@Article{info:doi/10.2196/66616,
author="Kapitan, Daniel
and Heddema, Femke
and Dekker, Andr{\'e}
and Sieswerda, Melle
and Verhoeff, Bart-Jan
and Berg, Matt",
title="Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="15",
volume="27",
pages="e66616",
keywords="FHIR",
keywords="OMOP",
keywords="openEHR",
keywords="health care informatics",
keywords="information standards",
keywords="secondary use",
keywords="digital platform",
keywords="data sharing",
keywords="data interoperability",
keywords="open source implementations",
keywords="open standards",
keywords="Fast Health Interoperability Resources",
keywords="Observational Medical Outcomes Partnership",
keywords="clinical care",
keywords="data exchange",
keywords="longitudinal analysis",
keywords="low income",
keywords="middle-income",
keywords="LMIC",
keywords="low and middle-income countries",
keywords="developing countries",
keywords="developing nations",
keywords="health information exchange",
doi="10.2196/66616",
url="https://www.jmir.org/2025/1/e66616",
url="http://www.ncbi.nlm.nih.gov/pubmed/40232773"
}


@Article{info:doi/10.2196/70852,
author="Mackey, Tim
and Cuomo, E. Raphael
and Xu, Qing
and McMann, J. Tiana
and Li, Zhuoran
and Cai, Mingxiang
and Wenzel, Christine
and Yang, S. Joshua",
title="Approach to Design and Evaluate Digital Tools to Enhance Young Adult Participation in Clinical Trials: Co-Design and Controlled Intercept Study",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="11",
volume="27",
pages="e70852",
keywords="health",
keywords="clinical trials",
keywords="COVID-19",
keywords="digital health",
keywords="coronavirus disease",
abstract="Background: Certain populations are underrepresented in clinical trials, limiting the generalizability of new treatments and their efficacy and uptake in these populations. It is essential to identify and understand effective strategies for enrolling young adults in clinical trials, as they represent a vital and key demographic for future clinical trial participation. Objective: This study aimed to develop, test, and evaluate digital tools designed to encourage the participation of young adults in the clinical trial process. An interdisciplinary approach, incorporating social listening, qualitative focus groups, and co-design workshops, was used to achieve this goal. Methods: Digital tools were designed and evaluated using a 4-phase approach that included: (1) social listening to characterize lived experiences with COVID-19 trials as self-reported by online users, (2) qualitative focus groups with young adults to explore specific lived attitudes and experiences related to COVID-19 clinical research hesitancy and engagement, (3) a series of cocreation and co-design workshops to build digital tools aimed at encouraging clinical trial participation, and (4) a controlled intercept study to assess the usability and specific outcome measures of the co-designed digital tools among young adults. Results: A significantly higher change in the likelihood of participating in a clinical trial post exposure was observed among study participants when exposed to prototypes of a mobile app ($\Delta$=0.74 on a 10-point scale, P<.01) and website ($\Delta$=0.93, P<.01) compared to those exposed to a Facebook ad ($\Delta$=0.21) but not a digital flyer ($\Delta$=0.58). Furthermore, those exposed to the mobile app (x?=5.76, P=.04) and electronic flier (x?=5.72, P=.04), but not the website (x?=5.55), exhibited significantly higher postexposure interest in learning about clinical trials when compared to participants exposed to the Facebook (Meta) ad (x?=5.06). Participants in the intercept study were more likely to consider joining a clinical trial after seeing a mobile app ($\Delta$=0.74, P<.01) or website ($\Delta$=0.93, P<.001) compared to a Facebook ad ($\Delta$=0.21), but the digital flyer ($\Delta$=0.58) did not show a significant difference. In addition, those who saw the mobile app (x?=5.76, P=.04) or the digital flyer (x?=5.72, P=.04) showed more interest in learning about clinical trials than those who saw the Facebook ad (x?=5.06), though the website (x?= 5.55) did not significantly impact interest. Conclusions: Mobile apps and web pages co-designed with young diverse adults may represent effective digital tools to advance shared goals of encouraging inclusive clinical trials. ",
doi="10.2196/70852",
url="https://www.jmir.org/2025/1/e70852"
}


@Article{info:doi/10.2196/54921,
author="Zola Matuvanga, Tr{\'e}sor
and Paviotti, Antea
and Bikioli Bolombo, Freddy
and Lemey, Gwen
and Larivi{\`e}re, Ynke
and Salloum, Maha
and Isekah Osang'ir, Bernard
and Esanga Longomo, Emmanuel
and Milolo, Solange
and Matangila, Junior
and Maketa, Vivi
and Mitashi, Patrick
and Van Damme, Pierre
and Muhindo-Mavoko, Hypolite
and Van geertruyden, Jean-Pierre",
title="Long-Term Experiences of Health Care Providers Using Iris Scanning as an Identification Tool in a Vaccine Trial in the Democratic Republic of the Congo: Qualitative Study",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="6",
volume="9",
pages="e54921",
keywords="iris scan",
keywords="vaccine trial",
keywords="iris",
keywords="perception",
keywords="experience",
keywords="views",
keywords="biometric identification",
keywords="Democratic Republic of the Congo",
abstract="Background: Iris scanning has increasingly been used for biometric identification over the past decade, with continuous advancements and expanding applications. To better understand the acceptability of this technology, we report the long-term experiences of health care providers and frontline worker participants with iris scanning as an identification tool in the EBL2007 Ebola vaccine trial conducted in the Democratic Republic of the Congo. Objective: This study aims to document the long-term experiences of using iris scanning for identity verification throughout the vaccine trial. Methods: Two years after the start of the EBL2007 vaccine trial (February to March 2022), 69 trial participants---including nurses, first aid workers, midwives, and community health workers---were interviewed through focus group discussions. Additionally, 13 in-depth individual interviews were conducted with physicians involved in the trial, iris scan operators, trial staff physicians, and trial participants who declined iris scanning. Qualitative content analysis was used to identify key themes. Results: Initially, interviewees widely accepted the iris scan and viewed it as a distinctive tool for identifying participants in the EBL2007 vaccine trial. However, over time, perceptions became less favorable. Some participants expressed concerns that their vision had diminished shortly after using the tool and continued to decline until the end of the study. Others reported experiencing perceived vision loss long after the trial had concluded. However, no vision impairment was reported as an adverse event or assessed in the trial as being linked to the iris scan, which uses a previously certified safe infrared light for scanning. Conclusions: Our findings highlight the sustained acceptability and perceived high accuracy of the iris scan tool for uniquely identifying adult participants in a vaccine trial over time. Continued efforts to systematically disseminate and reinforce information about the function and safety of this technology are essential. Clearly presenting iris scanning as a safe procedure could help dispel misconceptions, concerns, and perceived risks among potential users in vaccine trials. ",
doi="10.2196/54921",
url="https://formative.jmir.org/2025/1/e54921",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053756"
}


@Article{info:doi/10.2196/66718,
author="Gardner, Leslie Leah
and Raeisian Parvari, Pezhman
and Seidman, Mark
and Holden, J. Richard
and Fowler, R. Nicole
and Zarzaur, L. Ben
and Summanwar, Diana
and Barboi, Cristina
and Boustani, Malaz",
title="Improving the User Interface and Guiding the Development of Effective Training Material for a Clinical Research Recruitment and Retention Dashboard: Usability Testing Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="24",
volume="9",
pages="e66718",
keywords="recruitment strategies",
keywords="clinical research",
keywords="research subject recruitment",
keywords="agile science",
keywords="agile implementation",
keywords="human-computer interaction",
abstract="Background: Participant recruitment and retention are critical to the success of clinical trials, yet challenges such as low enrollment rates and high attrition remain ongoing obstacles. RecruitGPS is a scalable dashboard with integrated control charts to address these issues by providing real-time data monitoring and analysis, enabling researchers to better track and improve recruitment and retention. Objective: This study aims to identify the challenges and inefficiencies users encounter when interacting with the RecruitGPS dashboard. By identifying these issues, the study aims to inform strategies for improving the dashboard's user interface and create targeted, effective instructional materials that address user needs. Methods: Twelve clinical researchers from the Midwest region of the United States provided feedback through a 10-minute, video-recorded usability test session, during which participants were instructed to explore the various tabs of the dashboard, identify challenges, and note features that worked well while thinking aloud. Following the video session, participants took a survey on which they answered System Usability Scale (SUS) questions, ease of navigation questions, and a Net Promoter Score (NPS) question. Results: A quantitative analysis of survey responses revealed an average SUS score of 61.46 (SD 23.80; median 66.25) points, indicating a need for improvement in the user interface. The NPS was 8, with 4 of 12 (33\%) respondents classified as promoters and 3 of 12 (25\%) as detractors, indicating a slightly positive satisfaction. When participants compared RecruitGPS to other recruitment and study management tools they had used, 8 of 12 (67\%) of participants rated RecruitGPS as better or much better. Only 1 of 12 (8\%) participants rated RecruitGPS as worse but not much worse. A qualitative analysis of participants' interactions with the dashboard diagnosed a confusing part of the dashboard that could be eliminated or made optional and provided valuable insight for the development of instructional videos and documentation. Participants liked the dashboard's data visualization capabilities, including intuitive graphs and trend tracking; progress indicators, such as color-coded status indicators and comparison metrics; and the overall dashboard's layout and design, which consolidated relevant data on a single page. Users also valued the accuracy and real-time updates of data, especially the integration with external sources like Research Electronic Data Capture (REDCap). Conclusions: RecruitGPS demonstrates significant potential to improve the efficiency of clinical trials by providing researchers with real-time insights into participant recruitment and retention. This study offers valuable recommendations for targeted refinements to enhance the user experience and maximize the dashboard's effectiveness. Additionally, it highlights navigation challenges that can be addressed through the development of clear and focused instructional videos. ",
doi="10.2196/66718",
url="https://formative.jmir.org/2025/1/e66718"
}


@Article{info:doi/10.2196/58451,
author="Draucker, Burke Claire
and Carri{\'o}n, Andr{\'e}s
and Ott, A. Mary
and Hicks, I. Ariel
and Knopf, Amelia",
title="A 4-Site Public Deliberation Project on the Acceptability of Youth Self-Consent in Biomedical HIV Prevention Trials: Assessment of Facilitator Fidelity to Key Principles",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="13",
volume="9",
pages="e58451",
keywords="public deliberation",
keywords="deliberative democracy",
keywords="bioethics",
keywords="ethical conflict",
keywords="biomedical",
keywords="HIV prevention",
keywords="HIV research",
keywords="group facilitation",
keywords="fidelity assessment",
keywords="content analysis",
abstract="Background: Public deliberation is an approach used to engage persons with diverse perspectives in discussions and decision-making about issues affecting the public that are controversial or value laden. Because experts have identified the need to evaluate facilitator performance, our research team developed a framework to assess the fidelity of facilitator remarks to key principles of public deliberation. Objective: This report describes how the framework was used to assess facilitator fidelity in a 4-site public deliberation project on the acceptability of minor self-consent in biomedical HIV prevention research. Methods: A total of 88 individuals participated in 4 deliberation sessions held in 4 cities throughout the United States. The sessions, facilitated by 18 team members, were recorded and transcribed verbatim. Facilitator remarks were highlighted, and predetermined coding rules were used to code the remarks to 1 of 6 principles of quality deliberations. A variety of display tables were used to organize the codes and calculate the number of facilitator remarks that were consistent or inconsistent with each principle during each session across all sites. A content analysis was conducted on the remarks to describe how facilitator remarks aligned or failed to align with each principle. Results: In total, 735 remarks were coded to one of the principles; 516 (70.2\%) were coded as consistent with a principle, and 219 (29.8\%) were coded as inconsistent. A total of 185 remarks were coded to the principle of equal participation (n=138, 74.6\% as consistent; n=185, 25.4\% as inconsistent), 158 were coded to expression of diverse opinions (n=110, 69.6\% as consistent; n=48, 30.4\% as inconsistent), 27 were coded to respect for others (n=27, 100\% as consistent), 24 were coded to adoption of a societal perspective (n=11, 46\% as consistent; n=13, 54\% as inconsistent), 99 were coded to reasoned justification of ideas (n=81, 82\% as consistent; n=18, 18\% as inconsistent), and 242 were coded to compromise or movement toward consensus (n=149, 61.6\% as consistent; n=93, 38.4\% as inconsistent). Therefore, the counts provided affirmation that most of the facilitator remarks were aligned with the principles of deliberation, suggesting good facilitator fidelity. By considering how the remarks aligned or failed to align with the principles, areas where facilitator fidelity can be strengthened were identified. The results indicated that facilitators should focus more on encouraging quieter members to participate, refraining from expressing personal opinions, promoting the adoption of a societal perspective and reasoned justification of opinions, and inviting deliberants to articulate their areas of common ground. Conclusions: The results provide an example of how a framework for assessing facilitator fidelity was used in a 4-site deliberation project. The framework will be refined to better address issues related to balancing personal and public perspectives, managing plurality, and mitigating social inequalities. ",
doi="10.2196/58451",
url="https://formative.jmir.org/2025/1/e58451"
}


@Article{info:doi/10.2196/56062,
author="Sim{\'o}n-L{\'o}pez, Carmen Leticia
and Ortu{\~n}o-Soriano, Ismael
and Luengo-Gonz{\'a}lez, Raquel
and Posada-Moreno, Paloma
and Zaragoza-Garc{\'i}a, Ignacio
and S{\'a}nchez-G{\'o}mez, Rub{\'e}n",
title="Proposal and Strategy for Nursing-Led Research: Protocol for an Unfunded Clinical Trial",
journal="JMIR Res Protoc",
year="2025",
month="Feb",
day="10",
volume="14",
pages="e56062",
keywords="clinical trial",
keywords="academic trial",
keywords="nonfunded",
keywords="commercial",
keywords="nurse-led",
keywords="low intervention",
keywords="health product",
keywords="peripheral venous cannulation",
keywords="PVC",
keywords="protocol",
keywords="randomized controlled trial",
keywords="RCT",
keywords="adults",
keywords="healthy adults",
keywords="funding",
keywords="academic sponsors",
keywords="cause-effect results",
keywords="insurance",
abstract="Background: Clinical trials are known to provide cause-and-effect results and data with low levels of bias. However, a lack of funding for clinical trials, which are considered expensive, means that academic sponsors are rarely able to conduct them. Academic trials are considered highly relevant for the valuable results they provide for clinical questions. This is why initiatives to conduct unfunded clinical trials have been identified as an important issue to pay attention to in future studies. Therefore, we present our initiative through Rogers' theory, which is highlighted in the literature for diffusing innovative change across organizations. Objective: The purpose of this paper was to describe our case regarding management for conducting a nonfunded nurse-led clinical trial based on our previous low-interventional clinical trial across a specific health organization and with nurses. Methods: We conducted a low-intervention, nonexternally funded clinical trial using the human and material resources available on site. We managed our trial in a clinical trial unit where there were staff, sources, and ongoing commercial clinical trials. We conducted our trial based on an ongoing commercial trial, and, to do so, we needed behavioral changes. We relied on Rogers' theory, and we identified strengths and barriers to change by analyzing actors' characteristics, perceptions of the situation, motivation, and information. Afterward, we divided the staff according to their characteristics related to innovation and change into permanent staff (research staff with a culture of change) and nonpermanent staff (nursing staff with occasional attendance and resistance to change). First, we preselected only those nurses who were more aware of change (innovators and pioneers) to participate in our trial to avoid a massive rejection, and later, we asked others to join (late adopters). We followed Rogers' phases. For research staff who were aware of the funding, we focused on the ``persuasion phase,'' while for nursing staff, we mixed the ``knowledge and persuasion phases'' and used pioneers and early adopters as a positive example for other nurses as well as nonfinancial incentives (persuasion). Our trial consisted of different methods of vein cannulation, which was performed in the ongoing commercial trial. Thus, the entire development of our low-interventional clinical trial was conducted without interfering at any point with the parallel commercial clinical trial. Results: Our management allowed effective conduct of our study, and we met our aims without external funding and without ethical impact during the commercial clinical trial. Costs remained low, primarily because the major expenses were covered by the commercial clinical trial as an inherent part of its design. Conclusions: Our initiative to conduct a low-intervention clinical trial with no or limited funding was cost-effective. This initiative can be used by researchers with valuable academic research questions who do not have the external funding to conduct studies. Trial Registration: ClinicalTrials.gov NCT04027218; https://clinicaltrials.gov/study/NCT04027218 International Registered Report Identifier (IRRID): RR1-10.2196/56062 ",
doi="10.2196/56062",
url="https://www.researchprotocols.org/2025/1/e56062",
url="http://www.ncbi.nlm.nih.gov/pubmed/39927682"
}


@Article{info:doi/10.2196/64069,
author="Liu, Yingxin
and Zhang, Jingyi
and Thabane, Lehana
and Bai, Xuerui
and Kang, Lili
and Lip, H. Gregory Y.
and Van Spall, C. Harriette G.
and Xia, Min
and Li, Guowei",
title="Data-Sharing Statements Requested from Clinical Trials by Public, Environmental, and Occupational Health Journals: Cross-Sectional Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="7",
volume="27",
pages="e64069",
keywords="data sharing",
keywords="clinical trial",
keywords="public health",
keywords="International Committee of Medical Journal Editors",
keywords="ICMJE",
keywords="journal request",
keywords="clinical trials",
keywords="decision-making",
keywords="occupational health",
keywords="health informatics",
keywords="patient data",
abstract="Background: Data sharing plays a crucial role in health informatics, contributing to improving health information systems, enhancing operational efficiency, informing policy and decision-making, and advancing public health surveillance including disease tracking. Sharing individual participant data in public, environmental, and occupational health trials can help improve public trust and support by enhancing transparent reporting and reproducibility of research findings. The International Committee of Medical Journal Editors (ICMJE) requires all papers to include a data-sharing statement. However, it is unclear whether journals in the field of public, environmental, and occupational health adhere to this requirement. Objective: This study aims to investigate whether public, environmental, and occupational health journals requested data-sharing statements from clinical trials submitted for publication. Methods: In this bibliometric survey of ``Public, Environmental, and Occupational Health'' journals, defined by the Journal Citation Reports (as of June 2023), we included 202 journals with clinical trial reports published between 2019 and 2022. The primary outcome was a journal request for a data-sharing statement, as identified in the paper submission instructions. Multivariable logistic regression analysis was conducted to evaluate the relationship between journal characteristics and journal requests for data-sharing statements, with results presented as odds ratios (ORs) and corresponding 95\% CIs. We also investigated whether the journals included a data-sharing statement in their published trial reports. Results: Among the 202 public, environmental, and occupational health journals included, there were 68 (33.7\%) journals that did not request data-sharing statements. Factors significantly associated with journal requests for data-sharing statements included open access status (OR 0.43, 95\% CI 0.19-0.97), high journal impact factor (OR 2.31, 95\% CI 1.15-4.78), endorsement of Consolidated Standards of Reporting Trials (OR 2.43, 95\% CI 1.25-4.79), and publication in the United Kingdom (OR 7.18, 95\% CI 2.61-23.4). Among the 134 journals requesting data-sharing statements, 26.9\% (36/134) did not have statements in their published trial reports. Conclusions: Over one-third of the public, environmental, and occupational health journals did not request data-sharing statements in clinical trial reports. Among those journals that requested data-sharing statements in their submission guidance pages, more than one quarter published trial reports with no data-sharing statements. These results revealed an inadequate practice of requesting data-sharing statements by public, environmental, and occupational health journals, requiring more effort at the journal level to implement ICJME recommendations on data-sharing statements. ",
doi="10.2196/64069",
url="https://www.jmir.org/2025/1/e64069"
}


@Article{info:doi/10.2196/63550,
author="Ruta, R. Michael
and Gaidici, Tony
and Irwin, Chase
and Lifshitz, Jonathan",
title="ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="7",
volume="27",
pages="e63550",
keywords="ChatGPT",
keywords="data analysis",
keywords="statistics",
keywords="chatbot",
keywords="artificial intelligence",
keywords="biomedical research",
keywords="programmers",
keywords="bioinformatics",
keywords="data processing",
abstract="Background: ChatGPT, a conversational artificial intelligence developed by OpenAI, has rapidly become an invaluable tool for researchers. With the recent integration of Python code interpretation into the ChatGPT environment, there has been a significant increase in the potential utility of ChatGPT as a research tool, particularly in terms of data analysis applications. Objective: This study aimed to assess ChatGPT as a data analysis tool and provide researchers with a framework for applying ChatGPT to data management tasks, descriptive statistics, and inferential statistics. Methods: A subset of the National Inpatient Sample was extracted. Data analysis trials were divided into data processing, categorization, and tabulation, as well as descriptive and inferential statistics. For data processing, categorization, and tabulation assessments, ChatGPT was prompted to reclassify variables, subset variables, and present data, respectively. Descriptive statistics assessments included mean, SD, median, and IQR calculations. Inferential statistics assessments were conducted at varying levels of prompt specificity (``Basic,'' ``Intermediate,'' and ``Advanced''). Specific tests included chi-square, Pearson correlation, independent 2-sample t test, 1-way ANOVA, Fisher exact, Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test. Outcomes from consecutive prompt-based trials were assessed against expected statistical values calculated in Python (Python Software Foundation), SAS (SAS Institute), and RStudio (Posit PBC). Results: ChatGPT accurately performed data processing, categorization, and tabulation across all trials. For descriptive statistics, it provided accurate means, SDs, medians, and IQRs across all trials. Inferential statistics accuracy against expected statistical values varied with prompt specificity: 32.5\% accuracy for ``Basic'' prompts, 81.3\% for ``Intermediate'' prompts, and 92.5\% for ``Advanced'' prompts. Conclusions: ChatGPT shows promise as a tool for exploratory data analysis, particularly for researchers with some statistical knowledge and limited programming expertise. However, its application requires careful prompt construction and human oversight to ensure accuracy. As a supplementary tool, ChatGPT can enhance data analysis efficiency and broaden research accessibility. ",
doi="10.2196/63550",
url="https://www.jmir.org/2025/1/e63550"
}


@Article{info:doi/10.2196/60591,
author="DiCaro, Vincent Michael
and Yee, Brianna
and Lei, KaChon
and Batra, Kavita
and Dawn, Buddhadeb",
title="Mesenchymal Stem Cell Therapy for Acute Myocardial Infarction: Protocol for a Systematic Review and Meta-Analysis",
journal="JMIR Res Protoc",
year="2025",
month="Feb",
day="6",
volume="14",
pages="e60591",
keywords="mesenchymal stem cells",
keywords="mesenchymal stromal cells",
keywords="progenitor cells",
keywords="acute myocardial infarction",
keywords="outcomes",
keywords="stem cell",
keywords="myocardial",
keywords="protocol",
keywords="systematic review",
keywords="meta-analysis",
keywords="medical therapy",
keywords="therapy",
keywords="cardiac",
keywords="efficacy",
abstract="Background: Medical therapy and interventional approaches have improved outcomes in patients with acute myocardial infarction (MI). However, these strategies are inadequate for replacing cells lost during tissue ischemia, thereby leaving behind noncontractile scar tissue. The anti-inflammatory and immune modulating properties of mesenchymal stem cells (MSCs) may prove useful in inducing functional cardiac regeneration following acute MI. Objective: This is a protocol for systematic review and meta-analysis that will aggregate and synthesize high-level clinical data on the effects of MSC therapy for acute MI. The findings of this study may serve as evidence for clinicians and researchers in guiding the use of MSC therapy as an adjunct to reperfusion and optimal medical therapy in patients with acute MI. Methods: The proposed systematic review is registered with PROSPERO (International Prospective Register of Systematic Reviews). A systematic search of bibliographical databases, including Embase, PubMed, and Cochrane was conducted from inception to June 2023 to identify English-language human studies with adult patients receiving MSC therapy and optimal medical therapy for acute MI in comparison with respective controls. Article screening was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Data on functional cardiac outcomes and major adverse cardiac events were extracted and analyzed as primary outcomes. Results: Literature search and article screening commenced in June 2023. Data extraction and analysis will be completed by October 2024. The findings will be synthesized and reported by the end of November 2024. Conclusions: This systematic review and meta-analysis will summarize the best available updated evidence from published randomized controlled trials on the effects of MSC therapy for the treatment of acute MI. The findings of this systematic review and meta-analysis may shed light on the efficacy of MSC therapy in improving cardiac functional and structural parameters and reducing adverse cardiac events following acute MI. Trial Registration: PROSPERO CRD42024522398; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=522398 International Registered Report Identifier (IRRID): DERR1-10.2196/60591 ",
doi="10.2196/60591",
url="https://www.researchprotocols.org/2025/1/e60591"
}


@Article{info:doi/10.2196/57379,
author="Kim, Yesol
and Kim, Geonah
and Cho, Hyeonmi
and Kim, Yeonju
and Choi, Mona",
title="Application of Patient-Generated Health Data Among Older Adults With Cancer: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="4",
volume="27",
pages="e57379",
keywords="patient-generated health data",
keywords="wearable devices",
keywords="patient-reported outcomes",
keywords="patient-centered care",
keywords="older adults",
keywords="cancer",
keywords="scoping review",
abstract="Background: The advancement of information and communication technologies has spurred a growing interest in and increased applications of patient-generated health data (PGHD). In particular, PGHD may be promising for older adults with cancer who have increased survival rates and experience a variety of symptoms. Objective: This scoping review aimed to identify the characteristics of research on PGHD as applied to older adults with cancer and to assess the current use of PGHD. Methods: Guided by Arksey and O'Malley as well as the JBI (Joanna Briggs Institute) methodology for scoping reviews, 6 electronic databases were searched: PubMed, Embase, CINAHL, Cochrane Library, Scopus, and Web of Science. In addition, the reference lists of the selected studies were screened to identify gray literature. The researchers independently screened the literature according to the predefined eligibility criteria. Data from the selected studies were extracted, capturing study, participant, and PGHD characteristics. Results: Of the 1090 identified studies, 88 were selected. The publication trend gradually increased, with a majority of studies published since 2017 (69/88, 78\%). Almost half of the studies were conducted in North America (38/88, 43\%), followed by Europe (30/88, 34\%). The most common setting in which the studies were conducted was the participant's home (69/88, 78\%). The treatment status varied; the median sample size was 50 (IQR 33.8-84.0). The devices that were used to measure the PGHD were classified as research-grade wearable devices (57/113, 50.4\%), consumer-grade wearable devices (28/113, 24.8\%), or smartphones or tablet PCs for mobile apps (23/113, 20.4\%). More than half of the studies measured physical activity (69/123, 56.1\%), followed by patient-reported outcomes (23/123, 18.7\%), vital signs (13/123, 10.6\%), and sleep (12/123, 9.8\%). The PGHD were mainly collected passively (63/88, 72\%), and active collection methods were used from 2015 onward (20/88, 23\%). In this review, the stages of PGHD use were classified as follows: (1) identification, monitoring, review, and analysis (88/88, 100\%); (2) feedback and reporting (32/88, 39\%); (3) motivation (30/88, 34\%); and (4) education and coaching (19/88, 22\%). Conclusions: This scoping review provides a comprehensive summary of the overall characteristics and use stages of PGHD in older adults with various types and stages of cancer. Future research should emphasize the use of PGHD, which interacts with patients to provide patient-centered care through patient engagement. By enhancing symptom monitoring, enabling timely interventions, and promoting patient involvement, PGHD have the potential to improve the well-being of older adults with cancer, contributing to better health management and quality of life. Therefore, our findings may provide valuable insights into PGHD that health care providers and researchers can use for geriatric cancer care. Trial Registration: Open Science Framework Registry OSF.IO/FZRD5; https://doi.org/10.17605/OSF.IO/FZRD5 ",
doi="10.2196/57379",
url="https://www.jmir.org/2025/1/e57379"
}


