Joyce H S You, Scotty W C Luk, Dilys Y W Chow, Arthur D P Mak, Winnie W S Mak
Background: Internet-based cognitive behavioral therapy (CBT) provides psychological interventions to individuals with mild depressive symptoms.
Objective: This study aimed to examine the potential cost-effectiveness of internet-based guided-CBT in university students with mild depressive symptoms from the perspective of service providers in Hong Kong.
Methods: The outcomes of low-intensity guided internet-based CBT and in-person CBT in a hypothetical cohort of university students with mild depressive symptoms were examined using a 5-year decision-analytic model. Model inputs were obtained from published literature and local data. Model outcomes included direct medical cost, school dropouts, and quality-adjusted life years (QALYs). Sensitivity analyses were conducted on all model parameters.
Results: Compared to the in-person group, the internet group gained higher QALYs by 0.0211 QALYs, lowered school dropouts by 0.052%, and saved US $249 in the base-case analysis. In one-way sensitivity analysis, the internet group gained higher QALYs at a lower cost than the in-person group throughout the variation of all model inputs. Probabilistic sensitivity analysis showed that the internet group was cost-effective (at willingness-to-pay threshold was US $48,119/QALY) in 99.7% of the 10,000 Monte Carlo simulations.
Conclusions: Internet-based CBT appears to be the cost-effective option when compared to in-person CBT for university students with mild depressive symptoms from the perspective of service providers in Hong Kong.
{"title":"Internet-Based Mental Health Intervention for Depressive Symptoms in Young Adults: Cost-Effectiveness Analysis.","authors":"Joyce H S You, Scotty W C Luk, Dilys Y W Chow, Arthur D P Mak, Winnie W S Mak","doi":"10.2196/68167","DOIUrl":"10.2196/68167","url":null,"abstract":"<p><strong>Background: </strong>Internet-based cognitive behavioral therapy (CBT) provides psychological interventions to individuals with mild depressive symptoms.</p><p><strong>Objective: </strong>This study aimed to examine the potential cost-effectiveness of internet-based guided-CBT in university students with mild depressive symptoms from the perspective of service providers in Hong Kong.</p><p><strong>Methods: </strong>The outcomes of low-intensity guided internet-based CBT and in-person CBT in a hypothetical cohort of university students with mild depressive symptoms were examined using a 5-year decision-analytic model. Model inputs were obtained from published literature and local data. Model outcomes included direct medical cost, school dropouts, and quality-adjusted life years (QALYs). Sensitivity analyses were conducted on all model parameters.</p><p><strong>Results: </strong>Compared to the in-person group, the internet group gained higher QALYs by 0.0211 QALYs, lowered school dropouts by 0.052%, and saved US $249 in the base-case analysis. In one-way sensitivity analysis, the internet group gained higher QALYs at a lower cost than the in-person group throughout the variation of all model inputs. Probabilistic sensitivity analysis showed that the internet group was cost-effective (at willingness-to-pay threshold was US $48,119/QALY) in 99.7% of the 10,000 Monte Carlo simulations.</p><p><strong>Conclusions: </strong>Internet-based CBT appears to be the cost-effective option when compared to in-person CBT for university students with mild depressive symptoms from the perspective of service providers in Hong Kong.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e68167"},"PeriodicalIF":3.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12704703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145763997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siu Kit Yeung, Florence H T Leung, Jason C M Lee, Gabriel Man Hin Cheung, Ching Wan Li, Winnie W S Mak
<p><strong>Background: </strong>Digital mental health platforms often consist of many different forms of self-care exercises. To our knowledge, whether the number of choices presented to the users affects their uptake and experiences and poses negative consequences (ie, not choosing any exercises, choice dissatisfaction) for users, especially those experiencing anxiety and depressive symptoms or unpleasant state emotions, has not been empirically investigated.</p><p><strong>Objective: </strong>This study investigated the impact of choice set size on practice decisions, completion, satisfaction, and subjective experiences, as well as potential moderators including depression and anxiety symptoms, state emotions, and motivational and decisional attributes on these choice outcomes.</p><p><strong>Methods: </strong>Participants were recruited through university mass email and social media, and 652 participants were included in our analyses. Participants completed questions regarding anxiety and depressive symptoms, state emotions, and other psychological attributes. Then, they were randomly assigned to 1-choice, 4-choice, and 16-choice conditions, in which they may choose a self-care activity to practice or decide not to practice. Finally, they completed questions regarding completion, satisfaction, engagement, attitude, and perceived improvement in psychological state.</p><p><strong>Results: </strong>Presenting multiple choices resulted in a higher likelihood of practice (odds ratio 3.12, 95% CI 2.08 to 4.67 and 3.83, 95% CI 2.55 to 5.76; P<.001) and better decision satisfaction (16-choice vs 1-choice: d=0.36, 95% CI 0.17 to 0.56, P<.001; 4-choice vs 1-choice: d=0.24, 95% CI 0.05 to 0.43, P=.03) compared with presenting with a single choice. Tentative evidence indicates anxiety symptoms and state emotions were meaningful moderators. Specifically, for individuals with more anxiety symptoms and intense negative emotions, presenting a larger choice set (16 choices) resulted in more positive chosen exercise satisfaction, better attitudes toward chosen activity, and higher perceived improvement in mental health state after the activity, when compared with presenting with smaller choice sets (anxiety: β=-0.38, 95% CI -0.69 to -0.06 to -0.51, 95% CI -0.84 to -0.18; state emotions: β=-0.31, 95% CI -0.66 to 0.03 to -0.60, 95% CI -0.92 to -0.28). No evidence was found for the moderating effect of motivational and decisional attributes.</p><p><strong>Conclusions: </strong>The moderation results were contradictory to prior research and our expectation that a larger choice set may result in worse outcomes than a smaller choice set for people who were experiencing higher levels of psychological distress. We speculated that a possible reason for these findings may be that people with more anxiety symptoms and unpleasant emotions may have a stronger need to reduce these uncomfortable symptoms and emotions, and when presented with more choices on self-care activities
