Mayo Clinic Proceedings. Digital health最新文献

{"title":"Racial and Ethnic Representation and Study Engagement in a Siteless Digital Clinical Trial Using a Smartwatch: Findings From the Apple Heart Study","authors":"Kaylin T. Nguyen MD ,&nbsp;Jingzhi Yu BA ,&nbsp;Haley Hedlin PhD ,&nbsp;Adam T. Phillips MD ,&nbsp;Sumbul Desai MD ,&nbsp;Lauren Cheung MD ,&nbsp;Peter R. Kowey MD ,&nbsp;Sneha S. Jain MD ,&nbsp;John S. Rumsfeld MD, PhD ,&nbsp;Andrea M. Russo MD ,&nbsp;Christopher B. Granger MD ,&nbsp;Mellanie True Hills BS ,&nbsp;Manisha Desai PhD ,&nbsp;Kenneth W. Mahaffey MD ,&nbsp;Mintu P. Turakhia MD, MAS ,&nbsp;Marco V. Perez MD","doi":"10.1016/j.mcpdig.2025.100232","DOIUrl":"10.1016/j.mcpdig.2025.100232","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate differences in study engagement in diverse racial/ethnic groups that have been significantly underrepresented in atrial fibrillation and digital clinical trials.</div></div><div><h3>Patients and Methods</h3><div>This was a secondary analysis of participants from the Apple Heart Study, a prospective, siteless, single-arm pragmatic clinical trial from November 29, 2017, to January 31, 2019. Black, Hispanic, Asian, and White participants were monitored using an irregular rhythm notification algorithm designed to detect atrial fibrillation on a smartwatch. Logistic regression was performed to evaluate the relationship between race/ethnicity and completion of the first study visit after an irregular rhythm notification, adjusting for demographic characteristics and comorbidities.</div></div><div><h3>Results</h3><div>Of the 419,297 participants, 393,396 (93.8%) individuals self-identified as White, Black, Hispanic, or Asian. Overall, participants were 57% men and had a mean (SD) age of 41 (13) years. Among 2044 (0.52%) participants who received an irregular rhythm notification, non-White participants had lower odds of completing the initial virtual study visit compared with White participants (Black: OR, 0.61; 95% CI, 0.39-0.94; Hispanic: OR, 0.62; 95% CI, 0.40-0.95; Asian: OR, 0.40; 95% CI, 0.23-0.66) after multivariate adjustment. Among those who completed the initial study visit, there was no statistically significant difference in the odds of returning the electrocardiogram patch in the non-White groups compared with that of the White group.</div></div><div><h3>Conclusion</h3><div>Despite successful recruitment of racially and ethnically diverse participants, there were differences in subsequent engagement by non-White compared with that by White participants. Equitable representation and engagement of diverse racial and ethnic groups in digital clinical studies requires further study.</div></div><div><h3>Trial Registration</h3><div>Clinicaltrials.gov Identifier: <span><span>NCT03335800</span><svg><path></path></svg></span></div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Assessment of Positive Cardiac Remodeling in Hypertrophic Obstructive Cardiomyopathy Using an Artificial Intelligence-Based Electrocardiographic Platform in Patients Treated With Mavacamten”","authors":"","doi":"10.1016/j.mcpdig.2025.100209","DOIUrl":"10.1016/j.mcpdig.2025.100209","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Becomes of the Human Touch in the Age of Generative Artificial Intelligence?","authors":"Kishwen Kanna Yoga Ratnam MD, MPH, DrPH","doi":"10.1016/j.mcpdig.2025.100226","DOIUrl":"10.1016/j.mcpdig.2025.100226","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"United States Food and Drug Administration Regulation of Clinical Software in the Era of Artificial Intelligence and Machine Learning","authors":"Vidhi Singh BS,&nbsp;Susan Cheng MD, MPH,&nbsp;Alan C. Kwan MD, MS,&nbsp;Joseph Ebinger MD, MS","doi":"10.1016/j.mcpdig.2025.100231","DOIUrl":"10.1016/j.mcpdig.2025.100231","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation and Updating of Clinical Prediction Models: A Systematic Review","authors":"Alexander Saelmans MD ,&nbsp;Tom Seinen PhD ,&nbsp;Victor Pera PharmD ,&nbsp;Aniek F. Markus PhD ,&nbsp;Egill Fridgeirsson PhD ,&nbsp;Luis H. John MSc ,&nbsp;Lieke Schiphof-Godart PhD ,&nbsp;Peter Rijnbeek PhD ,&nbsp;Jenna Reps PhD ,&nbsp;Ross Williams PhD","doi":"10.1016/j.mcpdig.2025.100228","DOIUrl":"10.1016/j.mcpdig.2025.100228","url":null,"abstract":"<div><h3>Objective</h3><div>To summarize the implementation approaches and updating methods of clinically implemented models and consecutively advise researchers on the implementation and updating.