Lara Kleinschmidt, Juergen Wasem, Nikola Blase, Beatrice Nauendorf, Juliane Malsch, Matthias Brittner, Paul Brandenburg, André Aeustergerling, Theresa Hüer
<p><strong>Background: </strong>Although video consultations (VCs) are permitted in German outpatient care and have seen a notable rise during the COVID-19 pandemic, their use still does not seem to have become established in Germany.</p><p><strong>Objective: </strong>This survey aims to evaluate the attitudes of physicians and psychotherapists with regard to the use of VC after the COVID-19 pandemic, in particular in the context of types of treatment and suitable medical fields.</p><p><strong>Methods: </strong>A standardized questionnaire was sent out to all 34,095 physicians and psychotherapists in 4 German regions. The analysis consisted of both descriptive and inferential statistics. Subgroup analysis included gender, age groups, community size of practice location, VC experience, type and ownership of practice, and area of medical care. Binary logistic regression was conducted to determine whether physicians' and psychotherapists' individual factors, organizational factors, or area of medical care were associated with at least monthly VC provision or interest in VC provision.</p><p><strong>Results: </strong>The response rate was 17.9%, including a total of 5930 participants in the analysis. About 40% (2216/5863) of the physicians and psychotherapists surveyed stated that they offer VC at least once a month. In the area of medical care, the odds ratio (OR) of at least monthly VC provision in psychotherapeutic care was about 8.2 (95% CI 7.4-1.64; P<.001) compared to primary care, whereas in specialist care, the odds for monthly VC provision were approximately 50% lower than in primary care (OR 0.5, 95% CI 0.43-0.59; P<.001). Further, female participants have higher odds to provide VC at least once a month (OR 1.163, 95% CI 1.01-1.34; P=.03). The odds for monthly VC provision in older age groups are approximately 60% higher than in the <40 years old age group (OR 0.41, 95% CI 0.32-0.52; P<.001). Around 80% (4347/5442) of the participants expressed interest in VC use. The most common occasions for which treatment by VC was reported to be suitable were discussing test results (1422/1896, 75.0%), taking the patient's medical history (1195/2147, 55.7%), issuing prescriptions for drugs and remedies (793/1204, 65.9%), and the issuing of incapacity certificates for work (677/1042, 65.0%).</p><p><strong>Conclusions: </strong>There has been an increase in the self-reported uptake of VC among physicians and psychotherapists compared to prepandemic levels, although this remains at a relatively low level in primary and specialist care. A significant proportion of doctors and psychotherapists have expressed an interest in using VC after the pandemic period. However, this self-reported use is not yet reflected in actual usage data, suggesting the need for further investigation into the underlying factors influencing the gap and identifying potential enablers. Further, these self-assessments by service providers on suitable types of treatment and suitable med
背景：尽管德国门诊允许视频会诊，并且在2019冠状病毒病大流行期间视频会诊的数量显著增加，但在德国，视频会诊似乎尚未得到普及。目的：本调查旨在评估新冠肺炎大流行后医生和心理治疗师对使用VC的态度，特别是在治疗类型和适合的医学领域方面。方法：对德国4个地区的34,095名医生和心理治疗师进行标准化问卷调查。分析包括描述性统计和推断性统计。亚组分析包括性别、年龄组、执业地点的社区规模、风险投资经验、执业类型和所有权以及医疗保健地区。采用二元逻辑回归来确定医生和心理治疗师的个人因素、组织因素或医疗护理领域是否与至少每月提供VC或对VC提供的兴趣相关。结果：问卷回复率为17.9%，共纳入分析5930人。约40%（2216/5863）接受调查的医生和心理治疗师表示，他们每月至少提供一次VC。在医疗保健领域，心理治疗护理中每月至少提供VC的比值比（OR）约为8.2 (95% CI 7.4-1.64)；结论：与大流行前的水平相比，医生和心理治疗师自我报告的VC摄取有所增加，尽管这在初级和专科护理中仍处于相对较低的水平。很大一部分医生和心理治疗师表示有兴趣在大流行时期后使用VC。然而，这种自我报告的使用情况尚未反映在实际使用数据中，这表明需要进一步调查影响差距的潜在因素并确定潜在的促成因素。此外，服务提供者对适当的治疗类型和适当的医疗领域的这些自我评估可以为政治决策提供信息。
{"title":"Attitudes Toward Video Consultations From the Perspective of Physicians and Psychotherapists in German Outpatient Care After the COVID-19 Pandemic: Survey Study.","authors":"Lara Kleinschmidt, Juergen Wasem, Nikola Blase, Beatrice Nauendorf, Juliane Malsch, Matthias Brittner, Paul Brandenburg, André Aeustergerling, Theresa Hüer","doi":"10.2196/73757","DOIUrl":"10.2196/73757","url":null,"abstract":"<p><strong>Background: </strong>Although video consultations (VCs) are permitted in German outpatient care and have seen a notable rise during the COVID-19 pandemic, their use still does not seem to have become established in Germany.</p><p><strong>Objective: </strong>This survey aims to evaluate the attitudes of physicians and psychotherapists with regard to the use of VC after the COVID-19 pandemic, in particular in the context of types of treatment and suitable medical fields.</p><p><strong>Methods: </strong>A standardized questionnaire was sent out to all 34,095 physicians and psychotherapists in 4 German regions. The analysis consisted of both descriptive and inferential statistics. Subgroup analysis included gender, age groups, community size of practice location, VC experience, type and ownership of practice, and area of medical care. Binary logistic regression was conducted to determine whether physicians' and psychotherapists' individual factors, organizational factors, or area of medical care were associated with at least monthly VC provision or interest in VC provision.</p><p><strong>Results: </strong>The response rate was 17.9%, including a total of 5930 participants in the analysis. About 40% (2216/5863) of the physicians and psychotherapists surveyed stated that they offer VC at least once a month. In the area of medical care, the odds ratio (OR) of at least monthly VC provision in psychotherapeutic care was about 8.2 (95% CI 7.4-1.64; P<.001) compared to primary care, whereas in specialist care, the odds for monthly VC provision were approximately 50% lower than in primary care (OR 0.5, 95% CI 0.43-0.59; P<.001). Further, female participants have higher odds to provide VC at least once a month (OR 1.163, 95% CI 1.01-1.34; P=.03). The odds for monthly VC provision in older age groups are approximately 60% higher than in the <40 years old age group (OR 0.41, 95% CI 0.32-0.52; P<.001). Around 80% (4347/5442) of the participants expressed interest in VC use. The most common occasions for which treatment by VC was reported to be suitable were discussing test results (1422/1896, 75.0%), taking the patient's medical history (1195/2147, 55.7%), issuing prescriptions for drugs and remedies (793/1204, 65.9%), and the issuing of incapacity certificates for work (677/1042, 65.0%).</p><p><strong>Conclusions: </strong>There has been an increase in the self-reported uptake of VC among physicians and psychotherapists compared to prepandemic levels, although this remains at a relatively low level in primary and specialist care. A significant proportion of doctors and psychotherapists have expressed an interest in using VC after the pandemic period. However, this self-reported use is not yet reflected in actual usage data, suggesting the need for further investigation into the underlying factors influencing the gap and identifying potential enablers. Further, these self-assessments by service providers on suitable types of treatment and suitable med","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e73757"},"PeriodicalIF":6.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12774393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911959","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}