背景：数字心理健康平台通常由许多不同形式的自我保健练习组成。据我们所知，提供给用户的选择数量是否会影响他们的吸收和体验，并对用户，特别是那些经历焦虑和抑郁症状或不愉快状态情绪的用户产生负面影响（即不选择任何练习，选择不满意），尚未进行实证调查。目的：本研究探讨了选择集大小对练习决策、完成度、满意度和主观体验的影响，以及抑郁和焦虑症状、状态情绪、动机和决策属性等潜在调节因子对这些选择结果的影响。方法：通过大学群发邮件和社交媒体招募参与者，共纳入652名参与者。参与者完成了关于焦虑和抑郁症状、状态情绪和其他心理属性的问题。然后，他们被随机分配到1选择、4选择和16选择的条件下，在这些条件下，他们可以选择一项自我保健活动来练习，也可以决定不练习。最后，他们完成了关于完成度、满意度、参与度、态度和心理状态感知改善的问题。结果：呈现多个选择导致更高的练习可能性（优势比3.12,95% CI 2.08至4.67和3.83,95% CI 2.55至5.76）结论：适度结果与先前的研究和我们的预期相矛盾，即对于经历较高水平心理困扰的人来说，较大的选择集可能比较小的选择集导致更差的结果。我们推测，这些发现的一个可能原因可能是，焦虑症状和不愉快情绪较多的人可能更需要减少这些不舒服的症状和情绪，当自我保健活动有更多的选择时，这些自我保健活动可能更有可能解决他们的痛苦。
{"title":"Effects of Choice Set Sizes and Moderations of Anxiety and State Emotions on Mental Health Self-Care Uptake, Engagement, and User Experience: Experimental Study.","authors":"Siu Kit Yeung, Florence H T Leung, Jason C M Lee, Gabriel Man Hin Cheung, Ching Wan Li, Winnie W S Mak","doi":"10.2196/71165","DOIUrl":"10.2196/71165","url":null,"abstract":"<p><strong>Background: </strong>Digital mental health platforms often consist of many different forms of self-care exercises. To our knowledge, whether the number of choices presented to the users affects their uptake and experiences and poses negative consequences (ie, not choosing any exercises, choice dissatisfaction) for users, especially those experiencing anxiety and depressive symptoms or unpleasant state emotions, has not been empirically investigated.</p><p><strong>Objective: </strong>This study investigated the impact of choice set size on practice decisions, completion, satisfaction, and subjective experiences, as well as potential moderators including depression and anxiety symptoms, state emotions, and motivational and decisional attributes on these choice outcomes.</p><p><strong>Methods: </strong>Participants were recruited through university mass email and social media, and 652 participants were included in our analyses. Participants completed questions regarding anxiety and depressive symptoms, state emotions, and other psychological attributes. Then, they were randomly assigned to 1-choice, 4-choice, and 16-choice conditions, in which they may choose a self-care activity to practice or decide not to practice. Finally, they completed questions regarding completion, satisfaction, engagement, attitude, and perceived improvement in psychological state.</p><p><strong>Results: </strong>Presenting multiple choices resulted in a higher likelihood of practice (odds ratio 3.12, 95% CI 2.08 to 4.67 and 3.83, 95% CI 2.55 to 5.76; P<.001) and better decision satisfaction (16-choice vs 1-choice: d=0.36, 95% CI 0.17 to 0.56, P<.001; 4-choice vs 1-choice: d=0.24, 95% CI 0.05 to 0.43, P=.03) compared with presenting with a single choice. Tentative evidence indicates anxiety symptoms and state emotions were meaningful moderators. Specifically, for individuals with more anxiety symptoms and intense negative emotions, presenting a larger choice set (16 choices) resulted in more positive chosen exercise satisfaction, better attitudes toward chosen activity, and higher perceived improvement in mental health state after the activity, when compared with presenting with smaller choice sets (anxiety: β=-0.38, 95% CI -0.69 to -0.06 to -0.51, 95% CI -0.84 to -0.18; state emotions: β=-0.31, 95% CI -0.66 to 0.03 to -0.60, 95% CI -0.92 to -0.28). No evidence was found for the moderating effect of motivational and decisional attributes.</p><p><strong>Conclusions: </strong>The moderation results were contradictory to prior research and our expectation that a larger choice set may result in worse outcomes than a smaller choice set for people who were experiencing higher levels of psychological distress. We speculated that a possible reason for these findings may be that people with more anxiety symptoms and unpleasant emotions may have a stronger need to reduce these uncomfortable symptoms and emotions, and when presented with more choices on self-care activities","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e71165"},"PeriodicalIF":3.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12705060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145763986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gloria Obuobi-Donkor, Reham Shalaby, Belinda Agyapong, Samuel Obeng Nkrumah, Medard Adu, Ejemai Eboreime, Lori Wozney, Vincent Israel Opoku Agyapong
Background: Digital mental health tools, such as text messaging and online resources, are increasingly utilized to support well-being. However, user satisfaction across these formats remains insufficiently explored.
Objective: The study assessed participants' engagement, perceived impact, and overall satisfaction with the Text4Support program and the e-mental health resources.
Methods: This randomized controlled study was conducted in Nova Scotia, Canada. Participants were assigned to either the Text4Support group, which received daily supportive text messages, or the Control group, which received a single text message with a link to the Nova Scotia Health Mental Health and Addiction Program e-mental health resources. Responses to various aspects of the interventions were evaluated using a 5-point Likert scale, while overall satisfaction was measured on a scale from 0 to 10. The chi-square test and Fisher's exact test were employed for data analysis.
