Mayo Clinic Proceedings. Digital health最新文献

{"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":"2025-09-01","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":"Youth Uptake of Digital Sexual and Reproductive Health Services Across Sociodemographic Groups (2018-2022): A Total Population Study from Stockholm, Sweden","authors":"Lovisa Hellsten MPH ,&nbsp;Viktor H. Ahlqvist PhD ,&nbsp;Anna M. Nielsen RM, PhD ,&nbsp;Gunnar Brandén PhD ,&nbsp;Anna Mia Ekström MD, PhD ,&nbsp;Kyriaki Kosidou MD, PhD","doi":"10.1016/j.mcpdig.2025.100251","DOIUrl":"10.1016/j.mcpdig.2025.100251","url":null,"abstract":"<div><h3>Objective</h3><div>To examine uptake of in-person and digital sexual and reproductive health (SRH) services among adolescents and young adults, quantify uptake across time, and explore whether the introduction of digital services affected the sociodemographic composition of users.</div></div><div><h3>Patients and Methods</h3><div>This Swedish total population study included all Stockholm residents aged 12-22 years between January 1st 2018 and December 31st 2022. The primary outcome was in-person or digital visits (chat and video) of SRH services within a year, identified using regional health care registries. Sociodemographic predictors included sex, age, migrant background, parental education, and household income, analyzed with repeated-measures multivariable regressions.</div></div><div><h3>Results</h3><div>Among the 454,405 individuals, 23.96% had at some point used SRH services (80.01% women) between 2018 and 2022. In-person visits remained the predominant mode of contact. Women had higher annual utilization rate of both in-person (women: 15.27%; 95% CI, 15.13-15.40; men: 1.75%; 95% CI, 1.72-1.78) and digital visits (women: 2.23%; 95% CI, 2.16-2.30; men: 0.12%; 95% CI, 0.11-0.13). Significantly lower uptake was also observed in the lowest income quintile (digital: adjusted odds ratio [aOR], 0.34; 95% CI, 0.31-0.36; in-person: aOR, 0.43; 95% CI, 0.42-0.45) compared with the highest quintile (reference group). Among digital visits, chat was more equitably used than video consultations across sociodemographic groups, including smaller differences between the highest and lowest income quintiles (chat: aOR, 0.59; 95% CI, 0.54-0.65; video: aOR, 0.25; 95% CI, 0.23-0.27). Only modest reductions in socioeconomic disparities were observed after the introduction of digital services.</div></div><div><h3>Conclusions</h3><div>Sociodemographic disparities in utilization were not alleviated by the introduction of digital visits; in-person users were also the primary digital users. Chat could be more equitable than video, but further research is needed.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724163","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":"Designing Artificial Intelligence-Powered Health Care Assistants to Reach Vulnerable Populations: A Discrete Choice Experiment Among South African University Students","authors":"Amy Zheng MPH ,&nbsp;Lawrence Long PhD ,&nbsp;Caroline Govathson MSc ,&nbsp;Candice Chetty-Makkan PhD ,&nbsp;Sarah Morris BS ,&nbsp;Dino Rech MBA ,&nbsp;Matthew P. Fox DSc ,&nbsp;Sophie Pascoe PhD","doi":"10.1016/j.mcpdig.2025.100248","DOIUrl":"10.1016/j.mcpdig.2025.100248","url":null,"abstract":"<div><h3>Objective</h3><div>To understand what preferences are important to university students in South Africa when engaging with a hypothetical artificial intelligence-powered health care assistant (AIPHA) to access health information using a discrete choice experiment.</div></div><div><h3>Patients and Methods</h3><div>We conducted an unlabeled, forced choice discrete choice experiment among adult South African university students through Prolific, an online research platform, from June 26, 2024 to August 31, 2024. Each choice option described a hypothetical AIPHA using 8 attribute characteristics (cost, confidentiality, security, health care topics, language, persona, access, and services). Participants were presented with 10 choice sets each comprised of 2 choice options and asked to choose between the 2. A conditional logit model was used.