<p><strong>Background: </strong>The integration of the Internet of Things (IoT) into health care is transforming the industry by enhancing disease care and management, as well as supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices, which enable real-time health monitoring and advanced remote health management. Globally, the increased adoption of IoT in health care has improved efficiency, enhanced patient care, and generated substantial economic value.</p><p><strong>Objective: </strong>This review aims to conduct a comprehensive meta- and weight analysis of quantitative studies to identify the most influential predictors and theoretical frameworks explaining the adoption of IoT in health care.</p><p><strong>Methods: </strong>We searched databases, including Web of Science and PubMed, for quantitative studies on IoT health care adoption, with the last search conducted in early July 2025. Inclusion criteria comprised peer-reviewed articles written in English that employed a quantitative approach to IoT health care technology adoption. Studies were excluded if they did not report the significance of relationships, involved technologies without IoT features or were outside the scope, or examined target variables irrelevant to the analysis. The weight analysis identified the pathways with the most significant effects. A meta-analysis using a random-effects model was conducted to estimate combined effect sizes and their statistical significance. The results from both methods were then integrated to visualize the most frequently used theoretical frameworks. Risk of bias and heterogeneity were assessed using a funnel plot, Egger regression test, the I2 statistic, and subgroup analysis, which indicated no strong evidence of publication bias but revealed a high level of heterogeneity.</p><p><strong>Results: </strong>Analysis of 115 datasets from 109 papers identified the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the primary frameworks for explaining IoT adoption in health care. Incorporating context-specific variables-such as health consciousness, innovativeness, and trust-into these traditional technology acceptance frameworks enhances the understanding of IoT adoption. Although high heterogeneity suggests a need to refine theoretical models to account for regional contexts, universal adoption drivers such as performance expectancy and effort expectancy remain consistent.</p><p><strong>Conclusions: </strong>Behavioral intention is the most frequently studied variable in IoT health care adoption, whereas attitude, performance expectancy, effort expectancy, and task-technology fit remain underexplored. While adoption theories from the information systems field, such as the TAM, are predominantly used, integrating context-specific constructs and theories-such as trust and innovativeness-can provide deeper insights in
背景：物联网（IoT）与医疗保健的融合，通过加强疾病护理和管理，以及支持自我健康管理，正在改变行业。COVID-19大流行加速了物联网设备的采用，特别是可穿戴医疗设备，可实现实时健康监测和先进的远程健康管理。在全球范围内，物联网在医疗保健领域的日益普及提高了效率，增强了患者护理，并产生了巨大的经济价值。目的：本综述旨在对定量研究进行全面的meta和权重分析，以确定最具影响力的预测因素和理论框架，解释物联网在医疗保健中的应用。方法：我们检索了Web of Science和PubMed等数据库，进行物联网医疗采用的定量研究，最后一次检索是在2025年7月初。入选标准包括用英文撰写的同行评议文章，这些文章采用了量化方法来采用物联网医疗保健技术。如果研究没有报告关系的重要性，涉及没有物联网功能的技术或超出范围，或检查与分析无关的目标变量，则排除研究。权重分析确定了影响最显著的途径。采用随机效应模型进行meta分析，估计综合效应大小及其统计显著性。然后将两种方法的结果整合起来，以可视化最常用的理论框架。采用漏斗图、Egger回归检验、I2统计量和亚组分析评估偏倚和异质性风险，结果显示没有明显的发表偏倚证据，但异质性水平较高。结果：对来自109篇论文的115个数据集的分析确定了技术接受模型和技术接受和使用统一理论（UTAUT）是解释医疗保健中物联网采用的主要框架。将特定于环境的变量（如健康意识、创新和信任）纳入这些传统技术接受框架，可以增强对物联网采用的理解。尽管高度异质性表明需要改进理论模型以考虑区域背景，但普遍采用的驱动因素（如绩效预期和努力预期）仍然是一致的。结论：行为意向是物联网医疗采用中最常被研究的变量，而态度、绩效预期、努力预期和任务-技术契合度仍未得到充分探讨。虽然主要使用来自信息系统领域的采用理论，如TAM，但整合特定于情境的结构和理论（如信任和创新）可以为医疗保健中的物联网采用提供更深入的见解。行为意向的最强和最一致的预测因子是态度、表现期望、习惯、自我效能、功能一致性和利益。此外，社会影响力、便利条件、信任和审美吸引力显示出有希望或强烈的影响。相比之下，隐私和安全、障碍、脆弱性、严重性、兼容性、财务成本、健康和技术焦虑等变量通常不一致，或者在统计上不显著。
{"title":"Adoption of Internet of Things in Health Care: Weighted and Meta-Analytical Review of Theoretical Frameworks and Predictors.","authors":"Tiago Oliveira, Mijail Naranjo-Zolotov, Ricardo Martins, Stylianos Karatzas","doi":"10.2196/64091","DOIUrl":"10.2196/64091","url":null,"abstract":"<p><strong>Background: </strong>The integration of the Internet of Things (IoT) into health care is transforming the industry by enhancing disease care and management, as well as supporting self-health management. The COVID-19 pandemic has accelerated the adoption of IoT devices, particularly wearable medical devices, which enable real-time health monitoring and advanced remote health management. Globally, the increased adoption of IoT in health care has improved efficiency, enhanced patient care, and generated substantial economic value.</p><p><strong>Objective: </strong>This review aims to conduct a comprehensive meta- and weight analysis of quantitative studies to identify the most influential predictors and theoretical frameworks explaining the adoption of IoT in health care.</p><p><strong>Methods: </strong>We searched databases, including Web of Science and PubMed, for quantitative studies on IoT health care adoption, with the last search conducted in early July 2025. Inclusion criteria comprised peer-reviewed articles written in English that employed a quantitative approach to IoT health care technology adoption. Studies were excluded if they did not report the significance of relationships, involved technologies without IoT features or were outside the scope, or examined target variables irrelevant to the analysis. The weight analysis identified the pathways with the most significant effects. A meta-analysis using a random-effects model was conducted to estimate combined effect sizes and their statistical significance. The results from both methods were then integrated to visualize the most frequently used theoretical frameworks. Risk of bias and heterogeneity were assessed using a funnel plot, Egger regression test, the I2 statistic, and subgroup analysis, which indicated no strong evidence of publication bias but revealed a high level of heterogeneity.</p><p><strong>Results: </strong>Analysis of 115 datasets from 109 papers identified the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the primary frameworks for explaining IoT adoption in health care. Incorporating context-specific variables-such as health consciousness, innovativeness, and trust-into these traditional technology acceptance frameworks enhances the understanding of IoT adoption. Although high heterogeneity suggests a need to refine theoretical models to account for regional contexts, universal adoption drivers such as performance expectancy and effort expectancy remain consistent.</p><p><strong>Conclusions: </strong>Behavioral intention is the most frequently studied variable in IoT health care adoption, whereas attitude, performance expectancy, effort expectancy, and task-technology fit remain underexplored. While adoption theories from the information systems field, such as the TAM, are predominantly used, integrating context-specific constructs and theories-such as trust and innovativeness-can provide deeper insights in","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e64091"},"PeriodicalIF":6.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911930","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}
Jordi Piera-Jiménez, Núria Vilarasau Creus, Ada Maymó Costa, Xabier Michelena, Andrea Climent Fageda, Alèxia Farré, László Herczeg, Lekshmy Parameswaran, Gerard Carot-Sans, Luis Valle
<p><strong>Background: </strong>Digital health technologies promise to democratize health care access yet often exacerbate existing inequalities when developed through traditional top-down approaches that prioritize technology implementation and exclude end users from design processes. The COVID-19 pandemic accelerated digital transformation while simultaneously exposing how technology can both bridge and widen gaps in health care access. Understanding how to systematically integrate equity considerations into digital health transformation across entire health systems has become increasingly urgent.</p><p><strong>Objective: </strong>This study aims to cocreate actionable design principles for equitable digital health transformation through a large-scale participatory human-centered design (PHCD) process involving diverse stakeholders across Catalonia's health care ecosystem (northeast Spain), with the aim of establishing guidelines for information systems that support a person-centered, integrated, and longitudinal care delivery model.