@Article{info:doi/10.2196/67929,
author="Sugawara, Yuka
and Hirakawa, Yosuke
and Iwagami, Masao
and Inokuchi, Ryota
and Wakimizu, Rie
and Nangaku, Masaomi",
title="Metrics for Evaluating Telemedicine in Randomized Controlled Trials: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="31",
volume="27",
pages="e67929",
keywords="patient experience",
keywords="patient-reported outcome",
keywords="quality of life",
keywords="quality-adjusted life year",
keywords="telehealth",
keywords="eHealth",
keywords="mobile phone",
keywords="metrics",
keywords="telemedicine",
keywords="systematic review",
keywords="scoping review",
keywords="review",
keywords="telecommunications",
keywords="database",
keywords="health care",
keywords="patient-centeredness",
keywords="patient satisfaction",
keywords="patient outcome",
keywords="clinical parameter",
keywords="cost-effectiveness",
keywords="evaluation metrics",
keywords="mHealth",
keywords="mobile health",
abstract="Background: Telemedicine involves medical, diagnostic, and treatment-related services using telecommunication technology. Not only does telemedicine contribute to improved patient quality of life and satisfaction by reducing travel time and allowing patients to be seen in their usual environment, but it also has the potential to improve disease management by making it easier for patients to see a doctor. Recently, owing to IT developments, research on telemedicine has been increasing; however, its usefulness and limitations in randomized controlled trials remain unclear because of the multifaceted effects of telemedicine. Furthermore, the specific metrics that can be used as cross-disciplinary indicators when comparing telemedicine and face-to-face care also remain undefined. Objective: This review aimed to provide an overview of the general and cross-disciplinarity metrics used to compare telemedicine with in-person care in randomized controlled trials. In addition, we identified previously unevaluated indicators and suggested those that should be prioritized in future clinical trials. Methods: MEDLINE and Embase databases were searched for publications that met the inclusion criteria according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews). Original, English-language articles on randomized controlled trials comparing some forms of telemedicine with face-to-face care from January 2019 to March 2024 were included, and the basic information and general metrics used in these studies were summarized. Results: Of the 2275 articles initially identified, 79 were included in the final analysis. The commonly used metrics that can be used across medical specialties were divided into the following 3 categories: (1) patient-centeredness (67/79, 85\%), including patient satisfaction, workload, and quality of life; (2) patient outcomes (57/79, 72\%), including general clinical parameters such as death, admission, and adverse events; and (3) cost-effectiveness (40/79, 51\%), including cost assessment and quality-adjusted life year. Notably, only 25 (32\%) of 79 studies evaluated all the 3 categories. Other metrics, such as staff convenience, system usability, and environmental impact, were extracted as indicators in different directions from the three categories above, although few previous reports have evaluated them (staff convenience: 8/79, 10\%; system usability: 3/79, 4\%; and environmental impact: 2/79, 3\%). Conclusions: A significant variation was observed in the metrics used across previous studies. Notably, general indicators should be used to enhance the understandability of the results for people in other areas, even if disease-specific indicators are used. In addition, indicators should be established to include all three commonly used categories of measures to ensure a comprehensive evaluation: patient-centeredness, patient outcomes, and cost-effectiveness. Staff convenience, system usability, and environmental impact are important indicators that should be used in future trials. Moreover, standardization of the evaluation metrics is desired for future clinical trials and studies. Trial Registration: Open Science Forum Registries YH5S7; https://doi.org/10.17605/OSF.IO/YH5S7 ",
doi="10.2196/67929",
url="https://www.jmir.org/2025/1/e67929"
}


@Article{info:doi/10.2196/65446,
author="Lyon, R. Aaron
and Munson, A. Sean
and Pullmann, D. Michael
and Mosser, Brittany
and Aung, Tricia
and Fortney, John
and Dopp, Alex
and Osterhage, P. Katie
and Haile, G. Helen
and Bruzios, E. Kathryn
and Blanchard, E. Brittany
and Allred, Ryan
and Fuller, R. Macey
and Raue, J. Patrick
and Bennett, Ian
and Locke, Jill
and Bearss, Karen
and Walker, Denise
and Connors, Elizabeth
and Bruns, Eric
and Van Draanen, Jenna
and Darnell, Doyanne
and Are{\'a}n, A. Patricia",
title="Harnessing Human-Centered Design for Evidence-Based Psychosocial Interventions and Implementation Strategies in Community Settings: Protocol for Redesign to Improve Usability, Engagement, and Appropriateness",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="29",
volume="14",
pages="e65446",
keywords="implementation science",
keywords="human-centered design",
keywords="evidence-based psychosocial interventions",
keywords="mental health",
abstract="Background: Although substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice---particularly in more accessible, community settings---has been slow. Objective: This protocol outlines the renewal of the National Institute of Mental Health--funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center's second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center. Local redesign teams work collaboratively and share decision-making to carry out DDBT. Methods: All 4 core studies received institutional review board approval by June 2024, and each pilot project will pursue institutional review board approval when awarded. We will provide research infrastructure to 1 large effectiveness study and 3 exploratory pilot studies as part of the center grant. At least 4 additional small pilot studies will be solicited and funded by the center. All studies will explore the use of DDBT for clinical interventions and implementation strategies to identify modification targets to improve usability, engagement, and appropriateness in accessible nonspecialty settings (Discover phase); develop redesign solutions with local teams to address modification targets (Design and Build phase); and determine if redesign improves usability, engagement, and appropriateness (Test phase), as well as implementation outcomes. Center staff will collaborate with local redesign teams to develop and test clinical interventions and implementation strategies for community settings. We will collaborate with teams to use methods and centerwide measures that facilitate cross-project analysis of the effects of DDBT-driven redesign on outcomes of interest. Results: As of January 2025, three of the 4 core studies are underway. We will generate additional evidence on the robustness of DDBT and whether combining HCD and implementation science is an asset for improving clinical interventions and implementation strategies. Conclusions: During the first round of the center, we established that DDBT is a useful approach to systematically identify and address chronic challenges of implementing clinical interventions and implementation strategies. In this subsequent grant, we expect to increase evidence of DDBT's impact on clinical interventions and implementation strategies by expanding a list of common challenges that could benefit from modification, a list of exemplary solutions to address these challenges, and guidance on using the DDBT framework. These resources will contribute to broader discourse on how to enhance implementation of clinical interventions and implementation strategies that integrate HCD and implementation science. International Registered Report Identifier (IRRID): PRR1-10.2196/65446 ",
doi="10.2196/65446",
url="https://www.researchprotocols.org/2025/1/e65446"
}


@Article{info:doi/10.2196/58628,
author="Koh, Jodie
and Caron, Stacey
and Watters, N. Amber
and Vaidyanathan, Mahesh
and Melnick, David
and Santi, Alyssa
and Hudson, Kenneth
and Arguelles, Catherine
and Mathur, Priyanka
and Etemadi, Mozziyar",
title="Technological Adjuncts to Streamline Patient Recruitment, Informed Consent, and Data Management Processes in Clinical Research: Observational Study",
journal="JMIR Form Res",
year="2025",
month="Jan",
day="29",
volume="9",
pages="e58628",
keywords="digital health",
keywords="patient recruitment",
keywords="consent",
keywords="technological adjuncts",
keywords="data management",
keywords="clinical research processes",
keywords="automation",
keywords="digital platforms",
keywords="data warehouse",
keywords="patient data",
keywords="imaging data",
keywords="pregnancy",
keywords="clinical research methods",
abstract="Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies. Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration. Methods: Using one clinical research study as an example, we highlighted the use of technological adjuncts to automate and streamline research processes across various digital platforms, including a centralized database of electronic medical records (enterprise data warehouse [EDW]); a clinical research data management tool (REDCap [Research Electronic Data Capture]); and a locally managed, Health Insurance Portability and Accountability Act--compliant server. Eligible participants were identified through automated queries in the EDW, after which they received personalized email invitations with digital consent forms. After digital consent, patient data were transferred to a single Health Insurance Portability and Accountability Act--compliant server where each participant was assigned a unique QR code to facilitate data collection and integration. After the research study visit, data obtained were associated with existing electronic medical record data for each participant via a QR code system that collated participant consent, imaging data, and associated clinical data according to a unique examination ID. Results: Over a 19-month period, automated EDW queries identified 20,988 eligible patients, and 10,582 patients received personalized email invitations. In total, 1000 (9.45\%) patients signed consents to participate in the study. Of the consented patients, 549 unique patients completed 779 study visits; some patients consented to the study at more than 1 time period during their pregnancy. Conclusions: Technological adjuncts in clinical research decrease human labor while increasing participant reach and minimizing disruptions to clinic operations. Automating portions of the clinical research process benefits clinical research efforts by expanding and optimizing participant reach while reducing the limitations of labor and time in completing research studies. ",
doi="10.2196/58628",
url="https://formative.jmir.org/2025/1/e58628",
url="http://www.ncbi.nlm.nih.gov/pubmed/39879093"
}


@Article{info:doi/10.2196/63875,
author="Dorosan, Michael
and Chen, Ya-Lin
and Zhuang, Qingyuan
and Lam, Sean Shao Wei",
title="In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="16",
volume="14",
pages="e63875",
keywords="clinical decision support algorithms",
keywords="in silico evaluation",
keywords="clinical workflow simulation",
keywords="health care modeling",
keywords="digital twin",
keywords="quadruple aims",
keywords="clinical decision",
keywords="decision-making",
keywords="decision support",
keywords="workflow",
keywords="support system",
keywords="protocol",
keywords="scoping review",
keywords="algorithm-based",
keywords="screening",
keywords="thematic analysis",
keywords="descriptive analysis",
keywords="clinical decision-making",
abstract="Background: Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials. Objective: This paper presents a review protocol for preimplementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials. Methods: We propose a scoping review protocol that follows an enhanced Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to investigate the scope and research gaps in the in silico evaluation of algorithm-based CDS models---specifically CDS decision-making end points and objectives, evaluation metrics used, and simulation paradigms used to assess potential impacts. The databases searched are PubMed, Embase, CINAHL, PsycINFO, Cochrane, IEEEXplore, Web of Science, and arXiv. A 2-stage screening process identified pertinent articles. The information extracted from articles was iteratively refined. The review will use thematic, trend, and descriptive analyses to meet scoping aims. Results: We conducted an automated search of the databases above in May 2023, with most title and abstract screenings completed by November 2023 and full-text screening extended from December 2023 to May 2024. Concurrent charting and full-text analysis were carried out, with the final analysis and manuscript preparation set for completion in July 2024. Publication of the review results is targeted from July 2024 to February 2025. As of April 2024, a total of 21 articles have been selected following a 2-stage screening process; these will proceed to data extraction and analysis. Conclusions: We refined our data extraction strategy through a collaborative, multidisciplinary approach, planning to analyze results using thematic analyses to identify approaches to in silico evaluation. Anticipated findings aim to contribute to developing a unified in silico evaluation framework adaptable to various clinical workflows, detailing clinical decision-making characteristics, impact measures, and reusability of methods. The study's findings will be published and presented in forums combining artificial intelligence and machine learning, clinical decision-making, and health technology impact analysis. Ultimately, we aim to bridge the development-deployment gap through in silico evaluation-based potential impact assessments. International Registered Report Identifier (IRRID): DERR1-10.2196/63875 ",
doi="10.2196/63875",
url="https://www.researchprotocols.org/2025/1/e63875"
}


@Article{info:doi/10.2196/67378,
author="Liu, Chaofeng
and Liu, Yan
and Yi, Chunyan
and Xie, Tao
and Tian, Jingjun
and Deng, Peishen
and Liu, Changyu
and Shan, Yan
and Dong, Hangyu
and Xu, Yanhua",
title="Application of a 3D Fusion Model to Evaluate the Efficacy of Clear Aligner Therapy in Malocclusion Patients: Prospective Observational Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="15",
volume="27",
pages="e67378",
keywords="clear aligners",
keywords="CBCT",
keywords="intraoral scanning",
keywords="fusion model",
keywords="artificial intelligence",
keywords="efficacy evaluation",
keywords="orthodontic treatment",
abstract="Background: Investigating the safe range of orthodontic tooth movement is essential for maintaining oral and maxillofacial stability posttreatment. Although clear aligners rely on pretreatment digital models, their effect on periodontal hard tissues remains uncertain. By integrating cone beam computed tomography--derived cervical and root data with crown data from digital intraoral scans, a 3D fusion model may enhance precision and safety. Objective: This study aims to construct a 3D fusion model based on artificial intelligence software that matches cone beam computed tomography and intraoral scanning data using the Andrews' Six Element standard. The model will be used to assess the 3D effects of clear aligners on tooth movement, to provide a reference for the design of pretreatment target positions. Methods: Between May 2022 and May 2024, a total of 320 patients who completed clear aligner therapy at our institution were screened; 136 patients (aged 13-35 years, fully erupted permanent dentition and periodontal pocket depth <3 mm) met the criteria. Baseline (``simulation'') and posttreatment (``fusion'') models were compared. Outcomes included upper core discrepancy (UCD), upper incisors anteroposterior discrepancy (UAP), lower Spee curve deep discrepancy (LSD), upper anterior teeth width discrepancy (UAW), upper canine width discrepancy (UCW), upper molar width discrepancy (UMW), and total scores. Subanalyses examined sex, age stage (adolescent vs adult), and treatment method (extraction vs nonextraction). Results: The study was funded in May 2022, with data collection beginning the same month and continuing until May 2024. Of 320 initial participants, 136 met the inclusion criteria. Data analysis is ongoing, and final results are expected by late 2024. Among the 136 participants, 90 (66\%) were female, 46 (34\%) were male, 64 (47\%) were adolescents, 72 (53\%) were adults, 38 (28\%) underwent extraction, and 98 (72\%) did not. Total scores did not differ significantly by sex (mean difference 0.01, 95\% CI --0.13 to 0.15; P=.85), age stage (mean difference 0.03, 95\% CI --0.10 to 0.17; P=.60), or treatment method (mean difference 0.07, 95\% CI --0.22 to 0.07; P=.32). No significant differences were found in UCD (mean difference 0.001, 95\% CI --0.02 to 0.01; P=.90) or UAP (mean difference 0.01, 95\% CI --0.03 to 0.00; P=.06) by treatment method. However, adolescents exhibited smaller differences in UCD, UAW, UCW, and UMW yet larger differences in UAP and LSD (df=134; P<.001). Extraction cases showed smaller LSD, UAW, and UCW but larger UMW differences compared with nonextraction (df=134; P<.001). Conclusions: The 3D fusion model provides a reliable clinical reference for target position design and treatment outcome evaluation in clear aligner systems. The construction and application of a 3D fusion model in clear aligner orthodontics represent a significant leap forward, offering substantial clinical benefits while establishing a new standard for precision, personalization, and evidence-based treatment planning in the field. Trial Registration: Chinese Clinical Trial Registry ChiCTR2400094304, https://www.chictr.org.cn/hvshowproject.html?id=266090\&v=1.0 ",
doi="10.2196/67378",
url="https://www.jmir.org/2025/1/e67378"
}


@Article{info:doi/10.2196/60189,
author="Klein, Dave
and Montgomery, Aisha
and Begale, Mark
and Sutherland, Scott
and Sawyer, Sherilyn
and McCauley, L. Jacob
and Husbands, Letheshia
and Joshi, Deepti
and Ashbeck, Alan
and Palmer, Marcy
and Jain, Praduman",
title="Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="15",
volume="27",
pages="e60189",
keywords="longitudinal studies",
keywords="cohort studies",
keywords="health disparities",
keywords="minority populations",
keywords="vulnerable populations",
keywords="precision medicine",
keywords="biomedical research",
keywords="decentralization",
keywords="digital health technology",
keywords="database management system",
abstract="Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. Objective: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). Methods: We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. Results: We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87\% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46\%), rural dwelling individuals (n=49,118, 8\%), those over the age of 65 years (n=190,333, 31\%), and individuals with low socioeconomic status (n=122,795, 20\%). Conclusions: We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research. ",
doi="10.2196/60189",
url="https://www.jmir.org/2025/1/e60189"
}


@Article{info:doi/10.2196/60413,
author="Zheng, Yi Wu
and Shvetcov, Artur
and Slade, Aimy
and Jenkins, Zoe
and Hoon, Leonard
and Whitton, Alexis
and Logothetis, Rena
and Ravindra, Smrithi
and Kurniawan, Stefanus
and Gupta, Sunil
and Huckvale, Kit
and Stech, Eileen
and Agarwal, Akash
and Funke Kupper, Joost
and Cameron, Stuart
and Rosenberg, Jodie
and Manoglou, Nicholas
and Senadeera, Manisha
and Venkatesh, Svetha
and Mouzakis, Kon
and Vasa, Rajesh
and Christensen, Helen
and Newby, M. Jill",
title="Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="14",
volume="27",
pages="e60413",
keywords="recruitment",
keywords="Facebook",
keywords="retention, COVID-19",
keywords="artificial intelligence",
abstract="Background: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment methods are more effective than traditional methods such as newspapers, media, or flyers is inconsistent. Here we present insights from our experience recruiting tertiary education students for a digital mental health artificial intelligence--driven adaptive trial---Vibe Up. Objective: We evaluated the effectiveness of recruitment via Facebook and Instagram compared to traditional methods for a treatment trial and compared different recruitment methods' retention rates. With recruitment coinciding with COVID-19 lockdowns across Australia, we also compared the cost-effectiveness of social media recruitment during and after lockdowns. Methods: Recruitment was completed for 2 pilot trials and 6 minitrials from June 2021 to May 2022. To recruit participants, paid social media advertising on Facebook and Instagram was used, alongside mailing lists of university networks and student organizations or services, media releases, announcements during classes and events, study posters or flyers on university campuses, and health professional networks. Recruitment data, including engagement metrics collected by Meta (Facebook and Instagram), advertising costs, and Qualtrics data on recruitment methods and survey completion rates, were analyzed using RStudio with R (version 3.6.3; R Foundation for Statistical Computing). Results: In total, 1314 eligible participants (aged 22.79, SD 4.71 years; 1079, 82.1\% female) were recruited to 2 pilot trials and 6 minitrials. The vast majority were recruited via Facebook and Instagram advertising (n=1203; 92\%). Pairwise comparisons revealed that the lead institution's website was more effective in recruiting eligible participants than Facebook (z=3.47; P=.003) and Instagram (z=4.23; P<.001). No differences were found between recruitment methods in retaining participants at baseline, at midpoint, and at study completion. Wilcoxon tests found significant differences between lockdown (pilot 1 and pilot 2) and postlockdown (minitrials 1-6) on costs incurred per link click (lockdown: median Aus \$0.35 [US \$0.22], IQR Aus \$0.27-\$0.47 [US \$0.17-\$0.29]; postlockdown: median Aus \$1.00 [US \$0.62], IQR Aus \$0.70-\$1.47 [US \$0.44-\$0.92]; W=9087; P<.001) and the amount spent per hour to reach the target sample size (lockdown: median Aus \$4.75 [US \$2.95], IQR Aus \$1.94-6.34 [US \$1.22-\$3.97]; postlockdown: median Aus \$13.29 [US \$8.26], IQR Aus \$4.70-25.31 [US \$2.95-\$15.87]; W=16044; P<.001). Conclusions: Social media advertising via Facebook and Instagram was the most successful strategy for recruiting distressed tertiary students into this artificial intelligence--driven adaptive trial, providing evidence for the use of this recruitment method for this type of trial in digital mental health research. No recruitment method stood out in terms of participant retention. Perhaps a reflection of the added distress experienced by young people, social media recruitment during the COVID-19 lockdown period was more cost-effective. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621001092886; https://tinyurl.com/39f2pdmd;  Australian New Zealand Clinical Trials Registry ACTRN12621001223820; https://tinyurl.com/bdhkvucv ",
doi="10.2196/60413",
url="https://www.jmir.org/2025/1/e60413"
}


@Article{info:doi/10.2196/60504,
author="Bikou, Georgia Alexia
and Deligianni, Elena
and Dermiki-Gkana, Foteini
and Liappas, Nikolaos
and Teri{\'u}s-Padr{\'o}n, Gabriel Jos{\'e}
and Beltr{\'a}n Jaunsar{\'a}s, Eugenia Maria
and Cabrera-Umpi{\'e}rrez, Fernanda Maria
and Kontogiorgis, Christos",
title="Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="18",
volume="26",
pages="e60504",
keywords="clinical research",
keywords="e-recruitment",
keywords="patient matching",
keywords="clinical trials",
keywords="digital platforms",
keywords="enrollment",
keywords="electronic consent",
abstract="Background: Pharmaceutical product development relies on thorough and costly clinical trials. Participant recruitment and monitoring can be challenging. The incorporation of cutting-edge technologies such as blockchain and artificial intelligence has revolutionized clinical research (particularly in the recruitment stage), enhanced secure data storage and analysis, and facilitated participant monitoring while protecting their personal information. Objective: This study aims to investigate the use of novel digital platforms and their features, such as e-recruitment, e-consent, and matching, aiming to optimize and expedite clinical research. Methods: A review with a systematic approach was conducted encompassing literature from January 2000 to October 2024. The MEDLINE, ScienceDirect, Scopus, and Google Scholar databases were examined thoroughly using a customized search string. Inclusion criteria focused on digital platforms involving clinical trial recruitment phases that were in English and had international presence, scientific validation, regulatory approval, and no geographic limitations. Literature reviews and unvalidated digital platforms were excluded. The selected studies underwent meticulous screening by the research team, ensuring a thorough analysis of novel digital platforms and their use and features for clinical trials. Results: A total of 24 digital platforms were identified that supported clinical trial recruitment phases. In general, most of them (n=22, 80\%) are headquartered and operating in the United States, providing a range of functionalities including electronic consent (n=14, 60\% of the platforms), participant matching, and monitoring of patients' health status. These supplementary features enhance the overall effectiveness of the platforms in facilitating the recruitment process for clinical trials. The analysis and digital platform findings refer to a specific time frame when the investigation took place, and a notable surge was observed in the adoption of these novel digital tools, particularly following the COVID-19 outbreak. Conclusions: This study underscores the vital role of the identified digital platforms in clinical trials, aiding in recruitment, enhancing patient engagement, accelerating procedures, and personalizing vital sign monitoring. Despite their impact, challenges in accessibility, compatibility, and transparency require careful consideration. Addressing these challenges is crucial for optimizing digital tool integration into clinical research, allowing researchers to harness the benefits while managing the associated risks effectively. ",
doi="10.2196/60504",
url="https://www.jmir.org/2024/1/e60504"
}


@Article{info:doi/10.2196/57750,
author="Maru, Shoko
and Matthias, D. Michael
and Kuwatsuru, Ryohei
and Simpson Jr, J. Ross",
title="Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="25",
volume="26",
pages="e57750",
keywords="artificial intelligence",
keywords="machine learning",
keywords="deep learning",
keywords="trends",
keywords="health care",
keywords="cross-sectional study",
keywords="health disparities",
keywords="data-source disparities",
keywords="publication bias",
keywords="registry",
keywords="ClinicalTrials.gov",
abstract="Background: The rapid growth of research in artificial intelligence (AI) and machine learning (ML) continues. However, it is unclear whether this growth reflects an increase in desirable study attributes or merely perpetuates the same issues previously raised in the literature. Objective: This study aims to evaluate temporal trends in AI/ML studies over time and identify variations that are not apparent from aggregated totals at a single point in time. Methods: We identified AI/ML studies registered on ClinicalTrials.gov with start dates between January 1, 2010, and December 31, 2023. Studies were included if AI/ML-specific terms appeared in the official title, detailed description, brief summary, intervention, primary outcome, or sponsors' keywords. Studies registered as systematic reviews and meta-analyses were excluded. We reported trends in AI/ML studies over time, along with study characteristics that were fast-growing and those that remained unchanged during 2010-2023. Results: Of 3106 AI/ML studies, only 7.6\% (n=235) were regulated by the US Food and Drug Administration. The most common study characteristics were randomized (56.2\%; 670/1193; interventional) and prospective (58.9\%; 1126/1913; observational) designs; a focus on diagnosis (28.2\%; 335/1190) and treatment (24.4\%; 290/1190); hospital/clinic (44.2\%; 1373/3106) or academic (28\%; 869/3106) sponsorship; and neoplasm (12.9\%; 420/3245), nervous system (12.2\%; 395/3245), cardiovascular (11.1\%; 356/3245) or pathological conditions (10\%; 325/3245; multiple counts per study possible). Enrollment data were skewed to the right: maximum 13,977,257; mean 16,962 (SD 288,155); median 255 (IQR 80-1000). The most common size category was 101-1000 (44.8\%; 1372/3061; excluding withdrawn or missing), but large studies (n>1000) represented 24.1\% (738/3061) of all studies: 29\% (551/1898) of observational studies and 16.1\% (187/1163) of trials. Study locations were predominantly in high-income countries (75.3\%; 2340/3106), followed by upper-middle-income (21.7\%; 675/3106), lower-middle-income (2.8\%; 88/3106), and low-income countries (0.1\%; 3/3106). The fastest-growing characteristics over time were high-income countries (location); Europe, Asia, and North America (location); diagnosis and treatment (primary purpose); hospital/clinic and academia (lead sponsor); randomized and prospective designs; and the 1-100 and 101-1000 size categories. Only 5.6\% (47/842) of completed studies had results available on ClinicalTrials.gov, and this pattern persisted. Over time, there was an increase in not only the number of newly initiated studies, but also the number of completed studies without posted results. Conclusions: Much of the rapid growth in AI/ML studies comes from high-income countries in high-resource settings, albeit with a modest increase in upper-middle-income countries (mostly China). Lower-middle-income or low-income countries remain poorly represented. The increase in randomized or prospective designs, along with 738 large studies (n>1000), mostly ongoing, may indicate that enough studies are shifting from an in silico evaluation stage toward a prospective comparative evaluation stage. However, the ongoing limited availability of basic results on ClinicalTrials.gov contrasts with this field's rapid advancements and the public registry's role in reducing publication and outcome reporting biases. ",
doi="10.2196/57750",
url="https://www.jmir.org/2024/1/e57750"
}


@Article{info:doi/10.2196/58578,
author="Chen, David
and Cao, Christian
and Kloosterman, Robert
and Parsa, Rod
and Raman, Srinivas",
title="Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="23",
volume="26",
pages="e58578",
keywords="artificial intelligence",
keywords="clinical trial",
keywords="completion",
keywords="AI",
keywords="cross-sectional study",
keywords="application",
keywords="intervention",
keywords="trial design",
keywords="logistic regression",
keywords="Europe",
keywords="clinical",
keywords="trials testing",
keywords="health care",
keywords="informatics",
keywords="health information",
abstract="Background: Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. Objective: This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. Methods: Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95\% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. Results: We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95\% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95\% CI 1.00-1.00; P=.02). Conclusions: Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure. ",
doi="10.2196/58578",
url="https://www.jmir.org/2024/1/e58578",
url="http://www.ncbi.nlm.nih.gov/pubmed/39312296"
}


@Article{info:doi/10.2196/58432,
author="French, Blandine
and Babbage, Camilla
and Bird, Katherine
and Marsh, Lauren
and Pelton, Mirabel
and Patel, Shireen
and Cassidy, Sarah
and Rennick-Egglestone, Stefan",
title="Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge",
journal="JMIR Ment Health",
year="2024",
month="Sep",
day="16",
volume="11",
pages="e58432",
keywords="web-based research",
keywords="web-based studies",
keywords="qualitative studies",
keywords="surveys",
keywords="mental health",
keywords="data integrity, misrepresentation",
doi="10.2196/58432",
url="https://mental.jmir.org/2024/1/e58432"
}


@Article{info:doi/10.2196/58939,
author="Hall, L. Charlotte
and G{\'o}mez Bergin, D. Aislinn
and Rennick-Egglestone, Stefan",
title="Research Into Digital Health Intervention for Mental Health: 25-Year Retrospective on the Ethical and Legal Challenges",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="9",
volume="26",
pages="e58939",
keywords="digital mental health intervention",
keywords="research ethics",
keywords="compliance",
keywords="regulation",
keywords="digital health",
keywords="mobile health",
keywords="mhealth",
keywords="intervention",
keywords="interventions",
keywords="mental health",
keywords="retrospective",
keywords="ethical",
keywords="legal",
keywords="challenge",
keywords="challenges",
doi="10.2196/58939",
url="https://www.jmir.org/2024/1/e58939"
}