Results: A total of 69 in the control group and 130 in the Text4Support group completed the satisfaction survey. The overall mean satisfaction score in the control group was 5.1 (SD 2.3), and the overall mean satisfaction score for the Text4Support group was 7.1 (SD 2.2). Compared to the control 3 group, participants in the Text4Support group reported greater engagement and positive program impact. Whereas 53% of Text4Support recipients always read the messages, only 39.1% of the control group sometimes accessed the e-health resources. Participants allocated to the Text4Support group were reported to sometimes take positive action upon reading the messages (42.3% vs. 33.3%). A significantly higher proportion of Text4Support users strongly agreed or agreed that the messages were supportive (81.4% vs 41.5%), positive (88.4% vs 49.2%), and helpful in coping with stress (44.2% vs 11.9%), loneliness (40.3% vs 13.4%), and improving mental well-being (51.2% vs 17.9%). In contrast, the majority of responses from the control group were largely neutral.
Conclusions: Results showed that Text4Support group participants were significantly more satisfied with the program than those receiving standard e-health resources. This highlights that daily supportive text messaging is an effective, low-cost adjunct to care delivery and mental health improvement. These findings suggest that aggregate, brief, and low-cost text-based interventions have great potential for increasing health access and engagement, particularly among traditionally disadvantaged populations with limited access to traditional services.
{"title":"Evaluating User Engagement and Satisfaction with Digital Mental Health Interventions: A Randomized Controlled Trial of a Text Messaging Program and e-Mental Health Resources.","authors":"Gloria Obuobi-Donkor, Reham Shalaby, Belinda Agyapong, Samuel Obeng Nkrumah, Medard Adu, Ejemai Eboreime, Lori Wozney, Vincent Israel Opoku Agyapong","doi":"10.2196/76587","DOIUrl":"10.2196/76587","url":null,"abstract":"<p><strong>Background: </strong>Digital mental health tools, such as text messaging and online resources, are increasingly utilized to support well-being. However, user satisfaction across these formats remains insufficiently explored.</p><p><strong>Objective: </strong>The study assessed participants' engagement, perceived impact, and overall satisfaction with the Text4Support program and the e-mental health resources.</p><p><strong>Methods: </strong>This randomized controlled study was conducted in Nova Scotia, Canada. Participants were assigned to either the Text4Support group, which received daily supportive text messages, or the Control group, which received a single text message with a link to the Nova Scotia Health Mental Health and Addiction Program e-mental health resources. Responses to various aspects of the interventions were evaluated using a 5-point Likert scale, while overall satisfaction was measured on a scale from 0 to 10. The chi-square test and Fisher's exact test were employed for data analysis.</p><p><strong>Results: </strong>A total of 69 in the control group and 130 in the Text4Support group completed the satisfaction survey. The overall mean satisfaction score in the control group was 5.1 (SD 2.3), and the overall mean satisfaction score for the Text4Support group was 7.1 (SD 2.2). Compared to the control 3 group, participants in the Text4Support group reported greater engagement and positive program impact. Whereas 53% of Text4Support recipients always read the messages, only 39.1% of the control group sometimes accessed the e-health resources. Participants allocated to the Text4Support group were reported to sometimes take positive action upon reading the messages (42.3% vs. 33.3%). A significantly higher proportion of Text4Support users strongly agreed or agreed that the messages were supportive (81.4% vs 41.5%), positive (88.4% vs 49.2%), and helpful in coping with stress (44.2% vs 11.9%), loneliness (40.3% vs 13.4%), and improving mental well-being (51.2% vs 17.9%). In contrast, the majority of responses from the control group were largely neutral.</p><p><strong>Conclusions: </strong>Results showed that Text4Support group participants were significantly more satisfied with the program than those receiving standard e-health resources. This highlights that daily supportive text messaging is an effective, low-cost adjunct to care delivery and mental health improvement. These findings suggest that aggregate, brief, and low-cost text-based interventions have great potential for increasing health access and engagement, particularly among traditionally disadvantaged populations with limited access to traditional services.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12822869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145763972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Osamah Ahmad, Stephen Mason, Sarah Stanley, Amara Callistus Nwosu
Background: The use of artificial intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic health records; however, there are few data on the views of palliative care health care professionals on the role of AI in practice. Determining the opinions of palliative care health care professionals on the potential uses of AI in palliative care will be useful for policymakers and practitioners to determine and inform the meaningful use of AI in palliative care practice.
Objective: This study aimed to explore the views of palliative care health care professionals on the use of AI for the analysis of patient data in palliative care.
Methods: This was a phenomenological study using qualitative semistructured interviews with palliative care health care professionals with a minimum of 1 year of clinical experience in a hospice in the North West of England. Data were analyzed using inductive thematic analysis.
Results: We interviewed 6 palliative care professionals, including physicians, nurses, and occupational therapists. AI was viewed positively, although most participants had not used it in practice. None of the participants had received training in AI and stated that education in AI would be beneficial. Participants described the potential benefits of AI in palliative care, including the identification of people requiring palliative care interventions and the evaluation of patient experiences. Participants highlighted security and ethical concerns regarding AI related to data governance, efficacy, patient confidentiality, and consent issues.
Conclusions: This study highlights the importance of staff perceptions of AI in palliative care. Our findings support the role of AI in enhancing care, addressing educational needs, and tackling trust, ethics, and governance issues. This study lays the groundwork for guidelines on AI implementation, urging further research on the methodological, ethical, and practical aspects of AI in palliative care.