</p><p><strong>Methods: </strong>We conducted a qualitative PHCD research study involving 265 participants representing diverse stakeholder groups: citizens and informal caregivers (n=106), health care professionals (n=83), health care managers and leaders (n=50), and experts representing various domains of digital health innovation (n=26). Through two sequential rounds of participation between June 2024 and April 2025, we used design thinking methodologies and cocreation tools in 24 sessions across Catalan geography and 7 topic-specific expert sessions. Data collection used innovative visual tools, including journey mapping, care model animations, future scenario storyboarding, and facilitated ideation techniques. Analysis followed an inductive-deductive approach combining affinity mapping, thematic synthesis, and participant validation to transform stakeholder proposals into actionable design principles.</p><p><strong>Results: </strong>Participants identified critical barriers to digital health equity, including digital literacy gaps, fragmented information systems, a lack of user involvement in design, and insufficient consideration of vulnerable populations' needs. The cocreation process yielded 10 fundamental principles: (1) the person and their care circle at the center, (2) health for everyone, everywhere, (3) tools for more compassionate care, (4) a better professional experience, (5) an active role of the population, (6) personalized and proactive care, (7) systematic use of data for decision-making, (8) integrated quality data working for health, (9) an information system that builds trust, and (10) collaboration as a driver of innovation.</p><p><strong>Conclusions: </strong>This study shows how large-scale, rigorously conducted PHCD can uncover and address equity barriers in health information systems. Beyond producing 10 actionable design principles, it highlights how engaging diverse stakeholders can tu
{"title":"Cocreating Principles for Digital Health Equity: Cross-Sectional, Qualitative Study for Participatory Human-Centered Design in Catalonia.","authors":"Jordi Piera-Jiménez, Núria Vilarasau Creus, Ada Maymó Costa, Xabier Michelena, Andrea Climent Fageda, Alèxia Farré, László Herczeg, Lekshmy Parameswaran, Gerard Carot-Sans, Luis Valle","doi":"10.2196/84129","DOIUrl":"10.2196/84129","url":null,"abstract":"<p><strong>Background: </strong>Digital health technologies promise to democratize health care access yet often exacerbate existing inequalities when developed through traditional top-down approaches that prioritize technology implementation and exclude end users from design processes. The COVID-19 pandemic accelerated digital transformation while simultaneously exposing how technology can both bridge and widen gaps in health care access. Understanding how to systematically integrate equity considerations into digital health transformation across entire health systems has become increasingly urgent.</p><p><strong>Objective: </strong>This study aims to cocreate actionable design principles for equitable digital health transformation through a large-scale participatory human-centered design (PHCD) process involving diverse stakeholders across Catalonia's health care ecosystem (northeast Spain), with the aim of establishing guidelines for information systems that support a person-centered, integrated, and longitudinal care delivery model.</p><p><strong>Methods: </strong>We conducted a qualitative PHCD research study involving 265 participants representing diverse stakeholder groups: citizens and informal caregivers (n=106), health care professionals (n=83), health care managers and leaders (n=50), and experts representing various domains of digital health innovation (n=26). Through two sequential rounds of participation between June 2024 and April 2025, we used design thinking methodologies and cocreation tools in 24 sessions across Catalan geography and 7 topic-specific expert sessions. Data collection used innovative visual tools, including journey mapping, care model animations, future scenario storyboarding, and facilitated ideation techniques. Analysis followed an inductive-deductive approach combining affinity mapping, thematic synthesis, and participant validation to transform stakeholder proposals into actionable design principles.</p><p><strong>Results: </strong>Participants identified critical barriers to digital health equity, including digital literacy gaps, fragmented information systems, a lack of user involvement in design, and insufficient consideration of vulnerable populations' needs. The cocreation process yielded 10 fundamental principles: (1) the person and their care circle at the center, (2) health for everyone, everywhere, (3) tools for more compassionate care, (4) a better professional experience, (5) an active role of the population, (6) personalized and proactive care, (7) systematic use of data for decision-making, (8) integrated quality data working for health, (9) an information system that builds trust, and (10) collaboration as a driver of innovation.</p><p><strong>Conclusions: </strong>This study shows how large-scale, rigorously conducted PHCD can uncover and address equity barriers in health information systems. Beyond producing 10 actionable design principles, it highlights how engaging diverse stakeholders can tu","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e84129"},"PeriodicalIF":6.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12774400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911907","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}
<p><strong>Background: </strong>Frailty constitutes a growing challenge for health and social care systems around the world. In England, 35% of adults aged 65 years and older live with frailty, with international estimates indicating that almost half of all hospital inpatients within the same age group are frail. This population often experiences multimorbidity and frequent care transitions. Written documentation and verbal handovers may lack the precision and nuance required to understand an older adult's presentation and support needs. Video recordings of individual patients, capturing aspects of their functional abilities and condition, may help to enhance multidisciplinary team communication and care continuity, yet little is known about their use in the care of older inpatients with frailty.</p><p><strong>Objective: </strong>We aimed to evaluate the acceptability, feasibility of implementation, and perceived effectiveness of video-based patient records (the Isla Health Digital Pathway Platform) for supporting the assessment and care of older inpatients with frailty within the acute hospital setting.</p><p><strong>Methods: </strong>A nonrandomized mixed methods pilot study was conducted within 3 acute medicine wards for older adults. The video-based patient records intervention, permitting videos to be embedded securely within the electronic patient record, was implemented over a 3-month period alongside usual care. Patient enrollment and retention figures; qualitative interviews with patients, carers, and clinical staff; and video capture and view metrics were used to address the study objectives. The Theoretical Framework of Acceptability of Healthcare Interventions was applied to the framework analysis of interview data, capturing concepts such as intervention ethicality, burden, and coherence. Patient and public involvement and engagement informed each research stage.</p><p><strong>Results: </strong>Twenty-nine patients were enrolled (56.9%); 1 patient withdrew before receiving the intervention. Modal reasons given by patients for nonparticipation included not wanting to take part in research (n=8) or feeling too unwell (n=2). Staff identified multiple opportunities for capturing patient videos, including documentation of mobility assessments or seizures. The intervention was considered acceptable on the grounds that safeguards were always in place, including secure data storage and upholding of patient dignity. Implementation barriers and facilitators were identified; factors such as difficulties in capturing videos within busy ward environments and scheduling issues were voiced by participants. Video view metrics and data from interviews collectively suggested low rates of engagement with videos by clinical staff once captured. Potential intervention impacts included perceived enhancements to clinical assessment and person-centered care.</p><p><strong>Conclusions: </strong>Our findings suggest that the intervention is largely acceptable
