Internet of Things",
keywords="artificial intelligence",
abstract="Background: Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments. Objective: This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults. Methods: A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability. Results: Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18). Conclusions: The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability. Trial Registration: ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121 ",
doi="10.2196/55842",
url="https://mhealth.jmir.org/2024/1/e55842",
url="http://www.ncbi.nlm.nih.gov/pubmed/38885033"
}


@Article{info:doi/10.2196/55953,
author="V{\"o}geli, Benjamin
and Arenja, Nisha
and Sch{\"u}tz, Narayan
and Nef, Tobias
and Buluschek, Philipp
and Saner, Hugo",
title="Evaluation of Ambient Sensor Systems for the Early Detection of Heart Failure Decompensation in Older Patients Living at Home Alone: Protocol for a Prospective Cohort Study",
journal="JMIR Res Protoc",
year="2024",
month="May",
day="31",
volume="13",
pages="e55953",
keywords="heart failure",
keywords="home telemonitoring",
keywords="digital health",
keywords="biomarker",
keywords="ambient sensor system",
abstract="Background: The results of telemedicine intervention studies in patients with heart failure (HF) to reduce rehospitalization rate and mortality by early detection of HF decompensation are encouraging. However, the benefits are lower than expected. A possible reason for this could be the fact that vital signs, including blood pressure, heart rate, heart rhythm, and weight changes, may not be ideal indicators of the early stages of HF decompensation but are more sensitive for acute events triggered by ischemic episodes or rhythm disturbances. Preliminary results indicate a potential role of ambient sensor--derived digital biomarkers in this setting. Objective: The aim of this study is to identify changes in ambient sensor system--derived digital biomarkers with a high potential for early detection of HF decompensation. Methods: This is a prospective interventional cohort study. A total of 24 consecutive patients with HF aged 70 years and older, living alone, and hospitalized for HF decompensation will be included. Physical activity in the apartment and toilet visits are quantified using a commercially available, passive, infrared motion sensing system (DomoHealth SA). Heart rate, respiration rate, and toss-and-turns in bed are recorded by using a commercially available Emfit QS device (Emfit Ltd), which is a contact-free piezoelectric sensor placed under the participant's mattress. Sensor data are visualized on a dedicated dashboard for easy monitoring by health professionals. Digital biomarkers are evaluated for predefined signs of HF decompensation, including particularly decreased physical activity; time spent in bed; increasing numbers of toilet visits at night; and increasing heart rate, respiration rate, and motion in bed at night. When predefined changes in digital biomarkers occur, patients will be called in for clinical evaluation, and N-terminal pro b-type natriuretic peptide measurement (an increase of >30\% considered as significant) will be performed. The sensitivity and specificity of the different biomarkers and their combinations for the detection of HF decompensation will be calculated. Results: The study is in the data collection phase. Study recruitment started in February 2024. Data analysis is scheduled to start after all data are collected. As of manuscript submission, 5 patients have been recruited. Results are expected to be published by the end of 2025. Conclusions: The results of this study will add to the current knowledge about opportunities for telemedicine to monitor older patients with HF living at home alone by evaluating the potential of ambient sensor systems for this purpose. Timely recognition of HF decompensation could enable proactive management, potentially reducing health care costs associated with preventable emergency presentations or hospitalizations. Trial Registration: ClinicalTrials.gov NCT06126848; https://clinicaltrials.gov/study/NCT06126848 International Registered Report Identifier (IRRID): PRR1-10.2196/55953 ",
doi="10.2196/55953",
url="https://www.researchprotocols.org/2024/1/e55953",
url="http://www.ncbi.nlm.nih.gov/pubmed/38820577"
}


@Article{info:doi/10.2196/51587,
author="Fink, Franziska
and Kalter, Ivonne
and Steindorff, Jenny-Victoria
and Helmbold, Konrad Hans
and Paulicke, Denny
and Jahn, Patrick",
title="Identifying Factors of User Acceptance of a Drone-Based Medication Delivery: User-Centered Design Approach",
journal="JMIR Hum Factors",
year="2024",
month="Apr",
day="30",
volume="11",
pages="e51587",
keywords="human-drone interaction",
keywords="medical supplies",
keywords="participative research",
keywords="user-centered design",
keywords="technology acceptance",
abstract="Background: The use of drones in the health care sector is increasingly being discussed against the background of the aging population and the growing shortage of skilled workers. In particular, the use of drones to provide medication in rural areas could bring advantages for the care of people with and without a need for care. However, there are hardly any data available that focus on the interaction between humans and drones. Objective: This study aims to disclose and analyze factors associated with user acceptance of drone-based medication delivery to derive practice-relevant guidance points for participatory technology development (for apps and drones). Methods: A controlled mixed methods study was conducted that supports the technical development process of an app design for drone-assisted drug delivery based on a participatory research design. For the quantitative analysis, established and standardized survey instruments to capture technology acceptance, such as the System Usability Scale; Technology Usage Inventory (TUI); and the Motivation, Engagement, and Thriving in User Experience model, were used. To avoid possible biasing effects from a continuous user development (eg, response shifts and learning effects), an ad hoc group was formed at each of the 3 iterative development steps and was subsequently compared with the consisting core group, which went through all 3 iterations. Results: The study found a positive correlation between the usability of a pharmacy drone app and participants' willingness to use it (r=0.833). Participants' perception of usefulness positively influenced their willingness to use the app (r=0.487; TUI). Skepticism had a negative impact on perceived usability and willingness to use it (r=?0.542; System Usability Scale and r=?0.446; TUI). The study found that usefulness, skepticism, and curiosity explained most of the intention to use the app (F3,17=21.12; P<.001; R2=0.788; adjusted R2=0.751). The core group showed higher ratings on the intention to use the pharmacy drone app than the ad hoc groups. Results of the 2-tailed t tests showed a higher rating on usability for the third iteration of the core group compared with the first iteration. Conclusions: With the help of the participatory design, important aspects of acceptance could be revealed by the people involved in relation to drone-assisted drug delivery. For example, the length of time spent using the technology is an important factor for the intention to use the app. Technology-specific factors such as user-friendliness or curiosity are directly related to the use acceptance of the drone app. Results of this study showed that the more participants perceived their own competence in handling the app, the more they were willing to use the technology and the more they rated the app as usable. ",
doi="10.2196/51587",
url="https://humanfactors.jmir.org/2024/1/e51587",
url="http://www.ncbi.nlm.nih.gov/pubmed/38687589"
}


@Article{info:doi/10.2196/51874,
author="Nakajima, Yuki
and Kitayama, Asami
and Ohta, Yuji
and Motooka, Nobuhisa
and Kuno-Mizumura, Mayumi
and Miyachi, Motohiko
and Tanaka, Shigeho
and Ishikawa-Takata, Kazuko
and Tripette, Julien",
title="Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="25",
volume="8",
pages="e51874",
keywords="smart home system",
keywords="physical behavior",
keywords="physical activity",
keywords="activity tracker",
keywords="floor vibration",
keywords="housework-related activity",
keywords="home-based activity",
keywords="mobile phone",
abstract="Background: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. Objective: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). Methods: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. Results: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration--extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. Conclusions: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day. ",
doi="10.2196/51874",
url="https://formative.jmir.org/2024/1/e51874",
url="http://www.ncbi.nlm.nih.gov/pubmed/38662415"
}


@Article{info:doi/10.2196/46903,
author="Sahu, Sundar Kirti
and Dubin, A. Joel
and Majowicz, E. Shannon
and Liu, Sam
and Morita, P. Plinio",
title="Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things--Based Thermostat Data and Google Mobility Insights",
journal="JMIR Public Health Surveill",
year="2024",
month="Mar",
day="20",
volume="10",
pages="e46903",
keywords="population-level health indicators",
keywords="internet of things",
keywords="public health surveillance",
keywords="mobility",
keywords="risk factors",
keywords="chronic diseases",
keywords="chronic",
keywords="risk",
keywords="surveillance",
keywords="movement",
keywords="sensor",
keywords="population",
abstract="Background: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. Objective: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Methods: Motion sensor data were acquired from the ecobee ``Donate Your Data'' initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces---Ontario, Quebec, Alberta, and British Columbia---during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. Results: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. Conclusions: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts. ",
doi="10.2196/46903",
url="https://publichealth.jmir.org/2024/1/e46903",
url="http://www.ncbi.nlm.nih.gov/pubmed/38506901"
}


@Article{info:doi/10.2196/44422,
author="Cheng, Weibin
and Cao, Xiaowen
and Lian, Wanmin
and Tian, Junzhang",
title="An Introduction to Smart Home Ward--Based Hospital-at-Home Care in China",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Jan",
day="30",
volume="12",
pages="e44422",
keywords="smart home ward",
keywords="telemonitoring",
keywords="telemedicine",
keywords="home care",
keywords="hospital at home",
keywords="healthcare delivery",
keywords="implementation",
keywords="smart ward",
keywords="medical monitoring",
keywords="medical care",
keywords="rehabilitation",
keywords="health care",
doi="10.2196/44422",
url="https://mhealth.jmir.org/2024/1/e44422"
}


@Article{info:doi/10.2196/47146,
author="Vaussenat, Fabrice
and Bhattacharya, Abhiroop
and Payette, Julie
and Benavides-Guerrero, A. Jaime
and Perrotton, Alexandre
and Gerlein, Felipe Luis
and Cloutier, G. Sylvain",
title="Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study",
journal="JMIR Biomed Eng",
year="2023",
month="Oct",
day="25",
volume="8",
pages="e47146",
keywords="relative humidity sensor",
keywords="design",
keywords="develop",
keywords="development",
keywords="tidal volume",
keywords="pulmonary volume",
keywords="COPD",
keywords="pulmonary",
keywords="respiratory",
keywords="sensor",
keywords="sensors",
keywords="wearables",
keywords="humidity",
keywords="medical device",
keywords="breathing",
keywords="wearable",
keywords="ventilation",
keywords="air",
abstract="Background: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. Objective: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. Methods: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. Results: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of --0.25 and 0.33. The RR bias was 0.018, and the LoAs were --1.89 and 1.89. Conclusions: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals. ",
doi="10.2196/47146",
url="https://biomedeng.jmir.org/2023/1/e47146",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875670"
}


@Article{info:doi/10.2196/48730,
author="Heeres, J. Tjitske
and Tran, Mikael Tri
and Noort, A.C. Bart",
title="Drivers and Barriers to Implementing the Internet of Things in the Health Care Supply Chain: Mixed Methods Multicase Study",
journal="J Med Internet Res",
year="2023",
month="Sep",
day="20",
volume="25",
pages="e48730",
keywords="digital health",
keywords="drivers and barriers",
keywords="healthcare logistics",
keywords="healthcare supply chain",
keywords="implementation",
keywords="Internet of Things",
keywords="supply chain management",
abstract="Background: Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary. Objective: This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain. Methods: We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers. Results: The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome. Conclusions: The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain. ",
doi="10.2196/48730",
url="https://www.jmir.org/2023/1/e48730",
url="http://www.ncbi.nlm.nih.gov/pubmed/37728990"
}


@Article{info:doi/10.2196/46653,
author="Burns, L. Michael
and Sinha, Anik
and Hoffmann, Alexander
and Wu, Zewen
and Medina Inchauste, Tomas
and Retsky, Aaron
and Chesney, David
and Kheterpal, Sachin
and Shah, Nirav",
title="Development and Testing of a Data Capture Device for Use With Clinical Incentive Spirometers: Testing and Usability Study",
journal="JMIR Biomed Eng",
year="2023",
month="Sep",
day="7",
volume="8",
pages="e46653",
keywords="incentive",
keywords="spirometry",
keywords="Internet-of-Things",
keywords="electronic health records",
keywords="web-based intervention",
keywords="medical device",
keywords="medical tool",
keywords="data collection",
keywords="spirometry data",
keywords="incentive spirometer",
keywords="data analysis",
keywords="algorithm",
keywords="effectiveness",
abstract="Background: The incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications. Objective: The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer. Methods: An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad. Results: In user testing, sensor locations were captured at an accuracy of 99\% (SD 1.4\%) for volume and 100\% accuracy for flow. Median and average volumes were within 7.5\% (SD 6\%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100\% sensitivity and a 99\% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay. Conclusions: An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care. ",
doi="10.2196/46653",
url="https://biomedeng.jmir.org/2023/1/e46653",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875693"
}


@Article{info:doi/10.2196/44114,
author="Suleski, Tance
and Ahmed, Mohiuddin",
title="A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things",
journal="J Med Internet Res",
year="2023",
month="Aug",
day="29",
volume="25",
pages="e44114",
keywords="health care",
keywords="authentication",
keywords="contextual data model",
keywords="Internet of Health Care Things",
keywords="multifactor",
keywords="mobile phone",
doi="10.2196/44114",
url="https://www.jmir.org/2023/1/e44114",
url="http://www.ncbi.nlm.nih.gov/pubmed/37490633"
}


@Article{info:doi/10.2196/44850,
author="Hermsen, Sander
and Verbiest, Vera
and Buijs, Marije
and Wentink, Eva",
title="Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study",
journal="JMIR Hum Factors",
year="2023",
month="Aug",
day="11",
volume="10",
pages="e44850",
keywords="digital health",
keywords="internet of things",
keywords="human factors",
keywords="health tracking",
keywords="device",
keywords="automated",
keywords="biomarker",
keywords="personal health",
keywords="personal hygiene",
keywords="hygiene",
keywords="data",
keywords="privacy",
keywords="innovation",
keywords="mobile phone",
abstract="Background: Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive. Objective: This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience? Methods: Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences. Results: Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants. Conclusions: This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted. ",
doi="10.2196/44850",
url="https://humanfactors.jmir.org/2023/1/e44850",
url="http://www.ncbi.nlm.nih.gov/pubmed/37566450"
}


@Article{info:doi/10.2196/47325,
author="Kim, Hyunsoo
and Jang, Jin Seong
and Lee, Dong Hee
and Ko, Hoon Jae
and Lim, Young Jee",
title="Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study",
journal="JMIR Form Res",
year="2023",
month="Aug",
day="7",
volume="7",
pages="e47325",
keywords="analysis",
keywords="auto-mapping",
keywords="monitoring",
keywords="healthcare",
keywords="health-monitoring",
keywords="online",
keywords="piezo-resistance sensor",
keywords="pressure mat",
keywords="real-time",
keywords="sensing mats",
keywords="smart home technology",
keywords="smart home",
keywords="spatial map",
keywords="technology",
keywords="textile",
abstract="Background: The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as ``death by loneliness.'' Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society. Objective: Non--face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients' health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats. Methods: The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube--based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application. Results: This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube--based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology. Conclusions: This system allows for the analysis of patients' behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability. ",
doi="10.2196/47325",
url="https://formative.jmir.org/2023/1/e47325",
url="http://www.ncbi.nlm.nih.gov/pubmed/37548993"
}


@Article{info:doi/10.2196/45477,
author="Bottani, Eleonora
and Bellini, Valentina
and Mordonini, Monica
and Pellegrino, Mattia
and Lombardo, Gianfranco
and Franchi, Beatrice
and Craca, Michelangelo
and Bignami, Elena",
title="Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study",
journal="JMIR Res Protoc",
year="2023",
month="Jul",
day="5",
volume="12",
pages="e45477",
keywords="internet of things",
keywords="artificial intelligence",
keywords="machine learning",
keywords="perioperative organization",
keywords="operating rooms",
abstract="Background: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital. Objective: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources. Methods: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number. Results: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block. Conclusions: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system. Trial Registration: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621 International Registered Report Identifier (IRRID): DERR1-10.2196/45477 ",
doi="10.2196/45477",
url="https://www.researchprotocols.org/2023/1/e45477",
url="http://www.ncbi.nlm.nih.gov/pubmed/37405821"
}


@Article{info:doi/10.2196/37347,
author="Morita, P. Plinio
and Sahu, Sundar Kirti
and Oetomo, Arlene",
title="Health Monitoring Using Smart Home Technologies: Scoping Review",
journal="JMIR Mhealth Uhealth",
year="2023",
month="Apr",
day="13",
volume="11",
pages="e37347",
keywords="monitor",
keywords="smart home",
keywords="ambient assisted living",
keywords="active assisted living",
keywords="AAL",
keywords="assisted living",
keywords="review",
keywords="internet of things",
keywords="aging",
keywords="gerontology",
keywords="elder",
keywords="older adult",
keywords="older people",
keywords="geriatric",
keywords="digital health",
keywords="eHealth",
keywords="smart technology",
keywords="older population",
keywords="independent living",
keywords="big data",
keywords="machine learning",
keywords="algorithm",
keywords="deep learning",
abstract="Background: The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. Objective: This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. Methods: Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. Results: Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78\% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60\% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62\% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. Conclusions: This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps---in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector. ",
doi="10.2196/37347",
url="https://mhealth.jmir.org/2023/1/e37347",
url="http://www.ncbi.nlm.nih.gov/pubmed/37052984"
}


