Journal of Medical Internet Research最新文献

{"title":"Effectiveness of the Self-Directed mHealth Exercise Intervention re.flex in Patients With Knee Osteoarthritis: Randomized Controlled Trial.","authors":"Valerie Dieter, Peter Martus, David Seißler, Lina Maria Serna-Higuita, Pia Janssen, Inga Krauss","doi":"10.2196/71558","DOIUrl":"https://doi.org/10.2196/71558","url":null,"abstract":"<p><strong>Background: </strong>About 1 in 2 patients with knee osteoarthritis (OA) receives a referral or recommendation for exercise. Digital health applications could counteract this undersupply.</p><p><strong>Objective: </strong>We aimed to investigate the effectiveness of a 12-week self-directed mobile health exercise intervention (re.flex) when used in addition to usual care compared to a control group receiving usual care only on pain reduction and improvement in physical function in patients with knee OA.</p><p><strong>Methods: </strong>This monocentric, 2-arm, randomized controlled parallel-group trial included patients from Germany with moderate to severe knee OA. Participants were mainly recruited via newspapers. Randomization was 1:1 into an intervention group (re.flex+usual care) and a control group (usual care) using computer-generated blocks. Participants were unmasked to group assignment. The re.flex group conducted a 12-week self-directed app-based and sensor-assisted exercise program with 3 sessions per week in addition to usual care. Primary outcomes were OA-specific knee pain and physical function (using the subscales pain and activities in daily living of the Knee Osteoarthritis Outcome Score, 0-100) at 3 months. Secondary outcomes included adherence and safety. Multiple imputation was used to account for missing data. Intervention effects were calculated using a baseline-adjusted analyses of covariance (ANCOVA). Bonferroni correction with an alpha level of .025 was applied.</p><p><strong>Results: </strong>Between January 25, 2023, and August 11, 2023, a total of 195 participants were enrolled. Of them, 98 participants were allocated to re.flex, and 97 participants to usual care. The primary analysis included 194 participants. The mean age was 61.9 (SD 7.7) years, and the majority were female (132/194, 68%). Pain reduction was significantly larger in re.flex than in usual care, with an adjusted mean difference between study groups of 4.8 (95% CI 0.7-8.9; P=.02; Cohen d=0.35) points. Improvement in physical function was not statistically significant (beta coefficient [β]=3.9 points, 95% CI 0.0-7.9, P=.049). A total of 12 adverse events were linked to re.flex, none of which were serious. Participants adhered to 77% (2705/3528) of all scheduled exercise sessions.</p><p><strong>Conclusions: </strong>The self-directed sensor-based mobile health exercise intervention re.flex demonstrates superiority over usual care for pain reduction and justifies this kind of intervention as an alternative exercise delivery mode for patients with knee OA.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71558"},"PeriodicalIF":6.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145258317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multifaceted Digital Intervention for the Prevention of Type 2 Diabetes Mellitus in Primary Care (PREDIABETEXT): Cluster Randomized Trial.","authors":"Sofía Mira-Martínez, Narges Malih, Escarlata Angullo-Martínez, Rocío Zamanillo-Campos, Aina M Yañez, Miquel Bennasar-Veny, Rocío Gómez-Juanes, Jadwiga Konieczna, Rafael Jiménez, Maria J Serrano-Ripoll, Maria Antonia Fiol-deRoque, Alfonso Leiva, Aina M Galmes-Panades, Jerónima Miralles-Xamena, Maria Clara Vidal-Thomàs, Cristina Gómez-Cobo, Elena Gervilla, José Iván Oña-Gil, Ignacio Ricci-Cabello","doi":"10.2196/70981","DOIUrl":"https://doi.org/10.2196/70981","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The diabetes epidemic continues to surge worldwide, demanding urgent and innovative solutions. Digital health interventions, particularly those targeting behavior change, hold promise due to their affordability and scalability. However, research in this field remains in its early stages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the effectiveness of PREDIABETEXT (Prediabetes Text Message Digital Intervention for the Prevention of Type 2 Diabetes Mellitus), a digital health intervention, in reducing glycated hemoglobin (HbA&lt;sub&gt;1c&lt;/sub&gt;) and in improving secondary clinical, physiological, and behavioral outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a 6-month, 3-arm, pragmatic cluster randomized clinical trial. We recruited patients with prediabetes (HbA&lt;sub&gt;1c&lt;/sub&gt; levels between 6% and 6.4% in the previous 3 months or 2 consecutive fasting plasma glucose measurements of 110-125 mg/dL) registered at primary care centers in the Balearic Islands, Spain. The PREDIABETEXT intervention consisted of 3 personalized SMS text messages per week aimed at supporting lifestyle behavior changes and online training for their primary health care professionals. A total of 58 professionals (clusters) from 16 centers participated in the study and were randomized (1:1:1) to intervention group