{"title":"Acceptability, Feasibility, and Perceived Effectiveness of Video-Based Patient Records for Supporting Care Delivery to Older Adults With Frailty: Nonrandomized Mixed Methods Pilot Study.","authors":"Phoebe Averill, Rachael Lear, Ricky Odedra, Susannah Long, Alex Taylor, Pi-Jung Charville, Jessica Fernandes, Uzoamaka Ekeogu, Jessica Leombruno, Sophia Ellis, Erik Mayer","doi":"10.2196/77318","DOIUrl":"10.2196/77318","url":null,"abstract":"<p><strong>Background: </strong>Frailty constitutes a growing challenge for health and social care systems around the world. In England, 35% of adults aged 65 years and older live with frailty, with international estimates indicating that almost half of all hospital inpatients within the same age group are frail. This population often experiences multimorbidity and frequent care transitions. Written documentation and verbal handovers may lack the precision and nuance required to understand an older adult's presentation and support needs. Video recordings of individual patients, capturing aspects of their functional abilities and condition, may help to enhance multidisciplinary team communication and care continuity, yet little is known about their use in the care of older inpatients with frailty.</p><p><strong>Objective: </strong>We aimed to evaluate the acceptability, feasibility of implementation, and perceived effectiveness of video-based patient records (the Isla Health Digital Pathway Platform) for supporting the assessment and care of older inpatients with frailty within the acute hospital setting.</p><p><strong>Methods: </strong>A nonrandomized mixed methods pilot study was conducted within 3 acute medicine wards for older adults. The video-based patient records intervention, permitting videos to be embedded securely within the electronic patient record, was implemented over a 3-month period alongside usual care. Patient enrollment and retention figures; qualitative interviews with patients, carers, and clinical staff; and video capture and view metrics were used to address the study objectives. The Theoretical Framework of Acceptability of Healthcare Interventions was applied to the framework analysis of interview data, capturing concepts such as intervention ethicality, burden, and coherence. Patient and public involvement and engagement informed each research stage.</p><p><strong>Results: </strong>Twenty-nine patients were enrolled (56.9%); 1 patient withdrew before receiving the intervention. Modal reasons given by patients for nonparticipation included not wanting to take part in research (n=8) or feeling too unwell (n=2). Staff identified multiple opportunities for capturing patient videos, including documentation of mobility assessments or seizures. The intervention was considered acceptable on the grounds that safeguards were always in place, including secure data storage and upholding of patient dignity. Implementation barriers and facilitators were identified; factors such as difficulties in capturing videos within busy ward environments and scheduling issues were voiced by participants. Video view metrics and data from interviews collectively suggested low rates of engagement with videos by clinical staff once captured. Potential intervention impacts included perceived enhancements to clinical assessment and person-centered care.</p><p><strong>Conclusions: </strong>Our findings suggest that the intervention is largely acceptable","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e77318"},"PeriodicalIF":6.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12774403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911975","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}
Sandy Hannibal, Dörte Behrendt, Michèle Wessa, Sarah K Schäfer, Nina Dalkner, Dirk Lehr
<p><strong>Background: </strong>Promoting individual resilience-that is, maintaining or regaining mental health despite stressful circumstances-is regarded as an important endeavor to prevent mental illness. However, digital resilience interventions designed to enhance mental health have yielded mixed results. Such heterogeneous effects reflect a variety of unsolved conceptual challenges in interventional resilience research. These range from grounding interventions in resilience frameworks, using theory or targeting etiologically important resilience factors as intervention content, to a lack of knowledge about the mechanisms underlying effects, and using techniques specifically developed to foster psychosocial resources. The web- and app-based resilience intervention RESIST was designed to address these challenges, mainly by using both the Positive Appraisal Style Theory of Resilience as its theoretical foundation and interventional techniques from Strengths-Based Cognitive Behavioral Therapy.</p><p><strong>Objective: </strong>This study's primary aim was to evaluate the effectiveness of RESIST on perceived stress in a general working population as a means of universal prevention, relative to a waitlist control group. A secondary study aim was to explore the resilience factors of self-efficacy, optimism, self-compassion, and perceived social support, the intervention targets as potential mediators of its effect on stress and self-perceived resilience.</p><p><strong>Methods: </strong>In total, 352 employees were randomly assigned to either a self-help version of RESIST or a waitlist control group. Data were collected via the web at baseline, postintervention, and at 3- and 6-month (intervention group [IG] only) follow-ups. The primary outcome was perceived stress, measured with the Perceived Stress Scale-10. Secondary outcomes included self-perceived resilience, the resilience factors targeted, and other mental and work-related health outcomes.</p><p><strong>Results: </strong>The IG reported significantly less stress than controls postintervention (Δ=-3.14; d=-0.54, 95% CI -0.75 to -0.34, and P<.001) and at 3-month follow-up (Δ=-2.79; d=-0.47, 95% CI -0.71 to -0.22, and P=.002). These improvements in the IG were maintained at 6-month follow-up. Favorable between-group differences also were detected for self-perceived resilience and the resilience factors. IG participants completed on average 2.2 (SD 2.3) web-based sessions and used the app's core feature a median of 14 times (IQR 4.00-33.75, range 1-220). The positive effects of the intervention on stress and resilience were primarily mediated by changes in optimism and self-compassion. No evidence was found that self-efficacy and social support also acted as mediators.</p><p><strong>Conclusions: </strong>In a sample of employees experiencing heightened work-burden levels, RESIST was effective in reducing perceived stress and increasing self-perceived resilience as well as the targeted resilienc
背景：促进个体恢复力，即在压力环境下保持或恢复精神健康，被认为是预防精神疾病的重要努力。然而，旨在增强心理健康的数字复原力干预措施产生了好坏参半的结果。这种异质效应反映了干预弹性研究中各种尚未解决的概念挑战。这些措施包括在恢复力框架中进行基础干预，使用理论或针对病因学上重要的恢复力因素作为干预内容，缺乏对潜在影响机制的了解，以及使用专门开发的技术来促进社会心理资源。基于网络和应用程序的弹性干预RESIST旨在解决这些挑战，主要采用弹性的积极评价风格理论作为其理论基础，并采用基于优势的认知行为疗法的干预技术。目的：本研究的主要目的是评估抗压剂在普通工作人群中作为一种普遍预防手段的有效性，相对于候补名单对照组。第二个研究目的是探讨自我效能、乐观、自我同情和感知社会支持的心理弹性因素，干预目标作为其对压力和自我感知心理弹性影响的潜在中介。方法：总共有352名员工被随机分配到自助版本的RESIST或等待列表对照组。通过网络收集基线、干预后、3个月和6个月（仅干预组[IG]）随访时的数据。主要结果是感知压力，用感知压力量表-10测量。次要结果包括自我感知弹性、目标弹性因素以及其他心理和工作相关的健康结果。结果：干预后IG报告的压力明显低于对照组（Δ=-3.14; d=-0.54, 95% CI -0.75至-0.34）。结论：在工作负担水平较高的员工样本中，RESIST有效地降低了感知压力，增加了自我感知弹性以及目标弹性因素。中介分析表明，培养积极的未来观和对自己的自我同情态度可能是增强心理弹性的关键驱动因素。改变社会关系的质量和加强对自己能力的信念可能需要更多的时间、他人的参与或教练的个人支持，以确保有足够的学习机会。