@Article{info:doi/10.2196/52120,
author="Victoria-Castro, Maria Angela
and Arora, Tanima
and Simonov, Michael
and Biswas, Aditya
and Alausa, Jameel
and Subair, Labeebah
and Gerber, Brett
and Nguyen, Andrew
and Hsiao, Allen
and Hintz, Richard
and Yamamoto, Yu
and Soufer, Robert
and Desir, Gary
and Wilson, Perry Francis
and Villanueva, Merceditas",
title="Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study",
journal="JMIR Form Res",
year="2024",
month="Sep",
day="3",
volume="8",
pages="e52120",
keywords="COVID-19",
keywords="database",
keywords="data access",
keywords="interdepartmental communication",
keywords="collaborative scholarship",
keywords="clinical data",
keywords="repository",
keywords="researchers",
keywords="large-scale database",
keywords="innovation",
abstract="Background: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. Objective: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. Methods: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. Results: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. Conclusions: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19. ",
doi="10.2196/52120",
url="https://formative.jmir.org/2024/1/e52120"
}


@Article{info:doi/10.2196/47882,
author="Copland, R. Rachel
and Hanke, Sten
and Rogers, Amy
and Mpaltadoros, Lampros
and Lazarou, Ioulietta
and Zeltsi, Alexandra
and Nikolopoulos, Spiros
and MacDonald, M. Thomas
and Mackenzie, S. Isla",
title="The Digital Platform and Its Emerging Role in Decentralized Clinical Trials",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="3",
volume="26",
pages="e47882",
keywords="decentralized clinical trials",
keywords="digital platform",
keywords="digitalization",
keywords="clinical trials",
keywords="mobile phone",
doi="10.2196/47882",
url="https://www.jmir.org/2024/1/e47882"
}


@Article{info:doi/10.2196/51297,
author="Gierend, Kerstin
and Kr{\"u}ger, Frank
and Genehr, Sascha
and Hartmann, Francisca
and Siegel, Fabian
and Waltemath, Dagmar
and Ganslandt, Thomas
and Zeleke, Alamirrew Atinkut",
title="Provenance Information for Biomedical Data and Workflows: Scoping Review",
journal="J Med Internet Res",
year="2024",
month="Aug",
day="23",
volume="26",
pages="e51297",
keywords="provenance",
keywords="biomedical research",
keywords="data management",
keywords="scoping review",
keywords="health care data",
keywords="software life cycle",
abstract="Background: The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. Objective: This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. Methods: This scoping review followed the methodological framework developed by Arksey and O'Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. Results: The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6\%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. Conclusions: As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. International Registered Report Identifier (IRRID): RR2-10.2196/31750 ",
doi="10.2196/51297",
url="https://www.jmir.org/2024/1/e51297"
}


@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/10.2196/52180,
author="Wiertz, Svenja
and Boldt, Joachim",
title="Ethical, Legal, and Practical Concerns Surrounding the Implemention of New Forms of Consent for Health Data Research: Qualitative Interview Study",
journal="J Med Internet Res",
year="2024",
month="Aug",
day="7",
volume="26",
pages="e52180",
keywords="health data",
keywords="health research",
keywords="informed consent",
keywords="broad consent",
keywords="tiered consent",
keywords="consent management",
keywords="digital infrastructure",
keywords="data safety",
keywords="GDPR",
abstract="Background: In Europe, within the scope of the General Data Protection Regulation, more and more digital infrastructures are created to allow for large-scale access to patients' health data and their use for research. When the research is performed on the basis of patient consent, traditional study-specific consent appears too cumbersome for many researchers. Alternative models of consent are currently being discussed and introduced in different contexts. Objective: This study explores stakeholder perspectives on ethical, legal, and practical concerns regarding models of consent for health data research at German university medical centers. Methods: Semistructured focus group interviews were conducted with medical researchers at German university medical centers, health IT specialists, data protection officers, and patient representatives. The interviews were analyzed using a software-supported structuring qualitative content analysis. Results: Stakeholders regarded broad consent to be only marginally less laborious to implement and manage than tiered consent. Patient representatives favored specific consent, with tiered consent as a possible alternative. All stakeholders lamented that information material was difficult to understand. Oral information and videos were mentioned as a means of improvement. Patient representatives doubted that researchers had a sufficient degree of data security expertise to act as sole information providers. They were afraid of undue pressure if obtaining health data research consent were part of medical appointments. IT specialists and other stakeholders regarded the withdrawal of consent to be a major challenge and called for digital consent management solutions. On the one hand, the transfer of health data to non-European countries and for-profit organizations is seen as a necessity for research. On the other hand, there are data security concerns with regard to these actors. Research without consent is legally possible under certain conditions but deemed problematic by all stakeholder groups, albeit for differing reasons and to different degrees. Conclusions: More efforts should be made to determine which options of choice should be included in health data research consent. Digital tools could improve patient information and facilitate consent management. A unified and strict regulation for research without consent is required at the national and European Union level. Obtaining consent for health data research should be independent of medical appointments, and additional personnel should be trained in data security to provide information on health data research. ",
doi="10.2196/52180",
url="https://www.jmir.org/2024/1/e52180"
}


@Article{info:doi/10.2196/56237,
author="Amadi, David
and Kiwuwa-Muyingo, Sylvia
and Bhattacharjee, Tathagata
and Taylor, Amelia
and Kiragga, Agnes
and Ochola, Michael
and Kanjala, Chifundo
and Gregory, Arofan
and Tomlin, Keith
and Todd, Jim
and Greenfield, Jay",
title="Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study",
journal="Online J Public Health Inform",
year="2024",
month="Aug",
day="1",
volume="16",
pages="e56237",
keywords="FAIR data principles",
keywords="metadata",
keywords="machine-readable metadata",
keywords="DDI",
keywords="Data Documentation Initiative",
keywords="standardization",
keywords="JSON-LD",
keywords="JavaScript Object Notation for Linked Data",
keywords="OMOP CDM",
keywords="Observational Medical Outcomes Partnership Common Data Model",
keywords="data science",
keywords="data models",
abstract="Background: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. Objective: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. Methods: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. Results: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. Conclusions: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide. ",
doi="10.2196/56237",
url="https://ojphi.jmir.org/2024/1/e56237",
url="http://www.ncbi.nlm.nih.gov/pubmed/39088253"
}


@Article{info:doi/10.2196/54867,
author="Zondag, M. Anna G.
and Hollestelle, J. Marieke
and van der Graaf, Rieke
and Nathoe, M. Hendrik
and van Solinge, W. Wouter
and Bots, L. Michiel
and Vernooij, M. Robin W.
and Haitjema, Saskia
and ",
title="Comparison of the Response to an Electronic Versus a Traditional Informed Consent Procedure in Terms of Clinical Patient Characteristics: Observational Study",
journal="J Med Internet Res",
year="2024",
month="Jul",
day="11",
volume="26",
pages="e54867",
keywords="informed consent",
keywords="learning health care system",
keywords="e-consent",
keywords="cardiovascular risk management",
keywords="digital health",
keywords="research ethics",
abstract="Background: Electronic informed consent (eIC) is increasingly used in clinical research due to several benefits including increased enrollment and improved efficiency. Within a learning health care system, a pilot was conducted with an eIC for linking data from electronic health records with national registries, general practitioners, and other hospitals. Objective: We evaluated the eIC pilot by comparing the response to the eIC with the former traditional paper-based informed consent (IC). We assessed whether the use of eIC resulted in a different study population by comparing the clinical patient characteristics between the response categories of the eIC and former face-to-face IC procedure. Methods: All patients with increased cardiovascular risk visiting the University Medical Center Utrecht, the Netherlands, were eligible for the learning health care system. From November 2021 to August 2022, an eIC was piloted at the cardiology outpatient clinic. Prior to the pilot, a traditional face-to-face paper-based IC approach was used. Responses (ie, consent, no consent, or nonresponse) were assessed and compared between the eIC and face-to-face IC cohorts. Clinical characteristics of consenting and nonresponding patients were compared between and within the eIC and the face-to-face cohorts using multivariable regression analyses. Results: A total of 2254 patients were included in the face-to-face IC cohort and 885 patients in the eIC cohort. Full consent was more often obtained in the eIC than in the face-to-face cohort (415/885, 46.9\% vs 876/2254, 38.9\%, respectively). Apart from lower mean hemoglobin in the full consent group of the eIC cohort (8.5 vs 8.8; P=.0021), the characteristics of the full consenting patients did not differ between the eIC and face-to-face IC cohorts. In the eIC cohort, only age differed between the full consent and the nonresponse group (median 60 vs 56; P=.0002, respectively), whereas in the face-to-face IC cohort, the full consent group seemed healthier (ie, higher hemoglobin, lower glycated hemoglobin [HbA1c], lower C-reactive protein levels) than the nonresponse group. Conclusions: More patients provided full consent using an eIC. In addition, the study population remained broadly similar. The face-to-face IC approach seemed to result in a healthier study population (ie, full consenting patients) than the patients without IC, while in the eIC cohort, the characteristics between consent groups were comparable. Thus, an eIC may lead to a better representation of the target population, increasing the generalizability of results. ",
doi="10.2196/54867",
url="https://www.jmir.org/2024/1/e54867",
url="http://www.ncbi.nlm.nih.gov/pubmed/38990640"
}


@Article{info:doi/10.2196/52934,
author="Yuan, Yannan
and Mei, Yun
and Zhao, Shuhua
and Dai, Shenglong
and Liu, Xiaohong
and Sun, Xiaojing
and Fu, Zhiying
and Zhou, Liheng
and Ai, Jie
and Ma, Liheng
and Jiang, Min",
title="Data Flow Construction and Quality Evaluation of Electronic Source Data in Clinical Trials: Pilot Study Based on Hospital Electronic Medical Records in China",
journal="JMIR Med Inform",
year="2024",
month="Jun",
day="27",
volume="12",
pages="e52934",
keywords="clinical trials",
keywords="electronic source data",
keywords="EHRs",
keywords="electronic data capture systems",
keywords="data quality",
keywords="electronic health records",
abstract="Background: The traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital's electronic medical record. Using electronic source data opens a new path to extract patients' data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs. Objective: This study aims to explore how to extract clinical trial--related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors' environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow. Methods: A prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor's environment. Data validation was performed based on availability, completeness, and accuracy. Results: In a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor's environment with 100\% transcriptional accuracy, but the availability and completeness of the data could be improved. Conclusions: Data availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future. ",
doi="10.2196/52934",
url="https://medinform.jmir.org/2024/1/e52934"
}


@Article{info:doi/10.2196/55548,
author="Straand, J. Ingjerd
and Baxter, A. Kimberley
and F{\o}lstad, Asbj{\o}rn",
title="Remote Inclusion of Vulnerable Users in mHealth Intervention Design: Retrospective Case Analysis",
journal="JMIR Mhealth Uhealth",
year="2024",
month="Jun",
day="14",
volume="12",
pages="e55548",
keywords="user testing",
keywords="user participation in research",
keywords="COVID-19",
keywords="remote testing",
keywords="intervention design",
keywords="mobile phone",
abstract="Background: Mobile health (mHealth) interventions that promote healthy behaviors or mindsets are a promising avenue to reach vulnerable or at-risk groups. In designing such mHealth interventions, authentic representation of intended participants is essential. The COVID-19 pandemic served as a catalyst for innovation in remote user-centered research methods. The capability of such research methods to effectively engage with vulnerable participants requires inquiry into practice to determine the suitability and appropriateness of these methods. Objective: In this study, we aimed to explore opportunities and considerations that emerged from involving vulnerable user groups remotely when designing mHealth interventions. Implications and recommendations are presented for researchers and practitioners conducting remote user-centered research with vulnerable populations. Methods: Remote user-centered research practices from 2 projects involving vulnerable populations in Norway and Australia were examined retrospectively using visual mapping and a reflection-on-action approach. The projects engaged low-income and unemployed groups during the COVID-19 pandemic in user-based evaluation and testing of interactive, web-based mHealth interventions. Results: Opportunities and considerations were identified as (1) reduced barriers to research inclusion; (2) digital literacy transition; (3) contextualized insights: a window into people's lives; (4) seamless enactment of roles; and (5) increased flexibility for researchers and participants. Conclusions: Our findings support the capability and suitability of remote user methods to engage with users from vulnerable groups. Remote methods facilitate recruitment, ease the burden of research participation, level out power imbalances, and provide a rich and relevant environment for user-centered evaluation of mHealth interventions. There is a potential for a much more agile research practice. Future research should consider the privacy impacts of increased access to participants' environment via webcams and screen share and how technology mediates participants' action in terms of privacy. The development of support procedures and tools for remote testing of mHealth apps with user participants will be crucial to capitalize on efficiency gains and better protect participants' privacy. ",
doi="10.2196/55548",
url="https://mhealth.jmir.org/2024/1/e55548",
url="http://www.ncbi.nlm.nih.gov/pubmed/38875700"
}


@Article{info:doi/10.2196/52281,
author="Reed, D. Nicole
and Bull, Sheana
and Shrestha, Umit
and Sarche, Michelle
and Kaufman, E. Carol",
title="Combating Fraudulent Participation in Urban American Indian and Alaska Native Virtual Health Research: Protocol for Increasing Data Integrity in Online Research (PRIOR)",
journal="JMIR Res Protoc",
year="2024",
month="Jun",
day="13",
volume="13",
pages="e52281",
keywords="fraudulent survey participation",
keywords="online survey research",
keywords="American Indian and Alaska Native",
keywords="data integrity",
keywords="health research",
keywords="research trust",
keywords="online survey",
keywords="case study",
keywords="randomized control trial",
keywords="RCT",
keywords="social media",
keywords="recruitment",
keywords="young women",
keywords="women",
keywords="American Indian",
keywords="Native Americans",
keywords="Native American",
keywords="fraudulent",
keywords="data privacy",
abstract="Background: While the advantages of using the internet and social media for research recruitment are well documented, the evolving online environment also enhances motivations for misrepresentation to receive incentives or to ``troll'' research studies. Such fraudulent assaults can compromise data integrity, with substantial losses in project time; money; and especially for vulnerable populations, research trust. With the rapid advent of new technology and ever-evolving social media platforms, it has become easier for misrepresentation to occur within online data collection. This perpetuation can occur by bots or individuals with malintent, but careful planning can help aid in filtering out fraudulent data. Objective: Using an example with urban American Indian and Alaska Native young women, this paper aims to describe PRIOR (Protocol for Increasing Data Integrity in Online Research), which is a 2-step integration protocol for combating fraudulent participation in online survey research. Methods: From February 2019 to August 2020, we recruited participants for formative research preparatory to an online randomized control trial of a preconceptual health program. First, we described our initial protocol for preventing fraudulent participation, which proved to be unsuccessful. Then, we described modifications we made in May 2020 to improve the protocol performance and the creation of PRIOR. Changes included transferring data collection platforms, collecting embedded geospatial variables, enabling timing features within the screening survey, creating URL links for each method or platform of data collection, and manually confirming potentially eligible participants' identifying information. Results: Before the implementation of PRIOR, the project experienced substantial fraudulent attempts at study enrollment, with less than 1\% (n=6) of 1300 screened participants being identified as truly eligible. With the modified protocol, of the 461 individuals who completed a screening survey, 381 did not meet the eligibility criteria assessed on the survey. Of the 80 that did, 25 (31\%) were identified as ineligible via PRIOR. A total of 55 (69\%) were identified as eligible and verified in the protocol and were enrolled in the formative study. Conclusions: Fraudulent surveys compromise study integrity, validity of the data, and trust among participant populations. They also deplete scarce research resources including respondent compensation and personnel time. Our approach of PRIOR to prevent online misrepresentation in data was successful. This paper reviews key elements regarding fraudulent data participation in online research and demonstrates why enhanced protocols to prevent fraudulent data collection are crucial for building trust with vulnerable populations. Trial Registration: ClinicalTrials.gov NCT04376346; https://www.clinicaltrials.gov/study/NCT04376346 International Registered Report Identifier (IRRID): DERR1-10.2196/52281 ",
doi="10.2196/52281",
url="https://www.researchprotocols.org/2024/1/e52281",
url="http://www.ncbi.nlm.nih.gov/pubmed/38869930"
}


@Article{info:doi/10.2196/51530,
author="Kumarasamy, Vithusa
and Goodfellow, Nicole
and Ferron, Mae Era
and Wright, L. Amy",
title="Evaluating the Problem of Fraudulent Participants in Health Care Research: Multimethod Pilot Study",
journal="JMIR Form Res",
year="2024",
month="Jun",
day="4",
volume="8",
pages="e51530",
keywords="fraudulent participants",
keywords="threats to data integrity",
keywords="online recruitment",
keywords="multimethod study",
keywords="health care research",
keywords="bots",
keywords="social media",
abstract="Background: The shift toward online recruitment methods, accelerated by the COVID-19 pandemic, has brought to the forefront the growing concern of encountering fraudulent participants in health care research. The increasing prevalence of this issue poses a serious threat to the reliability and integrity of research data and subsequent findings. Objective: This study aims to explore the experiences of health care researchers (HCRs) who have encountered fraudulent participants while using online recruitment methods and platforms. The primary objective was to gain insights into how researchers detect and mitigate fraudulent behavior in their work and provide prevention recommendations. Methods: A multimethod sequential design was used for this pilot study, comprising a quantitative arm involving a web-based survey followed by a qualitative arm featuring semistructured interviews. The qualitative description approach framed the qualitative arm of the study. Sample sizes for the quantitative and qualitative arms were based on pragmatic considerations that in part stemmed from encountering fraudulent participants in a concurrent study. Content analysis was used to analyze open-ended survey questions and interview data. Results: A total of 37 HCRs participated, with 35\% (13/37) of them engaging in qualitative interviews. Online platforms such as Facebook, email, Twitter (subsequently rebranded X), and newsletters were the most used methods for recruitment. A total of 84\% (31/37) of participants indicated that fraudulent participation occurred in studies that mentioned incentives in their recruitment communications, with 71\% (26/37) of HCRs offering physical or electronic gift cards as incentives. Researchers identified several indicators of suspicious behavior, including email surges, discrepancies in contact or personal information, geographical inconsistencies, and suspicious responses to survey questions. HCRs emphasized the need for a comprehensive screening protocol that extends beyond eligibility checks and is seamlessly integrated into the study protocol, grant applications, and research ethics board submissions. Conclusions: This study sheds light on the intricate and pervasive problem of fraudulent participation in health care research using online recruitment methods. The findings underscore the importance of vigilance and proactivity among HCRs in identifying, preventing, and addressing fraudulent behavior. To effectively tackle this challenge, researchers are encouraged to develop a comprehensive prevention strategy and establish a community of practice, facilitating real-time access to solutions and support and the promotion of ethical research practices. This collaborative approach will enable researchers to effectively address the issue of fraudulent participation, ensuring the conduct of high-quality and ethically sound research in the digital age. ",
doi="10.2196/51530",
url="https://formative.jmir.org/2024/1/e51530",
url="http://www.ncbi.nlm.nih.gov/pubmed/38833292"
}


@Article{info:doi/10.2196/52572,
author="Jayasinghe, Thisakya Randi
and Ahern, Susannah
and Maharaj, D. Ashika
and Romero, Lorena
and Ruseckaite, Rasa",
title="Identifying Existing Guidelines, Frameworks, Checklists, and Recommendations for Implementing Patient-Reported Outcome Measures: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2024",
month="May",
day="21",
volume="13",
pages="e52572",
keywords="patient-reported outcome measures",
keywords="patient-reported outcomes",
keywords="quality of life",
keywords="clinical quality registry",
keywords="guidelines",
keywords="frameworks",
keywords="recommendations",
keywords="scoping review",
keywords="patient perspectives",
keywords="patient perspective",
keywords="patient-reported outcome",
keywords="patient-reported",
keywords="clinical setting",
keywords="clinical registry",
keywords="registry",
keywords="systematic review",
abstract="Background: Implementing patient-reported outcome measures (PROMs) to measure and evaluate health outcomes is increasing worldwide. Along with this emerging trend, it is important to identify which guidelines, frameworks, checklists, and recommendations exist, and if and how they have been used in implementing PROMs, especially in clinical quality registries (CQRs). Objective: This review aims to identify existing publications, as well as publications that discuss the application of actual guidelines, frameworks, checklists, and recommendations on PROMs' implementation for various purposes such as clinical trials, clinical practice, and CQRs. In addition, the identified publications will be used to guide the development of a new guideline for PROMs' implementation in CQRs, which is the aim of the broader project. Methods: A literature search of the databases MEDLINE, Embase, CINAHL, PsycINFO, and Cochrane Central Register of Controlled Trials will be conducted since the inception of the databases, in addition to using Google Scholar and gray literature to identify literature for the scoping review. Predefined inclusion and exclusion criteria will be used for all phases of screening. Existing publications of guidelines, frameworks, checklists, recommendations, and publications discussing the application of those methodologies for implementing PROMs in clinical trials, clinical practice, and CQRs will be included in the final review. Data relating to bibliographic information, aim, the purpose of PROMs use (clinical trial, practice, or registries), name of guideline, framework, checklist and recommendations, the rationale for development, and their purpose and implications will be extracted. Additionally, for publications of actual methodologies, aspects or domains of PROMs' implementation will be extracted. A narrative synthesis of included publications will be conducted. Results: The electronic database searches were completed in March 2024. Title and abstract screening, full-text screening, and data extraction will be completed in May 2024. The review is expected to be completed by the end of August 2024. Conclusions: The findings of this scoping review will provide evidence on any existing methodologies and tools for PROMs' implementation in clinical trials, clinical practice, and CQRs. It is anticipated that the publications will help us guide the development of a new guideline for PROMs' implementation in CQRs. Trial Registration: PROSPERO CRD42022366085; https://tinyurl.com/bdesk98x International Registered Report Identifier (IRRID): DERR1-10.2196/52572 ",
doi="10.2196/52572",
url="https://www.researchprotocols.org/2024/1/e52572",
url="http://www.ncbi.nlm.nih.gov/pubmed/38771621"
}


@Article{info:doi/10.2196/45719,
author="Jia, Yan
and Li, Qi
and Zhang, Xiaowen
and Yan, Yi
and Yan, Shiyan
and Li, Shunping
and Li, Wei
and Wu, Xiaowen
and Rong, Hongguo
and Liu, Jianping",
title="Application of Patient-Reported Outcome Measurements in Adult Tumor Clinical Trials in China: Cross-Sectional Study",
journal="J Med Internet Res",
year="2024",
month="May",
day="8",
volume="26",
pages="e45719",
keywords="patient-reported outcomes",
keywords="tumor",
keywords="cross-sectional study",
keywords="quality of life",
keywords="outcome study",
abstract="Background: International health policies and researchers have emphasized the value of evaluating patient-reported outcomes (PROs) in clinical studies. However, the characteristics of PROs in adult tumor clinical trials in China remain insufficiently elucidated. Objective: This study aims to assess the application and characteristics of PRO instruments as primary or secondary outcomes in adult randomized clinical trials related to tumors in China. Methods: This cross-sectional study identified tumor-focused randomized clinical trials conducted in China between January 1, 2010, and June 30, 2022. The ClinicalTrials.gov database and the Chinese Clinical Trial Registry were selected as the databases. Trials were classified into four groups based on the use of PRO instruments: (1) trials listing PRO instruments as primary outcomes, (2) trials listing PRO instruments as secondary outcomes, (3) trials listing PRO instruments as coprimary outcomes, and (4) trials without any mention of PRO instruments. Pertinent data, including study phase, settings, geographic regions, centers, participant demographics (age and sex), funding sources, intervention types, target diseases, and the names of PRO instruments, were extracted from these trials. The target diseases involved in the trials were grouped according to the American Joint Committee on Cancer Staging Manual, 8th Edition. Results: Among the 6445 trials examined, 2390 (37.08\%) incorporated PRO instruments as part of their outcomes. Within this subset, 26.82\% (641/2390) listed PRO instruments as primary outcomes, 52.72\% (1260/2390) as secondary outcomes, and 20.46\% (489/2390) as coprimary outcomes. Among the 2,155,306 participants included in these trials, PRO instruments were used to collect data from 613,648 (28.47\%) patients as primary or secondary outcomes and from 74,287 (3.45\%) patients as coprimary outcomes. The most common conditions explicitly using specified PRO instruments included thorax tumors (217/1280, 16.95\%), breast tumors (176/1280, 13.75\%), and lower gastrointestinal tract tumors (173/1280, 13.52\%). Frequently used PRO instruments included the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire--30, the visual analog scale, the numeric rating scale, the Traditional Chinese Medicine Symptom Scale, and the Pittsburgh Sleep Quality Index. Conclusions: Over recent years, the incorporation of PROs has demonstrated an upward trajectory in adult randomized clinical trials on tumors in China. Nonetheless, the infrequent measurement of the patient's voice remains noteworthy. Disease-specific PRO instruments should be more effectively incorporated into various tumor disease categories in clinical trials, and there is room for improvement in the inclusion of PRO instruments as clinical trial end points. ",
doi="10.2196/45719",
url="https://www.jmir.org/2024/1/e45719",
url="http://www.ncbi.nlm.nih.gov/pubmed/38718388"
}


@Article{info:doi/10.2196/46420,
author="Green, Shaw Sara
and Lee, Sung-Jae
and Chahin, Samantha
and Pooler-Burgess, Meardith
and Green-Jones, Monique
and Gurung, Sitaji
and Outlaw, Y. Angulique
and Naar, Sylvie",
title="Regulatory Issues in Electronic Health Records for Adolescent HIV Research: Strategies and Lessons Learned",
journal="JMIR Form Res",
year="2024",
month="May",
day="2",
volume="8",
pages="e46420",
keywords="electronic health record",
keywords="HIV",
keywords="pragmatic trial",
keywords="regulatory",
keywords="EHR",
keywords="pre-exposure prophylaxis",
keywords="retention",
keywords="attrition",
keywords="dropout",
keywords="legal",
keywords="regulation",
keywords="adherence",
keywords="ethic",
keywords="review board",
keywords="implementation",
keywords="data use",
keywords="privacy",
abstract="Background: Electronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance. Objective: As part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines. Methods: Eight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements. Results: Due to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites. Conclusions: The process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes. ",
doi="10.2196/46420",
url="https://formative.jmir.org/2024/1/e46420",
url="http://www.ncbi.nlm.nih.gov/pubmed/38696775"
}