{"title":"Exploring Perspectives of Health Care Professionals on AI in Palliative Care: Qualitative Interview Study.","authors":"Osamah Ahmad, Stephen Mason, Sarah Stanley, Amara Callistus Nwosu","doi":"10.2196/79514","DOIUrl":"10.2196/79514","url":null,"abstract":"<p><strong>Background: </strong>The use of artificial intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic health records; however, there are few data on the views of palliative care health care professionals on the role of AI in practice. Determining the opinions of palliative care health care professionals on the potential uses of AI in palliative care will be useful for policymakers and practitioners to determine and inform the meaningful use of AI in palliative care practice.</p><p><strong>Objective: </strong>This study aimed to explore the views of palliative care health care professionals on the use of AI for the analysis of patient data in palliative care.</p><p><strong>Methods: </strong>This was a phenomenological study using qualitative semistructured interviews with palliative care health care professionals with a minimum of 1 year of clinical experience in a hospice in the North West of England. Data were analyzed using inductive thematic analysis.</p><p><strong>Results: </strong>We interviewed 6 palliative care professionals, including physicians, nurses, and occupational therapists. AI was viewed positively, although most participants had not used it in practice. None of the participants had received training in AI and stated that education in AI would be beneficial. Participants described the potential benefits of AI in palliative care, including the identification of people requiring palliative care interventions and the evaluation of patient experiences. Participants highlighted security and ethical concerns regarding AI related to data governance, efficacy, patient confidentiality, and consent issues.</p><p><strong>Conclusions: </strong>This study highlights the importance of staff perceptions of AI in palliative care. Our findings support the role of AI in enhancing care, addressing educational needs, and tackling trust, ethics, and governance issues. This study lays the groundwork for guidelines on AI implementation, urging further research on the methodological, ethical, and practical aspects of AI in palliative care.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e79514"},"PeriodicalIF":3.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12723358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joanna Abraham, Alicia Meng, Nicolas Caravelli, Leah Traeger, May Nguyen, Vineet Arora, Valerie G Press
<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) affects approximately 16 million Americans and often results in avoidable readmissions due, in part, to medication errors and lack of education. Telehealth interventions can support medication reconciliation and inhaler education following hospital discharge for patients with COPD.</p><p><strong>Objective: </strong>This study aimed to design and prototype TELE-TOC (Telehealth Education: Leveraging Electronic Transitions of Care), a post-discharge, in-home, televisit intervention, and to map its workflow to ensure integration into the routine discharge care transition process for patients with COPD.</p><p><strong>Methods: </strong>A user-centered design approach across 3 phases was followed to develop and prototype TELE-TOC. Participants included adult patients hospitalized for COPD exacerbations, their caregivers, clinicians involved in COPD care, and organizational leaders. Data collection methods included semi-structured interviews, system usability scale surveys, and cognitive walkthroughs of the TELE-TOC prototype to assess participants' perceptions on usability and feasibility of TELE-TOC implementation as part of routine COPD discharge care transitions. Qualitative data were analyzed using inductive thematic analysis and an inductive-deductive approach guided by the Agency for Healthcare Research and Quality-endorsed Care Transitions Framework. Quantitative data were summarized using basic descriptive statistics.</p><p><strong>Results: </strong>Participants included 18 patients, 18 clinicians, 8 organizational leaders, and 2 caregivers. Phase 1 identified 3 interdependent stages of COPD hospital-to-home discharge: inpatient pre-discharge, at-home post-discharge, and outpatient clinic visit post-discharge. Key facilitators of discharge care transitions included the hospital's "meds-to-beds" program and high patient health literacy, while barriers to discharge included poor timing of education and conflicting patient priorities. Phase 2 delineated the core televisit components (eg, dedicated clinician, medication reconciliation, inhaler use, and self-management education) and flexible components (eg, reminder system and session frequency). Potential implementation enablers included multiple techniques for clinicians to access and support patient education and backup communication strategies in the event of technical issues. Potential implementation barriers included insufficient patient technology access and limited technology and health literacy, as well as limited clinician bandwidth for thorough COPD education and medication reconciliation. Phase 3 TELE-TOC prototype walkthroughs demonstrated a positive patient experience (average system usability scale score of 97.5/100), attributed to the benefits of videoconferencing technology for hands-on teaching and the use of the virtual teach-back method. Identified barriers included varying levels of patient technology l
{"title":"Patient-Centered Televisit for Chronic Obstructive Pulmonary Disease Discharge Transitions: User-Centered Design Study.","authors":"Joanna Abraham, Alicia Meng, Nicolas Caravelli, Leah Traeger, May Nguyen, Vineet Arora, Valerie G Press","doi":"10.2196/77953","DOIUrl":"10.2196/77953","url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) affects approximately 16 million Americans and often results in avoidable readmissions due, in part, to medication errors and lack of education. Telehealth interventions can support medication reconciliation and inhaler education following hospital discharge for patients with COPD.</p><p><strong>Objective: </strong>This study aimed to design and prototype TELE-TOC (Telehealth Education: Leveraging Electronic Transitions of Care), a post-discharge, in-home, televisit intervention, and to map its workflow to ensure integration into the routine discharge care transition process for patients with COPD.</p><p><strong>Methods: </strong>A user-centered design approach across 3 phases was followed to develop and prototype TELE-TOC. Participants included adult patients hospitalized for COPD exacerbations, their caregivers, clinicians involved in COPD care, and organizational leaders. Data collection methods included semi-structured interviews, system usability scale surveys, and cognitive walkthroughs of the TELE-TOC prototype to assess participants' perceptions on usability and feasibility of TELE-TOC implementation as part of routine COPD discharge care transitions. Qualitative data were analyzed using inductive thematic analysis and an inductive-deductive approach guided by the Agency for Healthcare Research and Quality-endorsed Care Transitions Framework. Quantitative data were summarized using basic descriptive statistics.</p><p><strong>Results: </strong>Participants included 18 patients, 18 clinicians, 8 organizational leaders, and 2 caregivers. Phase 1 identified 3 interdependent stages of COPD hospital-to-home discharge: inpatient pre-discharge, at-home post-discharge, and outpatient clinic visit post-discharge. Key facilitators of discharge care transitions included the hospital's \"meds-to-beds\" program and high patient health literacy, while barriers to discharge included poor timing of education and conflicting patient priorities. Phase 2 delineated the core televisit components (eg, dedicated clinician, medication reconciliation, inhaler use, and self-management education) and flexible components (eg, reminder system and session frequency). Potential implementation enablers included multiple techniques for clinicians to access and support patient education and backup communication strategies in the event of technical issues. Potential implementation barriers included insufficient patient technology access and limited technology and health literacy, as well as limited clinician bandwidth for thorough COPD education and medication reconciliation. Phase 3 TELE-TOC prototype walkthroughs demonstrated a positive patient experience (average system usability scale score of 97.5/100), attributed to the benefits of videoconferencing technology for hands-on teaching and the use of the virtual teach-back method. Identified barriers included varying levels of patient technology l","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e77953"},"PeriodicalIF":3.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12680292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaokang Shi, Zewu Jiang, Li Xiong, Ka-Chun Siu, Zhen Chen
Background: The emergence of Artificial Intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is accelerating the adoption of localized AI platforms, which are poised to become integral components in the coming years.
