JMIR Formative Research最新文献

{"title":"Feasibility and Acceptability of an eHealth-Based Physical Activity Coaching Intervention During Pulmonary Rehabilitation for People With Chronic Obstructive Pulmonary Disease: Mixed Methods Study.","authors":"Sofia Flora, Ana Sofia Grave, Sara Pimenta, Fátima Baptista, Chris Burtin, Joana Cruz","doi":"10.2196/83783","DOIUrl":"https://doi.org/10.2196/83783","url":null,"abstract":"<p><strong>Background: </strong>Physical inactivity is a modifiable and significant trait in people with chronic obstructive pulmonary disease (COPD). While traditional exercise-based pulmonary rehabilitation (PR) improves symptoms and exercise tolerance, its impact on physical activity (PA) levels remains limited. Digital health (eHealth) interventions may help address this gap.</p><p><strong>Objective: </strong>This study aimed to assess the feasibility and acceptability of integrating an eHealth PA coaching intervention into PR for people with COPD.</p><p><strong>Methods: </strong>Patients enrolled in an outpatient PR program were recruited for a 3-week PA coaching intervention, which used a smart band connected to a mobile patient app and a web application for health care professionals (HCPs). The intervention included PA monitoring (steps per day); weekly goal setting; and app notifications for goal updates, achievement, and motivational messages. Weekly telephone calls supported goal adjustment and identification of PA barriers. The acceptability of the intervention was explored through a patient focus group.</p><p><strong>Results: </strong>Five patients with COPD (mean 67, SD 9 years; n=4, 80% female; mean predicted forced expiratory volume at 1 second of 49%, SD 23%) participated with 100% retention and adherence to the intervention (daily synchronization). No adverse events or PA barriers were identified. One patient reported an app connection issue that was resolved by restarting the app. Patients found the app easy to use and helpful for their PA awareness and remote monitoring by HCPs. Weekly goal adjustments and contact with an HCP were valued. Limitations regarding the app use included a lack of personalization, goal setting restricted to steps, and occasional step miscounts.</p><p><strong>Conclusions: </strong>The intervention was feasible and well accepted. Future studies with a larger sample are needed to assess the impact of the intervention on PA outcomes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e83783"},"PeriodicalIF":2.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698767","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 Digital Inclusion Intervention to Improve Access to a Digital Health Intervention Among Digitally Excluded Adults: Mixed Methods Pilot Randomized Controlled Trial.","authors":"Christy Walklin, Juliet Briggs, Siobhan Freeman, Emmanuel Mangahis, Camila Dias, Sunil Bhandari, Kate Bramham, James O Burton, Jackie Campbell, Philip A Kalra, Jamie Macdonald, Maarten W Taal, David C Wheeler, Sharlene A Greenwood, Hannah M L Young","doi":"10.2196/91438","DOIUrl":"10.2196/91438","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The National Health Service 10-year health plan emphasizes an increasing shift toward digital health care delivery. However, there is limited research on how best to support, engage, and include individuals who are digitally excluded. As health care services become more digitally driven, evidence-based interventions are needed to address digital exclusion and ensure equitable access to care, particularly for people living with long-term conditions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the feasibility and acceptability of providing digital literacy training alongside a digital health intervention (DHI; Ex-Tab intervention), compared with providing a DHI alone. Kidney Beam, a DHI designed to promote physical activity and improve quality of life in people with chronic kidney disease (CKD), was used as an exemplar DHI.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This mixed methods, single-site pilot randomized controlled trial recruited 40 adults with CKD who were digitally excluded. Digital exclusion was defined as lacking access to a Wi-Fi-enabled digital device or having a Digital Health Care Literacy Scale (DHLS) score of &lt;7 (range 0-21). Participants were randomized 1:1 to receive either the Kidney Beam Ex-Tab intervention or Kidney Beam alone (control). The intervention group received a Wi-Fi-enabled iPad on loan with Kidney Beam preinstalled, digital literacy training, and ongoing support to access the 12-week Kidney Beam program (twice weekly live exercise and education sessions). The control group received sign-up instructions for Kidney Beam only. Feasibility outcomes were assessed against a priori progression criteria and included screening, recruitment, retention, adherence, safety, and acceptability. Secondary outcomes included the Kidney Disease Quality of Life Questionnaire, Chalder Fatigue Questionnaire, and Patient Health Questionnaire-4. Outcomes were measured at baseline and 12 weeks. Acceptability and user experience were explored through semistructured interviews with participants from both groups at 12 weeks (n=25).