Background: Eating is a primary daily activity crucial for maintaining independence and quality of life. Individuals with neuromuscular impairments often struggle with eating due to limitations in current assistive devices, which are predominantly passive and lack adaptive capabilities.
Objective: This study aims to introduce an adaptive feeding robot that integrates time series decomposition, autoregressive integrated moving average (ARIMA), and feed-forward neural networks (FFNN). The goal is to enhance feeding precision, efficiency, and personalization, thereby promoting autonomy for individuals with motor impairments.
Methods: The proposed feeding robot combines information from sensors and actuators to collect real-time data, that is, facial landmarks, mouth status (open or closed), fork-to-mouth and plate distances, as well as the force and angle required for food handling based on the food type. ARIMA and FFNN algorithms analyze data to predict user behavior and adjust feeding actions dynamically. A strain gauge sensor ensures precise force regulation, an ultrasonic sensor optimizes positioning, and facial recognition algorithms verify safety by monitoring mouth conditions and plate contents.
Results: The combined ARIMA+FFNN model achieved a mean squared error (MSE) of 0.008 and an R2 of 94%, significantly outperforming the standalone ARIMA (MSE=0.015; R2=85%) and FFNN (MSE=0.012; R2=88%). Feeding success rate improved from 75% to 90% over 150 iterations (P<.001), and response time decreased by 28% (from 3.6 s to 2.2 s). ANOVA revealed significant differences in success rates across scenarios (F3,146=12.34; P= .002), with scenario 1 outperforming scenario 3 (P=.030) and scenario 4 (P=.010). Object detection showed high accuracy (face detection precision=97%, recall=96%, 95% CI 94%-99%). Force application matched expected ranges with minimal deviation (24 [1] N for apples; 7 [0.5] N for strawberries).
Conclusions: Combining predictive algorithms and adaptive learning mechanisms enables the feeding robot to demonstrate substantial improvements in precision, responsiveness, and personalization. These advancements underline its potential to revolutionize assistive technology in rehabilitation, delivering safe and highly personalized feeding assistance to individuals with motor impairments, thereby enhancing their independence.
{"title":"Adaptive Feeding Robot With Multisensor Feedback and Predictive Control Using Autoregressive Integrated Moving Average-Feed-Forward Neural Network: Simulation Study.","authors":"Shabnam Sadeghi-Esfahlani, Vahaj Mohaghegh, Alireza Sanaei, Zainib Bilal, Nathon Arthur, Hassan Shirvani","doi":"10.2196/69877","DOIUrl":"10.2196/69877","url":null,"abstract":"<p><strong>Background: </strong>Eating is a primary daily activity crucial for maintaining independence and quality of life. Individuals with neuromuscular impairments often struggle with eating due to limitations in current assistive devices, which are predominantly passive and lack adaptive capabilities.</p><p><strong>Objective: </strong>This study aims to introduce an adaptive feeding robot that integrates time series decomposition, autoregressive integrated moving average (ARIMA), and feed-forward neural networks (FFNN). The goal is to enhance feeding precision, efficiency, and personalization, thereby promoting autonomy for individuals with motor impairments.</p><p><strong>Methods: </strong>The proposed feeding robot combines information from sensors and actuators to collect real-time data, that is, facial landmarks, mouth status (open or closed), fork-to-mouth and plate distances, as well as the force and angle required for food handling based on the food type. ARIMA and FFNN algorithms analyze data to predict user behavior and adjust feeding actions dynamically. A strain gauge sensor ensures precise force regulation, an ultrasonic sensor optimizes positioning, and facial recognition algorithms verify safety by monitoring mouth conditions and plate contents.</p><p><strong>Results: </strong>The combined ARIMA+FFNN model achieved a mean squared error (MSE) of 0.008 and an R2 of 94%, significantly outperforming the standalone ARIMA (MSE=0.015; R2=85%) and FFNN (MSE=0.012; R2=88%). Feeding success rate improved from 75% to 90% over 150 iterations (P<.001), and response time decreased by 28% (from 3.6 s to 2.2 s). ANOVA revealed significant differences in success rates across scenarios (F3,146=12.34; P= .002), with scenario 1 outperforming scenario 3 (P=.030) and scenario 4 (P=.010). Object detection showed high accuracy (face detection precision=97%, recall=96%, 95% CI 94%-99%). Force application matched expected ranges with minimal deviation (24 [1] N for apples; 7 [0.5] N for strawberries).</p><p><strong>Conclusions: </strong>Combining predictive algorithms and adaptive learning mechanisms enables the feeding robot to demonstrate substantial improvements in precision, responsiveness, and personalization. These advancements underline its potential to revolutionize assistive technology in rehabilitation, delivering safe and highly personalized feeding assistance to individuals with motor impairments, thereby enhancing their independence.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e69877"},"PeriodicalIF":2.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12844836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063618","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}