Objective: The primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students' needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback and to identify practical requirements across disciplines and academic stages to inform more effective platform design.
Methods: Based on the Task-Technology Fit (TTF) model and five hypotheses, an anonymous online questionnaire was conducted to assess AI usage in learning, gather student feedback on AI-powered medical education platforms, and evaluate expected functionalities. The survey was conducted from March 1 to May 31, 2025, using a convenience sampling method to recruit medical students from various disciplines across Shanghai, China. The sample size was determined to be 422, accounting for a 10% rate of invalid responses. The questionnaire was developed and distributed online via Wenjuanxing and promoted through WeChat groups and in-person interviews. Data analysis was conducted employing IBM SPSS Statistics (v 27.0).
Results: A total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was 5.06±2.05 times per week. Over 90% of students (388/428) used more than two AI tools in their daily tasks. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms.
Conclusions: AI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.
{"title":"Utilization of AI among Medical Students and Development of AI Education Platforms in Medical Institutions: A Cross-Sectional Study.","authors":"Xiaokang Shi, Zewu Jiang, Li Xiong, Ka-Chun Siu, Zhen Chen","doi":"10.2196/81652","DOIUrl":"https://doi.org/10.2196/81652","url":null,"abstract":"<p><strong>Background: </strong>The emergence of Artificial Intelligence (AI) is driving digital transformation and reshaping medical education in China. Numerous medical schools and institutions are actively implementing AI tools for case-based learning, literature analysis, and lecture support. This expanding application is accelerating the adoption of localized AI platforms, which are poised to become integral components in the coming years.</p><p><strong>Objective: </strong>The primary aim of this study was to investigate the current use of AI tools among medical students, including usage frequency, commonly used platforms, and purposes of use. The second aim was to explore students' needs and expectations toward AI-powered medical education platforms by collecting and assessing student feedback and to identify practical requirements across disciplines and academic stages to inform more effective platform design.</p><p><strong>Methods: </strong>Based on the Task-Technology Fit (TTF) model and five hypotheses, an anonymous online questionnaire was conducted to assess AI usage in learning, gather student feedback on AI-powered medical education platforms, and evaluate expected functionalities. The survey was conducted from March 1 to May 31, 2025, using a convenience sampling method to recruit medical students from various disciplines across Shanghai, China. The sample size was determined to be 422, accounting for a 10% rate of invalid responses. The questionnaire was developed and distributed online via Wenjuanxing and promoted through WeChat groups and in-person interviews. Data analysis was conducted employing IBM SPSS Statistics (v 27.0).</p><p><strong>Results: </strong>A total of 428 valid questionnaires were collected. The average frequency of AI-assisted learning among medical students was 5.06±2.05 times per week. Over 90% of students (388/428) used more than two AI tools in their daily tasks. Students from different disciplines, educational stages, and academic systems demonstrated different usage patterns and expectations for AI-powered medical education platforms.</p><p><strong>Conclusions: </strong>AI technology is widely accepted by medical students and is extensively applied across various aspects of medical education. Significant differences are observed in usage patterns across disciplines, educational stages, and academic systems. Understanding the actual needs of students is crucial for the construction of AI-powered medical education platforms.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145702308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rosemaree Kathleen Miller, Kathleen O'Moore, Katarina Kikas, Julie-Anne Therese Matheson, Alexis Estelle Whitton, Peter Baldwin, Sophie Li, Melissa Black, Laura Kampel, Nicole Cockayne, Fiona Tuttlebee, Caitlin Fraser, Victoria Carr, Kathleen Varghese, Jill Maree Newby
Background: In blended care, digital mental health interventions (DMHIs) integrate with face-to-face psychotherapy provided in person or via telehealth. To incorporate DMHIs into routine care for depression and anxiety, it is important to understand the needs and expectations of mental health professionals for blended DMHIs.
Objective: The study objective was to partner with Australian mental health professionals in the design of a transdiagnostic, cognitive behavioral therapy-based blended model of care for adults experiencing depression and anxiety.
Methods: Participants were Australian health professionals who treat adults with depression and anxiety. The participatory design process included a web-based survey (N=258), one-on-one interviews (N=14), and a 2-part focus group (N=6). Quantitative and qualitative data were collected through the web-based survey. In-depth qualitative feedback from interviews and the 2-part focus group was subjected to reflexive thematic analysis.
Results: Mental health professionals found blended care with face-to-face therapy more acceptable than telehealth and blended care with telehealth, with standalone DMHIs being the least preferred option. The most common ways in which mental health professionals thought a DMHI could integrate with face-to-face psychotherapy included homework completion (129/178, 72.5%), skills practice to support in-session therapy (128/178, 71.9%), and psychoeducation (127/178, 71.3%). Mental health professionals expect the blended DMHI to be easy to use, flexible, protective of client data, and to include evidence-based content from several therapeutic modalities (eg, cognitive behavioral therapy and mindfulness). Other preferences included mental health professionals being able to prescribe specific program modules to their clients, track the treatment progress of clients, and receive alerts if their clients' symptoms worsened. In terms of implementation, mental health professionals were concerned about the time and effort needed to use blended care. They suggested that ongoing training and support would help mental health professionals implement blended care with their clients. Monitoring client risk and progress via a web-based dashboard and downloadable summaries was also important.
Conclusions: Designing DMHIs that support psychotherapy for adults with depression and anxiety has the potential to increase access to evidence-based treatment. Involving mental health professionals in DMHI design is expected to increase their acceptance of DMHIs and facilitate the integration of these digital products into routine care.