{"title":"Effectiveness of and Mechanisms of Change in a Self-Help Web- and App-Based Resilience Intervention on Perceived Stress in the General Working Population: Randomized Controlled Trial.","authors":"Sandy Hannibal, Dörte Behrendt, Michèle Wessa, Sarah K Schäfer, Nina Dalkner, Dirk Lehr","doi":"10.2196/78335","DOIUrl":"10.2196/78335","url":null,"abstract":"<p><strong>Background: </strong>Promoting individual resilience-that is, maintaining or regaining mental health despite stressful circumstances-is regarded as an important endeavor to prevent mental illness. However, digital resilience interventions designed to enhance mental health have yielded mixed results. Such heterogeneous effects reflect a variety of unsolved conceptual challenges in interventional resilience research. These range from grounding interventions in resilience frameworks, using theory or targeting etiologically important resilience factors as intervention content, to a lack of knowledge about the mechanisms underlying effects, and using techniques specifically developed to foster psychosocial resources. The web- and app-based resilience intervention RESIST was designed to address these challenges, mainly by using both the Positive Appraisal Style Theory of Resilience as its theoretical foundation and interventional techniques from Strengths-Based Cognitive Behavioral Therapy.</p><p><strong>Objective: </strong>This study's primary aim was to evaluate the effectiveness of RESIST on perceived stress in a general working population as a means of universal prevention, relative to a waitlist control group. A secondary study aim was to explore the resilience factors of self-efficacy, optimism, self-compassion, and perceived social support, the intervention targets as potential mediators of its effect on stress and self-perceived resilience.</p><p><strong>Methods: </strong>In total, 352 employees were randomly assigned to either a self-help version of RESIST or a waitlist control group. Data were collected via the web at baseline, postintervention, and at 3- and 6-month (intervention group [IG] only) follow-ups. The primary outcome was perceived stress, measured with the Perceived Stress Scale-10. Secondary outcomes included self-perceived resilience, the resilience factors targeted, and other mental and work-related health outcomes.</p><p><strong>Results: </strong>The IG reported significantly less stress than controls postintervention (Δ=-3.14; d=-0.54, 95% CI -0.75 to -0.34, and P<.001) and at 3-month follow-up (Δ=-2.79; d=-0.47, 95% CI -0.71 to -0.22, and P=.002). These improvements in the IG were maintained at 6-month follow-up. Favorable between-group differences also were detected for self-perceived resilience and the resilience factors. IG participants completed on average 2.2 (SD 2.3) web-based sessions and used the app's core feature a median of 14 times (IQR 4.00-33.75, range 1-220). The positive effects of the intervention on stress and resilience were primarily mediated by changes in optimism and self-compassion. No evidence was found that self-efficacy and social support also acted as mediators.</p><p><strong>Conclusions: </strong>In a sample of employees experiencing heightened work-burden levels, RESIST was effective in reducing perceived stress and increasing self-perceived resilience as well as the targeted resilienc","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78335"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12775761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145911946","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}
Jelle Cbc de Jong, Femke Pm Hoevenaars, Lotte Gp Peters, Charlotte Mm Berendsen, Wilrike J Pasman, Martien Pm Caspers, Remon Dulos, Suzan Wopereis
<p><strong>Background: </strong>A digital-first strategy is increasingly implemented to reduce participant burden, accelerate recruitment, collect real-world data, and increase the diversity of the study population. However, fully remote studies lack face-to-face interaction, which may affect motivation, particularly in the delivery of personalized nutritional advice. Additionally, self-reported data may vary in terms of standardization and completeness.</p><p><strong>Objective: </strong>The study's primary objective is to evaluate the feasibility of conducting a fully remote, fully digital randomized controlled nutritional intervention, including participant experience and the capability to perform do-it-yourself anthropometric measurements at home. Secondary objectives are to determine whether self-collected data could detect changes in body weight and other anthropometric outcomes, and to compare the effectiveness of generic versus personalized nutrition advice, with and without personalized food boxes.</p><p><strong>Methods: </strong>We conducted a fully online, 3-arm randomized controlled trial including adults with overweight or obesity who were motivated to lose weight. Participants were assigned to a control group that received generic advice (n=43), a personalized intervention group that received personalized advice only (n=40), or a personalized intervention plus group that received personalized advice plus personalized food boxes (n=39). The 6-week intervention was delivered entirely digitally, and all anthropometric measurements, questionnaires, and dietary data were self-collected at home. Feasibility was assessed using adherence metrics, completion of self-measurements, and a user-experience questionnaire. Secondary analyses evaluated weight loss, changes in anthropometry, and exploratory associations, including sex differences.</p><p><strong>Results: </strong>Feasibility was high-102 out of 122 (83.6%) participants found the self-measured anthropometric assessments easy to perform, and 112 (91.8%) participants reported that completing questionnaires from home was easy. For secondary outcomes, participants receiving personalized, but not generic, nutritional advice significantly lost body weight (-1.0 kg; P=.002). Participants receiving personalized food boxes in addition to personalized nutritional advice lost significantly more body weight than the other 2 groups (-2.5 kg; P=.001) and also showed a decrease in hip circumference (-2.9 cm; P=.01). Personalized advice was not easier or more enjoyable to implement than generic nutritional advice, whereas the addition of personalized food boxes improved the ease of implementing personalized nutritional advice (P<.001). All participants, irrespective of the intervention arm, reduced intake of unhealthy food groups, including ready-made meals (113.6 g vs 78.5 g, -30.9%); sauces and gravy (18.8 g vs 10.0 g, -46.8%); sweet snacks (84.8 g vs 64.1 g, -24.4%); savory snacks (50.5 g vs 40.0 g,
{"title":"A Real-Life Digital Intervention for Personalized Nutrition in Adults With Overweight or Obesity: Remote Randomized Controlled Trial.","authors":"Jelle Cbc de Jong, Femke Pm Hoevenaars, Lotte Gp Peters, Charlotte Mm Berendsen, Wilrike J Pasman, Martien Pm Caspers, Remon Dulos, Suzan Wopereis","doi":"10.2196/73367","DOIUrl":"10.2196/73367","url":null,"abstract":"<p><strong>Background: </strong>A digital-first strategy is increasingly implemented to reduce participant burden, accelerate recruitment, collect real-world data, and increase the diversity of the study population. However, fully remote studies lack face-to-face interaction, which may affect motivation, particularly in the delivery of personalized nutritional advice. Additionally, self-reported data may vary in terms of standardization and completeness.</p><p><strong>Objective: </strong>The study's primary objective is to evaluate the feasibility of conducting a fully remote, fully digital randomized controlled nutritional intervention, including participant experience and the capability to perform do-it-yourself anthropometric measurements at home. Secondary objectives are to determine whether self-collected data could detect changes in body weight and other anthropometric outcomes, and to compare the effectiveness of generic versus personalized nutrition advice, with and without personalized food boxes.