@Article{info:doi/10.2196/43777,
author="David, B. Michael C.
and Kolanko, Magdalena
and Del Giovane, Martina
and Lai, Helen
and True, Jessica
and Beal, Emily
and Li, M. Lucia
and Nilforooshan, Ramin
and Barnaghi, Payam
and Malhotra, A. Paresh
and Rostill, Helen
and Wingfield, David
and Wilson, Danielle
and Daniels, Sarah
and Sharp, J. David
and Scott, Gregory",
title="Remote Monitoring of Physiology in People Living With Dementia: An Observational Cohort Study",
journal="JMIR Aging",
year="2023",
month="Mar",
day="9",
volume="6",
pages="e43777",
keywords="dementia",
keywords="remote monitoring",
keywords="physiology",
keywords="Internet of Things",
keywords="alerts",
keywords="monitoring",
keywords="technology",
keywords="detection",
keywords="blood pressure",
keywords="support",
keywords="feasibility",
keywords="system",
keywords="quality of life",
abstract="Background: Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years. Objective: Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system. Methods: We performed a longitudinal community-based cohort study of people with dementia using ``Minder,'' our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities (``alerts'') defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria. Results: A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2\% (IQR 33.2\%-83.7\%, range 2.3\%-100\%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45\% of people with dementia met criteria for hypertension. People with dementia with $\alpha$-synuclein--related dementia had lower systolic blood pressure; 30\% had clinically significant weight loss. Depending on the criteria used, 3.03\%-9.46\% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil. Conclusions: We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes. ",
doi="10.2196/43777",
url="https://aging.jmir.org/2023/1/e43777",
url="http://www.ncbi.nlm.nih.gov/pubmed/36892931"
}


@Article{info:doi/10.2196/42145,
author="Kim, Chan Joo
and Saguna, Saguna
and {\AA}hlund, Christer",
title="Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study",
journal="JMIR Hum Factors",
year="2023",
month="Mar",
day="8",
volume="10",
pages="e42145",
keywords="Internet of Things",
keywords="health monitoring",
keywords="older adults",
keywords="augmented reality",
keywords="user experience",
keywords="independent living",
keywords="design study",
keywords="mobile phone",
abstract="Background: The older population needs solutions for independent living and reducing the burden on caregivers while maintaining the quality and dignity of life. Objective: The aim of this study was to design, develop, and evaluate an older adult health care app that supports trained caregivers (ie, formal caregivers) and relatives (ie, informal caregivers). We aimed to identify the factors that affect user acceptance of interfaces depending on the user's role. Methods: We designed and developed an app with 3 user interfaces that enable remote sensing of an older adult's daily activities and behaviors. We conducted user evaluations (N=25) with older adults and their formal and informal caregivers to obtain an overall impression of the health care monitoring app in terms of user experience and usability. In our design study, the participants had firsthand experience with our app, followed by a questionnaire and individual interview to express their opinions on the app. Through the interview, we also identified their views on each user interface and interaction modality to identify the relationship between the user's role and their acceptance of a particular interface. The questionnaire answers were statistically analyzed, and we coded the interview answers based on keywords related to a participant's experience, for example, ease of use and usefulness. Results: We obtained overall positive results in the user evaluation of our app regarding key aspects such as efficiency, perspicuity, dependability, stimulation, and novelty, with an average between 1.74 (SD 1.02) and 2.18 (SD 0.93) on a scale of ?3.0 to 3.0. The overall impression of our app was favorable, and we identified that ``simple'' and ``intuitive'' were the main factors affecting older adults' and caregivers' preference for the user interface and interaction modality. We also identified a positive user acceptance of the use of augmented reality by 91\% (10/11) of the older adults to share information with their formal and informal caregivers. Conclusions: To address the need for a study to evaluate the user experience and user acceptance by older adults as well as both formal and informal caregivers regarding the user interfaces with multimodal interaction in the context of health monitoring, we designed, developed, and conducted user evaluations with the target user groups. Our results through this design study show important implications for designing future health monitoring apps with multiple interaction modalities and intuitive user interfaces in the older adult health care domain. ",
doi="10.2196/42145",
url="https://humanfactors.jmir.org/2023/1/e42145",
url="http://www.ncbi.nlm.nih.gov/pubmed/36884275"
}


@Article{info:doi/10.2196/43502,
author="Huang, Yitong
and Benford, Steve
and Li, Benqian
and Price, Dominic
and Blake, Holly",
title="Feasibility and Acceptability of an Internet of Things--Enabled Sedentary Behavior Intervention: Mixed Methods Study",
journal="J Med Internet Res",
year="2023",
month="Feb",
day="27",
volume="25",
pages="e43502",
keywords="internet of things",
keywords="health communication",
keywords="pervasive media",
keywords="ubiquitous computing",
keywords="smart objects",
keywords="wearable device",
keywords="behavior change wheel",
keywords="digital intervention",
keywords="sedentary behavior",
keywords="workplace intervention",
keywords="employee well-being",
abstract="Background: Encouraging office workers to break up prolonged sedentary behavior (SB) at work with regular microbreaks can be beneficial yet challenging. The Internet of Things (IoT) offers great promise for delivering more subtle and hence acceptable behavior change interventions in the workplace. We previously developed an IoT-enabled SB intervention, called WorkMyWay, by applying a combination of theory-informed and human-centered design approaches. According to the Medical Research Council's framework for developing and evaluating complex interventions such as WorkMyWay, process evaluation in the feasibility phase can help establish the viability of novel modes of delivery and identify facilitators and barriers to successful delivery. Objective: This study aims to evaluate the feasibility and acceptability of the WorkMyWay intervention and its technological delivery system. Methods: A mixed methods approach was adopted. A sample of 15 office workers were recruited to use WorkMyWay during work hours for 6 weeks. Questionnaires were administered before and after the intervention period to assess self-report occupational sitting and physical activity (OSPA) and psychosocial variables theoretically aligned with prolonged occupational SB (eg, intention, perceived behavioral control, prospective memory and retrospective memory of breaks, and automaticity of regular break behaviors). Behavioral and interactional data were obtained through the system database to determine adherence, quality of delivery, compliance, and objective OSPA. Semistructured interviews were conducted at the end of the study, and a thematic analysis was performed on interview transcripts. Results: All 15 participants completed the study (attrition=0\%) and on average used the system for 25 tracking days (out of a possible 30 days; adherence=83\%). Although no significant change was observed in either objective or self-report OSPA, postintervention improvements were significant in the automaticity of regular break behaviors (t14=2.606; P=.02), retrospective memory of breaks (t14=7.926; P<.001), and prospective memory of breaks (t14=--2.661; P=.02). The qualitative analysis identified 6 themes, which lent support to the high acceptability of WorkMyWay, though delivery was compromised by issues concerning Bluetooth connectivity and factors related to user behaviors. Fixing technical issues, tailoring to individual differences, soliciting organizational supports, and harnessing interpersonal influences could facilitate delivery and enhance acceptance. Conclusions: It is acceptable and feasible to deliver an SB intervention with an IoT system that involves a wearable activity tracking device, an app, and a digitally augmented everyday object (eg, cup). More industrial design and technological development work on WorkMyWay is warranted to improve delivery. Future research should seek to establish the broad acceptability of similar IoT-enabled interventions while expanding the range of digitally augmented objects as the modes of delivery to meet diverse needs. ",
doi="10.2196/43502",
url="https://www.jmir.org/2023/1/e43502",
url="http://www.ncbi.nlm.nih.gov/pubmed/36848183"
}


@Article{info:doi/10.2196/40797,
author="Lee, Peter
and Kim, Heepyung
and Zitouni, Sami M.
and Khandoker, Ahsan
and Jelinek, F. Herbert
and Hadjileontiadis, Leontios
and Lee, Uichin
and Jeong, Yong",
title="Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review",
journal="JMIR Mhealth Uhealth",
year="2022",
month="Nov",
day="15",
volume="10",
number="11",
pages="e40797",
keywords="Internet of Things",
keywords="IoT",
keywords="sensor technology",
keywords="smart helmet",
keywords="smart sensor",
keywords="wearable device",
keywords="mobile phone",
abstract="Background: As a form of the Internet of Things (IoT)--gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. Objective: This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? Methods: We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86\%; EBSCO: 149/1268, 11.75\%; ScienceDirect: 248/1268, 19.55\%; and Google Scholar: 784/1268, 61.82\%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. Results: Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32\%), and the 2 most frequent studies including simulation were industry (23/57, 40\%) and sports (23/57, 40\%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. Conclusions: A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality. ",
doi="10.2196/40797",
url="https://mhealth.jmir.org/2022/11/e40797",
url="http://www.ncbi.nlm.nih.gov/pubmed/36378505"
}


@Article{info:doi/10.2196/33351,
author="Sauz{\'e}on, H{\'e}l{\`e}ne
and Edjolo, Arlette
and Amieva, H{\'e}l{\`e}ne
and Consel, Charles
and P{\'e}r{\`e}s, Karine",
title="Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study",
journal="JMIR Res Protoc",
year="2022",
month="Oct",
day="26",
volume="11",
number="10",
pages="e33351",
keywords="ambient assisted living technology",
keywords="AAL",
keywords="Internet-of-Things",
keywords="IoT",
keywords="aging and frailty",
keywords="independent living",
keywords="effectiveness study",
abstract="Background: Ambient assisted living (AAL) technologies are viewed as a promising way to prolong aging in place, particularly when they are designed as closely as possible to the needs of the end users. However, very few evidence-based results have been provided to support its real value, notably for frail older adults who have a high risk of autonomy loss as well as entering a nursing home. Objective: We hypothesized that the benefit from an AAL with a user-centered design is effective for aging in place for frail older adults in terms of everyday functioning (instrumental activities of daily-life scale). In addition, our secondary hypotheses are that such an AAL decreases or neutralizes the frailty process and reduces the rates of institutionalization and hospitalization and that it improves the psychosocial health of participants and their caregivers when compared with the control condition. We also assume that a large proportion of equipped participants will have a satisfactory experience and will accept a subscription to an internet connection to prolong their participation. Methods: HomeAssist (HA) is an AAL platform offering a large set of apps for 3 main age-related need domains (activities of daily-living, safety, and social participation), relying on a basic set of entities (sensors, actuators, tablets, etc). The HA intervention involves monitoring based on assistive services to support activities related to independent living at home. The study design is quasi-experimental with a duration of 12 months, optionally extensible to 24 months. Follow-up assessments occurred at 0, 12, and 24 months. The primary outcome measures are related to everyday functioning. Secondary outcome measures include indices of frailty, cognitive functioning, and psychosocial health of the participants and their caregivers. Every 6 months, user experience and attitudes toward HA are also collected from equipped participants. Concomitantly, data on HA use will be collected. All measures of the study will be tested based on an intention-to-treat approach using a 2-tailed level of significance set at $\alpha$=.05, concerning our primary and secondary efficacy outcomes. Results: Descriptive analyses were conducted to characterize the recruited equipped participants compared with the others (excluded and refusals) on the data available at the eligibility visit, to describe the characteristics of the recruited sample at baseline, as well as those of the dropouts. Finally, recruitment at 12 months included equipped participants (n=73), matched with control participants (n=474, from pre-existing cohorts). The results of this study will be disseminated through scientific publications and conferences. This will provide a solid basis for the creation of a start-up to market the technology. Conclusions: This trial will inform the real-life efficacy of HA in prolonging aging in place for frail older adults and yield an informed analysis of AAL use and adoption in frail older individuals. International Registered Report Identifier (IRRID): DERR1-10.2196/33351 ",
doi="10.2196/33351",
url="https://www.researchprotocols.org/2022/10/e33351",
url="http://www.ncbi.nlm.nih.gov/pubmed/36287595"
}


@Article{info:doi/10.2196/40243,
author="Seth, Mattias
and Jalo, Hoor
and H{\"o}gstedt, {\AA}sa
and Medin, Otto
and Bj{\"o}rner, Ulrica
and Sj{\"o}qvist, Arne Bengt
and Candefjord, Stefan",
title="Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2022",
month="Sep",
day="20",
volume="11",
number="9",
pages="e40243",
keywords="interoperability",
keywords="Internet of Medical Things",
keywords="prehospital",
keywords="home care",
keywords="reference models",
keywords="mapping",
keywords="technologies",
keywords="scoping review, falls, cardiovascular disease, stroke",
keywords="medical emergency",
keywords="prehospital care",
abstract="Background: Population growth and aging have highlighted the need for more effective home and prehospital care. Interconnected medical devices and applications, which comprise an infrastructure referred to as the Internet of Medical Things (IoMT), have enabled remote patient monitoring and can be important tools to cope with these demographic changes. However, developing IoMT platforms requires profound knowledge of clinical needs and challenges related to interoperability and how these can be managed with suitable technologies. Objective: The purpose of this scoping review is to summarize the best practices and technologies to overcome interoperability concerns in IoMT platform development for medical emergencies in home and prehospital care. Methods: This scoping review will be conducted in accordance with Arksey and O'Malley's 5-stage framework and adhere to the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols) guidelines. Only peer-reviewed articles published in English will be considered. The databases/web search engines that will be used are IEEE Xplore, PubMed, Scopus, Google Scholar, National Center for Biotechnology Information, SAGE Journals, and ScienceDirect. The search process for relevant literature will be divided into 4 different steps. This will ensure that a suitable approach is followed in terms of search terms, limitations, and eligibility criteria. Relevant articles that meet the inclusion criteria will be screened in 2 stages: abstract and title screening and full-text screening. To reduce selection bias, the screening process will be performed by 2 reviewers. Results: The results of the preliminary search indicate that there is sufficient literature to form a good foundation for the scoping review. The search was performed in April 2022, and a total of 4579 articles were found. The main clinical focus is the prevention and management of falls, but other medical emergencies, such as heart disease and stroke, are also considered. Preliminary results show that little attention has been given to real-time IoMT platforms that can be deployed in real-world care settings. The final results are expected to be presented in a scoping review in 2023 and will be disseminated through scientific conference presentations, oral presentations, and publication in a peer-reviewed journal. Conclusions: This scoping review will provide insights and recommendations regarding how interoperable real-time IoMT platforms can be developed to handle medical emergencies in home and prehospital care. The findings of this research could be used by researchers, clinicians, and implementation teams to facilitate future development and interdisciplinary discussions. International Registered Report Identifier (IRRID): DERR1-10.2196/40243 ",
doi="10.2196/40243",
url="https://www.researchprotocols.org/2022/9/e40243",
url="http://www.ncbi.nlm.nih.gov/pubmed/36125863"
}


@Article{info:doi/10.2196/34239,
author="Cao, Yuanyuan
and Erdt, Mojisola
and Robert, Caroline
and Naharudin, Binte Nurhazimah
and Lee, Qi Shan
and Theng, Yin-Leng",
title="Decision-making Factors Toward the Adoption of Smart Home Sensors by Older Adults in Singapore: Mixed Methods Study",
journal="JMIR Aging",
year="2022",
month="Jun",
day="24",
volume="5",
number="2",
pages="e34239",
keywords="aging in place",
keywords="health care systems and management",
keywords="telehealth",
keywords="assistive technology",
keywords="assisted living facilities",
abstract="Background: An increasing aging population has become a pressing problem in many countries. Smart systems and intelligent technologies support aging in place, thereby alleviating the strain on health care systems. Objective: This study aims to identify decision-making factors involved in the adoption of smart home sensors (SHS) by older adults in Singapore. Methods: The study involved 3 phases: as an intervention, SHS were installed in older adults' homes (N=42) for 4 to 5 weeks; in-depth semistructured interviews were conducted with 18 older adults, 2 center managers, 1 family caregiver, and 1 volunteer to understand the factors involved in the decision-making process toward adoption of SHS; and follow-up feedback was collected from 42 older adult participants to understand the reasons for adopting or not adopting SHS. Results: Of the 42 participants, 31 (74\%) adopted SHS after the intervention, whereas 11 (26\%) did not adopt SHS. The reasons for not adopting SHS ranged from privacy concerns to a lack of family support. Some participants did not fully understand SHS functionality and did not perceive the benefits of using SHS. From the interviews, we found that the decision-making process toward the adoption of SHS technology involved intrinsic factors, such as understanding the technology and perceiving its usefulness and benefits, and more extrinsic factors, such as considering affordability and care support from the community. Conclusions: We found that training and a strong support ecosystem could empower older adults in their decision to adopt technology. We advise the consideration of human values and involvement of older adults in the design process to build user-centric assistive technology. ",
doi="10.2196/34239",
url="https://aging.jmir.org/2022/2/e34239",
url="http://www.ncbi.nlm.nih.gov/pubmed/35749213"
}


@Article{info:doi/10.2196/31485,
author="Karni, Liran
and Jusufi, Ilir
and Nyholm, Dag
and Klein, Oskar Gunnar
and Memedi, Mevludin",
title="Toward Improved Treatment and Empowerment of Individuals With Parkinson Disease: Design and Evaluation of an Internet of Things System",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="9",
volume="6",
number="6",
pages="e31485",
keywords="Internet of Things",
keywords="wearable technology",
keywords="Parkinson disease",
keywords="patient empowerment",
keywords="objective measures",
keywords="self-assessment",
keywords="self-management",
keywords="web interface",
abstract="Background: Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual's quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers. Objective: Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD. Methods: We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model. Results: Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD. Conclusions: The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research. ",
doi="10.2196/31485",
url="https://formative.jmir.org/2022/6/e31485",
url="http://www.ncbi.nlm.nih.gov/pubmed/35679097"
}