Neilane Bertoni, Andre Salem Szklo, Francisco Inacio Bastos
<p><strong>Background: </strong>The marketing of electronic nicotine delivery systems (ENDSs) has been prohibited in Brazil since 2009, and their regular use is less prevalent than in countries where these devices are not banned. To monitor the presence of ENDSs, it is important to prevent the development of a new generation of nicotine-dependent individuals. However, traditional surveys are costly for accessing rare populations. Therefore, to reach ENDS users aged ≥15 years, we used the online version of the respondent-driven sampling method (web-RDS), a peer chain recruitment method for contacting hard-to-reach groups.</p><p><strong>Objective: </strong>This paper aims to provide information on the implementation of the first web-RDS study in Brazil to recruit ENDS users.</p><p><strong>Methods: </strong>This study was conducted in Rio de Janeiro, the second largest city in Brazil. After a formative phase using qualitative in-depth interviews, we selected the first participants ("seeds") to complete an online quantitative questionnaire on the profile of their own ENDS use and the size of their contact network of ENDS users. Participants received 3 coupons to invite eligible peers. For participation and recruitment, each participant received a gift card worth approximately US $4. The target sample size was 300 ENDS users based on a conservative estimate and adjusted for design effect.</p><p><strong>Results: </strong>From August 2022 to May 2023, of the 12 seeds included, 508 attempts at access were recorded in the data collection system, of which 330 (65%) were eligible. Duplicate or ineligible attempts were identified and removed through automated and manual checks. Recruitment was initially slow due to the low monetary incentive, but it improved after the amount was increased. We found that 43.1% (75/174) of recruiters recruited only 1 eligible participant, 34.5% (60/174) recruited 2 eligible participants, and 22.4% (39/174) recruited 3 participants. Web-RDS was able to reach individuals in different areas of the city. Convergence was reached for target variables (ie, age and age at first use of electronic cigarettes). The median time to complete the questionnaire was 12 (IQR 8-17) minutes. Half (154/324, 47.5%) of the respondents reported that they knew up to 5 other ENDS users.</p><p><strong>Conclusions: </strong>The web-RDS methodology proved to be a feasible approach for accessing the population of ENDS users in Brazil. Incentives for participation and recruitment emerged as a determining factor in the data collection process. However, researchers needed to be aware of individuals attempting to circumvent the system by participating without being eligible or participating multiple times. Implications for optimizing web-RDS are discussed. On the basis of the method's performance in this study, web-RDS shows potential to support future repeated data collection processes that could help monitor changes in the profiles of ENDS users over time, s
{"title":"Implementation of Web-Based Respondent-Driven Sampling to Recruit Users of Electronic Nicotine Delivery Systems in Brazil: Cross-Sectional Survey.","authors":"Neilane Bertoni, Andre Salem Szklo, Francisco Inacio Bastos","doi":"10.2196/81573","DOIUrl":"https://doi.org/10.2196/81573","url":null,"abstract":"<p><strong>Background: </strong>The marketing of electronic nicotine delivery systems (ENDSs) has been prohibited in Brazil since 2009, and their regular use is less prevalent than in countries where these devices are not banned. To monitor the presence of ENDSs, it is important to prevent the development of a new generation of nicotine-dependent individuals. However, traditional surveys are costly for accessing rare populations. Therefore, to reach ENDS users aged ≥15 years, we used the online version of the respondent-driven sampling method (web-RDS), a peer chain recruitment method for contacting hard-to-reach groups.</p><p><strong>Objective: </strong>This paper aims to provide information on the implementation of the first web-RDS study in Brazil to recruit ENDS users.</p><p><strong>Methods: </strong>This study was conducted in Rio de Janeiro, the second largest city in Brazil. After a formative phase using qualitative in-depth interviews, we selected the first participants (\"seeds\") to complete an online quantitative questionnaire on the profile of their own ENDS use and the size of their contact network of ENDS users. Participants received 3 coupons to invite eligible peers. For participation and recruitment, each participant received a gift card worth approximately US $4. The target sample size was 300 ENDS users based on a conservative estimate and adjusted for design effect.