</div></div><div><h3>Results</h3><div>Three hundred participants were recruited and enrolled. Most participants were Black, born in South Africa, heterosexual, working for a wage, and had a mean age of 26.5 years (SD, 6.0). Language, security, and receiving personally tailored advice were the most important attributes for AIPHA. Participants strongly preferred the ability to communicate with the AIPHA in any South African language of their choosing instead of only English and receive information about health topics specific to their context including information on clinics geographically near them. The results were consistent when stratified by sex and socioeconomic status.</div></div><div><h3>Conclusion</h3><div>Participants had strong preferences for security and language, which is in line with previous studies where successful uptake and implementation of such health interventions clearly addressed these concerns. These results build the evidence base for how we might engage young adults in health care through technology effectively.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748792","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":"Predicting Tolerance to Anthracycline Chemotherapy Using Electrocardiogram-Based Artificial Intelligence in Sarcoma","authors":"Jack B. Korleski MD ,&nbsp;Regina M. Koch MD ,&nbsp;Thanh P. Ho MD ,&nbsp;Steven I. Robinson MBBS ,&nbsp;Scott H. Okuno MD ,&nbsp;Joerg Herrmann MD ,&nbsp;Brittany L. Siontis MD","doi":"10.1016/j.mcpdig.2025.100247","DOIUrl":"10.1016/j.mcpdig.2025.100247","url":null,"abstract":"<div><h3>Objective</h3><div>The objective of this study was to understand the utility of artificial intelligence-enabled electrocardiogram (AI-ECG) to assess the tolerability of anthracycline chemotherapy.</div></div><div><h3>Patients and Methods</h3><div>From December 18, 2006 to October 15, 2020, we identified adults with sarcoma who were treated with anthracycline chemotherapy at our institution who had an ECG within 1 year prior to treatment initiation. Utilizing previously defined AI-ECG nomograms, we obtained age and ejection fraction (EF) predictions. Changes in AI-ECG age were correlated with chemotherapy tolerance (the rates of dose reductions, treatment delays, and early discontinuation). We measured the sensitivity and specificity of the ECG to predict an EF of less than 50% or 35% prior to treatment and compared how changes in the AI-ECG EF prediction related to changes in echocardiography-based EF.</div></div><div><h3>Results</h3><div>Forty patients met the eligibility criteria. Sixty-eight percent of the patients were men. The median age was 56.5 years (18-76 years). We did not find differences in chemotherapy tolerance between patients who had an elevated or decreased ECG age. There was a trend `toward higher rates of dose reductions in patients with high ECG aging (odds ratio, 5.13; <em>P</em>=.32). The AI-ECG low EF prediction had a sensitivity of 100% and a specificity of 94% to isolate patients with an EF of less than 50% prior to treatment. Two patients’ EF decreased more than 10% after treatment, and both cases showed significant increases in the low EF prediction.</div></div><div><h3>Conclusion</h3><div>Overall, AI-based predictions on ECG tracings could be a way to monitor for decreases in EF during treatment with anthracycline chemotherapy. We recommend further studies to evaluate AI-ECG aging as a marker for chemotherapy tolerance.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686358","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":"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":"2025-09-01","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":"Artificial Intelligence in Digital Self-Diagnosis Tools: A Narrative Overview of Reviews","authors":"Aikaterini Mentzou PhD ,&nbsp;Amy Rogers MD ,&nbsp;Edzia Carvalho PhD ,&nbsp;Angela Daly PhD ,&nbsp;Maeve Malone HDip ,&nbsp;Xaroula Kerasidou PhD","doi":"10.1016/j.mcpdig.2025.100242","DOIUrl":"10.1016/j.mcpdig.2025.100242","url":null,"abstract":"<div><div>Digital self-diagnosis tools, or symptom checkers, many of which incorporate artificial intelligence technology, are intended to provide diagnostic information and triage advice to lay users. This narrative overview of reviews explores the common themes and issues raised by existing evidence synthesis literature on these tools to establish a common ground for interdisciplinary research. We searched 3 bibliographic databases (PubMed, Scopus and Web of Science) and Google Scholar using keyword combinations of <em>artificial</em>, <em>self-diagnosis</em>, <em>intelligence</em>, and <em>machine learning</em> for publications from 2019 to 2023. We included systematic reviews, meta-analyses, scoping reviews, narrative syntheses, and opinion pieces that discussed tools where users proactively entered personal health information to acquire a predicted diagnosis of their symptoms or triage advice. This overview reveals significant gaps in understanding the key areas of development, implementation, impact, and oversight of digital self-diagnosis tools. Additionally, the terminology used to describe these tools and their underlying technologies varies widely, encompassing technologies ranging from simple branching logic algorithms to complex deep neural networks. Our interdisciplinary analysis identified gaps and critical areas for future research across all stages of the lifecycles of these tools. The diverse challenges uncovered highlight the necessity for multiagency and multidisciplinary efforts promoting responsible development and implementation.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534516","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":"How is Engagement Defined Across Health Care Services and Technology Companies? A Systematic Review","authors":"Sanjay Basu MD, PhD ,&nbsp;Ariela Simerman BA ,&nbsp;Ari Hoffman MD","doi":"10.1016/j.mcpdig.2025.100256","DOIUrl":"10.1016/j.mcpdig.2025.100256","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically examine how digital health startups define and operationalize engagement in the post- coronavirus disease environment (2020-2025).</div></div><div><h3>Patients and Methods</h3><div>Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines adapted for web-based literature, we systematically reviewed publicly available information from digital health startups founded or significantly operating between 2020-2025. We extracted engagement definitions from company websites, white papers, blog posts, and press releases. Definitions were coded by type (explicit, implicit, or nondefinition) and dimensional focus (behavioral, cognitive, affective, and social). Inter-rater reliability was assessed using Cohen’s κ (κ=0.82). We conducted this systematic review from April 20, 2025, to May 21, 2025.</div></div><div><h3>Results</h3><div>We analyzed 64 engagement definitions from 30 digital health startups. Only 18.8% (n=12) were explicit definitions with clear measurement criteria, whereas 45.3% (n=29) were implicit definitions and 35.9% (n=23) were nondefinitions that mentioned engagement without defining it. The behavioral dimension dominated (64.1%, n=41), followed by social (28.1%, n=18), cognitive (21.9%, n=14), and affective dimensions (17.2%, n=11). Statistical analysis revealed significant associations between definition type and dimensional focus (<em>P</em>&lt;.05). Based on our findings, we developed a taxonomy of engagement definitions and a 5-level engagement definition maturity model.</div></div><div><h3>Conclusion</h3><div>Digital health startups predominantly use implicit or undefined engagement concepts with a strong behavioral focus. The proposed taxonomy and maturity model provide frameworks for standardizing engagement definitions across the digital health ecosystem, potentially improving measurement consistency, facilitating more meaningful comparisons between solutions, and establishing a baseline for evaluating effectiveness.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908449","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":"2025-09-01","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":"Medication Adherence Technologies: A Classification Taxonomy Based on Features","authors":"Bincy Baby PharmD, MSc ,&nbsp;Jasdeep Kaur Gill PharmD ,&nbsp;Sadaf Faisal BPharm, PhD ,&nbsp;Ghada Elba PharmD, MSc ,&nbsp;SooMin Park PharmD (c) ,&nbsp;Annette McKinnon ,&nbsp;Kirk Patterson BA ,&nbsp;Sara J.T. Guilcher PT, PhD ,&nbsp;Feng Chang PharmD ,&nbsp;Linda Lee MD ,&nbsp;Catherine Burns PhD ,&nbsp;Ryan Griffin PhD ,&nbsp;Tejal Patel BScPharm, PharmD","doi":"10.1016/j.mcpdig.2025.100237","DOIUrl":"10.1016/j.mcpdig.2025.100237","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a comprehensive classification system for medication adherence technologies based on an inventory of characteristics and features of existing technology.