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Between September 2023 and September 2024, a total of 169 individuals were screened and 40 were enrolled (median age 66.5 years; 20 male individuals; median DHLS score: 4). Twenty-one participants were randomized to the Kidney Beam Ex-Tab group and 19 to the Kidney Beam alone group. Of the 40 participants, 35 (88%) completed the 12-week follow-up (intervention: n=18; control: n=17). All prespecified feasibility criteria for recruitment, retention, adherence, and safety were met. Qualitative findings indicated that the tablet loan and digital literacy training were acceptable and highly valued, enhancing confidence, motivation, and DHI engagement. Providing loaned devices was particularly important for overcoming access barriers, especially for participants unable to afford their own device.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Providing Wi-F","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e91438"},"PeriodicalIF":2.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13085982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach.","authors":"Adithama Mulia, David Agustriawan, Marlinda Overbeek, Moeljono Widjaja, Vincent Kurniawan, Jheno Syechlo, Muhammad Imran Ahmad, Srinivasulu Yerukala Sathipati, Nilubon Kurubanjerdjit","doi":"10.2196/82587","DOIUrl":"10.2196/82587","url":null,"abstract":"<p><strong>Background: </strong>Prostate cancer progression exhibits significant variability influenced by biological and racial factors. DNA methylation profiling has shown potential in early cancer detection, but its integration with machine learning across racially diverse populations remains limited.</p><p><strong>Objective: </strong>This study aimed to develop a prostate cancer stage classifier for the majority White cohort using DNA methylation data and a multilayer perceptron (MLP) model in order to classify prostate cancer stages into early (stages I-II) and late (stages III-IV) stages and assess its performance when applied to other racial groups to highlight the need for race-specific models.</p><p><strong>Methods: </strong>Methylation and phenotype data from the TCGA-PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma) dataset were processed using differentially methylated position (DMP) analysis to identify CpG sites correlated with cancer stages. These features were further refined through recursive feature elimination (RFE) and used to train MLP models. Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the model and identify key DNA methylation features contributing to model predictions.</p><p><strong>Results: </strong>The best-performing model achieved 95% accuracy and up to 99% area under the curve on the majority race (White) training data using 90 selected features. However, performance declined sharply in racial minority groups, revealing the effects of sample imbalance and race-specific methylation patterns. Feature importance examination indicated strong patterns within certain CpG sites driving model predictions.</p><p><strong>Conclusions: </strong>We propose a race-aware MLP model for prostate cancer stage classification using DNA methylation data, which has been optimized through DMP and RFE-based feature selection. SHAP and LIME confirmed the predictive relevance of selected CpG sites, supporting model transparency. The results highlight high performance within the White cohort but reveal poor generalization to racial minority groups, emphasizing the importance of race-specific modeling strategies.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e82587"},"PeriodicalIF":2.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13086062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.","authors":"Padhraig Ryan, Orla Davoren, Glyn Elwyn","doi":"10.2196/68223","DOIUrl":"10.2196/68223","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Large language models use machine learning to produce natural language. These models have a range of potential applications in health care, such as patient education and diagnosis. However, evaluations of large language models in health care are still scarce.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to (1) evaluate the accuracy and efficiency of automated fact-checking by 2 large language models and (2) illustrate a process through which a large language model might support a patient in redrafting a prompt to include key information needed for patient safety.