@Article{info:doi/10.2196/54406,
author="Goel, Akash
and Kapoor, Bhavya
and Chan, Hillary
and Ladha, Karim
and Katz, Joel
and Clarke, Hance
and Pazmino-Canizares, Janneth
and Thomas, Zaaria
and Philip, Kaylyssa
and Mattina, Gabriella
and Ritvo, Paul",
title="Psychotherapy for Ketamine's Enhanced Durability in Chronic Neuropathic Pain: Protocol for a Pilot Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2024",
month="Apr",
day="17",
volume="13",
pages="e54406",
keywords="3-arm parallel group",
keywords="cognitive behavior therapy",
keywords="ketamine hydrochloride",
keywords="pain intensity",
keywords="pain interference",
keywords="psychotherapy",
keywords="randomized controlled trial",
abstract="Background: Chronic pain affects approximately 8 million Canadians ({\textasciitilde}20\%), impacting their physical and mental health while burdening the health care system with costs of upwards of US \$60 billion a year. Indeed, patients are often trialed on numerous medications over several years without reductions to their symptoms. Therefore, there is an urgent need to identify new therapies for chronic pain to improve patients' quality of life, increase the availability of treatment options, and reduce the burden on the health care system. Objective: The primary objective of this study is to examine the feasibility of a parallel 3-arm pilot randomized controlled trial whereby patients are randomized to either intravenous ketamine alone, cognitive behavioral therapy (CBT) and mindfulness meditation (MM) training (CBT/MM), or the combination of intravenous ketamine and CBT/MM. The secondary outcome is to assess the durability and efficacy of combination intravenous ketamine and CBT/MM for treatment of chronic pain as compared to CBT/MM or intravenous ketamine alone (assessed at week 20 of the study). Methods: This is a single-center, 16-week, 3-arm pilot study that will take place at the Chronic Pain Clinic at St. Michael's Hospital, Toronto, Ontario, which receives 1000 referrals per year. Patients will be enrolled in the study for a total of 20 weeks. Participants who are allocated CBT/MM therapy will receive remote weekly psychotherapy from week 1 to week 16, inclusive of health coaching administered through the NexJ Health Inc (NexJ Health) platform. Patients who are allocated ketamine-infusion therapy will receive monthly ketamine infusion treatments on weeks 2, 7, and 12. Patients who are allocated ketamine+CBT/MM will receive weekly psychotherapy from weeks 1 to 16, inclusive, as well as ketamine infusion treatments on weeks 2, 7, and 12. We will be assessing recruitment rates, consent rates, withdrawal rates, adherence, missing data, and adverse events as pilot outcome measures. Secondary clinical outcomes include changes relative to baseline in pain intensity and pain interference. Results: As of November 1, 2023, the recruitment process has not been initiated. Given the recruitment, consent, and intervention target of 30 participants for this feasibility study, with each patient undergoing monitoring and treatments for a course of 20 weeks, we expect to complete the study by December 2025. Conclusions: This study assesses the feasibility of conducting a 3-arm randomized controlled trial to examine the effects of ketamine administration with the concurrent use of CBT/MM in a population with chronic neuropathic pain. The results of this pilot randomized controlled trial will inform the development of a larger-scale randomized controlled trial. Future studies will be aimed at including a sufficiently powered sample that will inform decisions about optimal treatment calibration and treatment effect duration. Trial Registration: ClinicalTrials.gov NCT05639322; https://classic.clinicaltrials.gov/ct2/show/NCT05639322 International Registered Report Identifier (IRRID): PRR1-10.2196/54406 ",
doi="10.2196/54406",
url="https://www.researchprotocols.org/2024/1/e54406",
url="http://www.ncbi.nlm.nih.gov/pubmed/38630524"
}


@Article{info:doi/10.2196/49822,
author="Baek, Jinyoung
and Lawson, Jonathan
and Rahimzadeh, Vasiliki",
title="Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey",
journal="JMIR Form Res",
year="2024",
month="Mar",
day="20",
volume="8",
pages="e49822",
keywords="biomedical research",
keywords="survey",
keywords="surveys",
keywords="data sharing",
keywords="data management",
keywords="secondary use",
keywords="National Institutes of Health",
keywords="signing official",
keywords="information sharing",
keywords="exchange",
keywords="access",
keywords="data science",
keywords="accessibility",
keywords="policy",
keywords="policies",
abstract="Background: New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. Objective: The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health--funded research in the United States. Methods: We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. Results: In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21\% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into ``a National Institutes of Health--funded repository'' to meet the Data Management and Sharing requirements but also within the ``institution's own repository'' or a third-party repository. Conclusions: Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections. ",
doi="10.2196/49822",
url="https://formative.jmir.org/2024/1/e49822",
url="http://www.ncbi.nlm.nih.gov/pubmed/38506894"
}


@Article{info:doi/10.2196/50339,
author="Charles, M. Wendy
and van der Waal, B. Mark
and Flach, Joost
and Bisschop, Arno
and van der Waal, X. Raymond
and Es-Sbai, Hadil
and McLeod, J. Christopher",
title="Blockchain-Based Dynamic Consent and its Applications for Patient-Centric Research and Health Information Sharing: Protocol for an Integrative Review",
journal="JMIR Res Protoc",
year="2024",
month="Feb",
day="5",
volume="13",
pages="e50339",
keywords="best practices",
keywords="blockchain",
keywords="clinical trial",
keywords="data reuse",
keywords="data sharing",
keywords="dynamic consent",
keywords="health care data",
keywords="integrative research review",
keywords="scientific rigor",
keywords="technology implementation",
abstract="Background: Blockchain has been proposed as a critical technology to facilitate more patient-centric research and health information sharing. For instance, it can be applied to coordinate and document dynamic informed consent, a procedure that allows individuals to continuously review and renew their consent to the collection, use, or sharing of their private health information. Such has been suggested to facilitate ethical, compliant longitudinal research, and patient engagement. However, blockchain-based dynamic consent is a relatively new concept, and it is not yet clear how well the suggested implementations will work in practice. Efforts to critically evaluate implementations in health research contexts are limited. Objective: The objective of this protocol is to guide the identification and critical appraisal of implementations of blockchain-based dynamic consent in health research contexts, thereby facilitating the development of best practices for future research, innovation, and implementation. Methods: The protocol describes methods for an integrative review to allow evaluation of a broad range of quantitative and qualitative research designs. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) framework guided the review's structure and nature of reporting findings. We developed search strategies and syntax with the help of an academic librarian. Multiple databases were selected to identify pertinent academic literature (CINAHL, Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science) and gray literature (Electronic Theses Online Service, ProQuest Dissertations and Theses, Open Access Theses and Dissertations, and Google Scholar) for a comprehensive picture of the field's progress. Eligibility criteria were defined based on PROSPERO (International Prospective Register of Systematic Reviews) requirements and a criteria framework for technology readiness. A total of 2 reviewers will independently review and extract data, while a third reviewer will adjudicate discrepancies. Quality appraisal of articles and discussed implementations will proceed based on the validated Mixed Method Appraisal Tool, and themes will be identified through thematic data synthesis. Results: Literature searches were conducted, and after duplicates were removed, 492 articles were eligible for screening. Title and abstract screening allowed the removal of 312 articles, leaving 180 eligible articles for full-text review against inclusion criteria and confirming a sufficient body of literature for project feasibility. Results will synthesize the quality of evidence on blockchain-based dynamic consent for patient-centric research and health information sharing, covering effectiveness, efficiency, satisfaction, regulatory compliance, and methods of managing identity. Conclusions: The review will provide a comprehensive picture of the progress of emerging blockchain-based dynamic consent technologies and the rigor with which implementations are approached. Resulting insights are expected to inform best practices for future research, innovation, and implementation to benefit patient-centric research and health information sharing. Trial Registration: PROSPERO CRD42023396983; http://tinyurl.com/cn8a5x7t International Registered Report Identifier (IRRID): DERR1-10.2196/50339 ",
doi="10.2196/50339",
url="https://www.researchprotocols.org/2024/1/e50339",
url="http://www.ncbi.nlm.nih.gov/pubmed/38315514"
}


@Article{info:doi/10.2196/45821,
author="Persky, Susan
and Colloca, Luana",
title="Medical Extended Reality Trials: Building Robust Comparators, Controls, and Sham",
journal="J Med Internet Res",
year="2023",
month="Nov",
day="22",
volume="25",
pages="e45821",
keywords="augmented reality",
keywords="clinical trial design",
keywords="control conditions",
keywords="medical extended reality",
keywords="sham VR",
keywords="virtual reality",
doi="10.2196/45821",
url="https://www.jmir.org/2023/1/e45821",
url="http://www.ncbi.nlm.nih.gov/pubmed/37991836"
}


@Article{info:doi/10.2196/47052,
author="Ye, Jiancheng
and Xiong, Shangzhi
and Wang, Tengyi
and Li, Jingyi
and Cheng, Nan
and Tian, Maoyi
and Yang, Yang",
title="The Roles of Electronic Health Records for Clinical Trials in Low- and Middle-Income Countries: Scoping Review",
journal="JMIR Med Inform",
year="2023",
month="Nov",
day="22",
volume="11",
pages="e47052",
keywords="electronic health records",
keywords="clinical trials",
keywords="low- and middle-income countries",
abstract="Background: Clinical trials are a crucial element in advancing medical knowledge and developing new treatments by establishing the evidence base for safety and therapeutic efficacy. However, the success of these trials depends on various factors, including trial design, project planning, research staff training, and adequate sample size. It is also crucial to recruit participants efficiently and retain them throughout the trial to ensure timely completion. Objective: There is an increasing interest in using electronic health records (EHRs)---a widely adopted tool in clinical practice---for clinical trials. This scoping review aims to understand the use of EHR in supporting the conduct of clinical trials in low- and middle-income countries (LMICs) and to identify its strengths and limitations. Methods: A comprehensive search was performed using 5 databases: MEDLINE, Embase, Scopus, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature. We followed the latest version of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline to conduct this review. We included clinical trials that used EHR at any step, conducted a narrative synthesis of the included studies, and mapped the roles of EHRs into the life cycle of a clinical trial. Results: A total of 30 studies met the inclusion criteria: 13 were randomized controlled trials, 3 were cluster randomized controlled trials, 12 were quasi-experimental studies, and 2 were feasibility pilot studies. Most of the studies addressed infectious diseases (15/30, 50\%), with 80\% (12/15) of them about HIV or AIDS and another 40\% (12/30) focused on noncommunicable diseases. Our synthesis divided the roles of EHRs into 7 major categories: participant identification and recruitment (12/30, 40\%), baseline information collection (6/30, 20\%), intervention (8/30, 27\%), fidelity assessment (2/30, 7\%), primary outcome assessment (24/30, 80\%), nonprimary outcome assessment (13/30, 43\%), and extended follow-up (2/30, 7\%). None of the studies used EHR for participant consent and randomization. Conclusions: Despite the enormous potential of EHRs to increase the effectiveness and efficiency of conducting clinical trials in LMICs, challenges remain. Continued exploration of the appropriate uses of EHRs by navigating their strengths and limitations to ensure fitness for use is necessary to better understand the most optimal uses of EHRs for conducting clinical trials in LMICs. ",
doi="10.2196/47052",
url="https://medinform.jmir.org/2023/1/e47052",
url="http://www.ncbi.nlm.nih.gov/pubmed/37991820"
}


@Article{info:doi/10.2196/47119,
author="Rego, S{\'i}lvia
and Henriques, Rita Ana
and Serra, Silv{\'e}rio Sofia
and Costa, Teresa
and Rodrigues, Maria Ana
and Nunes, Francisco",
title="Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract",
journal="JMIR Res Protoc",
year="2023",
month="Oct",
day="26",
volume="12",
pages="e47119",
keywords="digital endpoint",
keywords="digital biomarker",
keywords="mobile health technologies",
keywords="mobile health",
keywords="mHealth",
keywords="remote monitoring",
keywords="wearable technology",
keywords="scoping review",
keywords="review method",
keywords="validate",
keywords="validation",
keywords="outcome measure",
keywords="sensor",
keywords="wearable",
abstract="Background: Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints. Objective: This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints. Methods: The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews. Results: A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal. Conclusions: A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit. International Registered Report Identifier (IRRID): PRR1-10.2196/47119 ",
doi="10.2196/47119",
url="https://www.researchprotocols.org/2023/1/e47119",
url="http://www.ncbi.nlm.nih.gov/pubmed/37883152"
}


@Article{info:doi/10.2196/46415,
author="Griggs, Stephanie
and Ash, I. Garrett
and Pignatiello, Grant
and Papik, AnnMarie
and Huynh, Johnathan
and Leuchtag, Mary
and Hickman Jr, L. Ronald",
title="Internet-Based Recruitment and Retention of Young Adults With Type 1 Diabetes: Cross-Sectional Study",
journal="JMIR Form Res",
year="2023",
month="Aug",
day="22",
volume="7",
pages="e46415",
keywords="type 1 diabetes",
keywords="internet-based recruitment",
keywords="young adult",
keywords="diabetes",
keywords="diabetic",
keywords="type 1",
keywords="recruit",
keywords="research platform",
keywords="T1D",
keywords="social media",
keywords="research subject",
keywords="research participant",
keywords="study participant",
abstract="Background: Multiple research strategies are required to recruit and engage a representative cohort of young adults in diabetes research. In this report, we describe an approach for internet-based recruitment for a repeated-measures descriptive study. Objective: The objective of this cross-sectional study was to determine whether internet-based recruitment through multiple social media platforms, a clinical research platform, and cooperation with community partnerships---College Diabetes Network and Beyond Type 1---would serve as an effective way to recruit a representative sample of young adults aged 18-25 years with type 1 diabetes (T1D). Methods: We conducted a repeated-measures descriptive study. We captured enrollment rates and participant characteristics acquired from each social media platform through survey data and Facebook analytics. This study was advertised via paid postings across a combination of different social media platforms (eg, Facebook, Instagram, Twitter, and Reddit). We used quarterly application postings, quarterly newsletters, and participation in the ResearchMatch registry to identify potentially eligible participants from February 3, 2021, to June 6, 2022. Results: ResearchMatch proved to be the most cost-effective strategy overall, yielding the highest gender and racial diversity compared to other internet platforms (eg, Facebook, Instagram, Twitter, and Reddit), application postings (eg, Beyond Type 1), and newsletters (eg, College Diabetes Network and a local area college). However, we propose that the combination of these approaches yielded a larger, more diverse sample compared to any individual strategy. Our recruitment cost was US \$16.69 per eligible participant, with a 1.27\% conversion rate and a 30\% eligibility rate. Conclusions: Recruiting young adults with T1D across multiple internet-based platforms was an effective strategy to yield a moderately diverse sample. Leveraging various recruitment strategies is necessary to produce a representative sample of young adults with T1D. As the internet becomes a larger forum for study recruitment, participants from underrepresented backgrounds may continue engaging in research through advertisements on the internet and other internet-based recruitment platforms. ",
doi="10.2196/46415",
url="https://formative.jmir.org/2023/1/e46415",
url="http://www.ncbi.nlm.nih.gov/pubmed/37606985"
}


@Article{info:doi/10.2196/49417,
author="M{\"u}ller Fiedler, Augusto
and Medeiros, Michelle
and Fiedler, Dalinda Haidi",
title="Targeted Glioblastoma Treatment via Synthesis and Functionalization of Gold Nanoparticles With De Novo--Engineered Transferrin-Like Peptides: Protocol for a Novel Method",
journal="JMIR Res Protoc",
year="2023",
month="Aug",
day="11",
volume="12",
pages="e49417",
keywords="gold nanoparticles",
keywords="glioblastoma",
keywords="blood-brain barrier",
keywords="transferrin-like peptides",
keywords="drug delivery",
keywords="brain tumor",
keywords="neuro-oncology",
keywords="chemotherapy",
keywords="nanoparticle functionalization",
keywords="pharmaceuticals",
abstract="Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with limited treatment options due to the blood-brain barrier's (BBB's) impedance and inherent resistance to chemotherapy. Gold nanoparticles (AuNPs) functionalized with transferrin-like peptides show promise in overcoming these challenges, enhancing drug delivery to the brain, and reducing chemotherapy resistance. Objective: The primary goal of this study is to establish a detailed protocol for synthesizing and stabilizing AuNPs, functionalizing them with de novo--engineered transferrin-like peptides, and conjugating them with the chemotherapeutic agent temozolomide. This strategy aims to improve drug delivery across the BBB and circumvent chemotherapy resistance. The secondary objective includes an assessment of the safety and potential for in vivo use of the synthesized nanoparticle complex. Methods: The proposal involves multiple steps with rigorous quality control of AuNP synthesis, stabilization with surfactants, and polyethylene glycol coating. The engineered transferrin-like peptides will be synthesized and attached to the AuNPs' surface, followed by the attachment of temozolomide and O6-methylguanine-DNA methyltransferase inhibitors. The resulting complex will undergo in vitro testing to assess BBB penetration, efficacy against GBM cells, and potential toxicity. Results: Initial preliminary experiments and simulations suggest successful synthesis and stabilization of AuNPs and effective attachment of transferrin-like peptides. We propose peptide attachment verification using Fourier transform infrared spectroscopy and surface plasmon resonance. Additionally, we will conduct pH stability tests to ensure our functionalized AuNPs retain their properties in acidic brain tumor microenvironments. Conclusions: The proposed functionalization of AuNPs with de novo--engineered transferrin-like peptides represents a novel approach to GBM treatment. Our strategy opens new avenues for drug delivery across the BBB and chemotherapy resistance reduction. While we primarily focus on in vitro studies and computational modeling at this stage, successful completion will lead to further development, including in vivo studies and nanoparticle design optimization. This proposal anticipates inspiring future research and funding in neuro-oncology, presenting a potentially innovative and effective treatment option for GBM. International Registered Report Identifier (IRRID): RR1-10.2196/49417 ",
doi="10.2196/49417",
url="https://www.researchprotocols.org/2023/1/e49417",
url="http://www.ncbi.nlm.nih.gov/pubmed/37531222"
}


@Article{info:doi/10.2196/46794,
author="Tian, Huichuan
and Zhang, Yao
and Ren, Jiajun
and Wang, Chaoran
and Mou, Ruiyu
and Li, Xiaojiang
and Jia, Yingjie",
title="Developing a Core Outcome Set for Assessing Clinical Safety Outcomes of Prostate Cancer in Clinical Trials of Traditional Chinese Medicine: Protocol for a Mixed Methods Study",
journal="JMIR Res Protoc",
year="2023",
month="Aug",
day="7",
volume="12",
pages="e46794",
keywords="core outcome set",
keywords="safety outcomes",
keywords="prostate cancer",
keywords="traditional Chinese medicine",
keywords="clinical trial",
keywords="study protocol",
keywords="quality of life",
keywords="efficacy",
keywords="clinical safety outcome",
keywords="men",
abstract="Background: Among the common malignant tumors in men worldwide, the incidence of prostate cancer ranks second to lung cancer. This disease will bring an economic burden to patients and their families and can reduce the quality of life of patients. Researchers have conducted numerous clinical trials on the efficacy and safety of different interventions in the treatment of prostate cancer with traditional Chinese medicine (TCM) combined with standard treatment regimens. However, the currently published clinical trials exhibit inconsistent and irregular reporting of outcome measures. Objective: The objective of this paper is to emphasize the need for a core outcome set (COS) to facilitate future prostate cancer research, aiming to improve the quality of trials and generate high-quality evidence. Methods: This mixed methods project has three phases, as follows: (1) a scoping review of the literature to identify outcomes that have been reported in clinical trials and systematic reviews of interventions involving TCM for the treatment of prostate cancer as well as a qualitative component using interviews to obtain the views of patients with prostate cancer, their families, and their caregivers who have a history of TCM treatment; (2) a Delphi survey among stakeholders to prioritize the core outcomes---Participants will include traditional Chinese and Western medicine clinicians in prostate cancer--related directions, nurses, and methodology experts who will participate in 2 rounds of the Delphi method expert consultation to score each outcome in the list of outcome indicators; and (3) a face-to-face consensus meeting to discuss and agree on the final COS for the application of TCM in the treatment of prostate cancer. Results: The protocol has been registered in PROSPERO (CRD42022356184) before the start of the review process, and we will initiate the review on August 1, 2023; results should be expected by September 1, 2023. The Delphi survey among stakeholders is expected to start in October 2023. Conclusions: The development of a core outcome set for assessing clinical safety outcomes of prostate cancer in clinical trials of TCM will provide a significant first step to assist Chinese doctors, researchers, and policy makers. Trial Registration: PROSPERO CRD42022356184; https://tinyurl.com/ysakz74r International Registered Report Identifier (IRRID): PRR1-10.2196/46794 ",
doi="10.2196/46794",
url="https://www.researchprotocols.org/2023/1/e46794",
url="http://www.ncbi.nlm.nih.gov/pubmed/37549007"
}


@Article{info:doi/10.2196/42175,
author="Fu, Zhiying
and Liu, Xiaohong
and Zhao, Shuhua
and Yuan, Yannan
and Jiang, Min",
title="Reducing Clinical Trial Monitoring Resources and Costs With Remote Monitoring: Retrospective Study Comparing On-Site Versus Hybrid Monitoring",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="27",
volume="25",
pages="e42175",
keywords="clinical trial",
keywords="management",
keywords="on-site monitoring",
keywords="hybrid monitoring model",
keywords="remote monitoring",
keywords="hybrid",
keywords="monitoring",
keywords="cost",
keywords="economic",
keywords="trial monitoring",
keywords="research quality",
keywords="scientific research",
keywords="trials methodology",
keywords="trial management",
keywords="research management",
abstract="Background: Clinical research associates (CRAs) monitor the progress of a trial, verify the data collected, and ensure that the trial is carried out and reported in accordance with the trial protocol, standard operating procedures, and relevant laws and regulations. In response to monitoring challenges during the COVID-19 pandemic, Peking University Cancer Hospital launched a remote monitoring system and established a monitoring model, combining on-site and remote monitoring of clinical trials. Considering the increasing digitization of clinical trials, it is important to determine the optimal monitoring model for the general benefit of centers conducting clinical trials worldwide. Objective: We sought to summarize our practical experience of a hybrid model of remote and on-site monitoring of clinical trials and provide guidance for clinical trial monitoring management. Methods: We evaluated 201 trials conducted by our hospital that used on-site monitoring alone or a hybrid monitoring model, of which 91 trials used on-site monitoring alone (arm A) and 110 used a hybrid model of remote and on-site monitoring (arm B). We reviewed trial monitoring reports from June 20, 2021, to June 20, 2022, and used a customized questionnaire to collect and compare the following information: monitoring cost of trials in the 2 models as a sum of the CRAs' transportation (eg, taxi fare and air fare), accommodation, and meal costs; differences in monitoring frequency; the number of monitored documents; and monitoring duration. Results: From June 20, 2021, to June 20, 2022, a total of 320 CRAs representing 201 sponsors used the remote monitoring system for source data review and the verification of data from 3299 patients in 320 trials. Arm A trials were monitored 728 times and arm B trials were monitored 849 times. The hybrid model in arm B had 52.9\% (449/849) remote visits and 48.1\% (409/849) on-site visits. The number of patients' visits that could be reviewed in the hybrid monitoring model increased by 34\% (4.70/13.80; P=.004) compared with that in the traditional model, whereas the duration of monitoring decreased by 13.8\% (3.96/28.61; P=.03) and the total cost of monitoring decreased by 46.2\% (CNY {\textyen}188.74/408.80; P<.001). These differences were shown by nonparametric testing to be statistically significant (P<.05). Conclusions: The hybrid monitoring model can ensure timely detection of monitoring issues, improve monitoring efficiency, and reduce the cost of clinical trials and should therefore be applied more broadly in future clinical studies. ",
doi="10.2196/42175",
url="https://www.jmir.org/2023/1/e42175",
url="http://www.ncbi.nlm.nih.gov/pubmed/37368468"
}


@Article{info:doi/10.2196/38159,
author="Gamble, Eoin
and Linehan, Conor
and Heavin, Ciara",
title="Establishing Requirements for Technology to Support Clinical Trial Retention: Systematic Scoping Review and Analysis Using Self-determination Theory",
journal="J Med Internet Res",
year="2023",
month="Apr",
day="13",
volume="25",
pages="e38159",
keywords="clinical trial",
keywords="clinical research",
keywords="retention strategies",
keywords="participant retention",
keywords="technology strategy",
keywords="decentralized clinical trial",
keywords="participant motivation",
keywords="patient centric",
keywords="engagement strategies",
keywords="self-determination theory",
abstract="Background: Retaining participants in clinical trials is an established challenge. Currently, the industry is moving to a technology-mediated, decentralized model for running trials. The shift presents an opportunity for technology design to aid the participant experience and promote retention; however, there are many open questions regarding how this can be best supported. We advocate the adoption of a stronger theoretical position to improve the quality of design decisions for clinical trial technology to promote participant engagement. Objective: This study aimed to identify and analyze the types of retention strategies used in published clinical trials that successfully retain participants. Methods: A systematic scoping review was carried out on 6 electronic databases for articles published from 1990 to September 2020, namely CINAHL, The Cochrane Library, EBSCO, Embase, PsycINFO, and PubMed, using the concepts ``retention,'' ``strategy,'' ``clinal trial,'' and ``clinical research.'' This was followed by an analysis of the included articles through the lens of self-determination theory, an evidence-based theory of human motivation. Results: A total of 26 articles were included in this review. The motivational strategies identified in the clinical trials in our sample were categorized into 8 themes: autonomy; competence; relatedness; controlled motivation; branding, communication material, and marketing literature; contact, tracking, and scheduling methods and data collection; convenience to contribute to data collection; and organizational competence. The trials used a wide range of motivational strategies. Notably, the trials often relied on controlled motivation interventions and underused strategies to support intrinsic motivation. Moreover, traditional clinical trials relied heavily on human interaction and ``relatedness'' to support motivation and retention, which may cause problems in the move to technology-led decentralized trials. We found inconsistency in the data-reporting methods and that motivational theory--based approaches were not evident in strategy design. Conclusions: This study offers direction and a framework to guide digital technology design decisions for future decentralized clinical trials to enhance participant retention during clinical trials. This research defines previous clinical trial retention strategies in terms of participant motivation, identifies motivational strategies, and offers a rationale for selecting strategies that will improve retention. It emphasizes the benefits of using theoretical frameworks to analyze strategic approaches and aid decision-making to improve the quality of technology design decisions. ",
doi="10.2196/38159",
url="https://www.jmir.org/2023/1/e38159",
url="http://www.ncbi.nlm.nih.gov/pubmed/37052985"
}


@Article{info:doi/10.2196/39262,
author="Applequist, Janelle
and Burroughs, Cristina
and Merkel, A. Peter
and Rothenberg, Marc
and Trapnell, Bruce
and Desnick, Robert
and Sahin, Mustafa
and Krischer, Jeffrey",
title="Direct-to-Consumer Recruitment Methods via Traditional and Social Media to Aid in Research Accrual for Clinical Trials for Rare Diseases: Comparative Analysis Study",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="14",
volume="25",
pages="e39262",
keywords="direct-to-consumer advertising",
keywords="clinical trial recruitment",
keywords="clinical trial accrual",
keywords="research recruitment",
keywords="research participant recruitment",
keywords="social media recruitment",
keywords="web-based recruitment",
keywords="patient-centered research",
keywords="rare diseases",
keywords="clinical trial",
abstract="Background: Recruitment into clinical trials is a challenging process, with as many as 40\% of studies failing to meet their target sample sizes. The principles of direct-to-consumer (DTC) advertising rely upon novel marketing strategies. The ability to reach expansive audiences in the web-based realm presents a unique opportunity for researchers to overcome various barriers to enrollment in clinical trials. Research has investigated the use of individual web-based platforms to aid in recruitment and accrual into trials; however, a gap in the literature exists, whereby multiple mass communication platforms have yet to be investigated across a range of clinical trials. Objective: There is a need to better understand how individual factors combine to collectively influence trial recruitment. We aimed to test whether DTC recruitment of potentially eligible study participants via social media platforms (eg, Facebook [Meta Platforms Inc] and Twitter [Twitter Inc]) was an effective strategy or whether this acted as an enhancement to traditional (eg, email via contact registries) recruitment strategies through established clinical research sites. Methods: This study tested multiple DTC web-based recruitment efforts (Facebook, Twitter, email, and patient advocacy group [PAG] involvement) across 6 national and international research studies from 5 rare disease consortia. Targeted social media messaging, social media management software, and individual study websites with prescreening questions were used in the Protocol for Increasing Accrual Using Social Media (PRISM). Results: In total, 1465 PRISM website referrals occurred across all 6 studies. Organic (unpaid) Facebook posts (676/1465, 46.14\%) and Rare Diseases Clinical Research Network patient contact registry emails (461/1465, 31.47\%) represented the most successful forms of engagement. PRISM was successful in accumulating a 40.1\% (136/339) lead generation (those who screened positive and consented to share their contact information to be contacted by a clinical site coordinator). Despite the large number of leads generated from PRISM recruitment efforts, the number of patients who were subsequently enrolled in studies was low. Across 6 studies, 3 participants were ultimately enrolled, meaning that 97.8\% (133/136) of leads dropped off. Conclusions: The results indicate that although accrual results were low, this is consistent with previously documented challenges of studying populations with rare diseases. Targeted messaging integrated throughout the recruitment process (eg, referral, lead, and accrual) remains an area for further research. Key elements to consider include structuring the communicative workflow in such a way that PAG involvement is central to the process, with clinical site coordinators actively involved after an individual consents to share their contact information. Customized approaches are needed for each population and research study, with observational studies best suited for social media recruitment. As evidenced by lead generation, results suggest that web-based recruitment efforts, coupled with targeted messaging and PAG partnerships, have the potential to supplement clinical trial accrual. ",
doi="10.2196/39262",
url="https://www.jmir.org/2023/1/e39262",
url="http://www.ncbi.nlm.nih.gov/pubmed/36917158"
}