</div></div><div><h3>Participants and Methods</h3><div>Using a 3-stage approach methodology—development, validation, and evaluation, the study adopted the taxonomy development method and was conducted from February 1, 2023 to July 31, 2024. In the development stage, medication adherence technologies were defined, end users were identified, and a meta-characteristic was determined; using both empirical-to-conceptual and conceptual-to-empirical approaches, dimensions and characteristics were identified. The taxonomy was validated through the Delphi consensus approach and classifying 20 sample medication adherence technologies and evaluated by mapping to codes identified from a qualitative study.</div></div><div><h3>Results</h3><div>After undergoing 8 iterations, which included incorporating feedback from a Delphi consensus survey, the final taxonomy comprised 7 dimensions, 25 subdimensions, and 320 characteristics. These key dimensions include Physical Features, Display, Connectivity, System Alert, Data Collection and Management, Operations, and Integration. The taxonomy was considered complete and valuable once all preestablished ending conditions were met, and its applicability and comprehensiveness were verified by comparing various medication adherence technologies and mapping to codes identified from a qualitative study.</div></div><div><h3>Conclusion</h3><div>This study successfully establishes the first comprehensive classification system for medication adherence technologies based on features, addressing a critical gap in literature. The taxonomy provides a structured framework for categorizing and evaluating technologies, supporting usability testing and the selection of appropriate devices tailored to the unique needs of older adults.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596753","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":"Healthy Heart Assistant, a WhatsApp-Based Generative Pretrained Transformer Technology, for Self-Care in Hypertensive Patients","authors":"Samuel E. Antia MD, MSc ,&nbsp;Collins N. Ugwu MD, MSc ,&nbsp;Vishal Ghodka BE ,&nbsp;Babangida S. Chori MSc ,&nbsp;Muhammad S. Nazir MD, MSc ,&nbsp;Chizoba A. Odili PhD ,&nbsp;Godsent C. Isiguzo MD, PhD ,&nbsp;Sri Vasireddy MS, MBA ,&nbsp;Augustine N. Odili MD, PhD","doi":"10.1016/j.mcpdig.2025.100243","DOIUrl":"10.1016/j.mcpdig.2025.100243","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the feasibility, usability, and efficacy of innovative generative pretrained transformer chatbot in improving self-care in hypertensive patients in a resource-limited setting.</div></div><div><h3>Patients and Methods</h3><div>A single-arm nonblinded clinical trial was deployed in a busy cardiology clinic in a low-resource setting. Artificial intelligence–enabled chatbot (Healthy Heart Assistant) was activated in smartphones of 50 adults on treatment for hypertension. Participants were trained on how to use the Healthy Heart Assistant including setting medication and appointment reminders. Baseline questionnaires were administered at enrollment and 30 days later to explore acceptability, feasibility and usability of the bot. We used chatbot usability questionnaire and self-made Healthy Heart Assistant satisfaction questionnaire to assess bot usability and patients’ satisfaction, respectively. The study began on April 5, 2024, through July 15, 2024.</div></div><div><h3>Results</h3><div>Of 200 hypertensive clinic attendees, 70 (35%) had internet-enabled bot-compatible cell phones, of which 50 hypertensive patients were recruited to participate in the study. Among 50 participants, 2 (4%) were lost to follow-up; 19 (39.6%) were women; and 40 (83.3%) had attained tertiary level of education. Mean time of training to use bot was 5.7 minutes, with 35 (70.8%) of participants being able to use the bot within 5 minutes. The median frequency of chats for participants within the timeframe was an average of 1.5 chats/day. Chatbot usability questionnaire score was 69.5%, whereas self-made Healthy Heart Assistant satisfaction questionnaire score was 90%.</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study shows that generative artificial intelligence can be applied with reasonable success in hypertension self-care in low-resource settings and has potential for being effective.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549292","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}