</p><p><strong>Results: </strong>From August 2022 to May 2023, of the 12 seeds included, 508 attempts at access were recorded in the data collection system, of which 330 (65%) were eligible. Duplicate or ineligible attempts were identified and removed through automated and manual checks. Recruitment was initially slow due to the low monetary incentive, but it improved after the amount was increased. We found that 43.1% (75/174) of recruiters recruited only 1 eligible participant, 34.5% (60/174) recruited 2 eligible participants, and 22.4% (39/174) recruited 3 participants. Web-RDS was able to reach individuals in different areas of the city. Convergence was reached for target variables (ie, age and age at first use of electronic cigarettes). The median time to complete the questionnaire was 12 (IQR 8-17) minutes. Half (154/324, 47.5%) of the respondents reported that they knew up to 5 other ENDS users.</p><p><strong>Conclusions: </strong>The web-RDS methodology proved to be a feasible approach for accessing the population of ENDS users in Brazil. Incentives for participation and recruitment emerged as a determining factor in the data collection process. However, researchers needed to be aware of individuals attempting to circumvent the system by participating without being eligible or participating multiple times. Implications for optimizing web-RDS are discussed. On the basis of the method's performance in this study, web-RDS shows potential to support future repeated data collection processes that could help monitor changes in the profiles of ENDS users over time, s","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e81573"},"PeriodicalIF":2.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063546","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}
<p><strong>Background: </strong>Life goal setting contributes substantially to well-being and quality of life, particularly among middle-aged and older adults. However, delivering remote goal-setting support remains challenging due to limited professional resources and accessibility barriers. Recent advancements in mobile health (mHealth) technologies, telemedicine, and generative artificial intelligence (AI) present new opportunities for scalable, personalized health behavior interventions. Nevertheless, few studies have compared AI-driven life goal interventions with conventional human-facilitated approaches in real-world settings.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility and user experience of an AI-supported mHealth intervention for remote life goal setting based on flow theory. We compared the AI-supported approach to occupational therapist (OT)-facilitated support and explored the differential characteristics of AI-guided and human-guided interventions for self-management and motivation enhancement.</p><p><strong>Methods: </strong>An exploratory, within-participant, 2-condition comparison with a counterbalanced order was conducted among 28 community-dwelling adults (aged between 20 and 76 years) who were smartphone users. Each participant selected 2 personal life goals and completed remote adjusting the challenge-skill balance (R-ACS) sessions, a structured telemedicine process based on flow theory. One goal was supported by an OT, while the other was facilitated by a generative AI chatbot integrated into an mHealth platform. Following each session, participants completed a 4-item rubric-based questionnaire (5-point Likert scale), assessing the quantity and quality of questions, appropriateness of suggestions, and perceived contribution to goal attainment. Free-text feedback was also collected. Quantitative data were analyzed using Wilcoxon signed-rank tests with effect size calculations and Benjamini-Hochberg correction for multiple comparisons. Qualitative differences were explored using text mining (term frequency-inverse document frequency analysis) and sentiment evaluation.</p><p><strong>Results: </strong>Both AI-supported and OT-facilitated R-ACS sessions were feasible and successfully delivered tailored suggestions for all participants. AI-supported sessions received higher scores on all rubric items than OT-facilitated sessions, with a statistically significant difference in suggestion appropriateness (z score=3.13; P=.002; r=0.418; false discovery rate-adjusted P=.008). Term frequency-inverse document frequency analysis of free-text comments revealed that AI-supported sessions emphasized actionability, motivation, and immediacy, while OT-facilitated sessions highlighted reflection, self-understanding, and emotional safety. Participants expressed high acceptance of both intervention types, with AI-supported interactions perceived as particularly accessible and conducive to health behavior change