@Article{info:doi/10.2196/39993,
author="Wu, Shanshan
and Ji, Hongqing
and Won, Junyeon
and Jo, Eun-Ah
and Kim, Yun-Sik
and Park, Jung-Jun",
title="The Effects of Exergaming on Executive and Physical Functions in Older Adults With Dementia: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="7",
volume="25",
pages="e39993",
keywords="exergame",
keywords="exergaming",
keywords="executive function",
keywords="physical function",
keywords="reaction time",
keywords="N2",
keywords="P3b",
keywords="physical",
keywords="function",
keywords="game",
keywords="dementia",
keywords="RCT",
keywords="cognitive function",
keywords="older adults",
keywords="aerobic exercise",
keywords="exercise",
keywords="neuronal",
keywords="activity",
keywords="task",
keywords="stimulation",
keywords="intervention",
abstract="Background: Despite increasing interest in the effects of exergaming on cognitive function, little is known about its effects on older adults with dementia. Objective: The purpose of this is to investigate the effects of exergaming on executive and physical functions in older adults with dementia compared to regular aerobic exercise. Methods: In total, 24 older adults with moderate dementia participated in the study. Participants were randomized into either the exergame group (EXG, n=13, 54\%) or the aerobic exercise group (AEG, n=11, 46\%). For 12 weeks, EXG engaged in a running-based exergame and AEG performed a cycling exercise. At baseline and postintervention, participants underwent the Ericksen flanker test (accuracy \% and response time [RT]) while recording event-related potentials (ERPs) that included the N2 and P3b potentials. Participants also underwent the senior fitness test (SFT) and the body composition test pre- and postintervention. Repeated-measures ANOVA was performed to assess the effects of time (pre- vs postintervention), group (EXG vs AEG), and group{\texttimes}time interactions. Results: Compared to AEG, EXG demonstrated greater improvements in the SFT (F1.22=7.434, P=.01), reduction in body fat (F1.22=6.476, P=.02), and increase in skeletal mass (F1.22=4.525, P=.05), fat-free mass (F1.22=6.103, P=.02), and muscle mass (F1.22=6.636, P=.02). Although there was a significantly shorter RT in EXG postintervention (congruent P=.03, 95\% CI 13.581-260.419, incongruent P=.04, 95\% CI 14.621-408.917), no changes occurred in AEG. EXG also yielded a shorter N2 latency for central (Cz) cortices during both congruent conditions compared to AEG (F1.22=4.281, P=.05). Lastly, EXG presented a significantly increased P3b amplitude compared to AEG during the Ericksen flanker test (congruent: frontal [Fz] F1.22=6.546, P=.02; Cz F1.22=5.963, P=.23; parietal [Pz] F1.22=4.302, P=.05; incongruent: Fz F1.22=8.302, P=.01; Cz F1.22=15.199, P=.001; Pz F1.22=13.774, P=.001). Conclusions: Our results suggest that exergaming may be associated with greater improvements in brain neuronal activity and enhanced executive function task performance than regular aerobic exercise. Exergaming characterized by both aerobic exercise and cognitive stimulation can be used as an effective intervention to improve cognitive and physical functions in older adults with dementia. Trial Registration: Clinical Research Information Service KCT0008238; https://cris.nih.go.kr/cris/search/detailSearch.do/24170 ",
doi="10.2196/39993",
url="https://www.jmir.org/2023/1/e39993",
url="http://www.ncbi.nlm.nih.gov/pubmed/36881445"
}


@Article{info:doi/10.2196/37714,
author="Franklin, D. Patricia
and Oatis, A. Carol
and Zheng, Hua
and Westby, D. Marie
and Peter, Wilfred
and Laraque-Two Elk, Jeremie
and Rizk, Joseph
and Benbow, Ellen
and Li, Wenjun",
title="Web-Based System to Capture Consistent and Complete Real-world Data of Physical Therapy Interventions Following Total Knee Replacement: Design and Evaluation Study",
journal="JMIR Rehabil Assist Technol",
year="2022",
month="Oct",
day="27",
volume="9",
number="4",
pages="e37714",
keywords="structured data",
keywords="web-based clinical data capture",
keywords="physical therapy",
keywords="total knee replacement",
keywords="electronic health records",
keywords="real-world evidence",
keywords="real-world data",
keywords="data",
keywords="therapy",
keywords="knee",
keywords="knee replacement",
keywords="clinical intervention",
abstract="Background: Electronic health records (EHRs) have the potential to facilitate consistent clinical data capture to support excellence in patient care, quality improvement, and knowledge generation. Despite widespread EHR use, the vision to transform health care system and its data to a ``learning health care system'' generating knowledge from real-world data is limited by the lack of consistent, structured clinical data. Objective: The purpose of this paper was to demonstrate the design of a web-based structured clinical intervention data capture system and its evaluation in practice. The use case was ambulatory physical therapy (PT) treatment after total knee replacement (TKR), one of the most common and costly procedures today. Methods: To identify the PT intervention type and intensity (or dose) used to treat patients with knee arthritis following TKR, an iterative user-centered design process refined an initial list of PT interventions generated during preliminary chart reviews. Input from practicing physical therapists and national and international experts refined and categorized the interventions. Next, a web-based, hierarchical structured system for intervention and intensity documentation was designed and deployed. Results: The PT documentation system was implemented by 114 physical therapists agreeing to record all interventions at patient visits. Data for 161 patients with 2615 PT visits were entered by 83 physical therapists. No technical problems with data entry were reported, and data entry required less than 2 minutes per visit. A total of 42 (2\%) interventions could not be categorized and were recorded using free text. Conclusions: The use of user-centered design principles provides a road map for developing clinically feasible data capture systems that employ structured collection of uniform data for use by multiple practitioners across institutions to complement and augment existing EHRs. Secondarily, these data can be analyzed to define best practices and disseminate knowledge to practice. ",
doi="10.2196/37714",
url="https://rehab.jmir.org/2022/4/e37714",
url="http://www.ncbi.nlm.nih.gov/pubmed/36301608"
}


@Article{info:doi/10.2196/33591,
author="Gudi, Nachiket
and Kamath, Prashanthi
and Chakraborty, Trishnika
and Jacob, G. Anil
and Parsekar, S. Shradha
and Sarbadhikari, Nath Suptendra
and John, Oommen",
title="Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review",
journal="J Med Internet Res",
year="2022",
month="May",
day="4",
volume="24",
number="5",
pages="e33591",
keywords="clinical trial",
keywords="data sharing",
keywords="policy",
keywords="scoping review",
abstract="Background: Although well recognized for its scientific value, data sharing from clinical trials remains limited. Steps toward harmonization and standardization are increasing in various pockets of the global scientific community. This issue has gained salience during the COVID-19 pandemic. Even for agencies willing to share data, data exclusivity practices complicate matters; strict regulations by funders affect this even further. Finally, many low- and middle-income countries (LMICs) have weaker institutional mechanisms. This complex of factors hampers research and rapid response during public health emergencies. This drew our attention to the need for a review of the regulatory landscape governing clinical trial data sharing. Objective: This review seeks to identify regulatory frameworks and policies that govern clinical trial data sharing and explore key elements of data-sharing mechanisms as outlined in existing regulatory documents. Following from, and based on, this empirical analysis of gaps in existing policy frameworks, we aimed to suggest focal areas for policy interventions on a systematic basis to facilitate clinical trial data sharing. Methods: We followed the JBI scoping review approach. Our review covered electronic databases and relevant gray literature through a targeted web search. We included records (all publication types, except for conference abstracts) available in English that describe clinical trial data--sharing policies, guidelines, or standard operating procedures. Data extraction was performed independently by 2 authors, and findings were summarized using a narrative synthesis approach. Results: We identified 4 articles and 13 policy documents; none originated from LMICs. Most (11/17, 65\%) of the clinical trial agencies mandated a data-sharing agreement; 47\% (8/17) of these policies required informed consent by trial participants; and 71\% (12/17) outlined requirements for a data-sharing proposal review committee. Data-sharing policies have, a priori, milestone-based timelines when clinical trial data can be shared. We classify clinical trial agencies as following either controlled- or open-access data-sharing models. Incentives to promote data sharing and distinctions between mandated requirements and supportive requirements for informed consent during the data-sharing process remain gray areas, needing explication. To augment participant privacy and confidentiality, a neutral institutional mechanism to oversee dissemination of information from the appropriate data sets and more policy interventions led by LMICs to facilitate data sharing are strongly recommended. Conclusions: Our review outlines the immediate need for developing a pragmatic data-sharing mechanism that aims to improve research and innovations as well as facilitate cross-border collaborations. Although a one-policy-fits-all approach would not account for regional and subnational legislation, we suggest that a focus on key elements of data-sharing mechanisms can be used to inform the development of flexible yet comprehensive data-sharing policies so that institutional mechanisms rather than disparate efforts guide data generation, which is the foundation of all scientific endeavor. ",
doi="10.2196/33591",
url="https://www.jmir.org/2022/5/e33591",
url="http://www.ncbi.nlm.nih.gov/pubmed/35507397"
}


@Article{info:doi/10.2196/28696,
author="Fitzer, Kai
and Haeuslschmid, Renate
and Blasini, Romina
and Altun, Bet{\"u}l Fatma
and Hampf, Christopher
and Freiesleben, Sherry
and Macho, Philipp
and Prokosch, Hans-Ulrich
and Gulden, Christian",
title="Patient Recruitment System for Clinical Trials: Mixed Methods Study About Requirements at Ten University Hospitals",
journal="JMIR Med Inform",
year="2022",
month="Apr",
day="20",
volume="10",
number="4",
pages="e28696",
keywords="patient recruitment system",
keywords="clinical trial recruitment support system",
keywords="recruitment",
keywords="patient screening",
keywords="requirements",
keywords="user needs",
keywords="clinical trial",
keywords="interview",
keywords="survey",
keywords="electronic support",
keywords="clinical information systems",
keywords="eHealth",
abstract="Background: Clinical trials are the gold standard for advancing medical knowledge and improving patient outcomes. For their success, an appropriately sized cohort is required. However, patient recruitment remains one of the most challenging aspects of clinical trials. Information technology (IT) support systems---for instance, patient recruitment systems---may help overcome existing challenges and improve recruitment rates, when customized to the user needs and environment. Objective: The goal of our study is to describe the status quo of patient recruitment processes and to identify user requirements for the development of a patient recruitment system. Methods: We conducted a web-based survey with 56 participants as well as semistructured interviews with 33 participants from 10 German university hospitals. Results: We here report the recruitment procedures and challenges of 10 university hospitals. The recruitment process was influenced by diverse factors such as the ward, use of software, and the study inclusion criteria. Overall, clinical staff seemed more involved in patient identification, while the research staff focused on screening tasks. Ad hoc and planned screenings were common. Identifying eligible patients was still associated with significant manual efforts. The recruitment staff used Microsoft Office suite because tailored software were not available. To implement such software, data from disparate sources will need to be made available. We discussed concrete technical challenges concerning patient recruitment systems, including requirements for features, data, infrastructure, and workflow integration, and we contributed to the support of developing a successful system. Conclusions: Identifying eligible patients is still associated with significant manual efforts. To fully make use of the high potential of IT in patient recruitment, many technical and process challenges have to be solved first. We contribute and discuss concrete technical challenges for patient recruitment systems, including requirements for features, data, infrastructure, and workflow integration. ",
doi="10.2196/28696",
url="https://medinform.jmir.org/2022/4/e28696",
url="http://www.ncbi.nlm.nih.gov/pubmed/35442203"
}


@Article{info:doi/10.2196/33537,
author="Olaye, M. Iredia
and Belovsky, P. Mia
and Bataille, Lauren
and Cheng, Royce
and Ciger, Ali
and Fortuna, L. Karen
and Izmailova, S. Elena
and McCall, Debbe
and Miller, J. Christopher
and Muehlhausen, Willie
and Northcott, A. Carrie
and Rodriguez-Chavez, R. Isaac
and Pratap, Abhishek
and Vandendriessche, Benjamin
and Zisman-Ilani, Yaara
and Bakker, P. Jessie",
title="Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="14",
volume="24",
number="4",
pages="e33537",
keywords="digital medicine",
keywords="digital measures",
keywords="adherence",
keywords="compliance",
keywords="mobile phone",
abstract="Background: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. Objective: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. Methods: We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). Results: Our PubMed search terms identified 940 manuscripts; 100 (10.6\%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30\% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92\% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. Conclusions: We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports. ",
doi="10.2196/33537",
url="https://www.jmir.org/2022/4/e33537",
url="http://www.ncbi.nlm.nih.gov/pubmed/35436221"
}


@Article{info:doi/10.2196/34567,
author="Santonen, Teemu
and Petsani, Despoina
and Julin, Mikko
and Garschall, Markus
and Kropf, Johannes
and Van der Auwera, Vicky
and Bernaerts, Sylvie
and Losada, Raquel
and Almeida, Rosa
and Garatea, Jokin
and Mu{\~n}oz, Idoia
and Nagy, Eniko
and Kehayia, Eva
and de Guise, Elaine
and Nadeau, Sylvie
and Azevedo, Nancy
and Segkouli, Sofia
and Lazarou, Ioulietta
and Petronikolou, Vasileia
and Bamidis, Panagiotis
and Konstantinidis, Evdokimos",
title="Cocreating a Harmonized Living Lab for Big Data--Driven Hybrid Persona Development: Protocol for Cocreating, Testing, and Seeking Consensus",
journal="JMIR Res Protoc",
year="2022",
month="Jan",
day="6",
volume="11",
number="1",
pages="e34567",
keywords="Living Lab",
keywords="everyday living",
keywords="technology",
keywords="big data",
keywords="harmonization",
keywords="personas",
keywords="small-scale real-life testing",
keywords="mobile phone",
abstract="Background: Living Labs are user-centered, open innovation ecosystems based on a systematic user cocreation approach, which integrates research and innovation processes in real-life communities and settings. The Horizon 2020 Project VITALISE (Virtual Health and Wellbeing Living Lab Infrastructure) unites 19 partners across 11 countries. The project aims to harmonize Living Lab procedures and enable effective and convenient transnational and virtual access to key European health and well-being research infrastructures, which are governed by Living Labs. The VITALISE consortium will conduct joint research activities in the fields included in the care pathway of patients: rehabilitation, transitional care, and everyday living environments for older adults. This protocol focuses on health and well-being research in everyday living environments. Objective: The main aim of this study is to cocreate and test a harmonized research protocol for developing big data--driven hybrid persona, which are hypothetical user archetypes created to represent a user community. In addition, the use and applicability of innovative technologies will be investigated in the context of various everyday living and Living Lab environments. Methods: In phase 1, surveys and structured interviews will be used to identify the most suitable Living Lab methods, tools, and instruments for health-related research among VITALISE project Living Labs (N=10). A series of web-based cocreation workshops and iterative cowriting processes will be applied to define the initial protocols. In phase 2, five small-scale case studies will be conducted to test the cocreated research protocols in various real-life everyday living settings and Living Lab infrastructures. In phase 3, a cross-case analysis grounded on semistructured interviews will be conducted to identify the challenges and benefits of using the proposed research protocols. Furthermore, a series of cocreation workshops and the consensus seeking Delphi study process will be conducted in parallel to cocreate and validate the acceptance of the defined harmonized research protocols among wider Living Lab communities. Results: As of September 30, 2021, project deliverables Ethics and safety manual and Living lab standard version 1 have been submitted to the European Commission review process. The study will be finished by March 2024. Conclusions: The outcome of this research will lead to harmonized procedures and protocols in the context of big data--driven hybrid persona development among health and well-being Living Labs in Europe and beyond. Harmonized protocols enable Living Labs to exploit similar research protocols, devices, hardware, and software for interventions and complex data collection purposes. Economies of scale and improved use of resources will speed up and improve research quality and offer novel possibilities for open data sharing, multidisciplinary research, and comparative studies beyond current practices. Case studies will also provide novel insights for implementing innovative technologies in the context of everyday Living Lab research. International Registered Report Identifier (IRRID): DERR1-10.2196/34567 ",
doi="10.2196/34567",
url="https://www.researchprotocols.org/2022/1/e34567",
url="http://www.ncbi.nlm.nih.gov/pubmed/34989697"
}


@Article{info:doi/10.2196/30368,
author="Mudaranthakam, Pal Dinesh
and Brown, Alexandra
and Kerling, Elizabeth
and Carlson, E. Susan
and Valentine, J. Christina
and Gajewski, Byron",
title="The Successful Synchronized Orchestration of an Investigator-Initiated Multicenter Trial Using a Clinical Trial Management System and Team Approach: Design and Utility Study",
journal="JMIR Form Res",
year="2021",
month="Dec",
day="22",
volume="5",
number="12",
pages="e30368",
keywords="data management",
keywords="data quality",
keywords="metrics",
keywords="trial execution",
keywords="clinical trials",
keywords="cost",
keywords="accrual",
keywords="accrual inequality",
keywords="rare diseases",
keywords="healthcare",
keywords="health care",
keywords="health operations",
abstract="Background: As the cost of clinical trials continues to rise, novel approaches are required to ensure ethical allocation of resources. Multisite trials have been increasingly utilized in phase 1 trials for rare diseases and in phase 2 and 3 trials to meet accrual needs. The benefits of multisite trials include easier patient recruitment, expanded generalizability, and more robust statistical analyses. However, there are several problems more likely to arise in multisite trials, including accrual inequality, protocol nonadherence, data entry mistakes, and data integration difficulties. Objective: The Biostatistics \& Data Science department at the University of Kansas Medical Center developed a clinical trial management system (comprehensive research information system [CRIS]) specifically designed to streamline multisite clinical trial management. Methods: A National Institute of Child Health and Human Development--funded phase 3 trial, the ADORE (assessment of docosahexaenoic acid [DHA] on reducing early preterm birth) trial fully utilized CRIS to provide automated accrual reports, centralize data capture, automate trial completion reports, and streamline data harmonization. Results: Using the ADORE trial as an example, we describe the utility of CRIS in database design, regulatory compliance, training standardization, study management, and automated reporting. Our goal is to continue to build a CRIS through use in subsequent multisite trials. Reports generated to suit the needs of future studies will be available as templates. Conclusions: The implementation of similar tools and systems could provide significant cost-saving and operational benefit to multisite trials. Trial Registration: ClinicalTrials.gov NCT02626299; https://tinyurl.com/j6erphcj ",
doi="10.2196/30368",
url="https://formative.jmir.org/2021/12/e30368",
url="http://www.ncbi.nlm.nih.gov/pubmed/34941552"
}


@Article{info:doi/10.2196/33608,
author="Galea, T. Jerome
and Greene, Y. Karah
and Nguyen, Brandon
and Polonijo, N. Andrea
and Dub{\'e}, Karine
and Taylor, Jeff
and Christensen, Christopher
and Zhang, Zhiwei
and Brown, Brandon",
title="Evaluating the Impact of Incentives on Clinical Trial Participation: Protocol for a Mixed Methods, Community-Engaged Study",
journal="JMIR Res Protoc",
year="2021",
month="Nov",
day="23",
volume="10",
number="11",
pages="e33608",
keywords="incentives",
keywords="ethics",
keywords="research participation",
keywords="stakeholder advisory board",
keywords="HIV",
abstract="Background: Monetary incentives in research are frequently used to support participant recruitment and retention. However, there are scant empirical data regarding how researchers decide upon the type and amount of incentives offered. Likewise, there is little guidance to assist study investigators and institutional review boards (IRBs) in their decision-making on incentives. Monetary incentives, in addition to other factors such as the risk of harm or other intangible benefits, guide individuals' decisions to enroll in research studies. These factors emphasize the need for evidence-informed guidance for study investigators and IRBs when determining the type and amount of incentives to provide to research participants. Objective: The specific aims of our research project are to (1) characterize key stakeholders' views on and assessments of incentives in biomedical HIV research; (2) reach consensus among stakeholders on the factors that are considered when choosing research incentives, including consensus on the relative importance of such factors; and (3) pilot-test the use of the guidance developed via aims 1 and 2 by presenting stakeholders with vignettes of hypothetical research studies for which they will choose corresponding incentive types. Methods: Our 2-year study will involve monthly, active engagement with a stakeholder advisory board of people living with HIV, researchers, and IRB members. For aim 1, we will conduct a nationwide survey (N=300) among people living with HIV to understand their views regarding the incentives used in HIV research. For aim 2, we will collect qualitative data by conducting focus groups with people living with HIV (n=60) and key informant interviews with stakeholders involved in HIV research (people living with HIV, IRB members, and biomedical HIV researchers: n=36) to extend and deepen our understanding of how incentives in HIV research are perceived. These participants will also complete a conjoint analysis experiment to gain an understanding of the relative importance of key HIV research study attributes and the impact that these attributes have on study participation. The data from the nationwide survey (aim 1) will be triangulated with the qualitative and conjoint analysis data (aim 2) to create 25 vignettes that describe hypothetical HIV research studies. Finally, individuals from each stakeholder group will select the most appropriate incentive that they feel should be used in each of the 25 vignettes (aim 3). Results: The stakeholder advisory board began monthly meetings in March 2021. All study aims are expected to be completed by December 2022. Conclusions: By studying the role of incentives in HIV clinical trial participation, we will establish a decision-making paradigm to guide the choice of incentives for HIV research and, eventually, other types of similar research and facilitate the ethical recruitment of clinical research participants. Trial Registration: ClinicalTrials.gov NCT04809636; https://clinicaltrials.gov/ct2/show/NCT04809636 International Registered Report Identifier (IRRID): DERR1-10.2196/33608 ",
doi="10.2196/33608",
url="https://www.researchprotocols.org/2021/11/e33608",
url="http://www.ncbi.nlm.nih.gov/pubmed/34817381"
}


@Article{info:doi/10.2196/29259,
author="Lamer, Antoine
and Abou-Arab, Osama
and Bourgeois, Alexandre
and Parrot, Adrien
and Popoff, Benjamin
and Beuscart, Jean-Baptiste
and Tavernier, Beno{\^i}t
and Moussa, Djahoum Mouhamed",
title="Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="29",
volume="23",
number="10",
pages="e29259",
keywords="data reuse",
keywords="common data model",
keywords="Observational Medical Outcomes Partnership",
keywords="anesthesia",
keywords="data warehouse",
keywords="reproducible research",
abstract="Background: Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective: The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods: Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results: We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7\%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3\%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions: Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse. ",
doi="10.2196/29259",
url="https://www.jmir.org/2021/10/e29259",
url="http://www.ncbi.nlm.nih.gov/pubmed/34714250"
}


@Article{info:doi/10.2196/26890,
author="Borysowski, Jan
and G{\'o}rski, Andrzej",
title="ClinicalTrials.gov as a Source of Information About Expanded Access Programs: Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="28",
volume="23",
number="10",
pages="e26890",
keywords="ClinicalTrials.gov",
keywords="expanded access",
keywords="expanded access program",
keywords="compassionate use",
keywords="unapproved drug",
keywords="investigational drug",
abstract="Background: ClinicalTrials.gov (CT.gov) is the most comprehensive internet-based register of different types of clinical studies. Expanded access is the use of unapproved drugs, biologics, or medical devices outside of clinical trials. One of the key problems in expanded access is the availability to both health care providers and patients of information about unapproved treatments. Objective: We aimed to evaluate CT.gov as a potential source of information about expanded access programs. Methods: We assessed the completeness of information in the records of 228 expanded access programs registered with CT.gov from February 2017 through May 2020. Moreover, we examined what percentage of published expanded access studies has been registered with CT.gov. Logistic regression (univariate and multivariate) and mediation analyses were used to identify the predictors of the absence of some information and a study's nonregistration. Results: We found that some important data were missing from the records of many programs. Information that was missing most often included a detailed study description, facility information, central contact person, and eligibility criteria (55.3\%, 54.0\%, 41.7\%, and 17.5\% of the programs, respectively). Multivariate analysis showed that information about central contact person was more likely to be missing from records of studies registered in 2017 (adjusted OR 21.93; 95\% CI 4.42-172.29; P<.001). This finding was confirmed by mediation analysis (P=.02). Furthermore, 14\% of the programs were registered retrospectively. We also showed that only 33 of 77 (42.9\%) expanded access studies performed in the United States and published from 2014 through 2019 were registered with CT.gov. However, multivariate logistic regression analysis showed no significant association between any of the variables related to the studies and the odds of study nonregistration (P>.01). Conclusions: Currently, CT.gov is a quite fragmentary source of data on expanded access programs. This problem is important because CT.gov is the only publicly available primary source of information about specific programs. We suggest the actions that should be taken by different stakeholders to fully exploit this register as a source of information about expanded access. ",
doi="10.2196/26890",
url="https://www.jmir.org/2021/10/e26890",
url="http://www.ncbi.nlm.nih.gov/pubmed/34709189"
}


@Article{info:doi/10.2196/33192,
author="Li, Mengyang
and Cai, Hailing
and Nan, Shan
and Li, Jialin
and Lu, Xudong
and Duan, Huilong",
title="A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study",
journal="JMIR Med Inform",
year="2021",
month="Oct",
day="21",
volume="9",
number="10",
pages="e33192",
keywords="openEHR",
keywords="patient screening",
keywords="electronic health record",
keywords="clinical research",
abstract="Background: The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria. Objective: The study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance. Methods: A patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research. Results: In total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1\%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67\%). Conclusions: We developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers. ",
doi="10.2196/33192",
url="https://medinform.jmir.org/2021/10/e33192",
url="http://www.ncbi.nlm.nih.gov/pubmed/34673526"
}


@Article{info:doi/10.2196/28923,
author="Reuter, Katja
and Liu, Chang
and Le, NamQuyen
and Angyan, Praveen
and Finley, M. James",
title="General Practice and Digital Methods to Recruit Stroke Survivors to a Clinical Mobility Study: Comparative Analysis",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="13",
volume="23",
number="10",
pages="e28923",
keywords="clinical trial",
keywords="stroke",
keywords="falls",
keywords="digital media",
keywords="social media",
keywords="advertising",
keywords="participant recruitment",
keywords="Facebook",
keywords="Google",
keywords="clinical research",
keywords="research methods",
keywords="recruitment practices",
keywords="enrollment",
abstract="Background: Participant recruitment remains a barrier to conducting clinical research. The disabling nature of a stroke, which often includes functional and cognitive impairments, and the acute stage of illness at which patients are appropriate for many trials make recruiting patients particularly complex and challenging. In addition, people aged 65 years and older, which includes most stroke survivors, have been identified as a group that is difficult to reach and is commonly underrepresented in health research, particularly clinical trials. Digital media may provide effective tools to support enrollment efforts of stroke survivors in clinical trials. Objective: The objective of this study was to compare the effectiveness of general practice (traditional) and digital (online) methods of recruiting stroke survivors to a clinical mobility study. Methods: Recruitment for a clinical mobility study began in July 2018. Eligible study participants included individuals 18 years and older who had a single stroke and were currently ambulatory in the community. General recruiting practice included calling individuals listed in a stroke registry, contacting local physical therapists, and placing study flyers throughout a university campus. Between May 21, 2019, and June 26, 2019, the study was also promoted digitally using the social network Facebook and the search engine marketing tool Google AdWords. The recruitment advertisements (ads) included a link to the study page to which users who clicked were referred. Primary outcomes of interest for both general practice and digital methods included recruitment speed (enrollment rate) and sample characteristics. The data were analyzed using the Lilliefors test, the Welch two-sample t test, and the Mann-Whitney test. Significance was set at P=.05. All statistical analyses were performed in MATLAB 2019b. Results: Our results indicate that digital recruitment methods can address recruitment challenges regarding stroke survivors. Digital recruitment methods allowed us to enroll study participants at a faster rate (1.8 participants/week) compared to using general practice methods (0.57 participants/week). Our findings also demonstrate that digital and general recruitment practices can achieve an equivalent level of sample representativeness. The characteristics of the enrolled stroke survivors did not differ significantly by age (P=.95) or clinical scores (P=.22; P=.82). Comparing the cost-effectiveness of Facebook and Google, we found that the use of Facebook resulted in a lower cost per click and cost per enrollee per ad. Conclusions: Digital recruitment can be used to expedite participant recruitment of stroke survivors compared to more traditional recruitment practices, while also achieving equivalent sample representativeness. Both general practice and digital recruitment methods will be important to the successful recruitment of stroke survivors. Future studies could focus on testing the effectiveness of additional general practice and digital media approaches and include robust cost-effectiveness analyses. Examining the effectiveness of different messaging and visual approaches tailored to culturally diverse and underrepresented target subgroups could provide further data to move toward evidence-based recruitment strategies. ",
doi="10.2196/28923",
url="https://www.jmir.org/2021/10/e28923",
url="http://www.ncbi.nlm.nih.gov/pubmed/34643544"
}