@Article{info:doi/10.2196/34104,
author="Oetomo, Arlene
and Jalali, Niloofar
and Costa, Paro Paula Dornhofer
and Morita, Pelegrini Plinio",
title="Indoor Temperatures in the 2018 Heat Wave in Quebec, Canada: Exploratory Study Using Ecobee Smart Thermostats",
journal="JMIR Form Res",
year="2022",
month="May",
day="12",
volume="6",
number="5",
pages="e34104",
keywords="Internet of Things",
keywords="IoT",
keywords="heat waves",
keywords="public health",
keywords="smart home technology",
keywords="smart thermostats",
keywords="indoor temperature",
keywords="air conditioning",
keywords="heat alert response systems",
keywords="thermostat",
keywords="unsafe temperatures",
keywords="uHealth",
abstract="Background: Climate change, driven by human activity, is rapidly changing our environment and posing an increased risk to human health. Local governments must adapt their cities and prepare for increased periods of extreme heat and ensure that marginalized populations do not suffer detrimental health outcomes. Heat warnings traditionally rely on outdoor temperature data which may not reflect indoor temperatures experienced by individuals. Smart thermostats could be a novel and highly scalable data source for heat wave monitoring. Objective: The objective of this study was to explore whether smart thermostats can be used to measure indoor temperature during a heat wave and identify houses experiencing indoor temperatures above 26{\textdegree}C. Methods: We used secondary data---indoor temperature data recorded by ecobee smart thermostats during the Quebec heat waves of 2018 that claimed 66 lives, outdoor temperature data from Environment Canada weather stations, and indoor temperature data from 768 Quebec households. We performed descriptive statistical analyses to compare indoor temperatures differences between air conditioned and non--air conditioned houses in Montreal, Gatineau, and surrounding areas from June 1 to August 31, 2018. Results: There were significant differences in indoor temperature between houses with and without air conditioning on both heat wave and non--heat wave days (P<.001). Households without air conditioning consistently recorded daily temperatures above common indoor temperature standards. High indoor temperatures persisted for an average of 4 hours per day in non--air conditioned houses. Conclusions: Our findings were consistent with current literature on building warming and heat retention during heat waves, which contribute to increased risk of heat-related illnesses. Indoor temperatures can be captured continuously using smart thermostats across a large population. When integrated with local heat health action plans, these data could be used to strengthen existing heat alert response systems and enhance emergency medical service responses. ",
doi="10.2196/34104",
url="https://formative.jmir.org/2022/5/e34104",
url="http://www.ncbi.nlm.nih.gov/pubmed/35550317"
}


@Article{info:doi/10.2196/31486,
author="Read, A. Emily
and Gagnon, A. Danie
and Donelle, Lorie
and Ledoux, Kathleen
and Warner, Grace
and Hiebert, Brad
and Sharma, Ridhi",
title="Stakeholder Perspectives on In-home Passive Remote Monitoring to Support Aging in Place in the Province of New Brunswick, Canada: Rapid Qualitative Investigation",
journal="JMIR Aging",
year="2022",
month="May",
day="11",
volume="5",
number="2",
pages="e31486",
keywords="aging in place",
keywords="home care",
keywords="older adults",
keywords="passive remote monitoring",
abstract="Background: The province of New Brunswick (NB) has one of the oldest populations in Canada, providing an opportunity to develop and test innovative strategies to address the unique health challenges faced by older adults. Passive remote monitoring technology has the potential to support independent living among older adults. Limited research has examined the benefits of and barriers to the adoption of this technology among community-dwelling older adults. Objective: This study aimed to explore perceptions of in-home passive remote monitoring technology designed to support aging in place from the perspective of older adults, their family or friend caregivers, social workers, and government decision-makers in the province of NB, Canada. Methods: Between October 2018 and March 2020, a rapid qualitative investigation of 28 one-on-one interviews was conducted in person or via telephone. Participants included 2 home support services clients and 11 family or friend caregivers who had used passive remote monitoring technology in their homes; 8 social workers who had worked as case managers for home support services clients; and 7 individuals who were key government decision-makers in the adoption, policy development, and use of the technology in the province of NB. The interviews focused on the following topics: decision to adopt the passive remote monitoring system, barriers to adopting the passive remote monitoring system, benefits of the passive remote monitoring system, impact on client health outcomes, and privacy concerns. The interviews were audio recorded, transcribed, and analyzed by a team of 6 researchers. Data analysis was conducted using a rapid assessment process approach that included matrix analysis. Results: Participants reported that the use of the remote monitoring system allowed older adults to live at home longer and provided caregiver relief. Stakeholders were invested in meeting the home support (home care) needs of older adults. However, when it came to the use of remote monitoring, there was a lack of consensus about which clients it was well-suited for and the role that social workers should play in informing clients and caregivers about the service (role ambiguity, gatekeeping, and perceived conflicts of interest). Conclusions: Our findings highlight many benefits and challenges of the adoption of passive remote monitoring for clients, their family or friend caregivers, and public provincial health and social services systems. Passive remote monitoring is a valuable tool that can provide support to older adults and their family or friend caregivers when it is a good fit with client needs. Further work is needed in NB to increase public and social workers' awareness of the service and its benefits. ",
doi="10.2196/31486",
url="https://aging.jmir.org/2022/2/e31486",
url="http://www.ncbi.nlm.nih.gov/pubmed/35544304"
}


@Article{info:doi/10.2196/35277,
author="Timon, M. Claire
and Heffernan, Emma
and Kilcullen, M. Sophia
and Lee, Hyowon
and Hopper, Louise
and Quinn, Joe
and McDonald, David
and Gallagher, Pamela
and Smeaton, F. Alan
and Moran, Kieran
and Hussey, Pamela
and Murphy, Catriona",
title="Development of an Internet of Things Technology Platform (the NEX System) to Support Older Adults to Live Independently: Protocol for a Development and Usability Study",
journal="JMIR Res Protoc",
year="2022",
month="May",
day="5",
volume="11",
number="5",
pages="e35277",
keywords="independent living",
keywords="older adults",
keywords="Internet of Things",
keywords="wearable electronic devices",
keywords="activities of daily living",
keywords="mobile phone",
abstract="Background: In a rapidly aging population, new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety. Objective: The NEX project aims to develop an integrated Internet of Things system coupled with artificial intelligence to offer unobtrusive health and wellness monitoring to support older adults living independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of the NEX system. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment. Methods: The NEX project commenced in December 2019 and is expected to be completed by August 2022. Mixed methods research (web-based surveys and focus groups) was conducted with 426 participants, including older adults (aged ?60 years), family caregivers, health care professionals, and home care workers, to inform the development of the NEX system (phase 1). The primary outcome will be evaluated in 2 successive trials (the Friendly trial [phase 2] and the Action Research Cycle trial [phase 3]). The secondary objective will be explored in the Action Research Cycle trial (phase 3). For the Friendly trial, 7 older adult participants aged ?60 years and living alone in their own homes for a 10-week period were enrolled. A total of 30 older adult participants aged ?60 years and living alone in their own homes will be recruited for a 10-week data collection period (phase 3). Results: Phase 1 of the project (n=426) was completed in December 2020, and phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for the 10-week usability study) is June 2022. Conclusions: The NEX project has considered the specific everyday needs of older adults and other stakeholders, which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of Internet of Things technologies and artificial intelligence to identify and predict changes in the routines of older adults. The findings of this project will contribute to the eHealth research agenda, focusing on the improvement of health care provision and patient support in home and community environments. International Registered Report Identifier (IRRID): DERR1-10.2196/35277 ",
doi="10.2196/35277",
url="https://www.researchprotocols.org/2022/5/e35277",
url="http://www.ncbi.nlm.nih.gov/pubmed/35511224"
}


@Article{info:doi/10.2196/28920,
author="Bhimaraju, Hari
and Nag, Nitish
and Pandey, Vaibhav
and Jain, Ramesh",
title="Understanding ``Atmosome'', the Personal Atmospheric Exposome: Comprehensive Approach",
journal="JMIR Biomed Eng",
year="2021",
month="Nov",
day="23",
volume="6",
number="4",
pages="e28920",
keywords="exposome",
keywords="exposomics",
keywords="personal health",
keywords="indoor air quality",
keywords="health state estimation",
keywords="health informatics",
keywords="public health policy",
keywords="epidemiology",
keywords="embedded systems",
keywords="internet of things",
abstract="Background: Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the ``atmosome.'' The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health. Objective: The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health. Methods: An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access. Results: The experimental results yield air quality measurements with an accuracy of 90\% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality. Conclusions: Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases. ",
doi="10.2196/28920",
url="https://biomedeng.jmir.org/2021/4/e28920"
}


@Article{info:doi/10.2196/29610,
author="Oladele, Ayo Daniel
and Markus, Didam Elisha
and Abu-Mahfouz, M. Adnan",
title="Adaptability of Assistive Mobility Devices and the Role of the Internet of Medical Things: Comprehensive Review",
journal="JMIR Rehabil Assist Technol",
year="2021",
month="Nov",
day="15",
volume="8",
number="4",
pages="e29610",
keywords="internet of medical things framework",
keywords="internet of things",
keywords="adaptability",
keywords="multisensor fusion",
keywords="mobility aids",
keywords="user system interface",
keywords="assistive mobility devices",
keywords="mobile phone",
abstract="Background: With the projected upsurge in the percentage of people with some form of disability, there has been a significant increase in the need for assistive mobility devices. However, for mobility aids to be effective, such devices should be adapted to the user's needs. This can be achieved by improving the confidence of the acquired information (interaction between the user, the environment, and the device) following design specifications. Therefore, there is a need for literature review on the adaptability of assistive mobility devices. Objective: In this study, we aim to review the adaptability of assistive mobility devices and the role of the internet of medical things in terms of the acquired information for assistive mobility devices. We review internet-enabled assistive mobility technologies and non--internet of things (IoT) assistive mobility devices. These technologies will provide awareness of the status of adaptive mobility technology and serve as a source and reference regarding information to health care professionals and researchers. Methods: We performed a literature review search on the following databases of academic references and journals: Google Scholar, ScienceDirect, Institute of Electrical and Electronics Engineers, Springer, and websites of assistive mobility and foundations presenting studies on assistive mobility found through a generic Google search (including the World Health Organization website). The following keywords were used: assistive mobility OR assistive robots, assistive mobility devices, internet-enabled assistive mobility technologies, IoT Framework OR IoT Architecture AND for Healthcare, assisted navigation OR autonomous navigation, mobility AND aids OR devices, adaptability of assistive technology, adaptive mobility devices, pattern recognition, autonomous navigational systems, human-robot interfaces, motor rehabilitation devices, perception, and ambient assisted living. Results: We identified 13,286 results (excluding titles that were not relevant to this study). Then, through a narrative review, we selected 189 potential studies (189/13,286, 1.42\%) from the existing literature on the adaptability of assistive mobility devices and IoT frameworks for assistive mobility and conducted a critical analysis. Of the 189 potential studies, 82 (43.4\%) were selected for analysis after meeting the inclusion criteria. On the basis of the type of technologies presented in the reviewed articles, we proposed a categorization of the adaptability of smart assistive mobility devices in terms of their interaction with the user (user system interface), perception techniques, and communication and sensing frameworks. Conclusions: We discussed notable limitations of the reviewed literature studies. The findings revealed that an improvement in the adaptation of assistive mobility systems would require a reduction in training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical for achieving real-world testing requirements. Finally, the trade-off between cost and performance should be considered in the commercialization of these devices. ",
doi="10.2196/29610",
url="https://rehab.jmir.org/2021/4/e29610",
url="http://www.ncbi.nlm.nih.gov/pubmed/34779786"
}


@Article{info:doi/10.2196/19846,
author="Fatoum, Hanaa
and Hanna, Sam
and Halamka, D. John
and Sicker, C. Douglas
and Spangenberg, Peter
and Hashmi, K. Shahrukh",
title="Blockchain Integration With Digital Technology and the Future of Health Care Ecosystems: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Nov",
day="2",
volume="23",
number="11",
pages="e19846",
keywords="blockchain, Internet of Things",
keywords="digital",
keywords="artificial intelligence",
keywords="machine learning",
keywords="eHealth",
keywords="ledger",
keywords="distributed ledger technology",
abstract="Background: In the era of big data, artificial intelligence (AI), and the Internet of Things (IoT), digital data have become essential for our everyday functioning and in health care services. The sensitive nature of health care data presents several crucial issues such as privacy, security, interoperability, and reliability that must be addressed in any health care data management system. However, most of the current health care systems are still facing major obstacles and are lacking in some of these areas. This is where decentralized, secure, and scalable databases, most notably blockchains, play critical roles in addressing these requirements without compromising security, thereby attracting considerable interest within the health care community. A blockchain can be maintained and widely distributed using a large network of nodes, mostly computers, each of which stores a full replica of the data. A blockchain protocol is a set of predefined rules or procedures that govern how the nodes interact with the network, view, verify, and add data to the ledger. Objective: In this article, we aim to explore blockchain technology, its framework, current applications, and integration with other innovations, as well as opportunities in diverse areas of health care and clinical research, in addition to clarifying its future impact on the health care ecosystem. We also elucidate 2 case studies to instantiate the potential role of blockchains in health care. Methods: To identify related existing work, terms based on Medical Subject Headings were used. We included studies focusing mainly on health care and clinical research and developed a functional framework for implementation and testing with data. The literature sources for this systematic review were PubMed, Medline, and the Cochrane library, in addition to a preliminary search of IEEE Xplore. Results: The included studies demonstrated multiple framework designs and various implementations in health care including chronic disease diagnosis, management, monitoring, and evaluation. We found that blockchains exhibit many promising applications in clinical trial management such as smart-contract application, participant-controlled data access, trustless protocols, and data validity. Electronic health records (EHRs), patient-centered interoperability, remote patient monitoring, and clinical trial data management were found to be major areas for blockchain usage, which can become a key catalyst for health care innovations. Conclusions: The potential benefits of blockchains are limitless; however, concrete data on long-term clinical outcomes based on blockchains powered and supplemented by AI and IoT are yet to be obtained. Nonetheless, implementing blockchains as a novel way to integrate EHRs nationwide and manage common clinical problems in an algorithmic fashion has the potential for improving patient outcomes, health care experiences, as well as the overall health and well-being of individuals. ",
doi="10.2196/19846",
url="https://www.jmir.org/2021/11/e19846",
url="http://www.ncbi.nlm.nih.gov/pubmed/34726603"
}


@Article{info:doi/10.2196/28873,
author="Ranjan, Yatharth
and Althobiani, Malik
and Jacob, Joseph
and Orini, Michele
and Dobson, JB Richard
and Porter, Joanna
and Hurst, John
and Folarin, A. Amos",
title="Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH): Protocol for a Prospective Observational Study",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="7",
volume="10",
number="10",
pages="e28873",
keywords="mHealth",
keywords="COVID-19",
keywords="mobile health",
keywords="remote monitoring",
keywords="wearables",
keywords="internet of things",
keywords="lung diseases",
keywords="respiratory health",
keywords="mental health",
keywords="cardiopulmonary diseases",
abstract="Background: Chronic lung disorders like chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by exacerbations. They are unpleasant for patients and sometimes severe enough to cause hospital admission and death. Moreover, due to the COVID-19 pandemic, vulnerable populations with these disorders are at high risk, and their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution for gaining visibility into the health of people in their daily lives, making it useful for vulnerable populations. Objective: The primary objective is to assess the feasibility and acceptability of remote monitoring using wearables and mobile phones in patients with pulmonary diseases. The secondary objective is to provide power calculations for future studies centered around understanding the number of exacerbations according to sample size and duration. Methods: Twenty participants will be recruited in each of three cohorts (COPD, IPF, and posthospitalization COVID). Data collection will be done remotely using the RADAR-Base (Remote Assessment of Disease And Relapse) mobile health (mHealth) platform for different devices, including Garmin wearable devices and smart spirometers, mobile app questionnaires, surveys, and finger pulse oximeters. Passive data include wearable-derived continuous heart rate, oxygen saturation, respiration rate, activity, and sleep. Active data include disease-specific patient-reported outcome measures, mental health questionnaires, and symptom tracking to track disease trajectory. Analyses will assess the feasibility of lung disorder remote monitoring (including data quality, data completeness, system usability, and system acceptability). We will attempt to explore disease trajectory, patient stratification, and identification of acute clinical events such as exacerbations. A key aspect is understanding the potential of real-time data collection. We will simulate an intervention to acquire responses at the time of the event to assess model performance for exacerbation identification. Results: The Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH) study provides a unique opportunity to assess the use of remote monitoring in the evaluation of lung disorders. The study started in the middle of June 2021. The data collection apparatus, questionnaires, and wearable integrations were setup and tested by the clinical teams prior to the start of recruitment. While recruitment is ongoing, real-time exacerbation identification models are currently being constructed. The models will be pretrained daily on data of previous days, but the inference will be run in real time. Conclusions: The RALPMH study will provide a reference infrastructure for remote monitoring of lung diseases. It specifically involves information regarding the feasibility and acceptability of remote monitoring and the potential of real-time data collection and analysis in the context of chronic lung disorders. It will help plan and inform decisions in future studies in the area of respiratory health. Trial Registration: ISRCTN Registry ISRCTN16275601; https://www.isrctn.com/ISRCTN16275601 International Registered Report Identifier (IRRID): PRR1-10.2196/28873 ",
doi="10.2196/28873",
url="https://www.researchprotocols.org/2021/10/e28873",
url="http://www.ncbi.nlm.nih.gov/pubmed/34319235"
}