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A parallel comparison of 2 large language models and 3 human experts was conducted. A clinical scenario was devised in which a woman aged 23 years questions the safety of retinoid treatment for acne by sending prompts to 2 large language models (GPT-4o and OpenBioLLM-70B). GPT-4o and OpenBioLLM-70B were asked to suggest improvements to the patient's initial prompt to elicit key information for clinical decision-making. After the patient sent the revised prompt to the large language models, the models were then asked to fact-check the final response. To test the generalizability of automated fact-checking, a set of 20 clinical statements on disparate topics, mostly related to drug indications, contraindications, and side effects, was developed. The large language models also fact-checked these 20 medical statements. The results were compared against the evaluations of 3 clinical experts. The outcome measures were as follows: (1) percentage of accuracy of automated fact-checking, (2) time to complete fact-checking, and (3) a binary outcome for prompt redrafting (advising the patient to revise her prompt by naming her acne medication to address safety concerns).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For the scenario of a patient with acne, GPT-4o and OpenBioLLM-70B both had 86% agreement with the clinical experts' fact-checking. The large language models did not consistently convey the urgency of discontinuing isotretinoin treatment when pregnancy is suspected. In addition, the models did not adequately convey the importance of folic acid supplementation during pregnancy. For the set of 20 medical claims, GPT-4o fact-checking had 100% agreement with that of human experts, whereas OpenBioLLM-70B had 95% agreement. OpenBioLLM-70B diverged from human experts and GPT-4o on 1 question related to pediatric use of antihistamines. The expert fact-checks took a mean time of 18 (SD 3.74) minutes, GPT-4o took 42 seconds, and OpenBioLLM-70B took 33 minutes. The GPT-4o responses for the acne scenario had some inconsistencies but zero fabrication and no obvious omissions. In contrast, OpenBioLLM-70B omitted 1 key information item needed for patient safety.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;GPT-4o can interact with patients to improve the quality and comprehensiveness of the information contained in health-related prompts. GPT-4o and OpenBi","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e68223"},"PeriodicalIF":2.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Contextualized, Research-Based Flemish Assessment Framework for Digital Care, Assistance, and Support: Delphi Study.","authors":"Fien Buelens, Tom Seymoens, Jana Verplancke, Tom Van Daele","doi":"10.2196/88512","DOIUrl":"10.2196/88512","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rapid evolution of digital technologies has transformed health, mental health, and social care, offering new modalities of digital care, assistance, and support through web-based platforms, mobile apps, extended reality, wearables, and artificial intelligence systems. Despite this proliferation, there is little consensus on what constitutes \"high-quality\" digital care. Challenges persist regarding data security, interoperability, accessibility, sustainability, and professional competence, whereas existing standards and regulations provide fragmented guidance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop a contextualized, consensus-based quality assessment framework for digital care, assistance, and support in Flanders, Belgium. For this purpose, perspectives across technology, organizational processes, and professional competencies were integrated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The study used a multiphase design comprising (1) 10 expert interviews with Flemish government officials; (2) a narrative literature review of 303 peer-reviewed and gray literature sources; (3) a 3-round Delphi study with 50 experts across 5 domains (end users, facilitators, technology developers, deontology and ethics experts, and digital inclusion and media literacy experts); and (4) 4 complementary focus groups and 3 interviews with specialists in artificial intelligence, regulation, social work, mental health, and IT. The Delphi rounds gathered iterative feedback through open-ended elicitation, structured rating, and classification of quality criteria. Quantitative data were analyzed using descriptive statistics, whereas qualitative feedback was subjected to thematic analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 50 experts participated in round 1, a total of 40 (80%) participated in round 2, and 27 (54%) participated in round 3. Round 1 generated 577 unique