@Article{info:doi/10.2196/25621,
author="Perez, A. Edith
and Jaffee, M. Elizabeth
and Whyte, John
and Boyce, A. Cheryl
and Carpten, D. John
and Lozano, Guillermina
and Williams, M. Raymond
and Winkfield, M. Karen
and Bernstein, David
and Poblete, Sung",
title="Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study",
journal="JMIR Cancer",
year="2021",
month="Sep",
day="23",
volume="7",
number="3",
pages="e25621",
keywords="cancer",
keywords="clinical trials",
keywords="data mining",
keywords="text extraction",
keywords="social media",
keywords="race and ethnicity",
keywords="health communication",
keywords="health care disparities",
keywords="natural language processing",
abstract="Background: Racial and ethnic diversity in clinical trials for cancer treatment is essential for the development of treatments that are effective for all patients and for identifying potential differences in toxicity between different demographics. Mining of social media discussions about clinical trials has been used previously to identify patient barriers to enrollment in clinical trials; however, a comprehensive breakdown of sentiments and barriers by various racial and ethnic groups is lacking. Objective: The aim of this study is to use an innovative methodology to analyze web-based conversations about cancer clinical trials and to identify and compare conversation topics, barriers, and sentiments between different racial and ethnic populations. Methods: We analyzed 372,283 web-based conversations about cancer clinical trials, of which 179,339 (48.17\%) of the discussions had identifiable race information about the individual posting the conversations. Using sophisticated machine learning software and analyses, we were able to identify key sentiments and feelings, topics of interest, and barriers to clinical trials across racial groups. The stage of treatment could also be identified in many of the discussions, allowing for a unique insight into how the sentiments and challenges of patients change throughout the treatment process for each racial group. Results: We observed that only 4.01\% (372,283/9,284,284) of cancer-related discussions referenced clinical trials. Within these discussions, topics of interest and identified clinical trial barriers discussed by all racial and ethnic groups throughout the treatment process included health care professional interactions, cost of care, fear, anxiety and lack of awareness, risks, treatment experiences, and the clinical trial enrollment process. Health care professional interactions, cost of care, and enrollment processes were notably discussed more frequently in minority populations. Other minor variations in the frequency of discussion topics between ethnic and racial groups throughout the treatment process were identified. Conclusions: This study demonstrates the power of digital search technology in health care research. The results are also valuable for identifying the ideal content and timing for the delivery of clinical trial information and resources for different racial and ethnic groups. ",
doi="10.2196/25621",
url="https://cancer.jmir.org/2021/3/e25621",
url="http://www.ncbi.nlm.nih.gov/pubmed/34554099"
}


@Article{info:doi/10.2196/13790,
author="Schreiweis, Bj{\"o}rn
and Brandner, Antje
and Bergh, Bj{\"o}rn",
title="Applicability of Different Electronic Record Types for Use in Patient Recruitment Support Systems: Comparative Analysis",
journal="JMIR Form Res",
year="2021",
month="Sep",
day="21",
volume="5",
number="9",
pages="e13790",
keywords="clinical trials",
keywords="patient recruitment support system",
keywords="PRSS",
keywords="electronic medical record",
keywords="EMR",
keywords="electronic health record",
keywords="EHR",
keywords="personal health record",
keywords="PHR",
keywords="personal enterprise health record",
keywords="PEHR",
keywords="clinical trial recruitment support system",
keywords="CTRSS.",
abstract="Background: Clinical trials constitute an important pillar in medical research. It is beneficial to support recruitment for clinical trials using software tools, so-called patient recruitment support systems; however, such information technology systems have not been frequently used to date. Because medical information systems' underlying data collection methods strongly influence the benefits of implementing patient recruitment support systems, we investigated patient recruitment support system requirements and corresponding electronic record types such as electronic medical record, electronic health record, electronic medical case record, personal health record, and personal cross-enterprise health record. Objective: The aim of this study was to (1) define requirements for successful patient recruitment support system deployment and (2) differentiate and compare patient recruitment support system--relevant properties of different electronic record types. Methods: In a previous study, we gathered requirements for patient recruitment support systems from literature and unstructured interviews with stakeholders (15 patients, 3 physicians, 5 data privacy experts, 4 researchers, and 5 staff members of hospital administration). For this investigation, the requirements were amended and categorized based on input from scientific sessions. Based on literature with a focus on patient recruitment support system--relevant properties, different electronic record types (electronic medical record, electronic health record, electronic medical case record, personal health record and personal cross-enterprise health record) were described in detail. We also evaluated which patient recruitment support system requirements can be achieved for each electronic record type. Results: Patient recruitment support system requirements (n=16) were grouped into 4 categories (consent management, patient recruitment management, trial management, and general requirements). All 16 requirements could be partially met by at least 1 type of electronic record. Only 1 requirement was fully met by all 5 types. According to our analysis, personal cross-enterprise health records fulfill most requirements for patient recruitment support systems. They demonstrate advantages especially in 2 domains (1) supporting patient empowerment and (2) granting access to the complete medical history of patients. Conclusions: In combination with patient recruitment support systems, personal cross-enterprise health records prove superior to other electronic record types, and therefore, this integration approach should be further investigated. ",
doi="10.2196/13790",
url="https://formative.jmir.org/2021/9/e13790",
url="http://www.ncbi.nlm.nih.gov/pubmed/34546175"
}


@Article{info:doi/10.2196/28573,
author="Zola Matuvanga, Tr{\'e}sor
and Johnson, Ginger
and Larivi{\`e}re, Ynke
and Esanga Longomo, Emmanuel
and Matangila, Junior
and Maketa, Vivi
and Lapika, Bruno
and Mitashi, Patrick
and Mc Kenna, Paula
and De Bie, Jessie
and Van Geertruyden, Jean-Pierre
and Van Damme, Pierre
and Muhindo Mavoko, Hypolite",
title="Use of Iris Scanning for Biometric Recognition of Healthy Adults Participating in an Ebola Vaccine Trial in the Democratic Republic of the Congo: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="9",
volume="23",
number="8",
pages="e28573",
keywords="biometric identification",
keywords="iris recognition",
keywords="vaccine trial",
keywords="participants' visits",
keywords="acceptability",
keywords="feasibility",
keywords="Democratic Republic of the Congo",
keywords="mixed methods",
keywords="Ebola",
abstract="Background: A partnership between the University of Antwerp and the University of Kinshasa implemented the EBOVAC3 clinical trial with an Ebola vaccine regimen administered to health care provider participants in Tshuapa Province, Democratic Republic of the Congo. This randomized controlled trial was part of an Ebola outbreak preparedness initiative financed through Innovative Medicines Initiative-European Union. The EBOVAC3 clinical trial used iris scan technology to identify all health care provider participants enrolled in the vaccine trial, to ensure that the right participant received the right vaccine at the right visit. Objective: We aimed to assess the acceptability, accuracy, and feasibility of iris scan technology as an identification method within a population of health care provider participants in a vaccine trial in a remote setting. Methods: We used a mixed methods study. The acceptability was assessed prior to the trial through 12 focus group discussions (FGDs) and was assessed at enrollment. Feasibility and accuracy research was conducted using a longitudinal trial study design, where iris scanning was compared with the unique study ID card to identify health care provider participants at enrollment and at their follow-up visits. Results: During the FGDs, health care provider participants were mainly concerned about the iris scan technology causing physical problems to their eyes or exposing them to spiritual problems through sorcery. However, 99\% (85/86; 95\% CI 97.1-100.0) of health care provider participants in the FGDs agreed to be identified by the iris scan. Also, at enrollment, 99.0\% (692/699; 95\% CI 98.2-99.7) of health care provider participants accepted to be identified by iris scan. Iris scan technology correctly identified 93.1\% (636/683; 95\% CI 91.2-95.0) of the participants returning for scheduled follow-up visits. The iris scanning operation lasted 2 minutes or less for 96.0\% (656/683; 95\% CI 94.6-97.5), and 1 attempt was enough to identify the majority of study participants (475/683, 69.5\%; 95\% CI 66.1-73.0). Conclusions: Iris scans are highly acceptable as an identification tool in a clinical trial for health care provider participants in a remote setting. Its operationalization during the trial demonstrated a high level of accuracy that can reliably identify individuals. Iris scanning is found to be feasible in clinical trials but requires a trained operator to reduce the duration and the number of attempts to identify a participant. Trial Registration: ClinicalTrials.gov NCT04186000; https://clinicaltrials.gov/ct2/show/NCT04186000 ",
doi="10.2196/28573",
url="https://www.jmir.org/2021/8/e28573",
url="http://www.ncbi.nlm.nih.gov/pubmed/34378545"
}


@Article{info:doi/10.2196/26284,
author="Sato, Kenichiro
and Niimi, Yoshiki
and Ihara, Ryoko
and Suzuki, Kazushi
and Toda, Tatsushi
and Iwata, Atsushi
and Iwatsubo, Takeshi",
title="Efficacy and Cost-effectiveness of Promotion Methods to Recruit Participants to an Online Screening Registry for Alzheimer Disease Prevention Trials: Observational Study",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="22",
volume="23",
number="7",
pages="e26284",
keywords="online clinical study",
keywords="promotion",
keywords="advertisement",
keywords="cost-effectiveness",
keywords="Trial-Ready Cohort",
keywords="preclinical Alzheimer disease",
keywords="clinical trial",
keywords="Alzheimer",
keywords="dementia",
keywords="recruitment",
abstract="Background: Web-based screening may be suitable for identifying individuals with presymptomatic latent diseases for recruitment to clinical studies, as such people do not often visit hospitals in the presymptomatic stage. The promotion of such online screening studies is critical to their success, although it remains uncertain how the effectiveness of such promotion can differ, depending on the different promotion methods, domains of interest, or countries of implementation. Objective: The Japanese Trial-Ready Cohort (J-TRC) web study is our ongoing online screening registry to identify individuals with presymptomatic Alzheimer disease (AD), aimed at facilitating the clinical trials for AD prevention. Within the first 9 months of its 2019 launch, the J-TRC web study recruited thousands of online participants via multiple methods of promotion, including press releases, newspaper advertisements, web advertisements, or direct email invitations. Here, we aimed to quantitatively evaluate efficacy and cost-effectiveness of each of these multimodal promotion methods. Methods: We applied the vector-autoregression model to assess the degree of contribution of each type of promotion to the following target metrics: number of daily visitors to the J-TRC website, number of daily registrants to the J-TRC web study, daily rate of registration among visitors, daily rate of eligible participants among registrants, and median age of daily registrants. The average cost-effectiveness for each promotion method was also calculated using the total cost and the coefficients in the vector-autoregression model. Results: During the first 9 months of the reviewed period from October 31, 2019 to June 17, 2020, there were 48,334 website visitors and 4429 registrations (9.16\% of 48,334 visitors), of which 3081 (69.56\%) were eligible registrations. Initial press release reports and newspaper advertisements had a marked effect on increasing the number of daily visitors and daily registrants. Web advertisements significantly contributed to the increase in daily visitors (P<.001) but not to the daily registrants, and it also lowered the rate of registrations and the median age of daily registrants. Website visitors from the direct email invitation sent to other cognitive registries seem to have registered with the highest reliability. The calculated average cost-effectiveness for the initial press release was US \$24.60 per visitor and US \$96.10 per registrant, while the calculated average cost-effectiveness for the newspaper advertisements was US \$28.60 per visitor and US \$227.90 per registrant. Conclusions: Our multivariate time-series analysis showed that each promotion method had different features in their effect of recruiting participants to the J-TRC web study. Under the advertisement condition settings thus far, newspaper advertisements and initial press releases were the most effective promotion methods, with fair cost-effectiveness that was equivalent to earlier online studies. These results can provide important suggestions for future promotions for the recruitment of presymptomatic participants to AD clinical trials in Japan. ",
doi="10.2196/26284",
url="https://www.jmir.org/2021/7/e26284",
url="http://www.ncbi.nlm.nih.gov/pubmed/34292159"
}


@Article{info:doi/10.2196/26813,
author="Coert, Helena Rom{\'e}e Melanie
and Timmis, Kenneth James
and Boorsma, Andr{\'e}
and Pasman, J. Wilrike",
title="Stakeholder Perspectives on Barriers and Facilitators for the Adoption of Virtual Clinical Trials: Qualitative Study",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="6",
volume="23",
number="7",
pages="e26813",
keywords="virtual clinical trials",
keywords="decentralized clinical trials",
keywords="adoption",
keywords="do-it-yourself",
keywords="wearables",
keywords="diffusion of innovation theory",
keywords="clinical trials",
keywords="digital health",
keywords="virtual health",
abstract="Background: Conventional clinical trials are essential for generating high-quality evidence by measuring the efficacy of interventions in rigorously controlled clinical environments. However, their execution can be expensive and time-consuming. In addition, clinical trials face several logistical challenges regarding the identification, recruitment, and retention of participants; consistent data collection during trials; and adequate patient follow-up. This might lead to inefficient resource utilization. In order to partially address the current problems with conventional clinical trials, there exists the need for innovations. One such innovation is the virtual clinical trial (VCT). VCTs allow for the collection and integration of diverse data from multiple information sources, such as electronic health records, clinical and demographic data, patient-reported outcomes, anthropometric and activity measurements, and data collected by digital biomarkers or (small) samples that participants can collect themselves. Although VCTs have the potential to provide substantial value to clinical research and patients because they can lower clinical trial costs, increase the volume of data collected from patients' daily environment, and reduce the burden of patient participation, so far VCT adoption is not commonplace. Objective: This paper aims to better understand the barriers and facilitators to VCT adoption by determining the factors that influence individuals' considerations regarding VCTs from the perspective of various stakeholders. Methods: Based on online semistructured interviews, a qualitative study was conducted with pharmaceutical companies, food and health organizations, and an applied research organization in Europe. Data were thematically analyzed using Rogers' diffusion of innovation theory. Results: A total of 16 individuals with interest and experience in VCTs were interviewed, including persons from pharmaceutical companies (n=6), food and health organizations (n=4), and a research organization (n=6). Key barriers included a potentially low degree of acceptance by regulatory authorities, technical issues (standardization, validation, and data storage), compliance and adherence, and lack of knowledge or comprehension regarding the opportunities VCTs have to offer. Involvement of regulators in development processes, stakeholder exposure to the results of pilot studies, and clear and simple instructions and assistance for patients were considered key facilitators. Conclusions: Collaboration among all stakeholders in VCT development is crucial to increase knowledge and awareness. Organizations should invest in accurate data collection technologies, and compliance of patients in VCTs needs to be ensured. Multicriteria decision analysis can help determine if a VCT is a preferred option by stakeholders. The findings of this study can be a good starting point to accelerate the development and widespread implementation of VCTs. ",
doi="10.2196/26813",
url="https://www.jmir.org/2021/7/e26813",
url="http://www.ncbi.nlm.nih.gov/pubmed/34255673"
}


@Article{info:doi/10.2196/26004,
author="Ferrar, Jennifer
and Griffith, J. Gareth
and Skirrow, Caroline
and Cashdollar, Nathan
and Taptiklis, Nick
and Dobson, James
and Cree, Fiona
and Cormack, K. Francesca
and Barnett, H. Jennifer
and Munaf{\`o}, R. Marcus",
title="Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="18",
volume="23",
number="6",
pages="e26004",
keywords="digital assessment",
keywords="remote research",
keywords="measurement validity",
keywords="clinical outcomes",
keywords="ecological momentary assessment",
keywords="mobile phone",
doi="10.2196/26004",
url="https://www.jmir.org/2021/6/e26004",
url="http://www.ncbi.nlm.nih.gov/pubmed/34142972"
}


@Article{info:doi/10.2196/21459,
author="Her, Qoua
and Kent, Thomas
and Samizo, Yuji
and Slavkovic, Aleksandra
and Vilk, Yury
and Toh, Sengwee",
title="Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="23",
volume="9",
number="4",
pages="e21459",
keywords="distributed regression analysis",
keywords="distributed data networks",
keywords="privacy-protecting analytics",
keywords="vertically partitioned data",
keywords="informatics",
keywords="data networks",
keywords="data",
abstract="Background: In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression---a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information---with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown. Objective: The objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings. Methods: We gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA. Results: PopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center. Conclusions: PopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings. ",
doi="10.2196/21459",
url="https://medinform.jmir.org/2021/4/e21459",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890866"
}


@Article{info:doi/10.2196/23161,
author="Grabner, Michael
and Molife, Cliff
and Wang, Liya
and Winfree, B. Katherine
and Cui, Lin Zhanglin
and Cuyun Carter, Gebra
and Hess, M. Lisa",
title="Data Integration to Improve Real-world Health Outcomes Research for Non--Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration",
journal="JMIR Cancer",
year="2021",
month="Apr",
day="12",
volume="7",
number="2",
pages="e23161",
keywords="non--small cell lung cancer",
keywords="cancer",
keywords="data aggregation",
keywords="real-world data",
keywords="administrative claims data",
keywords="medical records",
keywords="electronic health record",
keywords="retrospective study",
keywords="population health",
keywords="health services research",
abstract="Background: The integration of data from disparate sources could help alleviate data insufficiency in real-world studies and compensate for the inadequacies of single data sources and short-duration, small sample size studies while improving the utility of data for research. Objective: This study aims to describe and evaluate a process of integrating data from several complementary sources to conduct health outcomes research in patients with non--small cell lung cancer (NSCLC). The integrated data set is also used to describe patient demographics, clinical characteristics, treatment patterns, and mortality rates. Methods: This retrospective cohort study integrated data from 4 sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Patients with lung cancer who initiated second-line (2L) therapy between November 01, 2015, and April 13, 2018, were identified in the claims and CCQP data. Eligible patients were 18 years or older and received atezolizumab, docetaxel, erlotinib, nivolumab, pembrolizumab, pemetrexed, or ramucirumab in the 2L setting. The main analysis cohort included patients with claims data and data from at least one additional data source (CCQP or MR). Patients without integrated data (claims only) were reported separately. Descriptive and univariate statistics were reported. Results: Data integration resulted in a main analysis cohort of 2195 patients with NSCLC; 2106 patients had CCQP and 407 patients had MR data. The claims-only cohort included 931 eligible patients. For the main analysis cohort, the mean age was 62.1 (SD 9.27) years, 48.56\% (1066/2195) were female, the median length of follow-up was 6.8 months, and for 37.77\% (829/2195), death was observed. For the claims-only cohort, the mean age was 66.6 (SD 12.69) years, 52.1\% (485/931) were female, the median length of follow-up was 8.6 months, and for 29.3\% (273/931), death was observed. The most frequent 2L treatment was immunotherapy (1094/2195, 49.84\%), followed by platinum-based regimens (472/2195, 21.50\%) and single-agent chemotherapy (441/2195, 20.09\%); mean duration of 2L therapy was 5.6 (SD 4.9, median 4) months. We describe challenges and learnings from the data integration process, and the benefits of the integrated data set, which includes a richer set of clinical and outcome data to supplement the utilization metrics available in administrative claims. Conclusions: The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments. ",
doi="10.2196/23161",
url="https://cancer.jmir.org/2021/2/e23161",
url="http://www.ncbi.nlm.nih.gov/pubmed/33843600"
}


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


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


@Article{info:doi/10.2196/21043,
author="Park, Ae Ji
and Sung, Dong Min
and Kim, Heon Ho
and Park, Rang Yu",
title="Weight-Based Framework for Predictive Modeling of Multiple Databases With Noniterative Communication Without Data Sharing: Privacy-Protecting Analytic Method for Multi-Institutional Studies",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="5",
volume="9",
number="4",
pages="e21043",
keywords="multi-institutional study",
keywords="distributed data",
keywords="data sharing",
keywords="privacy-protecting methods",
abstract="Background: Securing the representativeness of study populations is crucial in biomedical research to ensure high generalizability. In this regard, using multi-institutional data have advantages in medicine. However, combining data physically is difficult as the confidential nature of biomedical data causes privacy issues. Therefore, a methodological approach is necessary when using multi-institution medical data for research to develop a model without sharing data between institutions. Objective: This study aims to develop a weight-based integrated predictive model of multi-institutional data, which does not require iterative communication between institutions, to improve average predictive performance by increasing the generalizability of the model under privacy-preserving conditions without sharing patient-level data. Methods: The weight-based integrated model generates a weight for each institutional model and builds an integrated model for multi-institutional data based on these weights. We performed 3 simulations to show the weight characteristics and to determine the number of repetitions of the weight required to obtain stable values. We also conducted an experiment using real multi-institutional data to verify the developed weight-based integrated model. We selected 10 hospitals (2845 intensive care unit [ICU] stays in total) from the electronic intensive care unit Collaborative Research Database to predict ICU mortality with 11 features. To evaluate the validity of our model, compared with a centralized model, which was developed by combining all the data of 10 hospitals, we used proportional overlap (ie, 0.5 or less indicates a significant difference at a level of .05; and 2 indicates 2 CIs overlapping completely). Standard and firth logistic regression models were applied for the 2 simulations and the experiment. Results: The results of these simulations indicate that the weight of each institution is determined by 2 factors (ie, the data size of each institution and how well each institutional model fits into the overall institutional data) and that repeatedly generating 200 weights is necessary per institution. In the experiment, the estimated area under the receiver operating characteristic curve (AUC) and 95\% CIs were 81.36\% (79.37\%-83.36\%) and 81.95\% (80.03\%-83.87\%) in the centralized model and weight-based integrated model, respectively. The proportional overlap of the CIs for AUC in both the weight-based integrated model and the centralized model was approximately 1.70, and that of overlap of the 11 estimated odds ratios was over 1, except for 1 case. Conclusions: In the experiment where real multi-institutional data were used, our model showed similar results to the centralized model without iterative communication between institutions. In addition, our weight-based integrated model provided a weighted average model by integrating 10 models overfitted or underfitted, compared with the centralized model. The proposed weight-based integrated model is expected to provide an efficient distributed research approach as it increases the generalizability of the model and does not require iterative communication. ",
doi="10.2196/21043",
url="https://medinform.jmir.org/2021/4/e21043",
url="http://www.ncbi.nlm.nih.gov/pubmed/33818396"
}


@Article{info:doi/10.2196/23011,
author="Coetzee, Timothy
and Ball, Price Mad
and Boutin, Marc
and Bronson, Abby
and Dexter, T. David
and English, A. Rebecca
and Furlong, Patricia
and Goodman, D. Andrew
and Grossman, Cynthia
and Hernandez, F. Adrian
and Hinners, E. Jennifer
and Hudson, Lynn
and Kennedy, Annie
and Marchisotto, Jane Mary
and Matrisian, Lynn
and Myers, Elizabeth
and Nowell, Benjamin W.
and Nosek, A. Brian
and Sherer, Todd
and Shore, Carolyn
and Sim, Ida
and Smolensky, Luba
and Williams, Christopher
and Wood, Julie
and Terry, F. Sharon",
title="Data Sharing Goals for Nonprofit Funders of Clinical Trials",
journal="J Participat Med",
year="2021",
month="Mar",
day="29",
volume="13",
number="1",
pages="e23011",
keywords="clinical trial",
keywords="biomedical research",
keywords="data sharing",
keywords="patients",
doi="10.2196/23011",
url="https://jopm.jmir.org/2021/1/e23011",
url="http://www.ncbi.nlm.nih.gov/pubmed/33779573"
}


@Article{info:doi/10.2196/27767,
author="Haddad, Tufia
and Helgeson, M. Jane
and Pomerleau, E. Katharine
and Preininger, M. Anita
and Roebuck, Christopher M.
and Dankwa-Mullan, Irene
and Jackson, Purcell Gretchen
and Goetz, P. Matthew",
title="Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="26",
volume="9",
number="3",
pages="e27767",
keywords="clinical trial matching",
keywords="clinical decision support system",
keywords="machine learning",
keywords="artificial intelligence",
keywords="screening",
keywords="clinical trials",
keywords="eligibility",
keywords="breast cancer",
abstract="Background: Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process. Objective: This study aims to evaluate the ability of an AI clinical decision support system (CDSS) to identify eligible patients for a set of clinical trials. Methods: This study included the deidentified data from a cohort of patients with breast cancer seen at the medical oncology clinic of an academic medical center between May and July 2017 and assessed patient eligibility for 4 breast cancer clinical trials. CDSS eligibility screening performance was validated against manual screening. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for eligibility determinations were calculated. Disagreements between manual screeners and the CDSS were examined to identify sources of discrepancies. Interrater reliability between manual reviewers was analyzed using Cohen (pairwise) and Fleiss (three-way) $\kappa$, and the significance of differences was determined by Wilcoxon signed-rank test. Results: In total, 318 patients with breast cancer were included. Interrater reliability for manual screening ranged from 0.60-0.77, indicating substantial agreement. The overall accuracy of breast cancer trial eligibility determinations by the CDSS was 87.6\%. CDSS sensitivity was 81.1\% and specificity was 89\%. Conclusions: The AI CDSS in this study demonstrated accuracy, sensitivity, and specificity of greater than 80\% in determining the eligibility of patients for breast cancer clinical trials. CDSSs can accurately exclude ineligible patients for clinical trials and offer the potential to increase screening efficiency and accuracy. Additional research is needed to explore whether increased efficiency in screening and trial matching translates to improvements in trial enrollment, accruals, feasibility assessments, and cost. ",
doi="10.2196/27767",
url="https://medinform.jmir.org/2021/3/e27767",
url="http://www.ncbi.nlm.nih.gov/pubmed/33769304"
}


@Article{info:doi/10.2196/23328,
author="Park, Young Ho
and Bae, Hyun-Jin
and Hong, Gil-Sun
and Kim, Minjee
and Yun, JiHye
and Park, Sungwon
and Chung, Jung Won
and Kim, NamKug",
title="Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="17",
volume="9",
number="3",
pages="e23328",
keywords="generative adversarial network",
keywords="unsupervised deep learning",
keywords="computed tomography",
keywords="synthetic body images",
keywords="visual Turing test",
abstract="Background: Generative adversarial network (GAN)--based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. Objective: The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. Methods: We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with <5 years of experience [Group I], 4 radiologists with 5-10 years of experience [Group II], and 2 radiologists with >10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. Results: The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4\% vs 1500/3000, 50.0\%, respectively; P<.001). However, in terms of identifying synthetic images as fake, there was no significant difference in the specificity between the visual Turing test and random guessing (779/1500, 51.9\% vs 750/1500, 50.0\%, respectively; P=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0\%; Group II, 726/1200, 60.5\%; and Group III, 359/600, 59.8\%; P=.36). Interreader agreements were poor ($\kappa$=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. Conclusions: The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details. ",
doi="10.2196/23328",
url="https://medinform.jmir.org/2021/3/e23328",
url="http://www.ncbi.nlm.nih.gov/pubmed/33609339"
}