A (patient SMS text messaging), intervention group B (patient SMS text messaging+health care professional web-based training), or the control group (usual care). Following the 6-month intervention period, we conducted individual qualitative interviews with 8 patients and 7 health care professionals to evaluate their experiences with the intervention in terms of utility, satisfaction, and implementation barriers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 58 health care professionals (clusters) were included, allocated to the control group (n=20, 34%; 119/365, 32.6% patients), intervention group A (SMS text messaging only; n=18, 31%; 106/365, 29% patients), and intervention group B (SMS text messaging+training; n=20, 34%; 140/365, 38.4% patients). The mean age of the patients was 59.79 (SD 9.75) years, and 54.5% (199/365) were female. The results of the intention-to-treat analysis at the 6-month time point showed that intervention A led to a small, nonsignificant reduction in HbA&lt;sub&gt;1c&lt;/sub&gt; levels compared to the control group (β=-0.05, 95% CI -0.21 to 0.10; P=.50), whereas intervention B showed a similar nonsignificant reduction (β=-0.04, 95% CI -0.12 to 0.10; P=.56). No substantial differences were observed in the remaining secondary outcomes. Interviews revealed positive feedback from patients, who appreciated the intervention's dietary messages and their frequency and practicality. Participants suggested enhancements such as increased personalization, links to recipes, and nursing follow-ups. Health care professionals valued the online training but highlighted time constraints, suggesting shorter or blended forma","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70981"},"PeriodicalIF":6.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145258335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Care Professionals' Perspectives of Socially Assistive Robots in Health Care Settings: Systematic Review.","authors":"Yun Hsuan Lee, Fang Yu Hsu, Angela Shin-Yu Lien","doi":"10.2196/79634","DOIUrl":"10.2196/79634","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health care professionals (HCPs) are key stakeholders whose acceptance, preparedness, and ethical considerations influence the integration of socially assistive robots (SARs). This review explores HCPs' perspectives on SARs integration into clinical practice. While previous research has focused on patient outcomes, ethical considerations, or general SARs deployment, limited evidence exists on how HCPs perceive, engage with, and address SARs implementation challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to systematically analyze HCPs' perspectives on the clinical implementation of SARs, including acceptance, challenges, barriers, educational needs, and ethical concerns.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched 13 databases (PubMed, Cochrane Library, Scopus, IEEE Xplore, ScienceDirect, CINAHL, Epistemonikos, MEDLINE [OVID], Web of Science, Embase, UpToDate, CEPS, and ProQuest Dissertations and Theses Global), with the final search on July 29, 2025. Eligible studies involved research with HCPs, examining their attitudes, perceptions, acceptance, or willingness to use SARs through qualitative, quantitative, or mixed methods designs. Studies were excluded if they focused on non-HCP populations, did not primarily investigate SARs, or lacked original data. Risk of bias was assessed with the Mixed Methods Appraisal Tool. Findings were synthesized thematically and mapped to the Unified Theory of Acceptance and Use of Technology (UTAUT) framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 15 studies (6 qualitative, 5 quantitative, and 4 mixed methods) involving 3166 HCPs across 10 countries were included. Participants were predominantly nurses and midwives (1960/3166, 61.9%), female (2618/3166, 82.7%), and based in hospital and long-term care settings across Europe (1709/3166, 54%) and Asia (1266/3166, 40%). Study quality was generally moderate, with 1 high-quality and 2 low-quality studies. Within the UTAUT framework, HCPs anticipated benefits of SARs in workload reduction, enhanced care efficiency, and improved patient well-being. However, they expressed concerns about technological reliability, maintenance requirements, role clarity, and professional identity. Acceptance was generally favorable but varied by profession, workplace, and relational attributes. Training needs, usability, and design were critical adoption determinants. Ethical concerns centered on privacy, informed consent, and equitable access.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Although evidence was limited due to moderate methodological quality, small sample size, self-developed instruments, and inconsistent reporting, which constrain generalizability, this review synthesis suggests that HCPs perceive SARs as beneficial for reducing workload, enhancing efficiency, and supporting patient well-being. However, there are also concerns regard","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79634"},"PeriodicalIF":6.