</p><p><strong>Methods: </strong>We conducted a fully online, 3-arm randomized controlled trial including adults with overweight or obesity who were motivated to lose weight. Participants were assigned to a control group that received generic advice (n=43), a personalized intervention group that received personalized advice only (n=40), or a personalized intervention plus group that received personalized advice plus personalized food boxes (n=39). The 6-week intervention was delivered entirely digitally, and all anthropometric measurements, questionnaires, and dietary data were self-collected at home. Feasibility was assessed using adherence metrics, completion of self-measurements, and a user-experience questionnaire. Secondary analyses evaluated weight loss, changes in anthropometry, and exploratory associations, including sex differences.</p><p><strong>Results: </strong>Feasibility was high-102 out of 122 (83.6%) participants found the self-measured anthropometric assessments easy to perform, and 112 (91.8%) participants reported that completing questionnaires from home was easy. For secondary outcomes, participants receiving personalized, but not generic, nutritional advice significantly lost body weight (-1.0 kg; P=.002). Participants receiving personalized food boxes in addition to personalized nutritional advice lost significantly more body weight than the other 2 groups (-2.5 kg; P=.001) and also showed a decrease in hip circumference (-2.9 cm; P=.01). Personalized advice was not easier or more enjoyable to implement than generic nutritional advice, whereas the addition of personalized food boxes improved the ease of implementing personalized nutritional advice (P<.001). All participants, irrespective of the intervention arm, reduced intake of unhealthy food groups, including ready-made meals (113.6 g vs 78.5 g, -30.9%); sauces and gravy (18.8 g vs 10.0 g, -46.8%); sweet snacks (84.8 g vs 64.1 g, -24.4%); savory snacks (50.5 g vs 40.0 g,","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e73367"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906162","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}
<p><strong>Background: </strong>Online mental health communities increase access and equity for patients seeking psychological support. User demand and professional contributions are key to their sustainability. While previous research has examined factors influencing physicians' participation in online consultation platforms, limited attention has been given to how post characteristics affect the quantity and length of professional responses in nonprofit mental health communities.</p><p><strong>Objective: </strong>This study aims to examine how textual (ie, topic, sentiment, title length, and content length) and contextual (ie, page views and posting time) characteristics of inquiries in nonprofit mental health forums influence the quantity and length of responses from mental health professionals, providing insights for enhancing community interactions.</p><p><strong>Methods: </strong>We collected 18,572 question-and-answer records from a Chinese online mental health platform (August 2024-July 2025). Topic features were extracted using BERTopic, and sentiment features were obtained through a distilled Bidirectional Encoder Representations from Transformers-based sentiment classification model. Additional features were derived from post metadata. We compared 5 machine learning models and identified Light Gradient Boosting Machine as the best performer. We then applied Shapley Additive Explanations (SHAP) analysis to it to evaluate the feature contributions to the prediction of response quantity and length.</p><p><strong>Results: </strong>In virtual mental health communities, user inquiries fall into 7 topic categories: work, love, depression, boyfriends or girlfriends, school, marriage, and family. Depression-related topics negatively predict response quantity, whereas interpersonal, school, marriage, or family topics are positively correlated. SHAP analysis revealed that page views (SHAP value=0.187) and title length (SHAP value=0.073) are key factors in predicting response quantity, and content length (SHAP value=0.274), sentiment category (SHAP value=0.054), and title length (SHAP value=0.053) are key factors in predicting response length. Posts exhibiting negative emotions are positively related to both the predicted quantity and length of responses, and this effect becomes more pronounced as the degree of emotional intensity increases. Titles with 15-20 characters and content with more than 60 characters are positively correlated with responses, whereas titles with fewer than 7 characters have negative effects. Higher view counts and weekday posts also increase response likelihood.</p><p><strong>Conclusions: </strong>This study provides insights into how textual and contextual features of patient posts influence professional responses in nonprofit mental health forums. It enhances understanding of voluntary knowledge contribution behaviors in online mental health communities and offers practical guidance for optimizing platform functional de
{"title":"Predictors of Professional Responses in Nonprofit Mental Health Forums: Interpretable Machine Learning Analysis.","authors":"Shuang Geng, Yanghui Li, Jie Wang, Peixuan Chen, Xusheng Wu, Zhiqun Zhang","doi":"10.2196/74359","DOIUrl":"https://doi.org/10.2196/74359","url":null,"abstract":"<p><strong>Background: </strong>Online mental health communities increase access and equity for patients seeking psychological support. User demand and professional contributions are key to their sustainability. While previous research has examined factors influencing physicians' participation in online consultation platforms, limited attention has been given to how post characteristics affect the quantity and length of professional responses in nonprofit mental health communities.</p><p><strong>Objective: </strong>This study aims to examine how textual (ie, topic, sentiment, title length, and content length) and contextual (ie, page views and posting time) characteristics of inquiries in nonprofit mental health forums influence the quantity and length of responses from mental health professionals, providing insights for enhancing community interactions.</p><p><strong>Methods: </strong>We collected 18,572 question-and-answer records from a Chinese online mental health platform (August 2024-July 2025). Topic features were extracted using BERTopic, and sentiment features were obtained through a distilled Bidirectional Encoder Representations from Transformers-based sentiment classification model. Additional features were derived from post metadata. We compared 5 machine learning models and identified Light Gradient Boosting Machine as the best performer. We then applied Shapley Additive Explanations (SHAP) analysis to it to evaluate the feature contributions to the prediction of response quantity and length.</p><p><strong>Results: </strong>In virtual mental health communities, user inquiries fall into 7 topic categories: work, love, depression, boyfriends or girlfriends, school, marriage, and family. Depression-related topics negatively predict response quantity, whereas interpersonal, school, marriage, or family topics are positively correlated. SHAP analysis revealed that page views (SHAP value=0.187) and title length (SHAP value=0.073) are key factors in predicting response quantity, and content length (SHAP value=0.274), sentiment category (SHAP value=0.054), and title length (SHAP value=0.053) are key factors in predicting response length. Posts exhibiting negative emotions are positively related to both the predicted quantity and length of responses, and this effect becomes more pronounced as the degree of emotional intensity increases. Titles with 15-20 characters and content with more than 60 characters are positively correlated with responses, whereas titles with fewer than 7 characters have negative effects. Higher view counts and weekday posts also increase response likelihood.</p><p><strong>Conclusions: </strong>This study provides insights into how textual and contextual features of patient posts influence professional responses in nonprofit mental health forums. It enhances understanding of voluntary knowledge contribution behaviors in online mental health communities and offers practical guidance for optimizing platform functional de","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e74359"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906093","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}