</div></div><div><h3>Patients and Methods</h3><div>We included studies describing the implementation of prognostic binary prediction models in a clinical setting. We retrieved articles from Embase, Medline, and Web of Science from January 1, 2010, to January 1, 2024. We performed data extraction, based on Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis and Prediction Model Risk of Bias Assessment guidelines, and summarized.</div></div><div><h3>Results</h3><div>The search yielded 1872 articles. Following screening, 37 articles, describing 56 prediction models, were eligible for inclusion. The overall risk of bias was high in 86% of publications. In model development and internal validation, 32% of the models was assessed for calibration. External validation was performed for 27% of the models. Most models were implemented into the hospital information system (63%), followed by a web application (32%) and a patient decision aid tool (5%). Moreover, 13% of models have been updated following implementation.</div></div><div><h3>Conclusion</h3><div>Impact assessments generally showed successful model implementation and the ability to improve patient care, despite not fully adhering to prediction modeling best practice. Both impact assessment and updating could play a key role in identifying and lowering bias in models.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Battle of the Bots: Solving Clinical Cases in Osteoarticular Infections With Large Language Models","authors":"Fabio Borgonovo MD ,&nbsp;Takahiro Matsuo MD ,&nbsp;Francesco Petri MD ,&nbsp;Seyed Mohammad Amin Alavi MD ,&nbsp;Laura Chelsea Mazudie Ndjonko ,&nbsp;Andrea Gori MD ,&nbsp;Elie F. Berbari MD, MBA","doi":"10.1016/j.mcpdig.2025.100230","DOIUrl":"10.1016/j.mcpdig.2025.100230","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the ability of 15 different large language models (LLMs) to solve clinical cases with osteoarticular infections following published guidelines.</div></div><div><h3>Materials and Methods</h3><div>The study evaluated 15 LLMs across 5 categories of osteoarticular infections: periprosthetic joint infection, diabetic foot infection, native vertebral osteomyelitis, fracture-related infections, and septic arthritis. Models were selected systematically, including general-purpose and medical-specific systems, ensuring robust English support. In total, 126 text-based questions, developed by the authors from published guidelines and validated by experts, assessed diagnostic, management, and treatment strategies. Each model answered individually, with responses classified as correct or incorrect based on guidelines. All tests were conducted between April 17, 2025, and April 28, 2025. Results, presented as percentages of correct answers and aggregated scores, highlight performance trends. Mixed-effects logistic regression with a random question effect was used to quantify how each LLM compared in answering the study questions.</div></div><div><h3>Results</h3><div>The performance of 15 LLMs was evaluated, with the percentage of correct answers reported. OpenEvidence and Microsoft Copilot achieved the highest score (119/126 [94.4%]), excelling in multiple categories. ChatGPT-4o and Gemini 2.5 Pro scored 117 of the 126 (92.8%). When used as references, OpenEvidence was not inferior to any comparator and was superior to 5 LLMs. Performance varied across categories, highlighting the strengths and limitations of individual models.</div></div><div><h3>Conclusion</h3><div>OpenEvidence and Miccrosoft Copilot achieved the highest accuracy among evaluated LLMs, highlighting their potential for precisely addressing complex clinical cases. This study emphasizes the need for specialized, validated artificial intelligence tools in medical practice. Although promising, current models face limitations in real-world applications, requiring further refinement to support clinical decision making reliably.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Social Media to Combat Influenza Vaccine Misinformation and Improve Uptake: A Social Media Campaign and Repeated Cross-sectional Survey Analysis","authors":"Jessica B. Steier DrPH","doi":"10.1016/j.mcpdig.2025.100229","DOIUrl":"10.1016/j.mcpdig.2025.100229","url":null,"abstract":"<div><h3>Objective</h3><div>To combat influenza (flu)-vaccine misinformation and improve vaccine uptake using social media.