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Social Networking Service, Patient-Generated Health Data, and Population Health Informatics: National Cross-sectional Study of Patterns and Implications of Leveraging Digital Technologies to Support Mental Health and Well-being.","authors":"","doi":"10.2196/84389","DOIUrl":"https://doi.org/10.2196/84389","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/30898.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e84389"},"PeriodicalIF":6.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.","authors":"Ivana Nanevski, Sebastian Jäger, Matthias Schulte-Althoff, Eva-Maria Behnke, Daniel Fürstenau, Felix Biessmann","doi":"10.2196/71034","DOIUrl":"https://doi.org/10.2196/71034","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With 28%-35% of individuals aged 65 years and older experiencing incidents of falling, falls are the second leading cause of unintentional injury-related deaths globally. Limited availability of clinical staff often impedes the timely detection and prevention of potential falls. Advances in artificial intelligence (AI) could complement existing fall risk assessment and help better allocate nursing care resources. Yet, many studies are based on small datasets from a single institution, which can restrict the generalizability of the model, and do not investigate important aspects in AI model development, such as fairness across demographic groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to provide a comprehensive empirical evaluation of the potential of AI in nursing care, focusing on the case of fall risk prediction. To account for demographic and contextual differences in fall incidences, we analyze data from a university and a geriatric hospital in Germany. To the best of our knowledge, these are the largest fall risk prediction datasets to date with heterogeneous data distributions. We focus on 3 key objectives. First, does AI help in improving fall risk prediction? Second, how can AI models be trained safely across different hospitals? Finally, are these models fair?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study used 2 datasets for fall risk prediction: one from a university hospital with 931,726 participants, 10,442 of whom experienced falls, and another from a geriatric hospital with 12,773 participants, 1728 of whom have fallen. State-of-the-art AI models were trained with 3 approaches, including 2 decentralized learning paradigms. First, separate models were trained on data from each hospital; second, models were retrained on the respective other dataset; and federated learning (FL) was applied to both datasets. The performance of these models was compared with the rule-based systems as implemented in clinical practice for fall risk prediction. Additional analyses were conducted to test for model fairness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our findings demonstrate that AI models consistently outperform rule-based systems across all experimental setups, with the area under the receiver operating characteristic curve of 0.735 (90% CI 0.727-0.744) for the geriatric hospital, and 0.926 (90% CI 0.924-0.928) for the university hospital. FL did not improve the fall risk prediction in this setting. Our fairness analysis ruled out disparities in model performance between different sex groups, but we found fairness infringements across age groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that AI models consistently outperform traditional rule-based systems across heterogeneous datasets in predicting fall risk. However, it also reveals the challenges related to demographic shifts and label distribution imbalances, which limited the FL models' ability to generalize. While the fairness analysis i","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71034"},"PeriodicalIF":6.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis.","authors":"Xue-Jing Liu, Danny Valdez, Maria A Parker, Andi Mai, Eric R Walsh-Buhi","doi":"10.2196/73185","DOIUrl":"https://doi.org/10.2196/73185","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;New media have become vital sources of cancer-related health information. However, concerns about the quality of that information persist.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to identify characteristics of studies considering cancer-related information on new media (including social media and artificial intelligence chatbots); analyze patterns in information quality across different platforms, cancer types, and evaluation tools; and synthesize the quality levels of the information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We systematically searched PubMed, Web of Science, Scopus, and Medline databases for peer-reviewed studies published in English between 2014 and 2023. The validity of the included studies was assessed based on risk of bias, reporting quality, and ethical approval, using the Joanna Briggs Institute Critical Appraisal and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklists. Features of platforms, cancer types, evaluation tools, and trends were summarized. Ordinal logistic regression was used to estimate the associations between the conclusion of quality assessments and study features. A random-effects meta-analysis of proportions was conducted to synthesize the overall levels of information quality and corresponding 95% CIs for each assessment indicator.