{"title":"Needs and Expectations for the myNewWay Blended Digital and Face-to-Face Psychotherapy Model of Care for Depression and Anxiety (Part 2): Participatory Design Study including Mental Health Professionals.","authors":"Rosemaree Kathleen Miller, Kathleen O'Moore, Katarina Kikas, Julie-Anne Therese Matheson, Alexis Estelle Whitton, Peter Baldwin, Sophie Li, Melissa Black, Laura Kampel, Nicole Cockayne, Fiona Tuttlebee, Caitlin Fraser, Victoria Carr, Kathleen Varghese, Jill Maree Newby","doi":"10.2196/68789","DOIUrl":"10.2196/68789","url":null,"abstract":"<p><strong>Background: </strong>In blended care, digital mental health interventions (DMHIs) integrate with face-to-face psychotherapy provided in person or via telehealth. To incorporate DMHIs into routine care for depression and anxiety, it is important to understand the needs and expectations of mental health professionals for blended DMHIs.</p><p><strong>Objective: </strong>The study objective was to partner with Australian mental health professionals in the design of a transdiagnostic, cognitive behavioral therapy-based blended model of care for adults experiencing depression and anxiety.</p><p><strong>Methods: </strong>Participants were Australian health professionals who treat adults with depression and anxiety. The participatory design process included a web-based survey (N=258), one-on-one interviews (N=14), and a 2-part focus group (N=6). Quantitative and qualitative data were collected through the web-based survey. In-depth qualitative feedback from interviews and the 2-part focus group was subjected to reflexive thematic analysis.</p><p><strong>Results: </strong>Mental health professionals found blended care with face-to-face therapy more acceptable than telehealth and blended care with telehealth, with standalone DMHIs being the least preferred option. The most common ways in which mental health professionals thought a DMHI could integrate with face-to-face psychotherapy included homework completion (129/178, 72.5%), skills practice to support in-session therapy (128/178, 71.9%), and psychoeducation (127/178, 71.3%). Mental health professionals expect the blended DMHI to be easy to use, flexible, protective of client data, and to include evidence-based content from several therapeutic modalities (eg, cognitive behavioral therapy and mindfulness). Other preferences included mental health professionals being able to prescribe specific program modules to their clients, track the treatment progress of clients, and receive alerts if their clients' symptoms worsened. In terms of implementation, mental health professionals were concerned about the time and effort needed to use blended care. They suggested that ongoing training and support would help mental health professionals implement blended care with their clients. Monitoring client risk and progress via a web-based dashboard and downloadable summaries was also important.</p><p><strong>Conclusions: </strong>Designing DMHIs that support psychotherapy for adults with depression and anxiety has the potential to increase access to evidence-based treatment. Involving mental health professionals in DMHI design is expected to increase their acceptance of DMHIs and facilitate the integration of these digital products into routine care.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e68789"},"PeriodicalIF":3.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12677875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>Addressing the complex medical and psychosocial needs of older adults is increasingly difficult in resource-limited care settings. In this context, socially assistive robots (SARs) provide support and practical functions such as orientation and information delivery. Integrating large language models (LLMs) into SAR dialogue systems offers opportunities to improve interaction fluency and adaptability. Yet, in real-world use, acceptability also depends on minimizing both technical and conversational errors, ensuring successful user interactions, and adapting to individual user characteristics.</p><p><strong>Objective: </strong>This study aimed to evaluate the impact of integrating an LLM into a SAR dialogue system in a hospital geriatric unit by (1) comparing system performance and interaction success across 2 experimental waves, (2) examining the links between robot errors, interaction success, and multidimensional user engagement, and (3) exploring how user characteristics influence performance and perceptions of acceptability and usability.</p><p><strong>Methods: </strong>Over an 8-month period, 28 older adults (≥60 years of age) attending a geriatric day care hospital (Paris, France) participated in a single-session evaluation of a SAR. Interactions took place in the day care hospital and were video-recorded across 2 waves: wave 1 (basic dialogue system) and wave 2 (LLM-based system). From the recordings, system performance (error types and interaction success) and user engagement (verbal, physical, and emotional dimensions) were coded. Acceptability and usability were measured using the Acceptability E-Scale and the System Usability Scale. Sociodemographic data were collected, and quantitative results were supplemented with a thematic analysis of qualitative observations.</p><p><strong>Results: </strong>Following LLM integration, error-free interactions increased from 27.8% (10/36) to 70.2% (66/94; P<.001), comprehension failures decreased from 47.2% (17/36) to 17% (16/94; P<.001), and interaction success rose from 25% (9/36) to 74.5% (70/94; P<.001). Acceptability (Acceptability E-Scale: 12.8 vs 20.8; P=.003) and usability (System Usability Scale: 40.0 vs 60.4; P=.04) were significantly higher in wave 2. Engagement scores did not differ significantly between waves, though emotional engagement correlated positively with interaction success (r=0.28; P=.008), and age was negatively associated with both physical engagement (r=-0.30; P<.001) and acceptability (r=-0.20; P=.03).</p><p><strong>Conclusions: </strong>Behavioral engagement with a SAR in geriatric care is shaped by both system performance and individual user characteristics. Improvements in dialogue quality observed in wave 2, coinciding with the integration of the LLM, were associated with higher interaction success and enhanced user experience. Nevertheless, other contextual or group-related factors may also have contributed to this outcome. These findin