quality criteria, consolidated into 26 clusters organized under 3 pillars: technology, organization, and professional competencies. The relative importance across pillars was balanced (mean score 37.29, SD 12.38 for technology; 33.33, SD 10.39 for professional competencies; and 29.80, SD 10.45 for organizations). Accessibility, reliability, and safety ranked highest for the technology; vision, quality monitoring, and infrastructure ranked highest for organization; and support, digital competencies, and ethics ranked highest for professional competencies. The finalized framework included 112 criteria, of which 35 (31.3%) were designated as optional and 77 (68.8%) were designated as minimum requirements. Focus groups and interviews validated the framework's comprehensiveness and usability, emphasizing proportional implementation, user centrality, and alignment with European Union regulations. Stakeholders highlighted the need for tools, training, and governance mechanisms to ensure adoption and sustainability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study ","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e88512"},"PeriodicalIF":2.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147689478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public Perceptions of AI in Medicine and Implications for Future Medical Education: Cross-Sectional Survey.","authors":"Michael Constantin Kirchberger","doi":"10.2196/89123","DOIUrl":"10.2196/89123","url":null,"abstract":"<p><strong>Background: </strong>The integration of artificial intelligence (AI) into clinical practice is contingent on public trust. This trust often depends on physician oversight, yet a significant gap exists between the need for AI-competent physicians and the current state of medical education. While the perspectives of students and experts on this gap are known, the views of the US general public remain largely unquantified.</p><p><strong>Objective: </strong>This study aimed to assess US public perceptions regarding AI in medicine and the corresponding emergent needs for medical education. We specifically sought to quantify public trust in different diagnostic scenarios, concerns about physician overreliance on AI, support for mandatory AI education, and priorities for the future focus of medical training.</p><p><strong>Methods: </strong>We conducted a cross-sectional, web-based survey of adults in the United States in November 2025. Participants (N=524) were recruited via SurveyMonkey Audience. We calculated descriptive statistics, frequencies, proportions (percentages), and 95% CIs for all main survey items.</p><p><strong>Results: </strong>A total of 524 participants completed the survey. Most (n=329, 62.8%; 95% CI 58.6%-66.9%) placed the most trust in a physician's diagnosis based on their expertise alone; only 7.8% (n=41; 95% CI 5.5%-10.1%) trusted an AI-first diagnostic model. Trust was highly contingent on training: 93.9% (n=492) of participants rated formal physician training on AI limitations as \"essential\" or \"very important.\" Widespread concern about physician overreliance on AI was reported, with 81.1% (n=425) being \"very concerned\" or \"extremely concerned.\" Consequently, 85.1% (n=446) agreed or strongly agreed that training on AI use, ethics, and limitations should be mandatory in medical school. When asked about future educational priorities, 70.2% (n=368; 95% CI 66.3%-74.1%) believed that medical education should focus on human-centered skills (eg, empathy and communication) over clinical skills.</p><p><strong>Conclusions: </strong>The US public expressed conditional trust in medical AI, strongly preferring physician-led and critically supervised models. These findings reveal a clear public mandate for medical education reform. The public expects future physicians to be mandatorily trained to appraise AI, understand its limitations, and refocus their professional development on the human-centered skills that technology cannot replace.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e89123"},"PeriodicalIF":2.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Relapse Using Digital Technology in People in Recovery From Substance Use Disorders: Early Economic Evaluation With a Case Study of the Subreal App.","authors":"Janet Bouttell, Michał Bartler, Sarah Bolton","doi":"10.2196/87186","DOIUrl":"https://doi.org/10.2196/87186","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Many people relapse after achieving abstinence in substance use disorders. Health care providers may scan the horizon for new technologies to predict response that allow interventions to be targeted rather than routine. Currently, no such predictive technologies are available in the United Kingdom. The Subreal app is available for use in research contexts, but no clinical