</div></div><div><h3>Patients and Methods</h3><div>Unbiased Science used an online survey to identify flu vaccine-hesitant demographic groups and their specific objections to vaccination. Targeted educational content was then created and deployed through a variety of media formats, including podcasts, newsletters, reels, and infographics. A postcampaign survey determined the proportion of individuals who changed their minds about vaccination as a result of the educational content. The study was conducted between October 28, 2022 and February 7, 2023.</div></div><div><h3>Results</h3><div>In 3626 precampaign surveys, 187 individuals (5.1%) reported being unvaccinated and not planning to get the flu vaccine (the unvaccinated group). Multivariable analysis showed that geographic region (Northeast and Southeast), gender identity (male and other), race–ethnicity (non-Hispanic Black, and non-Hispanic other), and education level (high-school or less and some college) were independently associated with being unvaccinated. The main reasons were needlephobia, dismissal of flu severity, and concerns about vaccine components, multiple vaccines, and side effects. In 838 postcampaign surveys, 39 individuals (4.7%) indicated changing their mind about vaccination: of these, 27 (69.2%) said they were more likely to get vaccinated and 22 (56.4%) had gotten vaccinated. Twenty individuals (51.3%) said they changed their mind at least in part because of the targeted educational content.</div></div><div><h3>Conclusion</h3><div>Social media has the potential to change attitudes and behaviors around vaccination. When science messaging is deployed across several platforms and targeted to key demographic characteristics, it has the ability to combat misinformation and influence vaccine uptake.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model for Rapid Innovation for Engagement, Enrollment, and Data and Sample Collection in a Diverse Cohort Study: Insights from All of Us Participant Labs","authors":"Janna Ter Meer PhD ,&nbsp;Jessica Chen BS ,&nbsp;Romina Foster-Bonds MHS ,&nbsp;Andrea Goosen RN ,&nbsp;Gayle Valensky MPH ,&nbsp;Ethan Dinh-Luong BS ,&nbsp;Rachele Peterson MBA ,&nbsp;Geoffrey Ginsburg MD, PhD ,&nbsp;Chris Lunt BS ,&nbsp;Vik Kheterpal MD ,&nbsp;Eric Topol MD ,&nbsp;Allison Mandich MS ,&nbsp;Yentram Huyen PhD ,&nbsp;Julia Moore Vogel PhD","doi":"10.1016/j.mcpdig.2025.100227","DOIUrl":"10.1016/j.mcpdig.2025.100227","url":null,"abstract":"<div><h3>Objective</h3><div>To improve engagement and retention of a cohort that reflects the US population within the <em>All of Us</em> Research Program, we created and implemented an innovation infrastructure and initiatives.</div></div><div><h3>Participants and Methods</h3><div><em>All of Us</em> participant laboratories (APLs) established innovation-specific processes to rapidly ideate, select, implement, and evaluate cost-effective innovative initiatives, while mitigating risks. This was done within 4 priority areas: accelerating enrollment, enhancing engagement and retention, improving biospecimen collection, and broadening data types. Participants within the <em>All of Us</em> Research Program were engaged in this research between April 6, 2022 and May 6, 2024.</div></div><div><h3>Results</h3><div>We present a summary of APL processes and portfolio along with 5 specific initiatives that rapidly tested innovative ways to increase task completion and broaden biospecimen submission accessibility. Each initiative’s cost–benefit profile was evaluated by a committee of program leadership. Findings include the following: (1) offering compensation increased task completion, the degree of which was dependent on the context and amount of compensation; and (2) adding evening and weekend blood donation appointment times and distributing saliva collection kits through community partners increased donations from participants who have been historically underrepresented in biomedical research. On average, program staff predicted initiative effect sizes would be more than double their actual effect.</div></div><div><h3>Conclusion</h3><div>We found that large research studies can rapidly innovate to meet program goals, including a focus on diversity. We identified specific strategies and tactics to improve health research engagement and retention, with a focus on historically underrepresented in biomedical research communities, which can be used by numerous health research studies.