@Article{info:doi/10.2196/27047,
author="Tiersen, Federico
and Batey, Philippa
and Harrison, C. Matthew J.
and Naar, Lenny
and Serban, Alina-Irina
and Daniels, C. Sarah J.
and Calvo, A. Rafael",
title="Smart Home Sensing and Monitoring in Households With Dementia: User-Centered Design Approach",
journal="JMIR Aging",
year="2021",
month="Aug",
day="11",
volume="4",
number="3",
pages="e27047",
keywords="assistive technology",
keywords="independent living",
keywords="internet of things",
keywords="remote monitoring",
keywords="dementia",
keywords="human centered design",
keywords="user-centered design",
keywords="patient-centered care",
keywords="smart home",
keywords="digital health",
abstract="Background: As life expectancy grows, so do the challenges of caring for an aging population. Older adults, including people with dementia, want to live independently and feel in control of their lives for as long as possible. Assistive technologies powered by artificial intelligence and internet of things devices are being proposed to provide living environments that support the users' safety, psychological, and medical needs through remote monitoring and interventions. Objective: This study investigates the functional, psychosocial, and environmental needs of people living with dementia, their caregivers, clinicians, and health and social care service providers toward the design and implementation of smart home systems. Methods: We used an iterative user-centered design approach comprising 9 substudies. First, semistructured interviews (9 people with dementia, 9 caregivers, and 10 academic and clinical staff) and workshops (35 pairs of people with dementia and caregivers, and 12 health and social care clinicians) were conducted to define the needs of people with dementia, home caregivers, and professional stakeholders in both daily activities and technology-specific interactions. Then, the spectrum of needs identified was represented via patient--caregiver personas and discussed with stakeholders in a workshop (14 occupational therapists; 4 National Health Service pathway directors; and 6 researchers in occupational therapy, neuropsychiatry, and engineering) and 2 focus groups with managers of health care services (n=8), eliciting opportunities for innovative care technologies and public health strategies. Finally, these design opportunities were discussed in semistructured interviews with participants of a smart home trial involving environmental sensors, physiological measurement devices, smartwatches, and tablet-based chatbots and cognitive assessment puzzles (10 caregivers and 2 people with dementia). A thematic analysis revealed factors that motivate household members to use these technologies. Results: Outcomes of these activities include a qualitative and quantitative analysis of patient, caregiver, and clinician needs and the identification of challenges and opportunities for the design and implementation of remote monitoring systems in public health pathways. Conclusions: Participatory design methods supported the triangulation of stakeholder perspectives to aid the development of more patient-centered interventions and their translation to clinical practice and public health strategy. We discuss the implications and limitations of our findings, the value and the applicability of our methodology, and directions for future research. ",
doi="10.2196/27047",
url="https://aging.jmir.org/2021/3/e27047",
url="http://www.ncbi.nlm.nih.gov/pubmed/34383672"
}


@Article{info:doi/10.2196/24127,
author="Hui, Yan Chi
and McKinstry, Brian
and Fulton, Olivia
and Buchner, Mark
and Pinnock, Hilary",
title="Patients' and Clinicians' Perceived Trust in Internet-of-Things Systems to Support Asthma Self-management: Qualitative Interview Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Jul",
day="16",
volume="9",
number="7",
pages="e24127",
keywords="asthma",
keywords="self-management",
keywords="telehealth",
keywords="internet-of-thing",
keywords="trust",
abstract="Background: Asthma affects 235 million people worldwide. Supported self-management, including an action plan agreed with clinicians, improves asthma outcomes. Internet-of-things (IoT) systems with artificial intelligence (AI) can provide customized support for a range of self-management functions, but trust is vital to encourage patients' adoption of such systems. Many models for understanding trust exist, some explicitly designed for eHealth, but no studies have used these models to explore trust in the context of using IoT systems to support asthma self-management. Objective: In this study, we aim to use the McKnight model to explore the functionality, helpfulness, and reliability domains of patients' and clinicians' trust in IoT systems to deliver the 14 components of self-management support defined by the PRISMS (Practical Reviews in Self-Management Support) taxonomy. Methods: We used think-aloud techniques in semistructured interviews to explore the views of patients and clinicians. Patients were recruited from research registers and social media and purposively sampled to include a range of ages, genders, action plan ownership, asthma duration, hospital admissions, and experience with mobile apps. Clinicians (primary, secondary, and community-based) were recruited from professional networks. Interviews were transcribed verbatim, and thematic analysis was used to explore perceptions of the functionality, helpfulness, and reliability of IoT features to support components of supported self-management. Results: A total of 12 patients and 12 clinicians were interviewed. Regarding perceived functionality, most patients considered that an IoT system had functionality that could support a broad range of self-management tasks. They wanted a system to provide customized advice involving AI. With regard to perceived helpfulness, they considered that IoT systems could usefully provide integrated support for a number of recognized components of self-management support. In terms of perceived reliability, they believed they could rely on the system to log their asthma condition and provide preset action plan advice triggered by their logs. However, they were less confident that the system could operate continuously and without errors in providing advice. They were not confident that AI could generate new advice or reach diagnostic conclusions without the interpretation of their trusted clinicians. Clinicians wanted clinical evidence before trusting the system. Conclusions: IoT systems including AI were regarded as offering potentially helpful functionality in mediating the action plans developed with a trusted clinician, although our technologically adept participants were not yet ready to trust AI to generate novel advice. Research is needed to ensure that technological capability does not outstrip the trust of individuals using it. ",
doi="10.2196/24127",
url="https://mhealth.jmir.org/2021/7/e24127",
url="http://www.ncbi.nlm.nih.gov/pubmed/34269684"
}


@Article{info:doi/10.2196/24879,
author="Budimir, Sanja
and Fontaine, J. Johnny R.
and Huijts, A. Nicole M.
and Haans, Antal
and Loukas, George
and Roesch, B. Etienne",
title="Emotional Reactions to Cybersecurity Breach Situations: Scenario-Based Survey Study",
journal="J Med Internet Res",
year="2021",
month="May",
day="12",
volume="23",
number="5",
pages="e24879",
keywords="cybersecurity breach victims",
keywords="emotions",
keywords="personality",
keywords="mental health",
keywords="Internet of Things",
abstract="Background: With the ever-expanding interconnectedness of the internet and especially with the recent development of the Internet of Things, people are increasingly at risk for cybersecurity breaches that can have far-reaching consequences for their personal and professional lives, with psychological and mental health ramifications. Objective: We aimed to identify the dimensional structure of emotion processes triggered by one of the most emblematic scenarios of cybersecurity breach, the hacking of one's smart security camera, and explore which personality characteristics systematically relate to these emotion dimensions. Methods: A total of 902 participants from the United Kingdom and the Netherlands reported their emotion processes triggered by a cybersecurity breach scenario. Moreover, they reported on their Big Five personality traits, as well as on key indicators for resilient, overcontrolling (internalizing problems), and undercontrolling (aggression) personality types. Results: Principal component analyses revealed a clear 3-dimensional structure of emotion processes: emotional intensity, proactive versus fight/flight reactions, and affective versus cognitive/motivational reactions. Regression analyses revealed that more internalizing problems ($\beta$=.33, P<.001), resilience ($\beta$=.22, P<.001), and agreeableness ($\beta$=.12, P<.001) and less emotional stability ($\beta$=--.25, P<.001) have significant predictive value for higher emotional intensity. More internalizing problems ($\beta$=.26, P<.001), aggression ($\beta$=.25, P<.001), and extraversion ($\beta$=.07, P=.01) and less resilience ($\beta$=--.19, P<.001), agreeableness ($\beta$=--.34, P<.001), consciousness ($\beta$=--.19, P<.001), and openness ($\beta$=--.22, P<.001) have significant predictive value for comparatively more fight/flight than proactive reactions. Less internalizing problems ($\beta$=--.32, P<.001) and more emotional stability ($\beta$=.14, P<.001) and aggression ($\beta$=.13, P<.001) have significant predictive value for a comparatively higher salience for cognitive/motivational than affective reactions. Conclusions: To adequately describe the emotion processes triggered by a cybersecurity breach, two more dimensions are needed over and above the general negative affectivity dimension. This multidimensional structure is further supported by the differential relationships of the emotion dimensions with personality characteristics. The discovered emotion structure could be used for consistent predictions about who is at risk to develop long-term mental well-being issues due to a cybersecurity breach experience. ",
doi="10.2196/24879",
url="https://www.jmir.org/2021/5/e24879",
url="http://www.ncbi.nlm.nih.gov/pubmed/33978591"
}


@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/10.2196/25312,
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/10.2196/24280,
author="So, Hong Kei
and Ting, Wun Cheuk
and Lee, Ping Chui
and Lam, Tai-Ning Teddy
and Chiang, Chu Sau
and Cheung, Ting Yin",
title="Medication Management Service for Old Age Homes in Hong Kong Using Information Technology, Automation Technology, and the Internet of Things: Pre-Post Interventional Study",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="10",
volume="9",
number="2",
pages="e24280",
keywords="medication management",
keywords="old age homes",
keywords="information technology",
keywords="automation",
keywords="Internet of Things",
abstract="Background: Innovation in technology and automation has been increasingly used to improve conventional medication management processes.?In Hong Kong, the current practices of medication management in old age homes (OAHs) are time consuming, labor intensive, and error prone. To address this problem, we initiated an integrated medication management service combining information technology, automation technology, and the Internet of Things in a cluster network of OAHs. Objective: This pilot study aimed to evaluate the impact of the medication management program on (1) medication management efficiency, (2) medication safety, and (3) drug wastage in OAHs. We compared the time efficiency and the reductions in medication errors and medication wastage in OAHs before and at least 2 weeks after the implementation of the program. Methods: From November 2019 to February 2020, we recruited 2 OAHs (serving 178 residents) in Hong Kong into the prospective, pre-post interventional study. The interventional program consisted of electronic medication profiles, automated packaging, and electronic records of medication administration. Using 3-way analysis of variance, we compared the number of doses prepared and checked in 10-minute blocks before and after implementation. We received anonymous reports of medication errors from OAH staff and analyzed the results with the Fisher exact test. We also calculated the quantity and cost of wasted medications from drug disposal reports. Results: The number of doses prepared and checked in 10-minute blocks significantly increased postimplementation (pre: 41.3, SD 31.8; post: 70.6, SD 22.8; P<.001). There was also a significant reduction in medication errors (pre: 10/9504 doses, 0.1\%; post: 0/5731 doses; P=.02). The total costs of wasted medications during January 2020 in OAH 1 (77 residents) and OAH 2 (101 residents) were HK \$2566.03 (US \$328.98) and HK \$5249.48 (US \$673.01), respectively. Conclusions: Our pilot study suggested that an innovative medication management program with information technology, automation technology, and Internet of Things components improved the time efficiency of medication preparation and medication safety for OAHs. It is a promising solution to address the current limitations in medication management in OAHs in Hong Kong. ",
doi="10.2196/24280",
url="http://medinform.jmir.org/2021/2/e24280/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33565993"
}


@Article{info:doi/10.2196/17005,
author="Lam, Ching
and van Velthoven, Helena Michelle
and Meinert, Edward",
title="Developing a Blockchain-Based Supply Chain System for Advanced Therapies: Protocol for a Feasibility Study",
journal="JMIR Res Protoc",
year="2020",
month="Dec",
day="14",
volume="9",
number="12",
pages="e17005",
keywords="blockchain",
keywords="digital health",
keywords="IOT",
keywords="internet of things",
keywords="regenerative medicine",
abstract="Background: Advanced therapies, including cell and gene therapies, have shown therapeutic promise in curing life-threatening diseases, such as leukemia and lymphoma. However, these therapies can be complicated and expensive to deliver due to their sensitivity to environment; troublesome tissue, cell, or genetic material sourcing; and complicated regulatory requirements. Objective: This study aims to create a novel connected supply chain logistics and manufacturing management platform based on blockchain, with cell and gene therapy as a use case. Objectives are to define the requirements and perform feasibility evaluations on the use of blockchain for standardized manufacturing and establishment of a chain of custody for the needle-to-needle delivery of autologous cell and gene therapies. A way of lowering overall regulatory compliance costs for running a network of facilities operating similar or parallel processes will be evaluated by lowering the monitoring costs through publishing zero-knowledge proofs and product release by exception. Methods: The study will use blockchain technologies to digitally connect and integrate supply chain with manufacturing to address the security, scheduling, and communication issues between advanced therapy treatment centers and manufacturing facilities in order to realize a transparent, secure, automated, and cost-effective solution to the delivery of these life-saving therapies. An agile software development methodology will be used to develop, implement, and evaluate the system. The system will adhere to the EU and US good manufacturing practices and regulatory requirements. Results: This is a proposed study protocol, and upon acceptance, grant funding will be pursued for its execution in 2021. Conclusions: The successful implementation of the integrated blockchain solution to supply chain and manufacturing of advanced therapies can push the industry standards toward a safer and more secure therapy delivery process. International Registered Report Identifier (IRRID): PRR1-10.2196/17005 ",
doi="10.2196/17005",
url="http://www.researchprotocols.org/2020/12/e17005/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33315020"
}


@Article{info:doi/10.2196/22532,
author="de Boer, S. Pia
and van Deursen, M. Alexander J. A.
and van Rompay, L. Thomas J.",
title="Internet-of-Things Skills Among the General Population: Task-Based Performance Test Using Activity Trackers",
journal="JMIR Hum Factors",
year="2020",
month="Nov",
day="18",
volume="7",
number="4",
pages="e22532",
keywords="internet of things",
keywords="activity tracker",
keywords="mobile phone",
keywords="skills",
keywords="digital divide",
keywords="performance test",
abstract="Background: The health internet-of-things (IoT) can potentially provide insights into the present health condition, potential pitfalls, and support of a healthier lifestyle. However, to enjoy these benefits, people need skills to use the IoT. These IoT skills are expected to differ across the general population, thereby causing a new digital divide. Objective: This study aims to assess whether a sample of the general Dutch population can use health IoT by focusing on data and strategic IoT skills. Furthermore, we determine the role of gender, age, and education, and traditional internet skills. Methods: From April 1, 2019, to December 12, 2019, 100 individuals participated in this study. Participants were recruited via digital flyers and door-to-door canvassing. A selective quota sample was divided into equal subsamples of gender, age, and education. Additional inclusion criteria were smartphone possession and no previous experience of using activity trackers. This study was conducted in 3 waves over a period of 2 weeks. In wave 1, a questionnaire was administered to measure the operational, mobile, and information internet skills of the participants, and the participants were introduced to the activity tracker. After 1 week of getting acquainted with the activity tracker, a task-based performance test was conducted in wave 2 to measure the levels of data IoT skills and the strategic IoT skill component---action plan construction. A week after the participants were asked to use the activity tracker more deliberately, a performance test was then conducted in wave 3 to measure the level of the strategic IoT skill component---action plan execution. Results: The participants successfully completed 54\% (13.5/25) of the data IoT skill tasks. Regarding strategic IoT tasks, the completion rates were 56\% (10.1/18) for action plan construction and 43\% (3.9/9) for action plan execution. None of the participants were able to complete all the data IoT skill tasks, and none of the participants were able to complete all the strategic IoT skill tasks regarding action plan construction or its execution. Age and education were important determinants of the IoT skill levels of the participants, except for the ability to execute an action plan strategically. Furthermore, the level of information internet skills of the participants contributed to their level of data IoT skills. Conclusions: This study found that data and strategic IoT skills of Dutch citizens are underdeveloped with regard to health purposes. In particular, those who could benefit the most from health IoT were those who had the most trouble using it, that is, the older and lower-educated individuals. ",
doi="10.2196/22532",
url="http://humanfactors.jmir.org/2020/4/e22532/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33206049"
}


@Article{info:doi/10.2196/21209,
author="Jalali, Niloofar
and Sahu, Sundar Kirti
and Oetomo, Arlene
and Morita, Pelegrini Plinio",
title="Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Nov",
day="13",
volume="8",
number="11",
pages="e21209",
keywords="public health",
keywords="IoT",
keywords="anomaly detection",
keywords="behavioral monitoring",
keywords="deep learning",
keywords="variational autoencoder",
keywords="LSTM",
abstract="Background: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective: The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods: From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results: The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions: This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment. ",
doi="10.2196/21209",
url="http://mhealth.jmir.org/2020/11/e21209/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33185562"
}