Mirai So, Yoichi Sekizawa, Sora Hashimoto, Masami Kashimura, Hajime Yamakage, Norio Watanabe
<p><strong>Background: </strong>Depression remains a major global cause of disability; yet, access to optimal mental health services is limited. Self-guided internet-based cognitive behavioral therapy (iCBT) offers a scalable alternative but is generally less effective than guided programs, showing limited antidepressant effects and incomplete symptomatic and functional recovery. Adherence remains a major barrier. Recent advances in artificial intelligence (AI), particularly natural language processing, enable automated advisory and empathic feedback that may enhance engagement and therapeutic impact. Although previous trials have reported promising effects, most used heterogeneous control conditions, making it difficult to isolate the specific contribution of AI within fully self-guided interventions.</p><p><strong>Objective: </strong>This randomized controlled trial evaluated whether natural language processing-based AI feedback integrated into a fully self-guided iCBT program improves clinical outcomes and engagement compared with an otherwise identical iCBT program without AI support.</p><p><strong>Methods: </strong>We recruited 1187 adults aged 20-60 years online and randomly assigned them to AI-augmented iCBT (AI-iCBT; n=396), iCBT without AI (n=397), or a waitlist control (n=394). Both active groups received 6 weekly sessions combining video-based psychoeducation and cognitive restructuring exercises. The AI-iCBT program additionally provided automated empathic and advisory feedback. The primary outcome was depressive symptom severity (Patient Health Questionnaire-9 [PHQ-9]) at week 7 and month 3, analyzed using mixed-effects models for repeated measures under an intention-to-treat framework. Secondary outcomes included a dichotomous PHQ-9 score of ≥10, Quick Inventory of Depressive Symptomatology, Generalized Anxiety Disorder-7, Sheehan Disability Scale, and weekly participation rates. Exploratory analyses assessed the impact of AI functions on engagement and antidepressant effects in the efficacy analysis set (EAS).</p><p><strong>Results: </strong>In intention-to-treat analyses, no significant between-group differences were observed in mean PHQ-9 scores at week 7 or month 3, whereas engagement analyses showed a significant group × week interaction, with AI-iCBT participants demonstrating consistently higher odds of weekly participation (odds ratio 1.23, 95% CI 1.09-1.39; P<.001). Exploratory analyses indicated that activation of the empathic feedback function strongly predicted adherence (odds ratio 9.99, 95% CI 5.80-17.21; P<.001), while advisory feedback was not significant. In EAS analyses, iCBT showed significant short-term improvement versus control at postintervention, whereas at follow-up, only AI-iCBT showed a significantly lower proportion of participants with a PHQ-9 score of ≥10 compared with control (difference -0.15, 95% CI -0.30 to -0.01; P=.046). No serious adverse events were reported.</p><p><strong>Conclusions: </strong>
背景：抑郁症仍然是全球主要的致残原因；然而，获得最佳精神卫生服务的机会有限。自我指导的基于互联网的认知行为疗法（iCBT）提供了一种可扩展的替代方案，但通常不如指导方案有效，显示出有限的抗抑郁效果，症状和功能恢复不完全。坚持治疗仍然是一个主要障碍。人工智能（AI）的最新进展，特别是自然语言处理，可以实现自动咨询和移情反馈，从而提高参与度和治疗效果。尽管先前的试验报告了有希望的效果，但大多数试验使用的是异质控制条件，因此很难在完全自我引导的干预措施中分离出人工智能的具体贡献。目的：本随机对照试验评估基于自然语言处理的人工智能反馈集成到完全自主引导的iCBT项目中，与没有人工智能支持的相同iCBT项目相比，是否能改善临床结果和参与。方法：我们在线招募了1187名年龄在20-60岁之间的成年人，并将他们随机分配到AI增强iCBT （AI-iCBT; n=396）、不使用AI的iCBT （n=397）或等候名单对照组（n=394）。两个活跃组每周接受6次训练，包括基于视频的心理教育和认知重组练习。AI-iCBT程序还提供了自动移情和咨询反馈。主要终点是第7周和第3个月的抑郁症状严重程度（患者健康问卷-9 [PHQ-9]），在意向治疗框架下使用混合效应模型进行重复测量分析。次要结局包括PHQ-9评分≥10分、抑郁症状快速量表、广泛性焦虑障碍-7、Sheehan残疾量表和每周参与率。探索性分析评估了AI功能对疗效分析集（EAS）中参与和抗抑郁作用的影响。结果：在意向治疗分析中，在第7周或第3个月的平均PHQ-9评分中没有观察到显著的组间差异，而参与分析显示了显著的组与周的相互作用，AI- icbt参与者表现出持续较高的每周参与的几率（优势比1.23,95% CI 1.09-1.39）；结论：AI支持显著提高了对完全自我管理计划的依从性。在EAS分析中，AI-iCBT也显示随访时PHQ-9评分≥10的参与者比例显著低于对照组。移情反馈成为维持参与的关键机制，这表明人工智能交流可能有助于保持对可扩展的数字心理健康干预措施的参与。需要进一步的研究。试验注册：大学医院医学信息网络临床试验注册（UMIN-CTR） UMIN000019228；https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000022220。
{"title":"Effect of AI-Based Natural Language Feedback on Engagement and Clinical Outcomes in Fully Self-Guided Internet-Based Cognitive Behavioral Therapy for Depression: 3-Arm Randomized Controlled Trial.","authors":"Mirai So, Yoichi Sekizawa, Sora Hashimoto, Masami Kashimura, Hajime Yamakage, Norio Watanabe","doi":"10.2196/76902","DOIUrl":"10.2196/76902","url":null,"abstract":"<p><strong>Background: </strong>Depression remains a major global cause of disability; yet, access to optimal mental health services is limited. Self-guided internet-based cognitive behavioral therapy (iCBT) offers a scalable alternative but is generally less effective than guided programs, showing limited antidepressant effects and incomplete symptomatic and functional recovery. Adherence remains a major barrier. Recent advances in artificial intelligence (AI), particularly natural language processing, enable automated advisory and empathic feedback that may enhance engagement and therapeutic impact. Although previous trials have reported promising effects, most used heterogeneous control conditions, making it difficult to isolate the specific contribution of AI within fully self-guided interventions.</p><p><strong>Objective: </strong>This randomized controlled trial evaluated whether natural language processing-based AI feedback integrated into a fully self-guided iCBT program improves clinical outcomes and engagement compared with an otherwise identical iCBT program without AI support.</p><p><strong>Methods: </strong>We recruited 1187 adults aged 20-60 years online and randomly assigned them to AI-augmented iCBT (AI-iCBT; n=396), iCBT without AI (n=397), or a waitlist control (n=394). Both active groups received 6 weekly sessions combining video-based psychoeducation and cognitive restructuring exercises. The AI-iCBT program additionally provided automated empathic and advisory feedback. The primary outcome was depressive symptom severity (Patient Health Questionnaire-9 [PHQ-9]) at week 7 and month 3, analyzed using mixed-effects models for repeated measures under an intention-to-treat framework. Secondary outcomes included a dichotomous PHQ-9 score of ≥10, Quick Inventory of Depressive Symptomatology, Generalized Anxiety Disorder-7, Sheehan Disability Scale, and weekly participation rates. Exploratory analyses assessed the impact of AI functions on engagement and antidepressant effects in the efficacy analysis set (EAS).</p><p><strong>Results: </strong>In intention-to-treat analyses, no significant between-group differences were observed in mean PHQ-9 scores at week 7 or month 3, whereas engagement analyses showed a significant group × week interaction, with AI-iCBT participants demonstrating consistently higher odds of weekly participation (odds ratio 1.23, 95% CI 1.09-1.39; P<.001). Exploratory analyses indicated that activation of the empathic feedback function strongly predicted adherence (odds ratio 9.99, 95% CI 5.80-17.21; P<.001), while advisory feedback was not significant. In EAS analyses, iCBT showed significant short-term improvement versus control at postintervention, whereas at follow-up, only AI-iCBT showed a significantly lower proportion of participants with a PHQ-9 score of ≥10 compared with control (difference -0.15, 95% CI -0.30 to -0.01; P=.046). No serious adverse events were reported.</p><p><strong>Conclusions: </strong>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e76902"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906159","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}
Jan Gaessler, Bernhard Remschmidt, Ann-Kathrin Jopp, Behrouz Arefnia, Adrian Franke, Marcus Rieder
Unlabelled: Artificial intelligence-generated informed consent forms for oral surgery demonstrated higher quality and better readability than conventional web-based forms, though both fell short of recommended comprehension levels.