data specific to the app are yet available. Early health economic modeling can use data from the literature to explore characteristics essential for the new technology to be cost-effective. This information can guide developers in setting performance targets and pricing and estimating potential cost savings and/or cost-effectiveness for health care providers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study was supported by a UK industry funding body to explore the potential of digital technologies such as the Subreal app to offer cost savings or cost-effectiveness for health care providers. We explored the threshold price and clinical effectiveness required to deliver cost savings and cost-effectiveness in 2 subpopulations with substance use disorders in a UK setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Deterministic models were used to estimate costs per relapse and quality-adjusted life years over 1-, 5-, and 20-year time horizons for people who have achieved abstinence after treatment for alcohol or opioid misuse. The intervention was a digital technology predicting relapse, provided-in addition to standard care-for 1 year post achievement of abstinence. In Subreal, biomarker data are collected daily through the app, and artificial intelligence-enhanced risk assessment flags patients who require additional support. The comparator was event-driven, reactive response to relapse. Costs and quality-of-life estimates were calculated using Markov models with data from existing published sources. The base-case estimate of 15% reduction in first-year relapse rates was based on a previous study on a similar but simpler digital technology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Digital technologies such as the Subreal app have the potential to be cost-saving from a UK health and social care perspective, especially when used over a longer time horizon. Assuming a reduction of 15% in first-year relapse rates, digital technologies have the potential to be cost-saving, provided that they do not cost more than £300 (US $400.09) and £460 (US $613.47) per patient per annum for alcohol and opioid use disorders, respectively. No cost was included for postalert care, as it was assumed that this could be met within existing resources. Cost savings would be achieved predominantly through a reduction in treatment requirements as fewer people relapse. Price thresholds would reduce correspondingly if a &lt;15% reduction in relapse rates were achieved.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Developers of digital technologies that aim to reduce relapse need to focus on the generation of evi","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e87186"},"PeriodicalIF":2.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coverage, Traits, and Geographic Distribution of Online Surgeon Reviews: Large-Scale Cross-Sectional Analysis.","authors":"Michael Geng, Carlos Riveros, Yash B Shah, Sanjana Ranganathan, Kai Fok, Renil Sinu Titus, Vatsala Mundra, Eusebio Luna Velasquez, Dharam Kaushik, Allan S Detsky, Angela Jerath, Benjamin N Breyer, Yusuke Tsugawa, Christopher J D Wallis, Raj Satkunasivam","doi":"10.2196/79427","DOIUrl":"https://doi.org/10.2196/79427","url":null,"abstract":"<p><strong>Background: </strong>The use of online physician rating platforms has significantly increased and has been shown to influence physician selection. There are limited data on the use of these platforms for rating surgeons.</p><p><strong>Objective: </strong>In this study, we sought to assess the geographic distribution of and patterns in rating scores of surgeons in the United States. Additionally, we examined rating volumes across different surgical specialties and the association between peer-nominated and patient-initiated ratings on online rating platforms in the United States.</p><p><strong>Methods: </strong>We conducted a cross-sectional study by identifying 201,154 surgeons in the United States via the National Plan and Provider Enumeration System records and Doctors and Clinicians downloadable file. We assessed surgeon coverage on 3 online rating platforms and their geographic use patterns. We described the rating scores and volumes across different surgical specialties and assessed the relationship between rating platforms by comparing peer-nominated and patient-initiated online ratings.</p><p><strong>Results: </strong>A total of 78.86% (158,630/201,154) of the surgeons had ratings on at least 1 of the 3 patient-initiated websites across 11 specialties. Plastic surgeons, neurosurgeons, and orthopedic surgeons had the highest mean number of patient-initiated ratings. Surgeons with \"Top Doctor\" recognition from peers (23,171/201,154, 11.52%) were associated with an increased median patient-initiated rating (Healthgrades: 4.36, IQR 3.88-4.71 vs 4.20, IQR 3.64-4.64, P<.001, and r=0.09; Vitals: 4.30, IQR 4.00-4.60 vs 4.20, IQR 3.80-4.50, P<.001, and r=0.09; RateMDs: 4.20, IQR 3.80-4.50 vs 3.80, IQR 3.60-4.60, P<.001, and r=0.16). Geographic analysis indicated that 91.06% (295,816,471/324,870,510) of the US population lives in a county with a surgeon rated 10 times or more.