@Article{info:doi/10.2196/20135,
author="Kelly, T. Jaimon
and Campbell, L. Katrina
and Gong, Enying
and Scuffham, Paul",
title="The Internet of Things: Impact and Implications for Health Care Delivery",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="10",
volume="22",
number="11",
pages="e20135",
keywords="Internet of Things",
keywords="digital health",
keywords="smartphone",
keywords="delivery of health care",
keywords="mobile phone",
doi="10.2196/20135",
url="http://www.jmir.org/2020/11/e20135/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33170132"
}


@Article{info:doi/10.2196/21964,
author="Choi, K. Yong
and Thompson, J. Hilaire
and Demiris, George",
title="Use of an Internet-of-Things Smart Home System for Healthy Aging in Older Adults in Residential Settings: Pilot Feasibility Study",
journal="JMIR Aging",
year="2020",
month="Nov",
day="10",
volume="3",
number="2",
pages="e21964",
keywords="Internet of Things",
keywords="smart home",
keywords="independent living",
keywords="aging",
keywords="healthy aging",
abstract="Background: The Internet-of-Things (IoT) technologies can create smart residences that integrate technology within the home to enhance residents' safety as well as monitor their health and wellness. However, there has been little research on real-world testing of IoT smart home devices with older adults, and the feasibility and acceptance of such tools have not been systematically examined. Objective: This study aims to conduct a pilot study to investigate the feasibility of using IoT smart home devices in the actual residences of older adults to facilitate healthy aging. Methods: We conducted a 2-month feasibility study on community-dwelling older adults. Participants chose among different IoT devices to be installed and deployed within their homes. The IoT devices tested varied depending on the participant's preference: a door and window sensor, a multipurpose sensor (motion, temperature, luminosity, and humidity), a voice-operated smart speaker, and an internet protocol (IP) video camera. Results: We recruited a total of 37 older adults for this study, with 35 (95\%) successfully completing all procedures in the 2-month study. The average age of the sample was 78 (SD 9) years and primarily comprised women (29/37, 78\%), those who were educated (31/37, 86\%; bachelor's degree or higher), and those affected by chronic conditions (33/37, 89\%). The most widely chosen devices among the participants were multipurpose sensors and smart speakers. An IP camera was a significantly unpopular choice among participants in both phases. The participant feedback suggests that perceived privacy concerns, perceived usefulness, and curiosity to technology were strong factors when considering which device to have installed in their home. Conclusions: Overall, our deployment results revealed that the use of IoT smart home devices is feasible in actual residences of older adults. These findings may inform the follow-up assessment of IoT technologies and their impact on health-related outcomes and advance our understanding of the role of IoT home-based monitoring technologies to promote successful aging-in-place for older adults. Future trials should consider older adults' preferences for the different types of smart home devices to be installed in real-world residential settings. ",
doi="10.2196/21964",
url="http://aging.jmir.org/2020/2/e21964/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33170128"
}


@Article{info:doi/10.2196/17286,
author="Abdi, Sarah
and de Witte, Luc
and Hawley, Mark",
title="Emerging Technologies With Potential Care and Support Applications for Older People: Review of Gray Literature",
journal="JMIR Aging",
year="2020",
month="Aug",
day="11",
volume="3",
number="2",
pages="e17286",
keywords="artificial intelligence",
keywords="internet of things",
keywords="mobile phone",
keywords="robotics",
keywords="emerging technologies",
keywords="older people",
keywords="care and support",
abstract="Background: The number of older people with unmet care and support needs is increasing substantially due to the challenges facing the formal and informal care systems. Emerging technological developments have the potential to address some of the care and support challenges of older people. However, limited work has been done to identify emerging technological developments with the potential to meet the care and support needs of the aging population. Objective: This review aimed to gain an overview of emerging technologies with potential care and support applications for older people, particularly for those living at home. Methods: A scoping gray literature review was carried out by using the databases of 13 key organizations, hand searching reference lists of included documents, using funding data, and consulting technology experts. A narrative synthesis approach was used to analyze and summarize the findings of the literature review. Results: A total of 39 documents were included in the final analysis. From the analysis, 8 emerging technologies were identified that could potentially be used to meet older people's needs in various care and support domains. These emerging technologies were (1) assistive autonomous robots; (2) self-driving vehicles; (3) artificial intelligence--enabled health smart apps and wearables; (4) new drug release mechanisms; (5) portable diagnostics; (6) voice-activated devices; (7) virtual, augmented, and mixed reality; and (8) intelligent homes. These emerging technologies were at different levels of development, with some being trialed for care applications, whereas others being in the early phases of development. However, only a few documents mentioned including older people during the process of designing and developing these technologies. Conclusions: This review has identified key emerging technologies with the potential to contribute to the support and care needs of older people. However, to increase the adoption of these technologies by older people, there is a need to involve them and other stakeholders, such as formal and informal carers, in the process of designing and developing these technologies. ",
doi="10.2196/17286",
url="http://aging.jmir.org/2020/2/e17286/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32780020"
}


@Article{info:doi/10.2196/19104,
author="Adly, Sedky Aya
and Adly, Sedky Afnan
and Adly, Sedky Mahmoud",
title="Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="10",
volume="22",
number="8",
pages="e19104",
keywords="SARS-CoV-2",
keywords="COVID-19",
keywords="novel coronavirus",
keywords="artificial intelligence",
keywords="internet of things",
keywords="telemedicine",
keywords="machine learning",
keywords="modeling",
keywords="simulation",
keywords="robotics",
abstract="Background: Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective: The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods: We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results: Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions: We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed. ",
doi="10.2196/19104",
url="https://www.jmir.org/2020/8/e19104",
url="http://www.ncbi.nlm.nih.gov/pubmed/32584780"
}


@Article{info:doi/10.2196/17914,
author="Huang, Yitong
and Benford, Steve
and Price, Dominic
and Patel, Roma
and Li, Benqian
and Ivanov, Alex
and Blake, Holly",
title="Using Internet of Things to Reduce Office Workers' Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jul",
day="29",
volume="8",
number="7",
pages="e17914",
keywords="sedentary behavior",
keywords="workplace",
keywords="just-in-time adaptive intervention",
keywords="internet of things",
abstract="Background: Sedentary behavior (SB) is associated with various adverse health outcomes. The prevalence of prolonged sitting at work among office workers makes a case for SB interventions to target this setting and population. Everyday mundane objects with embedded microelectronics and ubiquitous computing represent a novel mode of delivering health behavior change interventions enabled by internet of things (IoTs). However, little is known about how to develop interventions involving IoT technologies. Objective: This paper reports the design and development of an IoT-enabled SB intervention targeting office workers. Methods: The process was guided by the behavior change wheel (BCW), a systematic framework for theory-informed and evidence-based development of behavior change interventions, complemented by the human-centered design (HCD) approach. Intervention design was shaped by findings from a diary-probed interview study (n=20), a stakeholder design workshop (n=8), and a series of theoretical mapping and collaborative technical design activities. Results: The resulting intervention named WorkMyWay targets a reduction in office workers' prolonged stationary behaviors at work and an increase in regular breaks by modifying behavioral determinants in 11 theoretical domains with 17 behavior change techniques. The delivery technology consists of a wearable activity tracker, a light-emitting diode reminder device attached to a vessel (ie, water bottle or cup), and a companion Android app connected to both devices over Bluetooth. The delivery plan consists of a 2-week baseline assessment, a 30-min face-to-face action planning session, and 6-week self-directed use of the delivery technology. Conclusions: This is the first study to demonstrate that it is possible to develop a complex IoT-enabled intervention by applying a combination of the BCW and HCD approaches. The next step is to assess the feasibility of WorkMyWay prior to testing intervention efficacy in a full-scale trial. The intervention mapping table that links individual intervention components with hypothesized mechanisms of action can serve as the basis for testing and clarifying theory-based mechanisms of action in future studies on WorkMyWay. ",
doi="10.2196/17914",
url="http://mhealth.jmir.org/2020/7/e17914/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32723716"
}


@Article{info:doi/10.2196/12417,
author="Saarikko, Johanna
and Niela-Vilen, Hannakaisa
and Ekholm, Eeva
and Hamari, Lotta
and Azimi, Iman
and Liljeberg, Pasi
and Rahmani, M. Amir
and L{\"o}yttyniemi, Eliisa
and Axelin, Anna",
title="Continuous 7-Month Internet of Things--Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study",
journal="JMIR Form Res",
year="2020",
month="Jul",
day="24",
volume="4",
number="7",
pages="e12417",
keywords="prenatal care",
keywords="postnatal care",
keywords="wearable electronics",
keywords="biosensing",
keywords="cloud computing",
keywords="mHealth",
keywords="physical activity",
keywords="sleep",
keywords="heart rate",
abstract="Background: Monitoring during pregnancy is vital to ensure the mother's and infant's health. Remote continuous monitoring provides health care professionals with significant opportunities to observe health-related parameters in their patients and to detect any pathological signs at an early stage of pregnancy, and may thus partially replace traditional appointments. Objective: This study aimed to evaluate the feasibility of continuously monitoring the health parameters (physical activity, sleep, and heart rate) of nulliparous women throughout pregnancy and until 1 month postpartum, with a smart wristband and an Internet of Things (IoT)--based monitoring system. Methods: This prospective observational feasibility study used a convenience sample of 20 nulliparous women from the Hospital District of Southwest Finland. Continuous monitoring of physical activity/step counts, sleep, and heart rate was performed with a smart wristband for 24 hours a day, 7 days a week over 7 months (6 months during pregnancy and 1 month postpartum). The smart wristband was connected to a cloud server. The total number of possible monitoring days during pregnancy weeks 13 to 42 was 203 days and 28 days in the postpartum period. Results: Valid physical activity data were available for a median of 144 (range 13-188) days (75\% of possible monitoring days), and valid sleep data were available for a median of 137 (range 0-184) days (72\% of possible monitoring days) per participant during pregnancy. During the postpartum period, a median of 15 (range 0-25) days (54\% of possible monitoring days) of valid physical activity data and 16 (range 0-27) days (57\% of possible monitoring days) of valid sleep data were available. Physical activity decreased from the second trimester to the third trimester by a mean of 1793 (95\% CI 1039-2548) steps per day (P<.001). The decrease continued by a mean of 1339 (95\% CI 474-2205) steps to the postpartum period (P=.004). Sleep during pregnancy also decreased from the second trimester to the third trimester by a mean of 20 minutes (95\% CI --0.7 to 42 minutes; P=.06) and sleep time shortened an additional 1 hour (95\% CI 39 minutes to 1.5 hours) after delivery (P<.001). The mean resting heart rate increased toward the third trimester and returned to the early pregnancy level during the postpartum period. Conclusions: The smart wristband with IoT technology was a feasible system for collecting representative data on continuous variables of health parameters during pregnancy. Continuous monitoring provides real-time information between scheduled appointments and thus may help target and tailor pregnancy follow-up. ",
doi="10.2196/12417",
url="http://formative.jmir.org/2020/7/e12417/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706696"
}


@Article{info:doi/10.2196/17508,
author="Ismail, Leila
and Materwala, Huned
and Karduck, P. Achim
and Adem, Abdu",
title="Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="7",
volume="22",
number="7",
pages="e17508",
keywords="big data",
keywords="blockchain",
keywords="data analytics",
keywords="eHealth",
keywords="electronic medical records",
keywords="health care",
keywords="health information management",
keywords="Internet of Things",
keywords="medical research",
keywords="mHealth",
abstract="Background: Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. Objective: This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. Methods: To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. Results: Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. Conclusions: There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan. ",
doi="10.2196/17508",
url="https://www.jmir.org/2020/7/e17508",
url="http://www.ncbi.nlm.nih.gov/pubmed/32348265"
}


@Article{info:doi/10.2196/11839,
author="Knopp, U. Melanie
and Binzel, Katherine
and Wright, L. Chadwick
and Zhang, Jun
and Knopp, V. Michael",
title="Enhancing Patient Experience With Internet Protocol Addressable Digital Light-Emitting Diode Lighting in Imaging Environments: A Phase I Study",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="12",
volume="22",
number="6",
pages="e11839",
keywords="ambient lighting",
keywords="patient comfort",
keywords="medical imaging",
keywords="color perception",
keywords="health care environment",
keywords="internet protocol--based light-emitting diode lighting",
abstract="Background: Conventional approaches to improve the quality of clinical patient imaging studies focus predominantly on updating or replacing imaging equipment; however, it is often not considered that patients can also highly influence the diagnostic quality of clinical imaging studies. Patient-specific artifacts can limit the diagnostic image quality, especially when patients are uncomfortable, anxious, or agitated. Imaging facility or environmental conditions can also influence the patient's comfort and willingness to participate in diagnostic imaging studies, especially when performed in visually unesthetic, anxiety-inducing, and technology-intensive imaging centers. When given the opportunity to change a single aspect of the environmental or imaging facility experience, patients feel much more in control of the otherwise unfamiliar and uncomfortable setting. Incorporating commercial, easily adaptable, ambient lighting products within clinical imaging environments allows patients to individually customize their environment for a more personalized and comfortable experience. Objective: The aim of this pilot study was to use a customizable colored light-emitting diode (LED) lighting system within a clinical imaging environment and demonstrate the feasibility and initial findings of enabling healthy subjects to customize the ambient lighting and color. Improving the patient experience within clinical imaging environments with patient-preferred ambient lighting and color may improve overall patient comfort, compliance, and participation in the imaging study and indirectly contribute to improving diagnostic image quality. Methods: We installed consumer-based internet protocol addressable LED lights using the ZigBee standard in different imaging rooms within a clinical imaging environment. We recruited healthy volunteers (n=35) to generate pilot data in order to develop a subsequent clinical trial. The visual perception assessment procedure utilized questionnaires with preprogrammed light/color settings and further assessed how subjects preferred ambient light and color within a clinical imaging setting. Results: Technical implementation using programmable LED lights was performed without any hardware or electrical modifications to the existing clinical imaging environment. Subject testing revealed substantial variabilities in color perception; however, clear trends in subject color preference were noted. In terms of the color hue of the imaging environment, 43\% (15/35) found blue and 31\% (11/35) found yellow to be the most relaxing. Conversely, 69\% (24/35) found red, 17\% (6/35) found yellow, and 11\% (4/35) found green to be the least relaxing. Conclusions: With the majority of subjects indicating that colored lighting within a clinical imaging environment would contribute to an improved patient experience, we predict that enabling patients to customize environmental factors like lighting and color to individual preferences will improve patient comfort and patient satisfaction. Improved patient comfort in clinical imaging environments may also help to minimize patient-specific imaging artifacts that can otherwise limit diagnostic image quality. Trial Registration: ClinicalTrials.gov NCT03456895; https://clinicaltrials.gov/ct2/show/NCT03456895 ",
doi="10.2196/11839",
url="http://www.jmir.org/2020/6/e11839/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32530434"
}


@Article{info:doi/10.2196/17079,
author="Ford, Helen
and Herbert, Jeremy
and Horsham, Caitlin
and Wall, Alexander
and Hacker, Elke",
title="Internet of Things Smart Sunscreen Station: Descriptive Proof-of-Concept Study",
journal="J Med Internet Res",
year="2020",
month="May",
day="28",
volume="22",
number="5",
pages="e17079",
keywords="skin neoplasms",
keywords="melanoma",
keywords="health promotion",
keywords="public health",
keywords="preventive medicine",
keywords="web applications",
abstract="Background: Skin cancer is the most prevalent but also most preventable cancer in Australia. Outdoor workers are at increased risk of developing skin cancer, and improvements in sun protection are needed. Sunscreen, when applied at the recommended concentration (2 mg/cm2), has been shown to block the harmful molecular effects of ultraviolet radiation in vivo. However, sunscreen is often not applied, reapplied sufficiently, or stored adequately to yield protection and reduce sunburns. Objective: The primary aim of this study was to test an Internet of Things approach by deploying a smart sunscreen station to an outdoor regional mining site. Methods: We deployed a smart sunscreen station and examined the key technological considerations including connectivity, security, and data management systems. Results: The smart sunscreen station was deployed for 12 days at a mining workplace (Dalby, Australia). The smart sunscreen station's electrical components remained operational during field testing, and data were received by the message queuing telemetry transport server automatically at the end of each day of field testing (12/12 days, 100\% connectivity). Conclusions: This study highlights that an Internet of Things technology approach can successfully measure sunscreen usage and temperature storage conditions. ",
doi="10.2196/17079",
url="http://www.jmir.org/2020/5/e17079/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32463378"
}