{"title":"Quality of Conventional versus Artificial Intelligence Oral Surgery Consent Forms: Comparative Analysis.","authors":"Jan Gaessler, Bernhard Remschmidt, Ann-Kathrin Jopp, Behrouz Arefnia, Adrian Franke, Marcus Rieder","doi":"10.2196/59851","DOIUrl":"10.2196/59851","url":null,"abstract":"<p><strong>Unlabelled: </strong>Artificial intelligence-generated informed consent forms for oral surgery demonstrated higher quality and better readability than conventional web-based forms, though both fell short of recommended comprehension levels.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e59851"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12768391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906180","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}
Joar von Bahr, Antti Suutala, Vinod Diwan, Andreas Mårtensson, Johan Lundin, Nina Linder
<p><strong>Background: </strong>Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and a decreased need for experts on-site. To our knowledge, no scoping or systematic review has previously examined the use of AI-supported digital microscopy in primary health care laboratories, and a scoping review could guide future research by providing insights into the challenges of implementing these novel methods.</p><p><strong>Objective: </strong>This scoping review aimed to map published peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject.</p><p><strong>Methods: </strong>A systematic search of the databases PubMed, Web of Science, Embase, and IEEE was conducted on October 2, 2024. The inclusion criteria in the scoping review were based on 3 concepts: using digital microscopy, AI, and comparison of the results with a standard diagnostic system, and 1 context, being performed in primary health care laboratories. Additional inclusion criteria were peer-reviewed diagnostic accuracy studies published in English, performed on humans and achieving a sample-level diagnosis. The study selection and data extraction were performed by 2 independent researchers (JVB and AS), and cases of disagreement were resolved through discussion with a third researcher (NL). The methodology is in accordance with the Joanna Briggs Institute methodology for scoping reviews.</p><p><strong>Results: </strong>A total of 3403 papers were screened during the paper identification process, of which 22 (0.6%) were included in the scoping review. The samples analyzed were as follows: blood (n=12) for blood cell and malaria detection, urine (n=4) for urinalysis and parasite detection, cytology of atypical oral (n=1) and cervical cells (n=2), stool (n=2) for parasite detection, and sputum (n=1) for ferning patterns indicating inflammation. Both conventional (n=15) and specifically developed methods (n=7) were used in sample preparation. The AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for complete blood counts, malaria detection, identification of stool and genitourinary parasites, screening for oral and cervical cellular atypia, detection of pulmonary inflammation, and urinalysis. Furthermore, AI-supported digital microscopy achieved higher sensitivity than manual microscopy in 6/7 (85.7%) studies that used a reference standard that allowed for this comparison.</p><p><strong>Conclusions: </strong>AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for diagnosing multiple targets in primary health care laboratories and may be particularly
背景：数字显微镜与人工智能（AI）的结合越来越多地应用于卫生保健领域，主要是在先进的实验室环境中。然而，人工智能支持的数字显微镜在初级卫生保健环境中可能特别有利，因为这种方法可以通过自动化改善诊断的获取，并减少对现场专家的需求。据我们所知，以前没有对在初级卫生保健实验室中使用人工智能支持的数字显微镜进行过范围界定或系统评价，范围界定审查可以通过提供对实施这些新方法的挑战的见解来指导未来的研究。目的：本范围综述旨在绘制已发表的关于初级卫生保健实验室中人工智能支持的数字显微镜的同行评议研究，以对该主题进行概述。方法：于2024年10月2日对PubMed、Web of Science、Embase、IEEE等数据库进行系统检索。范围审查的纳入标准基于3个概念：使用数字显微镜、人工智能和将结果与标准诊断系统进行比较，以及在初级卫生保健实验室进行的1个环境。其他入选标准是同行评议的诊断准确性研究，以英文发表，在人类身上进行，并达到样本水平的诊断。研究选择和数据提取由2名独立研究人员（JVB和AS）完成，如果有分歧，则通过与第三名研究人员（NL）讨论解决。该方法与乔安娜布里格斯研究所的范围审查方法一致。结果：在论文鉴定过程中共筛选到3403篇论文，其中22篇（0.6%）被纳入纳入范围评价。分析的样本如下：血液（n=12）用于血细胞和疟疾检测，尿液（n=4）用于尿液分析和寄生虫检测，非典型口腔细胞（n=1）和宫颈细胞（n=2），粪便（n=2）用于寄生虫检测，痰（n=1）用于提示炎症的苔藓样。样品制备中使用了常规方法（n=15）和专门开发的方法（n=7）。人工智能支持的数字显微镜在全血细胞计数、疟疾检测、粪便和泌尿生殖系统寄生虫鉴定、口腔和宫颈细胞异型性筛查、肺部炎症检测和尿液分析方面的诊断准确性与参考标准相当。此外，人工智能支持的数字显微镜在6/7（85.7%）的研究中获得了比人工显微镜更高的灵敏度，这些研究使用了允许这种比较的参考标准。结论：在初级卫生保健实验室中，人工智能支持的数字显微镜在诊断多靶点时达到了与参考标准相当的诊断准确性，可能特别有利于提高诊断敏感性。随着对可扩展性和成本效益等挑战的进一步研究，人工智能支持的数字显微镜可以改善诊断的可及性，特别是在专家稀缺和资源有限的情况下。
{"title":"AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Scoping Review.","authors":"Joar von Bahr, Antti Suutala, Vinod Diwan, Andreas Mårtensson, Johan Lundin, Nina Linder","doi":"10.2196/78500","DOIUrl":"10.2196/78500","url":null,"abstract":"<p><strong>Background: </strong>Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced laboratory settings. However, AI-supported digital microscopy could be especially advantageous in primary health care settings, since such methods could improve access to diagnostics via automation and a decreased need for experts on-site. To our knowledge, no scoping or systematic review has previously examined the use of AI-supported digital microscopy in primary health care laboratories, and a scoping review could guide future research by providing insights into the challenges of implementing these novel methods.</p><p><strong>Objective: </strong>This scoping review aimed to map published peer-reviewed studies on AI-supported digital microscopy in primary health care laboratories to generate an overview of the subject.</p><p><strong>Methods: </strong>A systematic search of the databases PubMed, Web of Science, Embase, and IEEE was conducted on October 2, 2024. The inclusion criteria in the scoping review were based on 3 concepts: using digital microscopy, AI, and comparison of the results with a standard diagnostic system, and 1 context, being performed in primary health care laboratories. Additional inclusion criteria were peer-reviewed diagnostic accuracy studies published in English, performed on humans and achieving a sample-level diagnosis. The study selection and data extraction were performed by 2 independent researchers (JVB and AS), and cases of disagreement were resolved through discussion with a third researcher (NL). The methodology is in accordance with the Joanna Briggs Institute methodology for scoping reviews.</p><p><strong>Results: </strong>A total of 3403 papers were screened during the paper identification process, of which 22 (0.6%) were included in the scoping review. The samples analyzed were as follows: blood (n=12) for blood cell and malaria detection, urine (n=4) for urinalysis and parasite detection, cytology of atypical oral (n=1) and cervical cells (n=2), stool (n=2) for parasite detection, and sputum (n=1) for ferning patterns indicating inflammation. Both conventional (n=15) and specifically developed methods (n=7) were used in sample preparation. The AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for complete blood counts, malaria detection, identification of stool and genitourinary parasites, screening for oral and cervical cellular atypia, detection of pulmonary inflammation, and urinalysis. Furthermore, AI-supported digital microscopy achieved higher sensitivity than manual microscopy in 6/7 (85.7%) studies that used a reference standard that allowed for this comparison.</p><p><strong>Conclusions: </strong>AI-supported digital microscopy achieved comparable diagnostic accuracy to the reference standard for diagnosing multiple targets in primary health care laboratories and may be particularly ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78500"},"PeriodicalIF":6.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12768395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145906149","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：