{"title":"Feasibility and User Experience of an AI-Supported mHealth Intervention for Remote Life Goal Setting Based on Flow Theory: Exploratory Within-Participant Study.","authors":"Ippei Yoshida","doi":"10.2196/78717","DOIUrl":"https://doi.org/10.2196/78717","url":null,"abstract":"<p><strong>Background: </strong>Life goal setting contributes substantially to well-being and quality of life, particularly among middle-aged and older adults. However, delivering remote goal-setting support remains challenging due to limited professional resources and accessibility barriers. Recent advancements in mobile health (mHealth) technologies, telemedicine, and generative artificial intelligence (AI) present new opportunities for scalable, personalized health behavior interventions. Nevertheless, few studies have compared AI-driven life goal interventions with conventional human-facilitated approaches in real-world settings.</p><p><strong>Objective: </strong>This study aimed to evaluate the feasibility and user experience of an AI-supported mHealth intervention for remote life goal setting based on flow theory. We compared the AI-supported approach to occupational therapist (OT)-facilitated support and explored the differential characteristics of AI-guided and human-guided interventions for self-management and motivation enhancement.</p><p><strong>Methods: </strong>An exploratory, within-participant, 2-condition comparison with a counterbalanced order was conducted among 28 community-dwelling adults (aged between 20 and 76 years) who were smartphone users. Each participant selected 2 personal life goals and completed remote adjusting the challenge-skill balance (R-ACS) sessions, a structured telemedicine process based on flow theory. One goal was supported by an OT, while the other was facilitated by a generative AI chatbot integrated into an mHealth platform. Following each session, participants completed a 4-item rubric-based questionnaire (5-point Likert scale), assessing the quantity and quality of questions, appropriateness of suggestions, and perceived contribution to goal attainment. Free-text feedback was also collected. Quantitative data were analyzed using Wilcoxon signed-rank tests with effect size calculations and Benjamini-Hochberg correction for multiple comparisons. Qualitative differences were explored using text mining (term frequency-inverse document frequency analysis) and sentiment evaluation.</p><p><strong>Results: </strong>Both AI-supported and OT-facilitated R-ACS sessions were feasible and successfully delivered tailored suggestions for all participants. AI-supported sessions received higher scores on all rubric items than OT-facilitated sessions, with a statistically significant difference in suggestion appropriateness (z score=3.13; P=.002; r=0.418; false discovery rate-adjusted P=.008). Term frequency-inverse document frequency analysis of free-text comments revealed that AI-supported sessions emphasized actionability, motivation, and immediacy, while OT-facilitated sessions highlighted reflection, self-understanding, and emotional safety. Participants expressed high acceptance of both intervention types, with AI-supported interactions perceived as particularly accessible and conducive to health behavior change","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e78717"},"PeriodicalIF":2.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063569","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}
James S Brooks, Paa-Kwesi Blankson, Peter Murphy Campbell, R Adams Cowley, Tsorng-Shyang Yang, Tijani Oseni, Anny Rodriguez, Muhammed Y Idris
<p><strong>Background: </strong>Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)-generated responses compared to those authored by human clinicians.</p><p><strong>Objective: </strong>This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice.</p><p><strong>Methods: </strong>We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit's r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice.</p><p><strong>Results: </strong>Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest.</p><p><strong>Conclusions: </strong>This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health infor
{"title":"Assessment of Physician Preferences for Large Language Model-Generated Responses Across Geographic Regions and Clinical Experience Levels: Preliminary Survey Study.","authors":"James S Brooks, Paa-Kwesi Blankson, Peter Murphy Campbell, R Adams Cowley, Tsorng-Shyang Yang, Tijani Oseni, Anny Rodriguez, Muhammed Y Idris","doi":"10.2196/82487","DOIUrl":"10.2196/82487","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)-generated responses compared to those authored by human clinicians.</p><p><strong>Objective: </strong>This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice.</p><p><strong>Methods: </strong>We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit's r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice.</p><p><strong>Results: </strong>Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest.</p><p><strong>Conclusions: </strong>This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health infor","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":" ","pages":"e82487"},"PeriodicalIF":2.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145793998","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}
Chaitali Sinha, Riddhi Thakkar, Saha Meheli, Dyuthi Dinesh
Background: Despite digital mental health services growing at a rapid pace to address global mental health needs, there exist challenges of low engagement and attrition. Ensuring continuity of care in the digital context can positively impact mental health care delivery and adherence to treatment, helping to establish digital mental health interventions (DMHIs) as a viable option for mental health support.