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 75 studies were included, encompassing 297,519 posts related to 17 cancer types across 15 media platforms. Studies focusing on video-based media (odds ratio [OR] 0.02, 95% CI 0.01-0.12), rare cancers (OR 0.32, 95% CI 0.16-0.65), and combined cancer types (OR 0.04, 95% CI 0.01-0.14) were statistically less likely to yield higher quality conclusions compared to those on text-based media and common cancers. The pooled estimates reported moderate overall quality (DISCERN 43.58, 95% CI 37.80-49.35; Global Quality Score 49.91, 95% CI 43.31-56.50), moderate technical quality (Journal of American Medical Association Benchmark Criteria 46.13, 95% CI 38.87-53.39; Health on the Net Foundation Code of Conduct 49.68, 95% CI 19.68-79.68), moderate-high understandability (Patient Education Material Assessment Tool for Understandability 66.92, 95% CI 59.86-73.99), moderate-low actionability (Patient Education Materials Assessment Tool for Actionability 37.24, 95% CI 18.08-58.68; usefulness 48.86, 95% CI 26.24-71.48), and moderate-low completeness (34.22, 95% CI 27.96-40.48). Furthermore, 27.15% (95% CI 21.36-33.35) of posts contained misinformation, 21.15% (95% CI 8.96-36.50) contained harmful information, and 12.46% (95% CI 7.52-17.39) contained commercial bias. Publication bias was detected only in misinformation studies (Egger test: bias -5.67, 95% CI -9.63 to -1.71; P=.006), with high heterogeneity across most outcomes (I²&gt;75%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Meta-analysis results revealed that the overall quality of cancer-related information on social media and artif","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73185"},"PeriodicalIF":6.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Taboo to Touchscreen: A Qualitative Study of Digital Sexual and Reproductive Health Intervention for Bangladeshi Adolescents.","authors":"Kamrul Hasan, Hassan Rushekh Mahmood, Saraban Tahura Ether, Tanvir Hayder, Soaiba Zannat, Abu Sayeed, A M Rumayan Hasan, Ahmed Ehsanur Rahman, Chungyi Chiu, Anisuddin Ahmed, Md Refat Uz Zaman Sajib","doi":"10.2196/78514","DOIUrl":"10.2196/78514","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Adolescents in Bangladesh, approximately one-fifth of the total population, face significant challenges accessing relevant sexual and reproductive health (SRH) information, with the added burdens of cultural taboo, limited accessibility, and poor communication channels. Traditional adolescent-friendly approaches have shown limited effectiveness in addressing these challenges. In response, Mukhorito was developed as a peer-led, mobile-based digital platform to facilitate SRH, education, and communication among ninth-grade students.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study explored the feasibility and constraints of piloting the Mukhorito app to enhance adolescent SRH education in Bangladesh. It also sought to determine the self-reported usage, usability, and effect on knowledge and peer communication of the app, as well as to identify implementation and adoption challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A qualitative design was applied in the context of a broader mixed-methods study. Data were collected through 6 in-depth interviews, 3 key informant interviews, and 1 focus group discussion from 19 participants, including students, peer leaders, teachers, and government representatives, across 3 secondary schools (a boys' school, girls' school, and coeducational school) in the Feni district. Thematic analysis was conducted using NVivo software (QSR International) under Braun and Clarke's guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The Mukhorito app was perceived as a valuable tool to meet the SRH needs of adolescents in Bangladesh owing to its well-structured, easily navigable content with relatable stories. Participants described increased awareness of critical SRH issues, including reproductive health, nutrition, early marriage, violence against adolescents, and drug addiction, and reported sharing this knowledge with their families and communities. The app was seen as creating a safe space to discuss previously taboo SRH topics, reducing cultural reluctance to communicate, and promoting peer-to-peer communication. Interactive sessions were noted to strengthen decision-making skills and leadership qualities. Many users reported feeling more comfortable discussing SRH issues without shyness. However, challenges such as limited smartphone access, unreliable internet connection, and economic factors hindered adoption, especially in rural settings. Participants suggested the integration of the Mukhorito app in school curricula, aligning it with the government adolescent health program, adding visually rich and interactive content (eg, short dramas, videos, and animations), and enabling offline access to address connectivity challenges and maximize the effectiveness of the app.