</p><p><strong>Conclusions: </strong>Both patient-initiated and peer-nominated rating platforms have a comprehensive coverage of surgeons in the United States, but this coverage differs significantly between surgical specialties. Further work should assess how publicly available online ratings drive surgeon selection and their association with patient experience and postoperative outcomes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e79427"},"PeriodicalIF":2.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Experience and Early Clinical Outcomes of a Mental Wellness Chatbot for Depression and Anxiety: Pilot Evaluation Mixed Methods Study.","authors":"Scott Graupensperger, Emily J Ward, Graham Baum, Kate H Bentley, Emily R Dworkin, Millard Brown, Adam Chekroud, Matt Hawrilenko","doi":"10.2196/90644","DOIUrl":"10.2196/90644","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence-powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known about how users experience early-stage prototypes or which therapeutic processes contribute to clinical improvement. A transparent evaluation of emerging chatbot prototypes is needed to clarify if, how, and why artificial intelligence companions work and to guide their continued development.</p><p><strong>Objective: </strong>This mixed methods pilot study evaluated user experience, acceptability, and preliminary clinical signals for an early-stage mental wellness chatbot. We also examined whether baseline symptom severity moderated clinical improvement.</p><p><strong>Methods: </strong>Three sequential cohorts (n=125) completed a 2-week, incentivized chatbot exposure (approximately 60 min per week). Participants provided first-impression ratings, qualitative feedback, and pre-post assessments of depressive symptoms (PHQ-8 [Patient Health Questionnaire-8]), anxiety symptoms (GAD-7 [Generalized Anxiety Disorder-7]), psychological distress, well-being, and loneliness. Statistical models estimated symptom change and tested interactions with baseline symptom severity. Mixed methods analysis integrated quantitative outcomes with large language model-assisted qualitative content analysis of open-ended responses.</p><p><strong>Results: </strong>Participants described the chatbot as accessible, easy to use, and emotionally validating, while citing limitations in personalization and conversational depth. Qualitative responses consistently highlighted early therapeutic processes such as emotional validation, goal setting, and perceived attunement. Regression models showed significant pre-post reductions in depressive (Hedges g=-0.32) and anxiety (g=-0.32) symptoms, alongside modest improvements in distress and well-being. Baseline severity moderated improvement, with marginal effects indicating larger predicted reductions at higher PHQ-8 and GAD-7 baseline scores (eg, PHQ-8=15: g=-0.84; GAD-7=15: g=-0.62).</p><p><strong>Conclusions: </strong>This pilot provides a comprehensive view of early chatbot development and suggests promising user experiences and preliminary symptom improvements under structured pilot conditions. By integrating experiential and exploratory clinical data, the study identifies candidate process targets to inform ongoing refinement. Findings support continued development and demonstrate procedural feasibility for progression to larger, longer-term trials evaluating engagement and clinical outcomes under more naturalistic conditions.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e90644"},"PeriodicalIF":2.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147690200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study.","authors":"Amika Shah, Anam Shahil-Feroz, Kathleen A Sheehan, Shannon Wright, Robert P Nolan, Gillian Strudwick, Sanjeev Sockalingam, Emily Seto","doi":"10.2196/82139","DOIUrl":"https://doi.org/10.2196/82139","url":null,"abstract":"<p><strong>Background: </strong>Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings.</p><p><strong>Objective: </strong>This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program.</p><p><strong>Methods: </strong>A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service.</p><p><strong>Results: </strong>A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection.</p><p><strong>Conclusions: </strong>The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e82139"},"PeriodicalIF":2.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147689509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}