{"title":"Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative Study on Performance, Engagement, and User Perceptions.","authors":"Lauriane Blavette, Sébastien Dacunha, Xavier Alameda-Pineda, Jeanne Cattoni, Anne-Sophie Rigaud, Maribel Pino","doi":"10.2196/81936","DOIUrl":"10.2196/81936","url":null,"abstract":"<p><strong>Background: </strong>Addressing the complex medical and psychosocial needs of older adults is increasingly difficult in resource-limited care settings. In this context, socially assistive robots (SARs) provide support and practical functions such as orientation and information delivery. Integrating large language models (LLMs) into SAR dialogue systems offers opportunities to improve interaction fluency and adaptability. Yet, in real-world use, acceptability also depends on minimizing both technical and conversational errors, ensuring successful user interactions, and adapting to individual user characteristics.</p><p><strong>Objective: </strong>This study aimed to evaluate the impact of integrating an LLM into a SAR dialogue system in a hospital geriatric unit by (1) comparing system performance and interaction success across 2 experimental waves, (2) examining the links between robot errors, interaction success, and multidimensional user engagement, and (3) exploring how user characteristics influence performance and perceptions of acceptability and usability.</p><p><strong>Methods: </strong>Over an 8-month period, 28 older adults (≥60 years of age) attending a geriatric day care hospital (Paris, France) participated in a single-session evaluation of a SAR. Interactions took place in the day care hospital and were video-recorded across 2 waves: wave 1 (basic dialogue system) and wave 2 (LLM-based system). From the recordings, system performance (error types and interaction success) and user engagement (verbal, physical, and emotional dimensions) were coded. Acceptability and usability were measured using the Acceptability E-Scale and the System Usability Scale. Sociodemographic data were collected, and quantitative results were supplemented with a thematic analysis of qualitative observations.</p><p><strong>Results: </strong>Following LLM integration, error-free interactions increased from 27.8% (10/36) to 70.2% (66/94; P<.001), comprehension failures decreased from 47.2% (17/36) to 17% (16/94; P<.001), and interaction success rose from 25% (9/36) to 74.5% (70/94; P<.001). Acceptability (Acceptability E-Scale: 12.8 vs 20.8; P=.003) and usability (System Usability Scale: 40.0 vs 60.4; P=.04) were significantly higher in wave 2. Engagement scores did not differ significantly between waves, though emotional engagement correlated positively with interaction success (r=0.28; P=.008), and age was negatively associated with both physical engagement (r=-0.30; P<.001) and acceptability (r=-0.20; P=.03).</p><p><strong>Conclusions: </strong>Behavioral engagement with a SAR in geriatric care is shaped by both system performance and individual user characteristics. Improvements in dialogue quality observed in wave 2, coinciding with the integration of the LLM, were associated with higher interaction success and enhanced user experience. Nevertheless, other contextual or group-related factors may also have contributed to this outcome. These findin","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e81936"},"PeriodicalIF":3.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12712570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raphael Jan Dressle, Dieter Riemann, Nicole Thoma, Christina Erler, Rodger Burmeister, Bianka Jogwitz, Katharina Domschke, Kai Spiegelhalder, Joachim Boldt, Svenja Wiertz, Bernd Feige
<p><strong>Background: </strong>A large amount of data are generated in health care facilities, yet it is rarely made available for secondary research use. The reasons are manifold. Most importantly, different stakeholders' needs must be balanced. However, there are currently hardly any feasible solutions for this.</p><p><strong>Objective: </strong>This study aimed to develop a data trust model with supporting user interface applications to provide a legally and ethically sound framework for secondary use of medical data. The development was based on extensive surveys of various stakeholders.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with researchers (data users) and institutional representatives of the Medical Center-University of Freiburg, and online questionnaires were administered to patients (data subjects), data users, and institutional representatives. The questionnaire for data subjects covered the dimensions of trust (measured with a 5-point Likert scale), quality of interaction and involvement (measured with a 4-point Likert scale), subjective and objective understanding, and usability (measured with the user version of the Mobile Application Rating Scale). For all other stakeholder groups, the questionnaire focused on usability measured using the user version of the Mobile Application Rating Scale. The surveys comprised a requirement elicitation followed by two rounds of evaluation. Independent-samples Welch t tests were used to compare group means between the first and second evaluations.</p><p><strong>Results: </strong>We devised SouveMed, a framework for secondary use of medical data, applied to the use case of sleep research data. The model includes secure onboarding of data subjects and using digital consent and a digital interface for data users for onboarding, defining research aims, querying the amount of available data, and, finally, either downloading data or having algorithms run on it. At its core is a data trust entity that matches descriptions, consents, and constraints of all stakeholders using digital representations and constraint-solving techniques. Fourteen participants took part in the requirements elicitation, 22 in the first evaluation, and 16 in the second evaluation. In both the first and the second evaluations, data subjects showed a high level of trust in the concept, with mean ratings on the trust scale of 4.23 (SD 0.46) in the first and 4.23 (SD 0.68) in the second evaluation (t15.78=0.03, P=.97). Regarding usability, the mean functionality score of the data user system increased from 3.56 (SD 0.77) in the first to 4.58 (SD 0.38) out of 5 points in the second evaluation (t10.69=-3.28, P=.008). The mean functionality score of the data subject system increased from 4.30 (SD 0.41) in the first to 4.50 (SD 0.74) in the second evaluation (t13.99=-0.75, P=.46).</p><p><strong>Conclusions: </strong>The SouveMed concept provides a comprehensive framework for the secondary use of medical
{"title":"Developing a Data Trust Model (Not Only) for Sleep Research: Conceptual Study and Quantitative Survey.","authors":"Raphael Jan Dressle, Dieter Riemann, Nicole Thoma, Christina Erler, Rodger Burmeister, Bianka Jogwitz, Katharina Domschke, Kai Spiegelhalder, Joachim Boldt, Svenja Wiertz, Bernd Feige","doi":"10.2196/66513","DOIUrl":"10.2196/66513","url":null,"abstract":"<p><strong>Background: </strong>A large amount of data are generated in health care facilities, yet it is rarely made available for secondary research use. The reasons are manifold. Most importantly, different stakeholders' needs must be balanced. However, there are currently hardly any feasible solutions for this.</p><p><strong>Objective: </strong>This study aimed to develop a data trust model with supporting user interface applications to provide a legally and ethically sound framework for secondary use of medical data. The development was based on extensive surveys of various stakeholders.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with researchers (data users) and institutional representatives of the Medical Center-University of Freiburg, and online questionnaires were administered to patients (data subjects), data users, and institutional representatives. The questionnaire for data subjects covered the dimensions of trust (measured with a 5-point Likert scale), quality of interaction and involvement (measured with a 4-point Likert scale), subjective and objective understanding, and usability (measured with the user version of the Mobile Application Rating Scale). For all other stakeholder groups, the questionnaire focused on usability measured using the user version of the Mobile Application Rating Scale. The surveys comprised a requirement elicitation followed by two rounds of evaluation. Independent-samples Welch t tests were used to compare group means between the first and second evaluations.