@Article{info:doi/10.2196/26718,
author="Dron, Louis
and Dillman, Alison
and Zoratti, J. Michael
and Haggstrom, Jonas
and Mills, J. Edward
and Park, H. Jay J.",
title="Clinical Trial Data Sharing for COVID-19--Related Research",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="12",
volume="23",
number="3",
pages="e26718",
keywords="COVID-19",
keywords="data-sharing",
keywords="clinical trials",
keywords="data",
keywords="research",
keywords="privacy",
keywords="security",
keywords="registry",
keywords="feasibility",
keywords="challenge",
keywords="recruitment",
keywords="error",
keywords="bias",
keywords="assessment",
keywords="interoperability",
keywords="dataset",
keywords="intervention",
keywords="cooperation",
doi="10.2196/26718",
url="https://www.jmir.org/2021/3/e26718",
url="http://www.ncbi.nlm.nih.gov/pubmed/33684053"
}


@Article{info:doi/10.2196/24055,
author="Allan, Stephanie
and Mcleod, Hamish
and Bradstreet, Simon
and Bell, Imogen
and Whitehill, Helen
and Wilson-Kay, Alison
and Clark, Andrea
and Matrunola, Claire
and Morton, Emma
and Farhall, John
and Gleeson, John
and Gumley, Andrew",
title="Perspectives of Trial Staff on the Barriers to Recruitment in a Digital Intervention for Psychosis and How to Work Around Them: Qualitative Study Within a Trial",
journal="JMIR Hum Factors",
year="2021",
month="Mar",
day="5",
volume="8",
number="1",
pages="e24055",
keywords="recruitment",
keywords="schizophrenia",
keywords="mHealth",
keywords="psychosis",
keywords="mental health",
abstract="Background: Recruitment processes for clinical trials of digital interventions for psychosis are seldom described in detail in the literature. Although trial staff have expertise in describing barriers to and facilitators of recruitment, a specific focus on understanding recruitment from the point of view of trial staff is rare, and because trial staff are responsible for meeting recruitment targets, a lack of research on their point of view is a key limitation. Objective: The primary aim of this study was to understand recruitment from the point of view of trial staff and discover what they consider important. Methods: We applied pluralistic ethnographic methods, including analysis of trial documents, observation, and focus groups, and explored the recruitment processes of the EMPOWER (Early Signs Monitoring to Prevent Relapse in Psychosis and Promote Well-being, Engagement, and Recovery) feasibility trial, which is a digital app--based intervention for people diagnosed with schizophrenia. Results: Recruitment barriers were categorized into 2 main themes: service characteristics (lack of time available for mental health staff to support recruitment, staff turnover, patient turnover [within Australia only], management styles of community mental health teams, and physical environment) and clinician expectations (filtering effects and resistance to research participation). Trial staff negotiated these barriers through strategies such as emotional labor (trial staff managing feelings and expressions to successfully recruit participants) and trying to build relationships with clinical staff working within community mental health teams. Conclusions: Researchers in clinical trials for digital psychosis interventions face numerous recruitment barriers and do their best to work flexibly and to negotiate these barriers and meet recruitment targets. The recruitment process appeared to be enhanced by trial staff supporting each other throughout the recruitment stage of the trial. ",
doi="10.2196/24055",
url="https://humanfactors.jmir.org/2021/1/e24055",
url="http://www.ncbi.nlm.nih.gov/pubmed/33666555"
}


@Article{info:doi/10.2196/26799,
author="Fu, Zhiying
and Jiang, Min
and Wang, Kun
and Li, Jian",
title="Minimizing the Impact of the COVID-19 Epidemic on Oncology Clinical Trials: Retrospective Study of Beijing Cancer Hospital",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="2",
volume="23",
number="3",
pages="e26799",
keywords="COVID-19",
keywords="clinical trials",
keywords="management strategy",
keywords="information technology",
abstract="Background: In view of repeated COVID-19 outbreaks in most countries, clinical trials will continue to be conducted under outbreak prevention and control measures for the next few years. It is very significant to explore an optimal clinical trial management model during the outbreak period to provide reference and insight for other clinical trial centers worldwide. Objective: The aim of this study was to explore the management strategies used to minimize the impact of the COVID-19 epidemic on oncology clinical trials. Methods: We implemented a remote management model to maintain clinical trials conducted at Beijing Cancer Hospital, which realized remote project approval, remote initiation, remote visits, remote administration and remote monitoring to get through two COVID-19 outbreaks in the capital city from February to April and June to July 2020. The effectiveness of measures was evaluated as differences in rates of protocol compliance, participants lost to follow-up, participant withdrawal, disease progression, participant mortality, and detection of monitoring problems. Results: During the late of the first outbreak, modifications were made in trial processing, participant management and quality control, which allowed the hospital to ensure the smooth conduct of 572 trials, with a protocol compliance rate of 85.24\% for 3718 participants across both outbreaks. No COVID-19 infections were recorded among participants or trial staff, and no major procedural errors occurred between February and July 2020. These measures led to significantly higher rates of protocol compliance and significantly lower rates of loss to follow-up or withdrawal after the second outbreak than after the first, without affecting rates of disease progression or mortality. The hospital provided trial sponsors with a remote monitoring system in a timely manner, and 3820 trial issues were identified. Conclusions: When public health emergencies occur, an optimal clinical trial model combining on-site and remote management could guarantee the health care and treatment needs of clinical trial participants, in which remote management plays a key role. ",
doi="10.2196/26799",
url="https://www.jmir.org/2021/3/e26799",
url="http://www.ncbi.nlm.nih.gov/pubmed/33591924"
}


@Article{info:doi/10.2196/23441,
author="Borobia, M. Alberto
and Garc{\'i}a-Garc{\'i}a, Irene
and D{\'i}az-Garc{\'i}a, Luc{\'i}a
and Rodr{\'i}guez-Mariblanca, Amelia
and Mart{\'i}nez de Soto, Luc{\'i}a
and Monserrat Villatoro, Jaime
and Seco Meseguer, Enrique
and Gonz{\'a}lez, J. Juan
and Fr{\'i}as Iniesta, Jes{\'u}s
and Ram{\'i}rez Garc{\'i}a, Elena
and Arribas, Ram{\'o}n Jose
and Carcas-Sansu{\'a}n, J. Antonio",
title="Health Care Workers' Reasons for Choosing Between Two Different COVID-19 Prophylaxis Trials in an Acute Pandemic Context: Single-Center Questionnaire Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="25",
volume="23",
number="2",
pages="e23441",
keywords="clinical trials",
keywords="COVID-19",
keywords="health care worker",
keywords="motivation",
keywords="personnel",
keywords="pre-exposure",
keywords="professional practice",
keywords="prophylaxis",
keywords="SARS-CoV-2",
keywords="volunteers, web-based survey",
keywords="workplace safety",
abstract="Background: In April 2020, two independent clinical trials to assess SARS-CoV-2 prophylaxis strategies among health care workers were initiated at our hospital: MeCOVID (melatonin vs placebo) and EPICOS (tenofovir disoproxil/emtricitabine vs hydroxychloroquine vs combination therapy vs placebo). Objective: This study aimed to evaluate the reasons why health care workers chose to participate in the MeCOVID and EPICOS trials, as well as why they chose one over the other. Methods: Both trials were offered to health care workers through an internal news bulletin. After an initial screening visit, all subjects were asked to respond to a web-based survey. Results: In the first month, 206 health care workers were screened and 160 were randomized. The survey participation was high at 73.3\%. Health care workers cited ``to contribute to scientific knowledge'' (n=80, 53.0\%), followed by ``to avoid SARS-CoV-2 infection'' (n=33, 21.9\%) and ``the interest to be tested for SARS-CoV-2'' (n=28, 18.5\%), as their primary reasons to participate in the trials. We observed significant differences in the expected personal benefits across physicians and nurses (P=.01). The vast majority of volunteers (n=202, 98.0\%) selected the MeCOVID trial, their primary reason being their concern regarding adverse reactions to treatments in the EPICOS trial (n=102, 69.4\%). Conclusions: Health care workers' reasons to participate in prophylaxis trials in an acute pandemic context appear to be driven largely by their desire to contribute to science and to gain health benefits. Safety outweighed efficacy when choosing between the two clinical trials. ",
doi="10.2196/23441",
url="https://www.jmir.org/2021/2/e23441",
url="http://www.ncbi.nlm.nih.gov/pubmed/33556032"
}


@Article{info:doi/10.2196/19242,
author="McKenna, Christopher Kevin
and Geoghegan, Cindy
and Swezey, Teresa
and Perry, Brian
and Wood, A. William
and Nido, Virginia
and Morin, L. Steve
and Grabert, K. Brigid
and Hallinan, P. Zachary
and Corneli, L. Amy",
title="Investigator Experiences Using Mobile Technologies in Clinical Research: Qualitative Descriptive Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Feb",
day="12",
volume="9",
number="2",
pages="e19242",
keywords="mHealth",
keywords="mobile technology",
keywords="mobile clinical trials",
keywords="digital health",
keywords="clinical research",
keywords="mobile devices",
keywords="digital health technology",
keywords="mobile applications",
keywords="clinical trial",
abstract="Background: The successful adoption of mobile technology for use in clinical trials relies on positive reception from key stakeholders, including clinical investigators; however, little information is known about the perspectives of investigators using mobile technologies in clinical trials. Objective: The aim of this study was to seek investigators' insights on the advantages and challenges of mobile clinical trials (MCTs); site-level budgetary, training, and other support needs necessary to adequately prepare for and implement MCTs; and the advantages and disadvantages for trial participants using mobile technologies in clinical trials. Methods: Using a qualitative descriptive study design, we conducted in-depth interviews with investigators involved in the conduct of MCTs. Data were analyzed using applied thematic analysis. Results: We interviewed 12 investigators who represented a wide variety of clinical specialties and reported using a wide range of mobile technologies. Investigators most commonly cited 3 advantages of MCTs over traditional clinical trials: more streamlined study operations, remote data capture, and improvement in the quality of studies and data collected. Investigators also reported that MCTs can be designed around the convenience of trial participants, and individuals may be more willing to participate in MCTs because they can take part from their homes. In addition, investigators recognized that MCTs can also involve additional burden for participants and described that operational challenges, technology adoption barriers, uncertainties about data quality, and time burden made MCTs more challenging than traditional clinical trials. Investigators stressed that additional training and dedicated staff effort may be needed to select a particular technology for use in a trial, helping trial participants learn and use the technology, and for staff troubleshooting the technology. Investigators also expressed that sharing data collected in real time with investigators and trial participants is an important aspect of MCTs that warrants consideration and potentially additional training and education. Conclusions: Investigator perspectives can inform the use of mobile technologies in future clinical trials by proactively identifying and addressing potential challenges. ",
doi="10.2196/19242",
url="http://mhealth.jmir.org/2021/2/e19242/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33576742"
}


@Article{info:doi/10.2196/22302,
author="Yagi, Kenta
and Maeda, Kazuki
and Sakaguchi, Satoshi
and Chuma, Masayuki
and Sato, Yasutaka
and Kane, Chikako
and Akaishi, Akiyo
and Ishizawa, Keisuke
and Yanagawa, Hiroaki",
title="Status of Institutional Review Board Meetings Conducted Through Web Conference Systems in Japanese National University Hospitals During the COVID-19 Pandemic: Questionnaire Study",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="19",
volume="22",
number="11",
pages="e22302",
keywords="COVID-19",
keywords="IRB",
keywords="Institutional Review Board",
keywords="REB",
keywords="Research Ethics Board",
keywords="web conference",
keywords="survey",
keywords="drug development",
keywords="teleconference",
keywords="clinical trial",
keywords="Japan",
keywords="hospital",
abstract="Background: With the global proliferation of the novel COVID-19 disease, conventionally conducting institutional review board (IRB) meetings has become a difficult task. Amid concerns about the suspension of drug development due to delays within IRBs, it has been suggested that IRB meetings should be temporarily conducted via the internet. Objective: This study aimed to elucidate the current status of IRB meetings conducted through web conference systems. Methods: A survey on conducting IRB meetings through web conference systems was administered to Japanese national university hospitals. Respondents were in charge of operating IRB offices at different universities. This study was not a randomized controlled trial. Results: The survey was performed at 42 facilities between the end of May and early June, 2020, immediately after the state of emergency was lifted in Japan. The survey yielded a response rate of 74\% (31/42). Additionally, while 68\% (21/31) of facilities introduced web conference systems for IRB meetings, 13\% (4/31) of the surveyed facilities postponed IRB meetings. Therefore, we conducted a further survey of 21 facilities that implemented web conference systems for IRB meetings. According to 71\% (15/21) of the respondents, there was no financial burden for implementing these systems, as they were free of charge. In 90\% (19/21) of the facilities, IRB meetings through web conference systems were already being conducted with personal electronic devices. Furthermore, in 48\% (10/21) of facilities, a web conference system was used in conjunction with face-to-face meetings. Conclusions: Due to the COVID-19 pandemic, the number of reviews in clinical trial core hospitals has decreased. This suggests that the development of pharmaceuticals has stagnated because of COVID-19. According to 71\% (15/21) of the respondents who conducted IRB meetings through web conference systems, the cost of introducing such meetings was US \$0, showing a negligible financial burden. Moreover, it was shown that online deliberations could be carried out in the same manner as face-to-face meetings, as 86\% (18/21) of facilities stated that the number of comments made by board members did not change. To improve the quality of IRB meetings conducted through web conference systems, it is necessary to further examine camera use and the content displayed on members' screens during meetings. Further examination of all members who use web conference systems is required. Our measures for addressing the requests and problems identified in our study could potentially be considered protocols for future IRB meetings, when the COVID-19 pandemic has passed and face-to-face meetings are possible again. This study also highlights the importance of developing web conference systems for IRB meetings to respond to future unforeseen pandemics. ",
doi="10.2196/22302",
url="http://www.jmir.org/2020/11/e22302/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33112758"
}


@Article{info:doi/10.2196/22006,
author="Beauchamp, Lyng Ulrikke
and Pappot, Helle
and Holl{\"a}nder-Mieritz, Cecilie",
title="The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Nov",
day="11",
volume="8",
number="11",
pages="e22006",
keywords="cancer treatment",
keywords="wearables",
keywords="adherence",
keywords="sensor technology",
abstract="Background: Interest in the use of wearables in medical care is increasing. Wearables can be used to monitor different variables, such as vital signs and physical activity. A crucial point for using wearables in oncology is if patients already under the burden of severe disease and oncological treatment can accept and adhere to the device. At present, there are no specific recommendations for the use of wearables in oncology, and little research has examined the purpose of using wearables in oncology. Objective: The purpose of this review is to explore the use of wearables in clinical trials during cancer treatment, with a special focus on adherence. Methods: PubMed and EMBASE databases were searched prior and up to October 3, 2019, with no limitation in the date of publication. The search strategy was aimed at studies using wearables for monitoring adult patients with cancer during active antineoplastic treatment. Studies were screened independently by 2 reviewers by title and abstract, selected for inclusion and exclusion, and the full-text was assessed for eligibility. Data on study design, type of wearable used, primary outcome, adherence, and device outcome were extracted. Results were presented descriptively. Results: Our systematic search identified 1269 studies, of which 25 studies met our inclusion criteria. The types of cancer represented in the studies were breast (7/25), gastrointestinal (4/25), lung (4/25), and gynecologic (1/25); 9 studies had multiple types of cancer. Oncologic treatment was primarily chemotherapy (17/25). The study-type distribution was pilot/feasibility study (12/25), observational study (10/25), and randomized controlled trial (3/25). The median sample size was 40 patients (range 7-180). All studies used a wearable with an accelerometer. Adherence varied across studies, from 60\%-100\% for patients wearing the wearable/evaluable sensor data and 45\%-94\% for evaluable days, but was differently measured and reported. Of the 25 studies, the most frequent duration for planned monitoring with a wearable was 8-30 days (13/25). Topics for wearable outcomes were physical activity (19/25), circadian rhythm (8/25), sleep (6/25), and skin temperature (1/25). Patient-reported outcomes (PRO) were used in 17 studies; of the 17 PRO studies, only 9 studies reported correlations between the wearable outcome and the PRO. Conclusions: We found that definitions of outcome measures and adherence varied across studies, and limited consensus among studies existed on which variables to monitor during treatment. Less heterogeneity, better consensus in terms of the use of wearables, and established standards for the definitions of wearable outcomes and adherence would improve comparisons of outcomes from studies using wearables. Adherence, and the definition of such, seems crucial to conclude on data from wearable studies in oncology. Additionally, research using advanced wearable devices and active use of the data are encouraged to further explore the potential of wearables in oncology during treatment. Particularly, randomized clinical studies are warranted to create consensus on when and how to implement in oncological practice. ",
doi="10.2196/22006",
url="http://mhealth.jmir.org/2020/11/e22006/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33174852"
}


@Article{info:doi/10.2196/22179,
author="Br{\o}gger-Mikkelsen, Mette
and Ali, Zarqa
and Zibert, R. John
and Andersen, Daniel Anders
and Thomsen, Francis Simon",
title="Online Patient Recruitment in Clinical Trials: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="4",
volume="22",
number="11",
pages="e22179",
keywords="online clinical trial",
keywords="web-based clinical trial",
keywords="hybrid clinical trial",
keywords="online recruitment",
keywords="remote recruitment",
keywords="recruitment",
keywords="clinical trial",
keywords="conversion rate",
abstract="Background: Recruitment for clinical trials continues to be a challenge, as patient recruitment is the single biggest cause of trial delays. Around 80\% of trials fail to meet the initial enrollment target and timeline, and these delays can result in lost revenue of as much as US \$8 million per day for drug developing companies. Objective: This study aimed to conduct a systematic review and meta-analysis examining the effectiveness of online recruitment of participants for clinical trials compared with traditional in-clinic/offline recruitment methods. Methods: Data on recruitment rates (the average number of patients enrolled in the study per month and per day of active recruitment) and conversion rates (the percentage of participants screened who proceed to enroll into the clinical trial), as well as study characteristics and patient demographics were collected from the included studies. Differences in online and offline recruitment rates and conversion rates were examined using random effects models. Further, a nonparametric paired Wilcoxon test was used for additional analysis on the cost-effectiveness of online patient recruitment. All data analyses were conducted in R language, and P<.05 was considered significant. Results: In total, 3861 articles were screened for inclusion. Of these, 61 studies were included in the review, and 23 of these were further included in the meta-analysis. We found online recruitment to be significantly more effective with respect to the recruitment rate for active days of recruitment, where 100\% (7/7) of the studies included had a better online recruitment rate compared with offline recruitment (incidence rate ratio [IRR] 4.17, P=.04). When examining the entire recruitment period in months we found that 52\% (12/23) of the studies had a better online recruitment rate compared with the offline recruitment rate (IRR 1.11, P=.71). For cost-effectiveness, we found that online recruitment had a significantly lower cost per enrollee compared with offline recruitment (US \$72 vs US \$199, P=.04). Finally, we found that 69\% (9/13) of studies had significantly better offline conversion rates compared with online conversion rates (risk ratio 0.8, P=.02). Conclusions: Targeting potential participants using online remedies is an effective approach for patient recruitment for clinical research. Online recruitment was both superior in regard to time efficiency and cost-effectiveness compared with offline recruitment. In contrast, offline recruitment outperformed online recruitment with respect to conversion rate. ",
doi="10.2196/22179",
url="https://www.jmir.org/2020/11/e22179",
url="http://www.ncbi.nlm.nih.gov/pubmed/33146627"
}


@Article{info:doi/10.2196/22810,
author="Darmawan, Ida
and Bakker, Caitlin
and Brockman, A. Tabetha
and Patten, A. Christi
and Eder, Milton",
title="The Role of Social Media in Enhancing Clinical Trial Recruitment: Scoping Review",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="26",
volume="22",
number="10",
pages="e22810",
keywords="social media",
keywords="clinical trial",
keywords="recruitment methods",
keywords="enrollment methods",
keywords="review",
abstract="Background: Recruiting participants into clinical trials continues to be a challenge, which can result in study delay or termination. Recent studies have used social media to enhance recruitment outcomes. An assessment of the literature on the use of social media for this purpose is required. Objective: This study aims to answer the following questions: (1) How is the use of social media, in combination with traditional approaches to enhance clinical trial recruitment and enrollment, represented in the literature? and (2) Do the data on recruitment and enrollment outcomes presented in the literature allow for comparison across studies? Methods: We conducted a comprehensive literature search across 7 platforms to identify clinical trials that combined social media and traditional methods to recruit patients. Study and participant characteristics, recruitment methods, and recruitment outcomes were evaluated and compared. Results: We identified 2371 titles and abstracts through our systematic search. Of these, we assessed 95 full papers and determined that 33 studies met the inclusion criteria. A total of 17 studies reported enrollment outcomes, of which 9 achieved or exceeded their enrollment target. The proportion of participants enrolled from social media in these studies ranged from 0\% to 49\%. Across all 33 studies, the proportion of participants recruited and enrolled from social media varied greatly. A total of 9 studies reported higher enrollment rates from social media than any other methods, and 4 studies reported the lowest cost per enrolled participant from social media. Conclusions: While the assessment of the use of social media to improve clinical trial participation is hindered by reporting inconsistencies, preliminary data suggest that social media can increase participation and reduce per-participant cost. The adoption of consistent standards for reporting recruitment and enrollment outcomes is required to advance our understanding and use of social media to support clinical trial success. ",
doi="10.2196/22810",
url="http://www.jmir.org/2020/10/e22810/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33104015"
}


@Article{info:doi/10.2196/15284,
author="Bond, M. Diana
and Hammond, Jeremy
and Shand, W. Antonia
and Nassar, Natasha",
title="Comparing a Mobile Phone Automated System With a Paper and Email Data Collection System: Substudy Within a Randomized Controlled Trial",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Aug",
day="25",
volume="8",
number="8",
pages="e15284",
keywords="mobile phones",
keywords="text messaging",
keywords="data collection methods",
keywords="clinical trial",
keywords="breastfeeding",
keywords="maternal health",
abstract="Background: Traditional data collection methods using paper and email are increasingly being replaced by data collection using mobile phones, although there is limited evidence evaluating the impact of mobile phone technology as part of an automated research management system on data collection and health outcomes. Objective: The aim of this study is to compare a web-based mobile phone automated system (MPAS) with a more traditional delivery and data collection system combining paper and email data collection (PEDC) in a cohort of breastfeeding women. Methods: We conducted a substudy of a randomized controlled trial in Sydney, Australia, which included women with uncomplicated term births who intended to breastfeed. Women were recruited within 72 hours of giving birth. A quasi-randomized number of women were recruited using the PEDC system, and the remainder were recruited using the MPAS. The outcomes assessed included the effectiveness of data collection, impact on study outcomes, response rate, acceptability, and cost analysis between the MPAS and PEDC methods. Results: Women were recruited between April 2015 and December 2016. The analysis included 555 women: 471 using the MPAS and 84 using the PEDC. There were no differences in clinical outcomes between the 2 groups. At the end of the 8-week treatment phase, the MPAS group showed an increased response rate compared with the PEDC group (56\% vs 37\%; P<.001), which was also seen at the 2-, 6-, and 12-month follow-ups. At the 2-month follow-up, the MPAS participants also showed an increased rate of self-reported treatment compliance (70\% vs 56\%; P<.001) and a higher recommendation rate for future use (95\% vs 64\%; P<.001) as compared with the PEDC group. The cost analysis between the 2 groups was comparable. Conclusions: MPAS is an effective and acceptable method for improving the overall management, treatment compliance, and methodological quality of clinical research to ensure the validity and reliability of findings. ",
doi="10.2196/15284",
url="http://mhealth.jmir.org/2020/8/e15284/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32763873"
}


@Article{info:doi/10.2196/12813,
author="Langford, Aisha
and Sherman, Scott
and Thornton, Rachel
and Nightingale, Kira
and Kwon, Simona
and Chavis-Keeling, Deborah
and Link, Nathan
and Cronstein, Bruce
and Hochman, Judith
and Trachtman, Howard",
title="Profiling Clinical Research Activity at an Academic Medical Center by Using Institutional Databases: Content Analysis",
journal="JMIR Public Health Surveill",
year="2020",
month="Aug",
day="24",
volume="6",
number="3",
pages="e12813",
keywords="database",
keywords="clinical studies as topic",
keywords="vulnerable populations",
keywords="pediatrics",
keywords="geriatrics",
abstract="Background: It is important to monitor the scope of clinical research of all types, to involve participants of all ages and subgroups in studies that are appropriate to their condition, and to ensure equal access and broad validity of the findings. Objective: We conducted a review of clinical research performed at New York University with the following objectives: (1) to determine the utility of institutional administrative data to characterize clinical research activity; (2) to assess the inclusion of special populations; and (3) to determine if the type, initiation, and completion of the study differed by age. Methods: Data for all studies that were institutional review board--approved between January 1, 2014, and November 2, 2016, were obtained from the research navigator system, which was launched in November 2013. One module provided details about the study protocol, and another module provided the characteristics of individual participants. Research studies were classified as observational or interventional. Descriptive statistics were used to assess the characteristics of clinical studies across the lifespan, by type, and over time. Results: A total of 22\%-24\% of studies included children (minimum age <18 years) and 4\%-5\% focused exclusively on pediatrics. Similarly, 64\%-72\% of studies included older patients (maximum age >65 years) but only 5\%-12\% focused exclusively on geriatrics. Approximately 85\% of the studies included both male and female participants. Of the remaining studies, those open only to girls or women were approximately 3 times as common as those confined to boys or men. A total of 56\%-58\% of projects focused on nonvulnerable patients. Among the special populations studied, children (12\%-15\%) were the most common. Noninterventional trial types included research on human data sets (24\%), observational research (22\%), survey research (16\%), and biospecimen research (8\%). The percentage of projects designed to test an intervention in a vulnerable population increased from 17\% in 2014 to 21\% in 2015. Conclusions: Pediatric participants were the special population that was most often studied based on the number of registered projects that included children and adolescents. However, they were much less likely to be successfully enrolled in research studies compared with adults older than 65 years. Only 20\% of the studies were interventional, and 20\%-35\% of participants in this category were from vulnerable populations. More studies are exclusively devoted to women's health issues compared with men's health issues. ",
doi="10.2196/12813",
url="http://publichealth.jmir.org/2020/3/e12813/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32831180"
}


@Article{info:doi/10.2196/18580,
author="Ruth, J. Caleb
and Huey, Lee Samantha
and Krisher, T. Jesse
and Fothergill, Amy
and Gannon, M. Bryan
and Jones, Elyse Camille
and Centeno-Tablante, Elizabeth
and Hackl, S. Laura
and Colt, Susannah
and Finkelstein, Leigh Julia
and Mehta, Saurabh",
title="An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="13",
volume="22",
number="8",
pages="e18580",
keywords="data science",
keywords="data collection",
keywords="database management systems",
keywords="global health",
keywords="public health",
keywords="data management",
keywords="health information management",
keywords="population surveillance",
keywords="longitudinal studies",
keywords="randomized controlled trial",
keywords="Electronic Data Capture (EDC)",
abstract="Background: When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective: This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods: We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results: ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions: ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement. ",
doi="10.2196/18580",
url="https://www.jmir.org/2020/8/e18580",
url="http://www.ncbi.nlm.nih.gov/pubmed/32788154"
}