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment","authors":"Andrew McGarry MD ,&nbsp;Oliver Roesler PhD ,&nbsp;Jackson Liscombe PhD ,&nbsp;Michael Neumann PhD ,&nbsp;Hardik Kothare PhD ,&nbsp;Abhishek Hosamath MBM ,&nbsp;Lakshmi Arbatti MS ,&nbsp;Anusha Badathala MD ,&nbsp;Stephen Ruhmel BS, MPH ,&nbsp;Bryan J. Hansen PhD ,&nbsp;Madeline Quall BA ,&nbsp;Sandrine Istas MBio ,&nbsp;Arthur Wallace MD, PhD ,&nbsp;David Suendermann-Oeft PhD ,&nbsp;Vikram Ramanarayanan PhD ,&nbsp;Ira Shoulson MD","doi":"10.1016/j.mcpdig.2025.100224","DOIUrl":"10.1016/j.mcpdig.2025.100224","url":null,"abstract":"<div><div>Traditional clinical trials in neurodegenerative disorders have utilized combinations of examination-based outcomes, global assessments by investigators and participants, and scales aimed at function, some of which are patient-reported outcomes. It is debatable whether these tools optimally convey therapeutic efficacy. A complementary approach using digital biomarkers to surpass exam-based limitations for detecting physical change coupled with a direct report from participants on what their sources of suffering are could be a useful advance in reporting beneficial effects of interventions, particularly if changes track together. We sought to determine the feasibility of remotely assessing speech, facial features, and cognition in an mild cognitive impairment (MCI) population, whether those extracted features could distinguish MCI from controls, and to explore what self-reported problems could reveal about the MCI experience. Our web-based platform was easy to use and revealed facial features in particular as capable of discriminating MCI from controls. Using the features that showed a statistically significant difference between cohorts (<em>P</em>&lt;.01) produced an area under the receiver operating curve of 0.75. Self-reported problems with cognition, gait, sleep, and behavior were more common in the MCI group. The MCI was associated with 6 times more difficulty with falls (n=6 vs 1). These data support the feasibility and discriminative utility of using remote monitoring technology in combination with participant self-report in an MCI population. Future work will investigate the extent to which multimodal biomarkers combined with self-report can characterize MCI longitudinally and for potential research applications as a measure of therapeutic effect.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Evaluation of eHealth Lifestyle Interventions for Preschool Children: A Scoping Review","authors":"Marissa C.J. Kooij MD ,&nbsp;Ashley J.P. Smit MSc ,&nbsp;Linda D. Breeman PhD ,&nbsp;Lieke Schiphof-Godart PhD ,&nbsp;Isra Al-Dhahir MSc ,&nbsp;Andrea W.M. Evers PhD ,&nbsp;Koen F.M. Joosten MD, PhD","doi":"10.1016/j.mcpdig.2025.100223","DOIUrl":"10.1016/j.mcpdig.2025.100223","url":null,"abstract":"<div><div>EHealth lifestyle interventions can promote positive lifestyle changes in preschool children, but they need to be evaluated to assess their effectiveness and identify areas for improvement. This scoping review aimed to examine evaluation methods, outcome measures, and methodologic strengths and weaknesses, to provide recommendations for the evaluation of eHealth lifestyle interventions for preschool children. A comprehensive literature search was conducted across 6 databases for articles published up to September 29, 2023. We identified 48 articles describing 31 interventions that met our predefined eligibility criteria. These interventions predominantly targeted children’s diet. The most frequently evaluated outcomes were effectiveness, acceptability, and usage. Effectiveness outcomes included, among others, dietary intake, anthropometrics, and child and parental behaviors. Acceptability was evaluated primarily as user satisfaction. Evaluation methods for effectiveness and acceptability included questionnaires, interviews, focus groups, and portable devices. Intervention usage was evaluated via logged use and self-reported data. On the basis of our findings, we present recommendations for future evaluation of eHealth interventions for preschool children. These recommendations focus on selecting relevant outcome measures and appropriate evaluation methods and on integrating and applying evaluation results.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}