</p><p><strong>Results: </strong>We devised SouveMed, a framework for secondary use of medical data, applied to the use case of sleep research data. The model includes secure onboarding of data subjects and using digital consent and a digital interface for data users for onboarding, defining research aims, querying the amount of available data, and, finally, either downloading data or having algorithms run on it. At its core is a data trust entity that matches descriptions, consents, and constraints of all stakeholders using digital representations and constraint-solving techniques. Fourteen participants took part in the requirements elicitation, 22 in the first evaluation, and 16 in the second evaluation. In both the first and the second evaluations, data subjects showed a high level of trust in the concept, with mean ratings on the trust scale of 4.23 (SD 0.46) in the first and 4.23 (SD 0.68) in the second evaluation (t15.78=0.03, P=.97). Regarding usability, the mean functionality score of the data user system increased from 3.56 (SD 0.77) in the first to 4.58 (SD 0.38) out of 5 points in the second evaluation (t10.69=-3.28, P=.008). The mean functionality score of the data subject system increased from 4.30 (SD 0.41) in the first to 4.50 (SD 0.74) in the second evaluation (t13.99=-0.75, P=.46).</p><p><strong>Conclusions: </strong>The SouveMed concept provides a comprehensive framework for the secondary use of medical","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e66513"},"PeriodicalIF":3.0,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Salima Meherali, Amyna Ismail Rehmani, Mariam Ahmad
Background: Adolescents need comprehensive education and resources to promote their sexual and reproductive health (SRH) and make informed decisions about their SRH. However, many adolescents fail to secure an opportunity to learn accurate and reliable SRH information, as they face sociocultural barriers, fear of prejudice, and societal stigma. Information available on digital platforms may not always be evidence-based and can further spread misinformation. Digital knowledge translation interventions that provide evidence-based SRH information in North America are limited.
Objective: This study aimed to co-design a digital knowledge translation intervention, reflective of adolescents' voices, to provide evidence-based, accessible, and accurate SRH resources.
Methods: We conducted a large multisite project across 3 cities in Canada. Using a community-based participatory approach and principles of human-centered design, we established adolescent advisory groups (AAGs) to actively engage them in the design, development, and implementation of the intervention.
Results: A total of 26 participants were recruited from Edmonton, Vancouver, and Toronto to be part of the advisory groups. AAG members participated in design-thinking sessions to brainstorm ideas for website design, identify informational needs for content development, provide iterative feedback on the design of the intervention, and suggest strategies to improve engagement and interaction. With their input, 6 priority areas were identified to develop SRH resources (ie, puberty, menstruation, sexually transmitted infections, healthy relationships, sexual assault, and contraception). Their feedback informed the design's language, visual appeal, and engagement factors. To promote meaningful engagement of AAGs at each step, we used strategies such as gamification activities, group discussions, and flexible scheduling, resulting in high retention and ownership of the process among AAG members.
Conclusions: Co-designing with adolescents strengthened the intervention's cultural relevance, youth-friendliness, and credibility. Our process emphasizes the significance of involving adolescents in co-designing SRH interventions, which results in more meaningful, long-term, and youth-friendly solutions.
{"title":"Adolescent Voices in Action-Co-Designing Digital Sexual and Reproductive Health Knowledge Translation Interventions: Community-Based Participatory Action Project.","authors":"Salima Meherali, Amyna Ismail Rehmani, Mariam Ahmad","doi":"10.2196/71148","DOIUrl":"10.2196/71148","url":null,"abstract":"<p><strong>Background: </strong>Adolescents need comprehensive education and resources to promote their sexual and reproductive health (SRH) and make informed decisions about their SRH. However, many adolescents fail to secure an opportunity to learn accurate and reliable SRH information, as they face sociocultural barriers, fear of prejudice, and societal stigma. Information available on digital platforms may not always be evidence-based and can further spread misinformation. Digital knowledge translation interventions that provide evidence-based SRH information in North America are limited.</p><p><strong>Objective: </strong>This study aimed to co-design a digital knowledge translation intervention, reflective of adolescents' voices, to provide evidence-based, accessible, and accurate SRH resources.</p><p><strong>Methods: </strong>We conducted a large multisite project across 3 cities in Canada. Using a community-based participatory approach and principles of human-centered design, we established adolescent advisory groups (AAGs) to actively engage them in the design, development, and implementation of the intervention.</p><p><strong>Results: </strong>A total of 26 participants were recruited from Edmonton, Vancouver, and Toronto to be part of the advisory groups. AAG members participated in design-thinking sessions to brainstorm ideas for website design, identify informational needs for content development, provide iterative feedback on the design of the intervention, and suggest strategies to improve engagement and interaction. With their input, 6 priority areas were identified to develop SRH resources (ie, puberty, menstruation, sexually transmitted infections, healthy relationships, sexual assault, and contraception). Their feedback informed the design's language, visual appeal, and engagement factors. To promote meaningful engagement of AAGs at each step, we used strategies such as gamification activities, group discussions, and flexible scheduling, resulting in high retention and ownership of the process among AAG members.</p><p><strong>Conclusions: </strong>Co-designing with adolescents strengthened the intervention's cultural relevance, youth-friendliness, and credibility. Our process emphasizes the significance of involving adolescents in co-designing SRH interventions, which results in more meaningful, long-term, and youth-friendly solutions.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e71148"},"PeriodicalIF":3.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12706438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145655814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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