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Mukhorito possesses strong potential as a culturally relevant, digital SRH education tool for Bangladeshi adolescents. The app enabled knowledge and openness in SRH discourse. Alignment with national heal","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e78514"},"PeriodicalIF":6.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12505400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolving Health Information-Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol.","authors":"Claire Wardle, Shaydanay Urbani, Eric Wang","doi":"10.2196/79961","DOIUrl":"10.2196/79961","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Online health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)-powered technologies such as voice assistants and large language models (LLMs). While existing health information-seeking behavior models have long explained how people find and evaluate health information, less is known about how users engage with these newer tools, particularly tools that provide \"one\" answer rather than the resources to investigate a number of different sources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore how people use and perceive AI- and voice-assisted technologies when searching for health information and to evaluate whether these tools are reshaping traditional patterns of health information seeking and credibility assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted in-depth qualitative research with 27 participants (ages 19-80 years) using a think-aloud protocol. Participants searched for health information across 3 platforms-Google, ChatGPT, and Alexa-while verbalizing their thought processes. Prompts included both a standardized hypothetical scenario and a personally relevant health query. Sessions were transcribed and analyzed using reflexive thematic analysis to identify patterns in search behavior, perceptions of trust and utility, and differences across platforms and user demographics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants integrated AI tools into their broader search routines rather than using them in isolation. ChatGPT was valued for its clarity, speed, and ability to generate keywords or summarize complex topics, even by users skeptical of its accuracy. Trust and utility did not always align; participants often used ChatGPT despite concerns about sourcing and bias. Google's AI Overviews were met with caution-participants frequently skipped them to review traditional search results. Alexa was viewed as convenient but limited, particularly for in-depth health queries. Platform choice was influenced by the seriousness of the health issue, context of use, and prior experience. One-third of participants were multilingual, and they identified challenges with voice recognition, cultural relevance, and data provenance. Overall, users exhibited sophisticated \"mix-and-match\" behaviors, drawing on multiple tools depending on context, urgency, and familiarity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings suggest the need for additional research into the ways in which search behavior in the era of AI- and voice-assisted technologies is becoming more dynamic and context-driven. While the sample size is small, participants in this study selectively engaged with AI- and voice-assisted tools based on perceived usefulness, not just trustworthiness, challenging assumptions that credibility is the primary driver of technology adoption. Findings highlight the need for digital health literacy efforts that help users evaluate both the capabilities and limitation","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79961"},"PeriodicalIF":6.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Childhood Maltreatment, Bullying, and Internet Addiction in Relation to Suicidal Ideation Among Adolescents: Cross-Sectional Mediation and Network Analysis.","authors":"Jiayi Lu, Sihong Li, Tianqing Fan, Xi Ni, Leyin Zhang, Hui Chen, Xianliang Chen, Huajia Tang, Yanyue Ye, Jiansong Zhou, Yanmei Shen","doi":"10.2196/79858","DOIUrl":"10.2196/79858","url":null,"abstract":"<p><strong>Background: </strong>Internet addiction (IA), childhood maltreatment (CM), and bullying are prevalent psychosocial stressors among adolescents and have each been associated with suicidal ideation (SI). However, existing research often treats these factors in isolation, overlooking their potential interrelationships and joint associations with SI.</p><p><strong>Objective: </strong>This study aimed to examine how CM, IA, and bullying are jointly related to SI at both the scale and symptom levels and identify key symptoms within the CM-IA-bullying-SI network that may serve as intervention targets to disrupt maladaptive associations across the network.</p><p><strong>Methods: </strong>A total of 6573 adolescents were recruited through cluster sampling. Mediation analyses were conducted to assess direct and indirect effects of CM on SI via IA and bullying. Network analysis was conducted to examine symptom-level associations among CM, IA, bullying, and SI and identify core and bridge symptoms within the network. Network comparison tests were conducted to assess differences in network structure by gender and history of nonsuicidal self-injury.