@Article{info:doi/10.2196/16605,
author="Stevens, Timothy
and McGinnis, S. Ryan
and Hewgill, Blake
and Choquette, H. Rebecca
and Tourville, W. Timothy
and Harvey, Jean
and Lachapelle, Richard
and Beynnon, D. Bruce
and Toth, J. Michael
and Skalka, Christian",
title="A Cyber-Physical System for Near Real-Time Monitoring of At-Home Orthopedic Rehabilitation and Mobile--Based Provider-Patient Communications to Improve Adherence: Development and Formative Evaluation",
journal="JMIR Hum Factors",
year="2020",
month="May",
day="11",
volume="7",
number="2",
pages="e16605",
keywords="device use tracking",
keywords="internet of things",
keywords="neuromuscular electrical stimulation",
keywords="exercise",
keywords="smart devices",
keywords="mHealth",
keywords="rehabilitation",
keywords="mobile health",
keywords="digital health",
abstract="Background: Knee extensor muscle performance is reduced after lower extremity trauma and orthopedic surgical interventions. At-home use of neuromuscular electrical stimulation (NMES) may improve functional recovery, but adherence to at-home interventions is low. Greater benefits from NMES may be realized with closer monitoring of adherence to at-home prescriptions and more frequent patient-provider interactions. Objective: This study aimed to develop a cyber-physical system to monitor at-home adherence to NMES prescription and facilitate patient-provider communications to improve adherence in near real time. Methods: The RehabTracker cyber-physical system was developed to accomplish this goal and comprises four components: (1) hardware modifications to a commercially available NMES therapy device to monitor device use and provide Bluetooth functionality; (2) an iPhone Operating System--based mobile health (mHealth) app that enables patient-provider communications in near real time; (3) a clinician portal to allow oversight of patient adherence with device use; and (4) a back-end server to store data, enable adherence analysis, and send automated push notifications to the patient. These four elements were designed to be fully compliant with the Health Insurance Portability and Accountability Act. The system underwent formative testing in a cohort of patients following anterior cruciate ligament rupture (n=7) to begin to assess face validity. Results: Compared with the NMES device software--tracked device use, the RehabTracker system recorded 83\% (40/48) of the rehabilitation sessions, with 100\% (32/32) of all sessions logged by the system in 4 out of 7 patients. In patients for whom tracking of automated push notifications was enabled, 100\% (29/29) of the push notifications sent by the back-end server were received by the patient. Process, hardware, and software issues contributing to these inaccuracies are detailed. Conclusions: RehabTracker represents a promising mHealth app for tracking and improving adherence with at-home NMES rehabilitation programs and warrants further refinement and testing. ",
doi="10.2196/16605",
url="http://humanfactors.jmir.org/2020/2/e16605/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32384052"
}


@Article{info:doi/10.2196/16614,
author="Lee, Heayon
and Park, Rang Yu
and Kim, Hae-Reong
and Kang, Young Na
and Oh, Gahee
and Jang, Il-Young
and Lee, Eunju",
title="Discrepancies in Demand of Internet of Things Services Among Older People and People With Disabilities, Their Caregivers, and Health Care Providers: Face-to-Face Survey Study",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="15",
volume="22",
number="4",
pages="e16614",
keywords="Internet of Things",
keywords="older adults",
keywords="disability",
keywords="health care",
keywords="mobile phone",
abstract="Background: Home Internet of Things (IoT) services and devices have the potential to aid older adults and people with disabilities in their living environments. IoT services and devices can also aid caregivers and health care providers in conveniently providing care to those in need. However, real-world data on the IoT needs of vulnerable people are lacking. Objective: The objective of this study is to conduct a face-to-face survey on the demand for IoT services among older people and people with disabilities, their caregivers, and health care providers in a real-world setting and to see if there are any differences in the aspects of need. Methods: We conducted a face-to-face survey with 500 participants between January 2019 and March 2019. A total of 300 vulnerable people (200 older adults aged ?65 years and 100 physically disabled people aged 30-64 years) were randomly sampled from either a population-based, prospective cohort study of aging---the Aging Study of Pyeongchang Rural Area (ASPRA)---or from the outpatient clinics at the Asan Medical Center, Seoul, South Korea. Simultaneously, their caregivers (n=150) and health care providers (n=50) participated in the survey. Detailed socioeconomic status, digital literacy, health and physical function, and home IoT service needs were determined. Among all commercially available IoT services, 27 services were classified into five categories: emergency and security, safety, health care, convenience (information), and convenience (operation). The weighted-ranking method was used to rank the IoT needs in different groups. Results: There were discrepancies in the demand of IoT services among the vulnerable groups, their caregivers, and health care providers. The home IoT service category that was required the most by the vulnerable groups and their caregivers was emergency and security. However, health care providers indicated that the safety category was most needed by the older adults and disabled people. Home IoT service requirements differed according to the different types of disabilities among the vulnerable groups. Participants with fewer disabilities were more willing to use IoT services than those with more disabilities. Conclusions: Our survey study shows that there were discrepancies in the demand of IoT services among the vulnerable groups, their caregivers, and health care providers. IoT service requirements differed according to the various types of disabilities. Home IoT technology should be established by combining patients' priorities and individualized functional assessments among vulnerable people. Trial Registration: Clinical Research Information Service (CRIS; KCT0004157); https://tinyurl.com/r83eyva ",
doi="10.2196/16614",
url="http://www.jmir.org/2020/4/e16614/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32293575"
}


@Article{info:doi/10.2196/16935,
author="Lam, Ching
and van Velthoven, Helena Michelle
and Meinert, Edward",
title="Application of Internet of Things in Cell-Based Therapy Delivery: Protocol for a Systematic Review",
journal="JMIR Res Protoc",
year="2020",
month="Mar",
day="31",
volume="9",
number="3",
pages="e16935",
keywords="internet of things",
keywords="IoT",
keywords="childhood obesity",
keywords="wearables",
keywords="physical activity tracking",
abstract="Background: Internet of Things (IoT), or Industry 4.0, represents a smart shift to more interconnected manufacturing processes where individual entities within the supply chain communicate with each other to achieve greater flexibility and responsiveness in general manufacturing and leaner manufacturing to reduce the cost of production. IoT has become instrumental in driving leaner manufacturing and more efficient systems in other industries such as transportation and logistics. Cell-based therapeutic products could potentially transform various diseases; however, the delivery of these products is complex and challenging. Objective: This study aims to understand the applicability of IoT in cell-based product supply chains and delivery. Methods: We will search Medline, EMBASE (OvidSP), Web of Science, Cochrane Library \&amp; HEED, Scopus, ACM digital library, INSPEC, ScienceDirect, and the IEEE Xplore Digital Library for studies published after 2008 using a combination of keywords and subject headings related to IoT used in cell therapies. Additionally, a Google search to identify gray literature will be conducted. Two authors will independently screen the titles and abstracts identified from the search and accept or reject the studies according to the study inclusion criteria. Any discrepancies will then be discussed and resolved. The quality of the selected literature will be assessed using the Critical Appraisal Skills Programme systematic review checklist. Results: Data from eligible publications will be abstracted into a predesigned form to map the current and future directions of the technologies, applications, benefits, and challenges in the implementation of IoT in regenerative medicine. This study will be published in a peer-reviewed journal in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This systematic review will be executed by June 2020, and the completed review will be published in a peer-reviewed journal to inform future developments in IoT application for the delivery of cell-based therapies. Conclusions: This review paper will provide an overview of all technologies available in the area and inspect the current IoT applications in cell-based therapies to identify the benefits, challenges, and future directions of using IoT to allow safe and cost-effective delivery of cell-based therapies. International Registered Report Identifier (IRRID): PRR1-10.2196/16935 ",
doi="10.2196/16935",
url="https://www.researchprotocols.org/2020/3/e16935",
url="http://www.ncbi.nlm.nih.gov/pubmed/32229464"
}


@Article{info:doi/10.2196/14583,
author="Della Mea, Vincenzo
and Popescu, Horia Mihai
and Gonano, Dario
and Petaros, Toma?
and Emili, Ivo
and Fattori, Grazia Maria",
title="A Communication Infrastructure for the Health and Social Care Internet of Things: Proof-of-Concept Study",
journal="JMIR Med Inform",
year="2020",
month="Feb",
day="25",
volume="8",
number="2",
pages="e14583",
keywords="health services for the aged",
keywords="remote sensing technology",
keywords="sensors and actuators",
keywords="embedded systems",
keywords="Internet of Things",
keywords="LoRaWAN",
abstract="Background: Increasing life expectancy and reducing birth rates indicate that the world population is becoming older, with many challenges related to quality of life for old and fragile people, as well as their informal caregivers. In the last few years, novel information and communication technology techniques generally known as the Internet of Things (IoT) have been developed, and they are centered around the provision of computation and communication capabilities to objects. The IoT may provide older people with devices that enable their functional independence in daily life by either extending their own capacity or facilitating the efforts of their caregivers. LoRa is a proprietary wireless transmission protocol optimized for long-range, low-power, low--data-rate applications. LoRaWAN is an open stack built upon LoRa. Objective: This paper describes an infrastructure designed and experimentally developed to support IoT deployment in a health care setup, and the management of patients with Alzheimer's disease and dementia has been chosen for a proof-of-concept study. The peculiarity of the proposed approach is that it is based on the LoRaWAN protocol stack, which exploits unlicensed frequencies and allows for the use of very low-power radio devices, making it a rational choice for IoT communication. Methods: A complete LoRaWAN-based infrastructure was designed, with features partly decided in agreement with caregivers, including outdoor patient tracking to control wandering; fall recognition; and capability of collecting data for further clinical studies. Further features suggested by caregivers were night motion surveillance and indoor tracking for large residential structures. Implementation involved a prototype node with tracking and fall recognition capabilities, a middle layer based on an existing network server, and a Web application for overall management of patients and caregivers. Tests were performed to investigate indoor and outdoor capabilities in a real-world setting and study the applicability of LoRaWAN in health and social care scenarios. Results: Three experiments were carried out. One aimed to test the technical functionality of the infrastructure, another assessed indoor features, and the last assessed outdoor features. The only critical issue was fall recognition, because a slip was not always easy to recognize. Conclusions: The project allowed the identification of some advantages and restrictions of the LoRaWAN technology when applied to the health and social care sectors. Free installation allows the development of services that reach ranges comparable to those available with cellular telephony, but without running costs like telephony fees. However, there are technological limitations, which restrict the scenarios in which LoRaWAN is applicable, although there is room for many applications. We believe that setting up low-weight infrastructure and carefully determining whether applications can be concretely implemented within LoRaWAN limits might help in optimizing community care activities while not adding much burden and cost in information technology management. ",
doi="10.2196/14583",
url="http://medinform.jmir.org/2020/2/e14583/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130158"
}


@Article{info:doi/10.2196/14300,
author="Venkataramanan, Revathy
and Thirunarayan, Krishnaprasad
and Jaimini, Utkarshani
and Kadariya, Dipesh
and Yip, Yung Hong
and Kalra, Maninder
and Sheth, Amit",
title="Determination of Personalized Asthma Triggers From Multimodal Sensing and a Mobile App: Observational Study",
journal="JMIR Pediatr Parent",
year="2019",
month="Jun",
day="27",
volume="2",
number="1",
pages="e14300",
keywords="personalized digital health",
keywords="medical internet of things",
keywords="asthma management",
keywords="patient-generated health data",
keywords="pediatric asthma",
keywords="asthma control",
keywords="medication adherence",
keywords="childhood asthma",
keywords="understanding and treatment of asthma",
abstract="Background: Asthma is a chronic pulmonary disease with multiple triggers. It can be managed by strict adherence to an asthma care plan and by avoiding these triggers. Clinicians cannot continuously monitor their patients' environment and their adherence to an asthma care plan, which poses a significant challenge for asthma management. Objective: In this study, pediatric patients were continuously monitored using low-cost sensors to collect asthma-relevant information. The objective of this study was to assess whether kHealth kit, which contains low-cost sensors, can identify personalized triggers and provide actionable insights to clinicians for the development of a tailored asthma care plan. Methods: The kHealth asthma kit was developed to continuously track the symptoms of asthma in pediatric patients and monitor the patients' environment and adherence to their care plan for either 1 or 3 months. The kit consists of an Android app--based questionnaire to collect information on asthma symptoms and medication intake, Fitbit to track sleep and activity, the Peak Flow meter to monitor lung functions, and Foobot to monitor indoor air quality. The data on the patient's outdoor environment were collected using third-party Web services based on the patient's zip code. To date, 107 patients consented to participate in the study and were recruited from the Dayton Children's Hospital, of which 83 patients completed the study as instructed. Results: Patient-generated health data from the 83 patients who completed the study were included in the cohort-level analysis. Of the 19\% (16/83) of patients deployed in spring, the symptoms of 63\% (10/16) and 19\% (3/16) of patients suggested pollen and particulate matter (PM2.5), respectively, to be their major asthma triggers. Of the 17\% (14/83) of patients deployed in fall, symptoms of 29\% (4/17) and 21\% (3/17) of patients suggested pollen and PM2.5, respectively, to be their major triggers. Among the 28\% (23/83) of patients deployed in winter, PM2.5 was identified as the major trigger for 83\% (19/23) of patients. Similar correlations were not observed between asthma symptoms and factors such as ozone level, temperature, and humidity. Furthermore, 1 patient from each season was chosen to explain, in detail, his or her personalized triggers by observing temporal associations between triggers and asthma symptoms gathered using the kHealth asthma kit. Conclusions: The continuous monitoring of pediatric asthma patients using the kHealth asthma kit generates insights on the relationship between their asthma symptoms and triggers across different seasons. This can ultimately inform personalized asthma management and intervention plans. ",
doi="10.2196/14300",
url="http://pediatrics.jmir.org/2019/1/e14300/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518318"
}


@Article{info:doi/10.2196/13558,
author="Bartlett Ellis, J. Rebecca
and Hill, H. James
and Kerley, Denise K.
and Sinha, Arjun
and Ganci, Aaron
and Russell, L. Cynthia",
title="The Feasibility of a Using a Smart Button Mobile Health System to Self-Track Medication Adherence and Deliver Tailored Short Message Service Text Message Feedback",
journal="JMIR Form Res",
year="2019",
month="Jun",
day="25",
volume="3",
number="2",
pages="e13558",
keywords="medication adherence",
keywords="medication compliance",
keywords="behavior change",
abstract="Background: As many as 50\% of people experience medication nonadherence, yet studies for detecting nonadherence and delivering real-time interventions to improve adherence are lacking. Mobile health (mHealth) technologies show promise to track and support medication adherence. Objective: The study aimed to evaluate the feasibility and acceptability of using an mHealth system for medication adherence tracking and intervention delivery. The mHealth system comprises a smart button device to self-track medication taking, a companion smartphone app, a computer algorithm used to determine adherence and then deliver a standard or tailored SMS (short message service) text message on the basis of timing of medication taking. Standard SMS text messages indicated that the smartphone app registered the button press, whereas tailored SMS text messages encouraged habit formation and systems thinking on the basis of the timing the medications were taken. Methods: A convenience sample of 5 adults with chronic kidney disease (CKD), who were prescribed antihypertensive medication, participated in a 52-day longitudinal study. The study was conducted in 3 phases, with a standard SMS text message sent in phases 1 (study days 1-14) and 3 (study days 46-52) and tailored SMS text messages sent during phase 2 (study days 15-45) in response to participant medication self-tracking. Medication adherence was measured using: (1) the smart button and (2) electronic medication monitoring caps. Concordance between these 2 methods was evaluated using percentage of measurements made on the same day and occurring within {\textpm}5 min of one another. Acceptability was evaluated using qualitative feedback from participants. Results: A total of 5 patients with CKD, stages 1-4, were enrolled in the study, with the majority being men (60\%), white (80\%), and Hispanic/Latino (40\%) of middle age (52.6 years, SD 22.49; range 20-70). The mHealth system was successfully initiated in the clinic setting for all enrolled participants. Of the expected 260 data points, 36.5\% (n=95) were recorded with the smart button and 76.2\% (n=198) with electronic monitoring. Concordant events (n=94), in which events were recorded with both the smart button and electronic monitoring, occurred 47\% of the time and 58\% of these events occurred within {\textpm}5 min of one another. Participant comments suggested SMS text messages were encouraging. Conclusions: It was feasible to recruit participants in the clinic setting for an mHealth study, and our system was successfully initiated for all enrolled participants. The smart button is an innovative way to self-report adherence data, including date and timing of medication taking, which were not previously available from measures that rely on recall of adherence. Although the selected smart button had poor concordance with electronic monitoring caps, participants were willing to use it to self-track medication adherence, and they found the mHealth system acceptable to use in most cases. ",
doi="10.2196/13558",
url="http://formative.jmir.org/2019/2/e13558/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31237568"
}