@Article{info:doi/10.2196/14591,
author="Wang, Karen
and Grossetta Nardini, Holly
and Post, Lori
and Edwards, Todd
and Nunez-Smith, Marcella
and Brandt, Cynthia",
title="Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="20",
volume="22",
number="7",
pages="e14591",
keywords="continental population groups",
keywords="multiracial populations",
keywords="multiethnic groups",
keywords="data standards",
keywords="health status disparities",
keywords="race factors",
keywords="demography",
abstract="Background: Data standards for race and ethnicity have significant implications for health equity research. Objective: We aim to describe a challenge encountered when working with a multiple--race and ethnicity assessment in the Eastern Caribbean Health Outcomes Research Network (ECHORN), a research collaborative of Barbados, Puerto Rico, Trinidad and Tobago, and the US Virgin Islands. Methods: We examined the data standards guiding harmonization of race and ethnicity data for multiracial and multiethnic populations, using the Office of Management and Budget (OMB) Statistical Policy Directive No. 15. Results: Of 1211 participants in the ECHORN cohort study, 901 (74.40\%) selected 1 racial category. Of those that selected 1 category, 13.0\% (117/901) selected Caribbean; 6.4\% (58/901), Puerto Rican or Boricua; and 13.5\% (122/901), the mixed or multiracial category.  A total of 17.84\% (216/1211) of participants selected 2 or more categories, with 15.19\% (184/1211) selecting 2 categories and 2.64\% (32/1211) selecting 3 or more categories. With aggregation of ECHORN data into OMB categories, 27.91\% (338/1211) of the participants can be placed in the ``more than one race'' category. Conclusions: This analysis exposes the fundamental informatics challenges that current race and ethnicity data standards present to meaningful collection, organization, and dissemination of granular data about subgroup populations in diverse and marginalized communities. Current standards should reflect the science of measuring race and ethnicity and the need for multidisciplinary teams to improve evolving standards throughout the data life cycle. ",
doi="10.2196/14591",
url="http://www.jmir.org/2020/7/e14591/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706693"
}


@Article{info:doi/10.2196/15878,
author="Robinson, Heather
and Appelbe, Duncan
and Dodd, Susanna
and Flowers, Susan
and Johnson, Sonia
and Jones, H. Steven
and Mateus, C{\'e}u
and Mezes, Barbara
and Murray, Elizabeth
and Rainford, Naomi
and Rosala-Hallas, Anna
and Walker, Andrew
and Williamson, Paula
and Lobban, Fiona",
title="Methodological Challenges in Web-Based Trials: Update and Insights From the Relatives Education and Coping Toolkit Trial",
journal="JMIR Ment Health",
year="2020",
month="Jul",
day="17",
volume="7",
number="7",
pages="e15878",
keywords="randomized controlled trial",
keywords="research design",
keywords="methods",
keywords="internet",
keywords="web",
keywords="mental health",
keywords="relatives",
keywords="carers",
abstract="International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-016965 ",
doi="10.2196/15878",
url="https://mental.jmir.org/2020/7/e15878",
url="http://www.ncbi.nlm.nih.gov/pubmed/32497018"
}


@Article{info:doi/10.2196/17589,
author="Chung, Alicia
and Seixas, Azizi
and Williams, Natasha
and Senathirajah, Yalini
and Robbins, Rebecca
and Newsome Garcia, Valerie
and Ravenell, Joseph
and Jean-Louis, Girardin",
title="Development of ``Advancing People of Color in Clinical Trials Now!'': Web-Based Randomized Controlled Trial Protocol",
journal="JMIR Res Protoc",
year="2020",
month="Jul",
day="14",
volume="9",
number="7",
pages="e17589",
keywords="health communication",
keywords="health care disparities",
keywords="eHealth",
abstract="Background: Participation in clinical trials among people of color remains low, compared with white subjects. This protocol describes the development of ``Advancing People of Color in Clinical Trials Now!'' (ACT Now!), a culturally tailored website designed to influence clinical trial decision making among people of color. Objective: This cluster randomized study aims to test the efficacy of a culturally tailored website to increase literacy, self-efficacy, and willingness to enroll in clinical trials among people of color. Methods: ACT Now! is a randomized trial including 2 groups: (1) intervention group (n=50) with access to the culturally tailored website and (2) control group (n=50) exposed to a standard clinical recruitment website. Clinical trial literacy and willingness to enroll in a clinical trial will be measured before and after exposure to the website corresponding to their assigned group (intervention or control). Surveys will be conducted at baseline and during the 1-month postintervention and 3-month follow-up. Website architecture and wireframing will be informed by the literature and experts in the field. Statistical analysis will be conducted using a two-tailed t test, with 80\% power, at .05 alpha level, to increase clinical trial literacy, self-efficacy, and willingness to enroll in clinical trials 3 months post intervention. Results: We will design a culturally tailored website that will provide leverage for community stakeholders to influence clinical trial literacy, self-efficacy, and willingness to enroll in clinical trials among racial and ethnic groups. ACT Now! applies a community-based participatory research approach through the use of a community steering committee (CSC). The CSC provides input during the research study conception, development, implementation, and enrollment. CSC relationships help foster trust among communities of color. ACT Now! has the potential to fill a gap in clinical trial enrollment among people of color through an accessible web-based website. This study was funded in July 2017 and obtained institutional review board approval in spring 2017. As of December 2019, we had enrolled 100 participants. Data analyses are expected to be completed by June 2020, and expected results are to be published in fall 2020. Conclusions: ACT Now! has the potential to fill an important gap in clinical trial enrollment among people of color through an accessible web-based website. Trial Registration: ClinicalTrials.gov NCT03243071; https://clinicaltrials.gov/ct2/show/NCT00102401 International Registered Report Identifier (IRRID): DERR1-10.2196/17589 ",
doi="10.2196/17589",
url="https://www.researchprotocols.org/2020/7/e17589",
url="http://www.ncbi.nlm.nih.gov/pubmed/32673274"
}


@Article{info:doi/10.2196/17832,
author="Zeng, Kun
and Pan, Zhiwei
and Xu, Yibin
and Qu, Yingying",
title="An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation",
journal="JMIR Med Inform",
year="2020",
month="Jul",
day="1",
volume="8",
number="7",
pages="e17832",
keywords="Deep learning",
keywords="Text classification",
keywords="Ensemble learning",
keywords="Eligibility criteria",
keywords="Clinical trial",
abstract="Background: Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. Objective: We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. Methods: We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. Results: Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. Conclusions: We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance. ",
doi="10.2196/17832",
url="https://medinform.jmir.org/2020/7/e17832",
url="http://www.ncbi.nlm.nih.gov/pubmed/32609092"
}


@Article{info:doi/10.2196/15749,
author="Becker, Linda
and Ganslandt, Thomas
and Prokosch, Hans-Ulrich
and Newe, Axel",
title="Applied Practice and Possible Leverage Points for Information Technology Support for Patient Screening in Clinical Trials: Qualitative Study",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="16",
volume="8",
number="6",
pages="e15749",
keywords="clinical trial",
keywords="patient screening",
keywords="electronic support",
keywords="clinical information systems",
keywords="inclusion criteria",
keywords="exclusion criteria",
keywords="feasibility studies",
keywords="mobile phone",
abstract="Background: Clinical trials are one of the most challenging and meaningful designs in medical research. One essential step before starting a clinical trial is screening, that is, to identify patients who fulfill the inclusion criteria and do not fulfill the exclusion criteria. The screening step for clinical trials might be supported by modern information technology (IT). Objective: This explorative study aimed (1) to obtain insights into which tools for feasibility estimations and patient screening are actually used in clinical routine and (2) to determine which method and type of IT support could benefit clinical staff. Methods: Semistandardized interviews were conducted in 5 wards (cardiology, gynecology, gastroenterology, nephrology, and palliative care) in a German university hospital. Of the 5 interviewees, 4 were directly involved in patient screening. Three of them were clinicians, 1 was a study nurse, and 1 was a research assistant. Results: The existing state of study feasibility estimation and the screening procedure were dominated by human communication and estimations from memory, although there were many possibilities for IT support. Success mostly depended on the experience and personal motivation of the clinical staff. Electronic support has been used but with little importance so far. Searches in ward-specific patient registers (databases) and searches in clinical information systems were reported. Furthermore, free-text searches in medical reports were mentioned. For potential future applications, a preference for either proactive or passive systems was not expressed. Most of the interviewees saw the potential for the improvement of the actual systems, but they were also largely satisfied with the outcomes of the current approach. Most of the interviewees were interested in learning more about the various ways in which IT could support and relieve them in their clinical routine. Conclusions: Overall, IT support currently plays a minor role in the screening step for clinical trials. The lack of IT usage and the estimations made from memory reported by all the participants might constrain cognitive resources, which might distract from clinical routine. We conclude that electronic support for the screening step for clinical trials is still a challenge and that education of the staff about the possibilities for electronic support in clinical trials is necessary. ",
doi="10.2196/15749",
url="http://medinform.jmir.org/2020/6/e15749/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32442156"
}


@Article{info:doi/10.2196/15073,
author="Her, Qoua
and Malenfant, Jessica
and Zhang, Zilu
and Vilk, Yury
and Young, Jessica
and Tabano, David
and Hamilton, Jack
and Johnson, Ron
and Raebel, Marsha
and Boudreau, Denise
and Toh, Sengwee",
title="Distributed Regression Analysis Application in Large Distributed Data Networks: Analysis of Precision and Operational Performance",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="4",
volume="8",
number="6",
pages="e15073",
keywords="distributed regression analysis",
keywords="distributed data networks",
keywords="privacy-protecting analytics",
keywords="pharmacoepidemiology",
keywords="PopMedNet",
abstract="Background: A distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multicenter studies. However, software that facilitates large-scale and efficient implementation of DRA is limited. Objective: This study aimed to assess the precision and operational performance of a DRA application comprising a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNet in a horizontally partitioned distributed data network. Methods: We executed the SAS-based DRA package to perform distributed linear, logistic, and Cox proportional hazards regression analysis on a real-world test case with 3 data partners. We used PopMedNet to iteratively and automatically transfer highly summarized information between the data partners and the analysis center. We compared the DRA results with the results from standard SAS procedures executed on the pooled individual-level dataset to evaluate the precision of the SAS-based DRA package. We computed the execution time of each step in the workflow to evaluate the operational performance of the PopMedNet-driven file transfer workflow. Results: All DRA results were precise (<10?12), and DRA model fit curves were identical or similar to those obtained from the corresponding pooled individual-level data analyses. All regression models required less than 20 min for full end-to-end execution. Conclusions: We integrated a SAS-based DRA package with PopMedNet and successfully tested the new capability within an active distributed data network. The study demonstrated the validity and feasibility of using DRA to enable more privacy-protecting analysis in multicenter studies. ",
doi="10.2196/15073",
url="https://medinform.jmir.org/2020/6/e15073",
url="http://www.ncbi.nlm.nih.gov/pubmed/32496200"
}


@Article{info:doi/10.2196/18938,
author="Hirano, Tomonobu
and Motohashi, Tomomitsu
and Okumura, Kosuke
and Takajo, Kentaro
and Kuroki, Taiyo
and Ichikawa, Daisuke
and Matsuoka, Yutaka
and Ochi, Eisuke
and Ueno, Taro",
title="Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="2",
volume="22",
number="6",
pages="e18938",
keywords="blockchain",
keywords="clinical trial",
keywords="data management",
keywords="validation",
keywords="breast cancer",
keywords="regulatory sandbox",
abstract="Background: The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. Objective: The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. Methods: We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. Results: We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. Conclusions: We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials. ",
doi="10.2196/18938",
url="https://www.jmir.org/2020/6/e18938",
url="http://www.ncbi.nlm.nih.gov/pubmed/32340974"
}


@Article{info:doi/10.2196/14379,
author="Park, Rang Yu
and Koo, HaYeong
and Yoon, Young-Kwang
and Park, Sumi
and Lim, Young-Suk
and Baek, Seunghee
and Kim, Reong Hae
and Kim, Won Tae",
title="Expedited Safety Reporting Through an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study",
journal="JMIR Med Inform",
year="2020",
month="Feb",
day="27",
volume="8",
number="2",
pages="e14379",
keywords="clinical trial",
keywords="adverse event",
keywords="early detection",
keywords="patient safety",
abstract="Background: Early detection or notification of adverse event (AE) occurrences during clinical trials is essential to ensure patient safety. Clinical trials take advantage of innovative strategies, clinical designs, and state-of-the-art technologies to evaluate efficacy and safety, however, early awareness of AE occurrences by investigators still needs to be systematically improved. Objective: This study aimed to build a system to promptly inform investigators when clinical trial participants make unscheduled visits to the emergency room or other departments within the hospital. Methods: We developed the Adverse Event Awareness System (AEAS), which promptly informs investigators and study coordinators of AE occurrences by automatically sending text messages when study participants make unscheduled visits to the emergency department or other clinics at our center. We established the AEAS in July 2015 in the clinical trial management system. We compared the AE reporting timeline data of 305 AE occurrences from 74 clinical trials between the preinitiative period (December 2014-June 2015) and the postinitiative period (July 2015-June 2016) in terms of three AE awareness performance indicators: onset to awareness, awareness to reporting, and onset to reporting. Results: A total of 305 initial AE reports from 74 clinical trials were included. All three AE awareness performance indicators were significantly lower in the postinitiative period. Specifically, the onset-to-reporting times were significantly shorter in the postinitiative period (median 1 day [IQR 0-1], mean rank 140.04 [SD 75.35]) than in the preinitiative period (median 1 day [IQR 0-4], mean rank 173.82 [SD 91.07], P?.001). In the phase subgroup analysis, the awareness-to-reporting and onset-to-reporting indicators of phase 1 studies were significantly lower in the postinitiative than in the preinitiative period (preinitiative: median 1 day, mean rank of awareness to reporting 47.94, vs postinitiative: median 0 days, mean rank of awareness to reporting 35.75, P=.01; and preinitiative: median 1 day, mean rank of onset to reporting 47.4, vs postinitiative: median 1 day, mean rank of onset to reporting 35.99, P=.03). The risk-level subgroup analysis found that the onset-to-reporting time for low- and high-risk studies significantly decreased postinitiative (preinitiative: median 4 days, mean rank of low-risk studies 18.73, vs postinitiative: median 1 day, mean rank of low-risk studies 11.76, P=.02; and preinitiative: median 1 day, mean rank of high-risk studies 117.36, vs postinitiative: median 1 day, mean rank of high-risk studies 97.27, P=.01). In particular, onset to reporting was reduced more in the low-risk trial than in the high-risk trial (low-risk: median 4-0 days, vs high-risk: median 1-1 day). Conclusions: We demonstrated that a real-time automatic alert system can effectively improve safety reporting timelines. The improvements were prominent in phase 1 and in low- and high-risk clinical trials. These findings suggest that an information technology-driven automatic alert system effectively improves safety reporting timelines, which may enhance patient safety. ",
doi="10.2196/14379",
url="http://medinform.jmir.org/2020/2/e14379/"
}


@Article{info:doi/10.2196/11050,
author="Lattie, G. Emily
and Kaiser, M. Susan
and Alam, Nameyeh
and Tomasino, N. Kathryn
and Sargent, Elizabeth
and Rubanovich, Kseniya Caryn
and Palac, L. Hannah
and Mohr, C. David",
title="A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Identification of Efficient and Cost-Effective Methods for Decision Making (Part 2)",
journal="J Med Internet Res",
year="2018",
month="Nov",
day="29",
volume="20",
number="11",
pages="e11050",
keywords="eHealth",
keywords="mHealth",
keywords="mental health",
keywords="recruitment",
abstract="Background: The ability to successfully recruit participants for electronic health (eHealth) clinical trials is largely dependent on the use of efficient and effective recruitment strategies. Determining which types of recruitment strategies to use presents a challenge for many researchers. Objective: The aim of this study was to present an analysis of the time-efficiency and cost-effectiveness of recruitment strategies for eHealth clinical trials, and it describes a framework for cost-effective trial recruitment. Methods: Participants were recruited for one of 5 eHealth trials of interventions for common mental health conditions. A multipronged recruitment approach was used, including digital (eg, social media and Craigslist), research registry-based, print (eg, flyers and posters on public transportation), clinic-based (eg, a general internal medicine clinic within an academic medical center and a large nonprofit health care organization), a market research recruitment firm, and traditional media strategies (eg, newspaper and television coverage in response to press releases). The time costs and fees for each recruitment method were calculated, and the participant yield on recruitment costs was calculated by dividing the number of enrolled participants by the total cost for each method. Results: A total of 777 participants were enrolled across all trials. Digital recruitment strategies yielded the largest number of participants across the 5 clinical trials and represented 34.0\% (264/777) of the total enrolled participants. Registry-based recruitment strategies were in second place by enrolling 28.0\% (217/777) of the total enrolled participants across trials. Research registry-based recruitment had a relatively high conversion rate from potential participants who contacted our center for being screened to be enrolled, and it was also the most cost-effective for enrolling participants in this set of clinical trials with a total cost per person enrolled at US \$8.99. Conclusions: On the basis of these results, a framework is proposed for participant recruitment. To make decisions on initiating and maintaining different types of recruitment strategies, the resources available and requirements of the research study (or studies) need to be carefully examined. ",
doi="10.2196/11050",
url="https://www.jmir.org/2018/11/e11050/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30497997"
}


@Article{info:doi/10.2196/11049,
author="Palac, L. Hannah
and Alam, Nameyeh
and Kaiser, M. Susan
and Ciolino, D. Jody
and Lattie, G. Emily
and Mohr, C. David",
title="A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Simple Architecture (Part 1)",
journal="J Med Internet Res",
year="2018",
month="Nov",
day="01",
volume="20",
number="11",
pages="e11049",
keywords="eHealth",
keywords="mHealth",
keywords="online recruitment",
keywords="REDCap",
keywords="referral management",
abstract="Background: The ability to identify, screen, and enroll potential research participants in an efficient and timely manner is crucial to the success of clinical trials. In the age of the internet, researchers can be confronted with large numbers of people contacting the program, overwhelming study staff and frustrating potential participants. Objective: This paper describes a ``do-it-yourself'' recruitment support framework (DIY-RSF) that uses tools readily available in many academic research settings to support remote participant recruitment, prescreening, enrollment, and management across multiple concurrent eHealth clinical trials. Methods: This work was conducted in an academic research center focused on developing and evaluating behavioral intervention technologies. A needs assessment consisting of unstructured individual and group interviews was conducted to identify barriers to recruitment and important features for the new system. Results: We describe a practical and adaptable recruitment management architecture that used readily available software, such as REDCap (Research Electronic Data Capture) and standard statistical software (eg, SAS, R), to create an automated recruitment framework that supported prescreening potential participants, consent to join a research registry, triaging for management of multiple trials, capture of eligibility information for each phase of a recruitment pipeline, and staff management tools including monitoring of participant flow and task assignment/reassignment features. The DIY-RSF was launched in July 2015. As of July 2017, the DIY-RSF has supported the successful recruitment efforts for eight trials, producing 14,557 participant records in the referral tracking database and 5337 participants in the center research registry. The DIY-RSF has allowed for more efficient use of staff time and more rapid processing of potential applicants. Conclusions: Using tools already supported at many academic institutions, we describe the architecture and utilization of an adaptable referral management framework to support recruitment for multiple concurrent clinical trials. The DIY-RSF can serve as a guide for leveraging common technologies to improve clinical trial recruitment procedures. ",
doi="10.2196/11049",
url="https://www.jmir.org/2018/11/e11049/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30389650"
}


@Article{info:doi/10.2196/jmir.9312,
author="Park, Rang Yu
and Yoon, Jo Young
and Koo, HaYeong
and Yoo, Soyoung
and Choi, Chang-Min
and Beck, Sung-Ho
and Kim, Won Tae",
title="Utilization of a Clinical Trial Management System for the Whole Clinical Trial Process as an Integrated Database: System Development",
journal="J Med Internet Res",
year="2018",
month="Apr",
day="24",
volume="20",
number="4",
pages="e103",
keywords="clinical trial",
keywords="information systems",
keywords="academic medical center",
keywords="information technology",
keywords="privacy",
abstract="Background: Clinical trials pose potential risks in both communications and management due to the various stakeholders involved when performing clinical trials. The academic medical center has a responsibility and obligation to conduct and manage clinical trials while maintaining a sufficiently high level of quality, therefore it is necessary to build an information technology system to support standardized clinical trial processes and comply with relevant regulations. Objective: The objective of the study was to address the challenges identified while performing clinical trials at an academic medical center, Asan Medical Center (AMC) in Korea, by developing and utilizing a clinical trial management system (CTMS) that complies with standardized processes from multiple departments or units, controlled vocabularies, security, and privacy regulations. Methods: This study describes the methods, considerations, and recommendations for the development and utilization of the CTMS as a consolidated research database in an academic medical center. A task force was formed to define and standardize the clinical trial performance process at the site level. On the basis of the agreed standardized process, the CTMS was designed and developed as an all-in-one system complying with privacy and security regulations. Results: In this study, the processes and standard mapped vocabularies of a clinical trial were established at the academic medical center. On the basis of these processes and vocabularies, a CTMS was built which interfaces with the existing trial systems such as the electronic institutional review board health information system, enterprise resource planning, and the barcode system. To protect patient data, the CTMS implements data governance and access rules, and excludes 21 personal health identifiers according to the Health Insurance Portability and Accountability Act (HIPAA) privacy rule and Korean privacy laws. Since December 2014, the CTMS has been successfully implemented and used by 881 internal and external users for managing 11,645 studies and 146,943 subjects. Conclusions: The CTMS was introduced in the Asan Medical Center to manage the large amounts of data involved with clinical trial operations. Inter- and intraunit control of data and resources can be easily conducted through the CTMS system. To our knowledge, this is the first CTMS developed in-house at an academic medical center side which can enhance the efficiency of clinical trial management in compliance with privacy and security laws. ",
doi="10.2196/jmir.9312",
url="http://www.jmir.org/2018/4/e103/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29691212"
}


@Article{info:doi/10.2196/jmir.6978,
author="Zhang, Jing
and Sun, Lei
and Liu, Yu
and Wang, Hongyi
and Sun, Ningling
and Zhang, Puhong",
title="Mobile Device--Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges",
journal="J Med Internet Res",
year="2017",
month="Mar",
day="08",
volume="19",
number="3",
pages="e66",
keywords="mEDC",
keywords="electronic data capture",
keywords="mobile data capture",
keywords="mhealth",
keywords="randomized controlled trial",
keywords="clinical research",
abstract="Background: Electronic data capture (EDC) systems have been widely used in clinical research, but mobile device--based electronic data capture (mEDC) system has not been well evaluated. Objective: The aim of our study was to evaluate the feasibility, advantages, and challenges of mEDC in data collection, project management, and telemonitoring in a randomized controlled trial (RCT). Methods: We developed an mEDC to support an RCT called ``Telmisartan and Hydrochlorothiazide Antihypertensive Treatment (THAT)'' study, which was a multicenter, double-blinded, RCT, with the purpose of comparing the efficacy of telmisartan and hydrochlorothiazide (HCTZ) monotherapy in high-sodium-intake patients with mild to moderate hypertension during a 60 days follow-up. Semistructured interviews were conducted during and after the trial to evaluate the feasibility, advantage, and challenge of mEDC. Nvivo version 9.0 (QSR International) was used to analyze records of interviews, and a thematic framework method was used to obtain outcomes. Results: The mEDC was successfully used to support the data collection and project management in all the 14 study hospitals. A total of 1333 patients were recruited with support of mEDC, of whom 1037 successfully completed all 4 visits. Across all visits, the average time needed for 141 questions per patient was 53 min, which were acceptable to both doctors and patients. All the interviewees, including 24 doctors, 53 patients, 1 clinical research associate (CRA), 1 project manager (PM), and 1 data manager (DM), expressed their satisfaction to nearly all the functions of the innovative mEDC in randomization, data collection, project management, quality control, and remote monitoring in real time. The average satisfaction score was 9.2 (scale, 0-10). The biggest challenge came from the stability of the mobile or Wi-Fi signal although it was not a problem in THAT study. Conclusions: The innovative mEDC has many merits and is well acceptable in supporting data collection and project management in a timely manner in clinical trial. ",
doi="10.2196/jmir.6978",
url="http://www.jmir.org/2017/3/e66/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28274907"
}


@Article{info:doi/10.2196/cancer.6701,
author="Perez, P. Raymond
and Finnigan, Shanda
and Patel, Krupa
and Whitney, Shanell
and Forrest, Annemarie",
title="Clinical Trial Electronic Portals for Expedited Safety Reporting: Recommendations from the Clinical Trials Transformation Initiative Investigational New Drug Safety Advancement Project",
journal="JMIR Cancer",
year="2016",
month="Dec",
day="15",
volume="2",
number="2",
pages="e16",
keywords="clinical trials",
keywords="investigational new drug application",
keywords="risk management",
abstract="Background: Use of electronic clinical trial portals has increased in recent years to assist with sponsor-investigator communication, safety reporting, and clinical trial management. Electronic portals can help reduce time and costs associated with processing paperwork and add security measures; however, there is a lack of information on clinical trial investigative staff's perceived challenges and benefits of using portals. Objective: The Clinical Trials Transformation Initiative (CTTI) sought to (1) identify challenges to investigator receipt and management of investigational new drug (IND) safety reports at oncologic investigative sites and coordinating centers and (2) facilitate adoption of best practices for communicating and managing IND safety reports using electronic portals. Methods: CTTI, a public-private partnership to improve the conduct of clinical trials, distributed surveys and conducted interviews in an opinion-gathering effort to record investigator and research staff views on electronic portals in the context of the new safety reporting requirements described in the US Food and Drug Administration's final rule (Code of Federal Regulations Title 21 Section 312). The project focused on receipt, management, and review of safety reports as opposed to the reporting of adverse events. Results: The top challenge investigators and staff identified in using individual sponsor portals was remembering several complex individual passwords to access each site. Also, certain tasks are time-consuming (eg, downloading reports) due to slow sites or difficulties associated with particular operating systems or software. To improve user experiences, respondents suggested that portals function independently of browsers and operating systems, have intuitive interfaces with easy navigation, and incorporate additional features that would allow users to filter, search, and batch safety reports. Conclusions: Results indicate that an ideal system for sharing expedited IND safety information is through a central portal used by all sponsors. Until this is feasible, electronic reporting portals should at least have consistent functionality. CTTI has issued recommendations to improve the quality and use of electronic portals. ",
doi="10.2196/cancer.6701",
url="http://cancer.jmir.org/2016/2/e16/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28410179"
}


@Article{info:doi/10.2196/jmir.2392,
author="Xiao, Lan
and Huang, Qiwen
and Yank, Veronica
and Ma, Jun",
title="An Easily Accessible Web-Based Minimization Random Allocation System for Clinical Trials",
journal="J Med Internet Res",
year="2013",
month="Jul",
day="19",
volume="15",
number="7",
pages="e139",
keywords="randomized controlled trials",
keywords="randomization",
keywords="minimization",
keywords="adaptive randomization",
keywords="Kullback--Leibler divergence",
keywords="Web-based",
abstract="Background: Minimization as an adaptive allocation technique has been recommended in the literature for use in randomized clinical trials. However, it remains uncommonly used due in part to a lack of easily accessible implementation tools. Objective: To provide clinical trialists with a robust, flexible, and readily accessible tool for implementing covariate-adaptive biased-coin randomization. Methods: We developed a Web-based random allocation system, MinimRan, that applies Pocock--Simon (for trials with 2 or more arms) and 2-way (currently limited to 2-arm trials) minimization methods for trials using only categorical prognostic factors or the symmetric Kullback--Leibler divergence minimization method for trials (currently limited to 2-arm trials) using continuous prognostic factors with or without categorical factors, in covariate-adaptive biased-coin randomization. Results: In this paper, we describe the system's essential statistical and computer programming features and provide as an example the randomization results generated by it in a recently completed trial. The system can be used in single- and double-blind trials as well as single-center and multicenter trials. Conclusions: We expect the system to facilitate the translation of the 3 validated random allocation methods into broad, efficient clinical research practice. ",
doi="10.2196/jmir.2392",
url="http://www.jmir.org/2013/7/e139/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23872035"
}