</p><p><strong>Results: </strong>Mediation analyses revealed that both IA and bullying partially mediated the association between CM and SI, with significant indirect effects via IA (c'=0.010, 95% CI 0.008-0.011; P<.001) and bullying (c'=0.004, 95% CI 0.002-0.005; P<.001). In the network, tolerance, time management, and compulsive internet use were identified as central symptoms, whereas SI, emotional abuse, and traditional bullying victimization served as bridge symptoms. Emotional abuse and cyberbullying victimization were most strongly linked to SI. Among individuals with a history of nonsuicidal self-injury, emotional abuse and emotional neglect showed stronger associations with SI. Sex subgroup analysis showed no significant difference in global strength (S=0.095; P=.69) but a significant difference in network structure (M=0.174; P=.01).</p><p><strong>Conclusions: </strong>This study revealed how CM, bullying, and IA are jointly related to SI among adolescents at both the scale and symptom levels. Key symptoms, including tolerance and time management, played central roles within the symptom network, with SI bridging multiple psychosocial domains. These findings underscore the need for multilevel, targeted interventions to disrupt maladaptive links and reduce suicide risk in adolescents.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79858"},"PeriodicalIF":6.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing Theory-Enhanced Messaging to Promote COVID-19 Vaccination Among Adults: Randomized Controlled Trial.","authors":"Rachael Piltch-Loeb, Yanhan Shen, Sasha Fleary, McKaylee Robertson, Josefina Nuñez Sahr, Kate Penrose, Jenna Sanborn, Surabhi Yadav, Avantika Srivastava, Denis Nash, Angela Parcesepe","doi":"10.2196/79228","DOIUrl":"https://doi.org/10.2196/79228","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Uptake of the COVID-19 vaccine has been low in the United States despite ongoing public health recommendations. This has been linked to many factors, including pandemic fatigue; reduced risk perception; dis- and misinformation; and, more recently, symptoms of depression and anxiety. Novel communication and messaging strategies are one potential approach to promote vaccine uptake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This randomized controlled trial aimed to fill research gaps by testing the effect of 2 communication-based approaches&lt;strong&gt;-&lt;/strong&gt;the use of a short attitudinal inoculation message and cognitive behavioral therapy (CBT) kernel messaging&lt;strong&gt;-&lt;/strong&gt;compared to standard public health messaging on vaccine uptake in a cohort of adult US residents.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We completed a 3-arm, parallel-group, assessor-blinded stratified randomized trial between April 15, 2024, and May 2, 2024. Individuals were eligible if they were aged ≥18 years and (1) had received at least one dose of the COVID-19 vaccine but (2) had not received COVID-19 vaccine doses since September 11, 2023, and (3) had not been infected with SARS-CoV-2 in the previous 3 months. We purposively sampled eligible individuals with and without symptoms of anxiety and depression. Participants were randomly allocated to the (1) attitudinal inoculation intervention, (2) CBT kernel intervention, or (3) standard public health messaging intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;By the 4-week follow-up, COVID-19 vaccination uptake was low overall (17/1403, 1.2%, 95% CI 0.6%-1.8%) and did not significantly differ by study arm&lt;strong&gt;-&lt;/strong&gt;1.5% (7/469) in the CBT kernel arm (95% CI 0.4%-2.8%), 0.9% (4/466) in the inoculation arm (95% CI 0%-1.8%), and 1.3% (6/468) in the standard arm (95% CI 0.3%-2.4%). Compared to the standard arm, the CBT kernel intervention yielded a risk difference (RD) of 0.3% (95% CI -1.3% to 1.8%) and a risk ratio (RR) of 1.21 (95% CI 0.41-3.59); the inoculation intervention yielded an RD of -0.4% (95% CI -1.8% to 1%) and an RR of 0.69 (95% CI 0.19-2.44). Reported SARS-CoV-2 infections and vaccine uptake did not differ by anxiety or depression symptoms. At baseline, approximately one-third of participants (466/1403, 33.21%) reported high willingness to receive another COVID-19 vaccine dose, with no significant differences across arms. At the 4-week follow-up, willingness remained similar across groups (CBT kernel vs standard arm: RD=-0.3%, 95% CI -6.3% to 5.8%, and RR=0.99, 95% CI 0.79-1.25; inoculation vs standard arm: RD=7%, 95% CI 0.8%-13.3%, and RR=1.23, 95% CI 0.98-1.53). Willingness did not differ by mental health status.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Successful efforts to increase uptake of the COVID-19 vaccine via theory-enhanced messaging remain elusive. Findings underscore the challenges of shifting behavior through messaging alone in a context of declining public trust and a diminished","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e79228"},"PeriodicalIF":6.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}