@Article{info:doi/10.2196/13588,
author="Chai, R. Peter
and Zhang, Haipeng
and Jambaulikar, D. Guruprasad
and Boyer, W. Edward
and Shrestha, Labina
and Kitmitto, Loay
and Wickner, G. Paige
and Salmasian, Hojjat
and Landman, B. Adam",
title="An Internet of Things Buttons to Measure and Respond to Restroom Cleanliness in a Hospital Setting: Descriptive Study",
journal="J Med Internet Res",
year="2019",
month="Jun",
day="19",
volume="21",
number="6",
pages="e13588",
keywords="operations research",
keywords="wireless technology",
keywords="hygiene",
keywords="toilet facilities",
keywords="workflow",
abstract="Background: Restroom cleanliness is an important factor in hospital quality. Due to its dynamic process, it can be difficult to detect the presence of dirty restrooms that need to be cleaned. Using an Internet of Things (IoT) button can permit users to designate restrooms that need cleaning and in turn, allow prompt response from housekeeping to maintain real-time restroom cleanliness. Objective: This study aimed to describe the deployment of an IoT button--based notification system to measure hospital restroom cleanliness reporting system usage and qualitative feedback from housekeeping staff on IoT button use. Methods: We deployed IoT buttons in 16 hospital restrooms. Over an 8-month period, housekeeping staff received real-time notifications and responded to button presses for restroom cleaning. All button presses were recorded. We reported average button usage by hospital area, time of day, and day of week. We also conducted interviews with housekeeping supervisors and staff to understand their acceptance of and experience with the system. Results: Over 8 months, 1920 requests to clean restrooms in the main hospital lobby and satellite buildings were received. The hospital lobby IoT buttons received over half (N=1055, 55\%) of requests for cleaning. Most requests occurred in afternoon hours from 3 PM to midnight. Requests for cleaning remained stable throughout the work week with fewer requests occurring over weekends. IoT button use was sustained throughout the study period. Interviews with housekeeping supervisors and staff demonstrated acceptance of the IoT buttons; actual use was centered around asynchronous communication between supervisors and staff in response to requests to clean restrooms. Conclusions: An IoT button system is a feasible method to generate on-demand request for restroom cleaning that is easy to deploy and that users will consistently engage with. Data from this system have the potential to enable responsive scheduling for restroom service and anticipate periods of high restroom utilization in a hospital. ",
doi="10.2196/13588",
url="http://www.jmir.org/2019/6/e13588/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31219046"
}


@Article{info:doi/10.2196/13583,
author="Zheng, Xiaochen
and Sun, Shengjing
and Mukkamala, Rao Raghava
and Vatrapu, Ravi
and Ordieres-Mer{\'e}, Joaqu{\'i}n",
title="Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies",
journal="J Med Internet Res",
year="2019",
month="Jun",
day="06",
volume="21",
number="6",
pages="e13583",
keywords="Internet of Things",
keywords="distributed ledger technologies",
keywords="data sharing",
keywords="health information interoperability",
keywords="IOTA Tangle",
keywords="masked authenticated messaging",
keywords="blockchain",
keywords="intelligent healthcare",
abstract="Background: Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. Objective: This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. Methods: The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. Results: A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. Conclusions: DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices. ",
doi="10.2196/13583",
url="https://www.jmir.org/2019/6/e13583/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31172963"
}


@Article{info:doi/10.2196/12293,
author="Chung, Hung-Yuan
and Chung, Yao-Liang
and Liang, Chih-Yen",
title="Design and Implementation of a Novel System for Correcting Posture Through the Use of a Wearable Necklace Sensor",
journal="JMIR Mhealth Uhealth",
year="2019",
month="May",
day="28",
volume="7",
number="5",
pages="e12293",
keywords="wearable sensing technology",
keywords="necklace",
keywords="posture correction",
keywords="image recognition",
keywords="internet of things",
abstract="Background: To our knowledge, few studies have examined the use of wearable sensing devices to effectively integrate information communication technologies and apply them to health care issues (particularly those pertaining to posture correction). Objective: A novel system for posture correction involving the application of wearable sensing technology was developed in this study. The system was created with the aim of preventing the unconscious development of bad postures (as well as potential spinal diseases over the long term). Methods: The newly developed system consists of a combination of 3 subsystems, namely, a smart necklace, notebook computer, and smartphone. The notebook computer is enabled to use a depth camera to read the relevant data, to identify the skeletal structure and joint reference points of a user, and to compute calculations relating to those reference points, after which the computer then sends signals to the smart necklace to enable calibration of the smart necklace's standard values (base values for posture assessment). The gravitational acceleration data of the user are collected and analyzed by a microprocessor unit-6050 sensor housed in the smart necklace when the smart necklace is worn, with those data being used by the smart necklace to determine the user's body posture. When poor posture is detected by the smart necklace, the smart necklace sends the user's smartphone a reminder to correct his or her posture; a mobile app that was also developed as part of the study allows the smart necklace to transmit such messages to the smartphone. Results: The system effectively enables a user to monitor and correct his or her own posture, which in turn will assist the user in preventing spine-related diseases and, consequently, in living a healthier life. Conclusions: The proposed system makes it possible for (1) the user to self-correct his or her posture without resorting to the use of heavy, thick, or uncomfortable corrective clothing; (2) the smart necklace's standard values to be quickly calibrated via the use of posture imaging; and (3) the need for complex wiring to be eliminated through the effective application of the Internet of Things as well as by implementing wireless communication between the smart necklace, notebook computer, and smartphone. ",
doi="10.2196/12293",
url="https://mhealth.jmir.org/2019/5/e12293/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31140439"
}


@Article{info:doi/10.2196/12077,
author="Meinert, Edward
and Van Velthoven, Michelle
and Brindley, David
and Alturkistani, Abrar
and Foley, Kimberley
and Rees, Sian
and Wells, Glenn
and de Pennington, Nick",
title="The Internet of Things in Health Care in Oxford: Protocol for Proof-of-Concept Projects",
journal="JMIR Res Protoc",
year="2018",
month="Dec",
day="04",
volume="7",
number="12",
pages="e12077",
keywords="Internet",
keywords="computer systems",
keywords="computing methodologies",
keywords="information systems",
keywords="information storage and retrieval",
keywords="dataset",
keywords="patient care",
keywords="health services",
keywords="Internet of Things",
keywords="Internet of Medical Things",
abstract="Background: Demands on health services across are increasing because of the combined challenges of an expanding and aging population, alongside complex comorbidities that transcend the classical boundaries of modern health care. Continuing to provide and coordinate care in the current manner is not a viable route to sustain the improvements in health outcomes observed in recent history. To ensure that there continues to be improvement in patient care, prevention of disease, and reduced burden on health systems, it is essential that we adapt our models of delivery. Providers of health and social care are evolving to face these pressures by changing the way they think about the care system and, importantly, how to involve patients in the planning and delivery of services. Objective: The objective of this paper is to provide (1) an overview of the current state of Internet of Things (IoT) and key implementation considerations, (2) key use cases demonstrating technology capabilities, (3) an overview of the landscape for health care IoT use in Oxford, and (4) recommendations for promoting the IoT via collaborations between higher education institutions and industry proof-of-concept (PoC) projects. Methods: This study describes the PoC projects that will be created to explore cost-effectiveness, clinical efficacy, and user adoption of Internet of Medical Things systems. The projects will focus on 3 areas: (1) bring your own device integration, (2) chronic disease management, and (3) personal health records. Results: This study is funded by Research England's Connecting Capability Fund. The study started in March 2018, and results are expected by the end of 2019. Conclusions: Embracing digital solutions to support the evolution and transformation of health services is essential. Importantly, this should not simply be undertaken by providers in isolation. It must embrace and exploit the advances being seen in the consumer devices, national rollout of high-speed broadband services, and the rapidly expanding medical device industry centered on mobile and wearable technologies. Oxford University Hospitals and its partner providers, patients, and stakeholders are building on their leading position as an exemplar site for digital maturity in the National Health Service to implement and evaluate technologies and solutions that will capitalize on the IoT. Although early in the application to health, the IoT and the potential it provides to make the patient a partner at the center of decisions about care represent an exciting opportunity. If achieved, a fully connected and interoperable health care environment will enable continuous acquisition and real-time analysis of patient data, offering unprecedented ability to monitor patients, manage disease, and potentially deliver early diagnosis. The clinical benefit of this is clear, but additional patient benefit and value will be gained from being able to provide expert care at home or close to home. International Registered Report Identifier (IRRID): DERR1-10.2196/12077 ",
doi="10.2196/12077",
url="http://www.researchprotocols.org/2018/12/e12077/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30514695"
}


@Article{info:doi/10.2196/12048,
author="Lee, Ho Jang
and Park, Rang Yu
and Kweon, Solbi
and Kim, Seulgi
and Ji, Wonjun
and Choi, Chang-Min",
title="A Cardiopulmonary Monitoring System for Patient Transport Within Hospitals Using Mobile Internet of Things Technology: Observational Validation Study",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Nov",
day="14",
volume="6",
number="11",
pages="e12048",
keywords="wearable device",
keywords="patient safety",
keywords="intrahospital transport",
keywords="oxygen saturation",
keywords="heart rate",
keywords="mobile application",
keywords="real-time monitoring",
abstract="Background: During intrahospital transport, adverse events are inevitable. Real-time monitoring can be helpful for preventing these events during intrahospital transport. Objective: We attempted to determine the viability of risk signal detection using wearable devices and mobile apps during intrahospital transport. An alarm was sent to clinicians in the event of oxygen saturation below 90\%, heart rate above 140 or below 60 beats per minute (bpm), and network errors. We validated the reliability of the risk signal transmitted over the network. Methods: We used two wearable devices to monitor oxygen saturation and heart rate for 23 patients during intrahospital transport for diagnostic workup or rehabilitation. To determine the agreement between the devices, records collected every 4 seconds were matched and imputation was performed if no records were collected at the same time by both devices. We used intraclass correlation coefficients (ICC) to evaluate the relationships between the two devices. Results: Data for 21 patients were delivered to the cloud over LTE, and data for two patients were delivered over Wi-Fi. Monitoring devices were used for 20 patients during intrahospital transport for diagnostic work up and for three patients during rehabilitation. Three patients using supplemental oxygen before the study were included. In our study, the ICC for the heart rate between the two devices was 0.940 (95\% CI 0.939-0.942) and that of oxygen saturation was 0.719 (95\% CI 0.711-0.727). Systemic error analyzed with Bland-Altman analysis was 0.428 for heart rate and --1.404 for oxygen saturation. During the study, 14 patients had 20 risk signals: nine signals for eight patients with less than 90\% oxygen saturation, four for four patients with a heart rate of 60 bpm or less, and seven for five patients due to network error. Conclusions: We developed a system that notifies the health care provider of the risk level of a patient during transportation using a wearable device and a mobile app. Although there were some problems such as missing values and network errors, this paper is meaningful in that the previously mentioned risk detection system was validated with actual patients. ",
doi="10.2196/12048",
url="http://mhealth.jmir.org/2018/11/e12048/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30429115"
}


@Article{info:doi/10.2196/jmir.9454,
author="Chai, Ray Peter
and Zhang, Haipeng
and Baugh, W. Christopher
and Jambaulikar, D. Guruprasad
and McCabe, C. Jonathan
and Gorman, M. Janet
and Boyer, W. Edward
and Landman, Adam",
title="Internet of Things Buttons for Real-Time Notifications in Hospital Operations: Proposal for Hospital Implementation",
journal="J Med Internet Res",
year="2018",
month="Aug",
day="10",
volume="20",
number="8",
pages="e251",
keywords="Internet of Things",
keywords="operations",
keywords="hospital systems",
keywords="health care",
abstract="Background: Hospital staff frequently performs the same process hundreds to thousands of times a day. Customizable Internet of Things buttons are small, wirelessly-enabled devices that trigger specific actions with the press of an integrated button and have the potential to automate some of these repetitive tasks. In addition, IoT buttons generate logs of triggered events that can be used for future process improvements. Although Internet of Things buttons have seen some success as consumer products, little has been reported on their application in hospital systems. Objective: We discuss potential hospital applications categorized by the intended user group (patient or hospital staff). In addition, we examine key technological considerations, including network connectivity, security, and button management systems. Methods: In order to meaningfully deploy Internet of Things buttons in a hospital system, we propose an implementation framework grounded in the Plan-Do-Study-Act method. Results: We plan to deploy Internet of Things buttons within our hospital system to deliver real-time notifications in public-facing tasks such as restroom cleanliness and critical supply restocking. We expect results from this pilot in the next year. Conclusions: Overall, Internet of Things buttons have significant promise; future rigorous evaluations are needed to determine the impact of Internet of Things buttons in real-world health care settings. ",
doi="10.2196/jmir.9454",
url="http://www.jmir.org/2018/8/e251/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30097420"
}


@Article{info:doi/10.2196/jmir.7533,
author="Guti{\'e}rrez Garc{\'i}a, Angeles Mar{\'i}a
and Mart{\'i}n Ruiz, Luisa Mar{\'i}a
and Rivera, Diego
and Vadillo, Laura
and Valero Duboy, Angel Miguel",
title="A Smart Toy to Enhance the Decision-Making Process at Children's Psychomotor Delay Screenings: A Pilot Study",
journal="J Med Internet Res",
year="2017",
month="May",
day="19",
volume="19",
number="5",
pages="e171",
keywords="research instruments",
keywords="questionnaires and tools",
keywords="Information retrieval",
keywords="Internet of things",
keywords="clinical information and decision making",
keywords="Web-based and mobile health interventions",
keywords="developmental delays",
keywords="smart toys",
abstract="Background: EDUCERE (``Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders'') is an ecosystem for ubiquitous detection, care, and early stimulation of children with developmental disorders. The objectives of this Spanish government-funded research and development project are to investigate, develop, and evaluate innovative solutions to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. Thirty multidisciplinary professionals and three nursery schools worked in the EDUCERE project between 2014 and 2017 and they obtained satisfactory results. Related to EDUCERE, we found studies based on providing networks of connected smart objects and the interaction between toys and social networks. Objective: This research includes the design, implementation, and validation of an EDUCERE smart toy aimed to automatically detect delays in psychomotor development. The results from initial tests led to enhancing the effectiveness of the original design and deployment. The smart toy, based on stackable cubes, has a data collector module and a smart system for detection of developmental delays, called the EDUCERE developmental delay screening system (DDSS). Methods: The pilot study involved 65 toddlers aged between 23 and 37 months (mean=29.02, SD 3.81) who built a tower with five stackable cubes, designed by following the EDUCERE smart toy model. As toddlers made the tower, sensors in the cubes sent data to a collector module through a wireless connection. All trials were video-recorded for further analysis by child development experts. After watching the videos, experts scored the performance of the trials to compare and fine-tune the interpretation of the data automatically gathered by the toy-embedded sensors. Results: Judges were highly reliable in an interrater agreement analysis (intraclass correlation 0.961, 95\% CI 0.937-0.967), suggesting that the process was successful to separate different levels of performance. A factor analysis of collected data showed that three factors, trembling, speed, and accuracy, accounted for 76.79\% of the total variance, but only two of them were predictors of performance in a regression analysis: accuracy (P=.001) and speed (P=.002). The other factor, trembling (P=.79), did not have a significant effect on this dependent variable. Conclusions: The EDUCERE DDSS is ready to use the regression equation obtained for the dependent variable ``performance'' as an algorithm for the automatic detection of psychomotor developmental delays. The results of the factor analysis are valuable to simplify the design of the smart toy by taking into account only the significant variables in the collector module. The fine-tuning of the toy process module will be carried out by following the specifications resulting from the analysis of the data to improve the efficiency and effectiveness of the product. ",
doi="10.2196/jmir.7533",
url="http://www.jmir.org/2017/5/e171/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28526666"
}


@Article{info:doi/10.2196/mhealth.6491,
author="Wray, Tyler
and Chan, A. Philip
and Simpanen, Erik
and Operario, Don",
title="eTEST: Developing a Smart Home HIV Testing Kit that Enables Active, Real-Time Follow-Up and Referral After Testing",
journal="JMIR Mhealth Uhealth",
year="2017",
month="May",
day="08",
volume="5",
number="5",
pages="e62",
keywords="HIV",
keywords="smartphone",
keywords="Internet",
keywords="counseling",
keywords="referral and consultation",
keywords="qualitative research",
abstract="Background: Men who have sex with men (MSM) are the group at highest risk for contracting human immunodeficiency virus (HIV) in the United States, but many do not test as frequently as recommended. Home-based self-testing (HBST) for HIV holds promise for promoting regular testing among these individuals, but currently available HBSTs have limited follow-up options, providing only a 1-800 number that participants can call. Failure to actively conduct follow-up counseling and referrals after HBST use could result in delays in seeking confirmatory testing and care among users receiving reactive (preliminary positive) test results. HBST also fails to connect users who test negative with other prevention services that can reduce their future risk for HIV. Objective: The aim of our study was to use qualitative research methods with high-risk MSM to inform development of a ``smart'' HBST kit. The kit utilizes existing Internet-of-Things (IoT) technologies to monitor HBST use in real-time and enable delivery of timely, active follow-up counseling and referrals over the phone. Methods: In phase 1, individual interviews (n=10) explored how participants might use HBST and their views and preferences for conducting counseling and referral after HBST. Based on these perspectives, we developed a smartphone app (iOS, Android) that uses data from light sensors on Bluetooth low energy (BLE) beacons to monitor when HBST kits are opened, facilitating timely follow-up phone contact with users. In phase 2, a usability study conducted among high-risk MSM (n=10) examined the acceptability and feasibility of this system and provided user perspectives after using the system along with HBST. Results: Phase 1 themes suggested that MSM preferred HBST, that most thought active follow-up after HBST would be valuable, and that doing so over the phone within 24 h after testing was preferable. Phase 2 results showed that the eTEST system successfully detected HBST use in nearly all cases. Participant perspectives also suggested that the timing, method (ie, phone call), and duration of follow-up were appropriate and helpful. Conclusions: Using BLE beacons and a smartphone app to enable follow-up counseling and referral over the phone after HBST use is feasible and acceptable to high-risk MSM. Future research is needed to compare the effects of follow-up counseling on rates of repeat testing and receipt of referral services (eg, testing for sexually transmitted infections and initiation of preexposure prophylaxis) and to explore the acceptability of the eTEST system over longer periods of time. ",
doi="10.2196/mhealth.6491",
url="http://mhealth.jmir.org/2017/5/e62/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28483744"
}


@Article{info:doi/10.2196/jmir.7131,
author="Balaguera, U. Henri
and Wise, Diana
and Ng, Yin Chun
and Tso, Han-Wen
and Chiang, Wan-Lin
and Hutchinson, M. Aimee
and Galvin, Tracy
and Hilborne, Lee
and Hoffman, Cathy
and Huang, Chi-Cheng
and Wang, Jason C.",
title="Using a Medical Intranet of Things System to Prevent Bed Falls in an Acute Care Hospital: A Pilot Study",
journal="J Med Internet Res",
year="2017",
month="May",
day="04",
volume="19",
number="5",
pages="e150",
keywords="accidental falls",
keywords="acute care",
keywords="nursing",
keywords="patient safety",
keywords="patient-centered care",
keywords="sensor devices and platforms",
keywords="health care technology",
keywords="mobile apps",
keywords="patient monitoring",
keywords="health technology assessment",
abstract="Background: Hospitalized patients in the United States experience falls at a rate of 2.6 to 17.1 per 1000 patient-days, with the majority occurring when a patient is moving to, from, and around the bed. Each fall with injury costs an average of US \$14,000. Objective: The aim was to conduct a technology evaluation, including feasibility, usability, and user experience, of a medical sensor-based Intranet of things (IoT) system in facilitating nursing response to bed exits in an acute care hospital. Methods: Patients 18 years and older with a Morse fall score of 45 or greater were recruited from a 35-bed medical-surgical ward in a 317-bed Massachusetts teaching hospital. Eligible patients were recruited between August 4, 2015 and July 31, 2016. Participants received a sensor pad placed between the top of their mattress and bed sheet. The sensor pad was positioned to monitor movement from patients' shoulders to their thighs. The SensableCare System was evaluated for monitoring patient movement and delivering timely alerts to nursing staff via mobile devices when there appeared to be a bed-exit attempt. Sensor pad data were collected automatically from the system. The primary outcomes included number of falls, time to turn off bed-exit alerts, and the number of attempted bed-exit events. Data on patient falls were collected by clinical research assistants and confirmed with the unit nurse manager. Explanatory variables included room locations (zones 1-3), day of the week, nursing shift, and Morse Fall Scale (ie, positive fall history, positive secondary diagnosis, positive ambulatory aid, weak impaired gait/transfer, positive IV/saline lock, mentally forgets limitations). We also assessed user experience via nurse focus groups. Qualitative data regarding staff interactions with the system were collected during two focus groups with 25 total nurses, each lasting approximately 1.5 hours. Results: A total of 91 patients used the system for 234.0 patient-days and experienced no bed falls during the study period. On average, patients were assisted/returned to bed 46 seconds after the alert system was triggered. Response times were longer during the overnight nursing shift versus day shift (P=.005), but were independent of the patient's location on the unit. Focus groups revealed that nurses found the system integrated well into the clinical nursing workflow and the alerts were helpful in patient monitoring. Conclusions: A medical IoT system can be integrated into the existing nursing workflow and may reduce patient bed fall risk in acute care hospitals, a high priority but an elusive patient safety challenge. By using an alerting system that sends notifications directly to nurses' mobile devices, nurses can equally respond to unassisted bed-exit attempts wherever patients are located on the ward. Further study, including a fully powered randomized controlled trial, is needed to assess effectiveness across hospital settings. ",
doi="10.2196/jmir.7131",
url="http://www.jmir.org/2017/5/e150/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28473306"
}


@Article{info:doi/10.2196/medinform.6693,
author="Fernandes, Oliveira Chrystinne
and Lucena, De Carlos Jos{\'e} Pereira",
title="A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support",
journal="JMIR Med Inform",
year="2017",
month="Mar",
day="27",
volume="5",
number="1",
pages="e9",
keywords="eHealth systems",
keywords="remote patient monitoring",
keywords="biometric sensors",
abstract="Background: Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. Objective: The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. Methods: A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework's flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health's operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. Results: IoT4Health's construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. Conclusions: We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion. ",
doi="10.2196/medinform.6693",
url="http://medinform.jmir.org/2017/1/e9/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28347973"
}


@Article{info:doi/10.2196/jmir.7050,
author="Chai, R. Peter
and Carreiro, Stephanie
and Innes, J. Brendan
and Rosen, K. Rochelle
and O'Cleirigh, Conall
and Mayer, H. Kenneth
and Boyer, W. Edward",
title="Digital Pills to Measure Opioid Ingestion Patterns in Emergency Department Patients With Acute Fracture Pain: A Pilot Study",
journal="J Med Internet Res",
year="2017",
month="Jan",
day="13",
volume="19",
number="1",
pages="e19",
keywords="medication adherence",
keywords="opioid",
keywords="digital pills",
keywords="digital health",
keywords="emergency medicine",
keywords="pain management",
abstract="Background: Nonadherence to prescribed regimens for opioid analgesic agents contributes to increasing opioid abuse and overdose death. Opioids are frequently prescribed on an as-needed basis, placing the responsibility to determine opioid dose and frequency with the patient. There is wide variability in physician prescribing patterns because of the lack of data describing how patients actually use as-needed opioid analgesics. Digital pill systems have a radiofrequency emitter that directly measures medication ingestion events, and they provide an opportunity to discover the dose, timing, and duration of opioid therapy. Objective: The purpose of this study was to determine the feasibility of a novel digital pill system to measure as-needed opioid ingestion patterns in patients discharged from the emergency department (ED) after an acute bony fracture. Methods: We used a digital pill with individuals who presented to a teaching hospital ED with an acute extremity fracture. The digital pill consisted of a digital radiofrequency emitter within a standard gelatin capsule that encapsulated an oxycodone tablet. When ingested, the gastric chloride ion gradient activated the digital pill, transmitting a radiofrequency signal that was received by a hip-worn receiver, which then transmitted the ingestion data to a cloud-based server. After a brief, hands-on training session in the ED, study participants were discharged home and used the digital pill system to ingest oxycodone prescribed as needed for pain for one week. We conducted pill counts to verify digital pill data and open-ended interviews with participants at their follow-up appointment with orthopedics or at one week after enrollment in the study to determine the knowledge, attitudes, beliefs, and practices regarding digital pills. We analyzed open-ended interviews using applied thematic analysis. Results: We recruited 10 study participants and recorded 96 ingestion events (87.3\%, 96/110 accuracy). Study participants reported being able to operate all aspects of the digital pill system after their training. Two participants stopped using the digital pill, reporting they were in too much pain to focus on the novel technology. The digital pill system detected multiple simultaneous ingestion events by the digital pill system. Participants ingested a mean 8 (SD 5) digital pills during the study period and four participants continued on opioids at the end of the study period. After interacting with the digital pill system in the real world, participants found the system highly acceptable (80\%, 8/10) and reported a willingness to continue to use a digital pill to improve medication adherence monitoring (90\%, 9/10). Conclusions: The digital pill is a feasible method to measure real-time opioid ingestion patterns in individuals with acute pain and to develop real-time interventions if opioid abuse is detected. Deploying digital pills is possible through the ED with a short instructional course. Patients who used the digital pill accepted the technology. ",
doi="10.2196/jmir.7050",
url="http://www.jmir.org/2017/1/e19/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28087496"
}


@Article{info:doi/10.2196/jmir.5876,
author="Roehrs, Alex
and da Costa, Andr{\'e} Cristiano
and Righi, Rosa Rodrigo da
and de Oliveira, Farias Kleinner Silva",
title="Personal Health Records: A Systematic Literature Review",
journal="J Med Internet Res",
year="2017",
month="Jan",
day="06",
volume="19",
number="1",
pages="e13",
keywords="personal health records",
keywords="patient access to records",
keywords="mobile health",
keywords="electronic health records",
keywords="taxonomy",
abstract="Background: Information and communication technology (ICT) has transformed the health care field worldwide. One of the main drivers of this change is the electronic health record (EHR). However, there are still open issues and challenges because the EHR usually reflects the partial view of a health care provider without the ability for patients to control or interact with their data. Furthermore, with the growth of mobile and ubiquitous computing, the number of records regarding personal health is increasing exponentially. This movement has been characterized as the Internet of Things (IoT), including the widespread development of wearable computing technology and assorted types of health-related sensors. This leads to the need for an integrated method of storing health-related data, defined as the personal health record (PHR), which could be used by health care providers and patients. This approach could combine EHRs with data gathered from sensors or other wearable computing devices. This unified view of patients' health could be shared with providers, who may not only use previous health-related records but also expand them with data resulting from their interactions. Another PHR advantage is that patients can interact with their health data, making decisions that may positively affect their health. Objective: This work aimed to explore the recent literature related to PHRs by defining the taxonomy and identifying challenges and open questions. In addition, this study specifically sought to identify data types, standards, profiles, goals, methods, functions, and architecture with regard to PHRs. Methods: The method to achieve these objectives consists of using the systematic literature review approach, which is guided by research questions using the population, intervention, comparison, outcome, and context (PICOC) criteria. Results: As a result, we reviewed more than 5000 scientific studies published in the last 10 years, selected the most significant approaches, and thoroughly surveyed the health care field related to PHRs. We developed an updated taxonomy and identified challenges, open questions, and current data types, related standards, main profiles, input strategies, goals, functions, and architectures of the PHR. Conclusions: All of these results contribute to the achievement of a significant degree of coverage regarding the technology related to PHRs. ",
doi="10.2196/jmir.5876",
url="http://www.jmir.org/2017/1/e13/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28062391"
}


@Article{info:doi/10.2196/jmir.5256,
author="Hale, M. Timothy
and Jethwani, Kamal
and Kandola, Singh Manjinder
and Saldana, Fidencio
and Kvedar, C. Joseph",
title="A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study",
journal="J Med Internet Res",
year="2016",
month="May",
day="6",
volume="18",
number="4",
pages="e91",
keywords="heart failure",
keywords="telemonitoring",
keywords="telehealth",
keywords="self-management",
keywords="self-care",
keywords="complex medication regimens",
keywords="medication management",
keywords="medication adherence",
keywords="hospitalization length of stay",
keywords="ED visits",
abstract="Background: Heart failure (HF) is a chronic condition affecting nearly 5.7 million Americans and is a leading cause of morbidity and mortality. With an aging population, the cost associated with managing HF is expected to more than double from US \$31 billion in 2012 to US \$70 billion by 2030. Readmission rates for HF patients are high---25\% are readmitted at 30 days and nearly 50\% at 6 months. Low medication adherence contributes to poor HF management and higher readmission rates. Remote telehealth monitoring programs aimed at improved medication management and adherence may improve HF management and reduce readmissions. Objective: The primary goal of this randomized controlled pilot study is to compare the MedSentry remote medication monitoring system versus usual care in older HF adult patients who recently completed a HF telemonitoring program. We hypothesized that remote medication monitoring would be associated with fewer unplanned hospitalizations and emergency department (ED) visits, increased medication adherence, and improved health-related quality of life (HRQoL) compared to usual care. Methods: Participants were randomized to usual care or use of the remote medication monitoring system for 90 days. Twenty-nine participants were enrolled and the final analytic sample consisted of 25 participants. Participants completed questionnaires at enrollment and closeout to gather data on medication adherence, health status, and HRQoL. Electronic medical records were reviewed for data on baseline classification of heart function and the number of unplanned hospitalizations and ED visits during the study period. Results: Use of the medication monitoring system was associated with an 80\% reduction in the risk of all-cause hospitalization and a significant decrease in the number of all-cause hospitalization length of stay in the intervention arm compared to usual care. Objective device data indicated high adherence rates (95\%-99\%) among intervention group participants despite finding no significant difference in self-reported adherence between study arms. The intervention group had poorer heart function and HRQoL at baseline, and HRQoL declined significantly in the intervention group compared to controls. Conclusions: The MedSentry medication monitoring system is a promising technology that merits continued development and evaluation. The MedSentry medication monitoring system may be useful both as a standalone system for patients with complex medication regimens or used to complement existing HF telemonitoring interventions. We found significant reductions in risk of all-cause hospitalization and the number of all-cause length of stay in the intervention group compared to controls. Although HRQoL deteriorated significantly in the intervention group, this may have been due to the poorer HF-functioning at baseline in the intervention group compared to controls. Telehealth medication adherence technologies, such as the MedSentry medication monitoring system, are a promising method to improve patient self-management,the quality of patient care, and reduce health care utilization and expenditure for patients with HF and other chronic diseases that require complex medication regimens. Trial Registration: ClinicalTrials.gov NCT01814696; https://clinicaltrials.gov/ct2/show/study/NCT01814696 (Archived by WebCite? at http://www.webcitation.org/6giqAVhno) ",
doi="10.2196/jmir.5256",
url="http://www.jmir.org/2016/4/e91/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27154462"
}


@Article{info:doi/10.2196/jmir.4767,
author="Sperrin, Matthew
and Rushton, Helen
and Dixon, G. William
and Normand, Alexis
and Villard, Joffrey
and Chieh, Angela
and Buchan, Iain",
title="Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="21",
volume="18",
number="1",
pages="e17",
keywords="weight gain",
keywords="weight loss",
keywords="body weight",
keywords="body mass index",
keywords="self-monitoring",
keywords="connected health technologies",
abstract="Background: Digital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes. Objective: The aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change. Methods: This was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change. Results: Among women, users of self-weighing technology had a mean BMI of 1.62 kg/m2 (95\% CI 1.03-2.22) lower than the general population (of the same age and height) (P<.001). Among men, users had a mean BMI of 1.26 kg/m2 (95\% CI 0.84-1.69) greater than the general population (of the same age and height) (P<.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI. Conclusions: Users of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics. ",
doi="10.2196/jmir.4767",
url="http://www.jmir.org/2016/1/e17/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26794900"
}


@Article{info:doi/10.2196/mhealth.2550,
author="Wang, Xingce
and Bie, Rongfang
and Sun, Yunchuan
and Wu, Zhongke
and Zhou, Mingquan
and Cao, Rongfei
and Xie, Lizhi
and Zhang, Dong",
title="The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection",
journal="JMIR Mhealth Uhealth",
year="2013",
month="Aug",
day="09",
volume="1",
number="2",
pages="e20",
keywords="cerebrovascular",
keywords="eHealth platform",
keywords="Ball B-Spline",
keywords="statistical segmentation",
keywords="volume rendering",
abstract="Background: In recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease. Objective: In order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied. Methods: We designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection.  Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users. Results: The implemented platform is running on a common personal computer. Experiments on 32 patients' brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist. Conclusions: The platform appears beneficial in diagnosis of the cerebrovascular disease.  The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things. ",
doi="10.2196/mhealth.2550",
url="http://mhealth.jmir.org/2013/2/e20/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25098861"
}


@Article{info:doi/10.2196/mhealth.2539,
author="Taka{\v c}, Boris
and Catal{\`a}, Andreu
and Rodr{\'i}guez Mart{\'i}n, Daniel
and van der Aa, Nico
and Chen, Wei
and Rauterberg, Matthias",
title="Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom",
journal="JMIR Mhealth Uhealth",
year="2013",
month="Jul",
day="15",
volume="1",
number="2",
pages="e14",
keywords="Parkinson disease",
keywords="Freezing of Gait",
keywords="context-aware system",
keywords="indoor localization",
keywords="person orientation",
abstract="Background: Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. Objective: The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. Methods: We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. Results: We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. Conclusions: The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position. ",
doi="10.2196/mhealth.2539",
url="http://mhealth.jmir.org/2013/2/e14/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25098265"
}


@Article{info:doi/10.2196/mhealth.2536,
author="Aztiria, Asier
and Farhadi, Golnaz
and Aghajan, Hamid",
title="User Behavior Shift Detection in Ambient Assisted Living Environments",
journal="JMIR Mhealth Uhealth",
year="2013",
month="Jun",
day="18",
volume="1",
number="1",
pages="e6",
keywords="shift detection",
keywords="intelligent environments",
keywords="disease detection",
doi="10.2196/mhealth.2536",
url="http://mhealth.jmir.org/2013/1/e6/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25100679"
}