Objective: This study aimed to examine the impact of adjunct app features of the mental health app Wysa and their ability to promote engagement and adherence to the text-based coaching sessions.
Methods: This retrospective mixed methods observational study was based on real-world app data from users (n=1213) who subscribed to text-based sessions with mental health coaches (MHCs) between February 1 and July 31, 2022. Their engagement with the adjunct app features, such as brief interventions with the conversational agent, self-management tools, and journaling, was analyzed quantitatively using descriptive statistics. Acceptability of the app features was also assessed using qualitative feedback data. Adherence to sessions with MHCs was compared between app feature users (n=1042, 85.9%) and nonfeature users (n=171, 14.1%) using inferential statistics. Subgroup analysis was not feasible in the absence of demographic and clinical user data, potentially limiting the generalizability of the findings.
Results: Findings demonstrated high use of the adjunct app features, which allowed communication with the MHCs in between sessions. The thematic analysis captures user experiences of helpfulness within the app and with the MHCs. The Mann-Whitney U test indicated that users who accessed one or more features completed significantly more sessions compared with users who did not use any feature (Mann-Whitney U=154,085.0; P<.001; rB=0.73) with a large effect size. The odds ratio analysis indicated that users were almost thrice as likely to complete sessions after using the adjunct app features (odds ratio 2.91, 95% CI 2.24-3.38; P<.001).
Conclusions: Inclusion of adjunct app features enhances continuity in care delivery between sessions with MHCs and is associated with improved engagement with DMHIs. Further efforts are needed to assess the impact of this approach in DMHIs on clinical mental health outcomes.
{"title":"Exploring the Role of App Features in Providing Continuity of Care to Users on a Digital Mental Health Platform (Wysa): Retrospective Mixed Methods Observational Study.","authors":"Chaitali Sinha, Riddhi Thakkar, Saha Meheli, Dyuthi Dinesh","doi":"10.2196/73033","DOIUrl":"10.2196/73033","url":null,"abstract":"<p><strong>Background: </strong>Despite digital mental health services growing at a rapid pace to address global mental health needs, there exist challenges of low engagement and attrition. Ensuring continuity of care in the digital context can positively impact mental health care delivery and adherence to treatment, helping to establish digital mental health interventions (DMHIs) as a viable option for mental health support.</p><p><strong>Objective: </strong>This study aimed to examine the impact of adjunct app features of the mental health app Wysa and their ability to promote engagement and adherence to the text-based coaching sessions.</p><p><strong>Methods: </strong>This retrospective mixed methods observational study was based on real-world app data from users (n=1213) who subscribed to text-based sessions with mental health coaches (MHCs) between February 1 and July 31, 2022. Their engagement with the adjunct app features, such as brief interventions with the conversational agent, self-management tools, and journaling, was analyzed quantitatively using descriptive statistics. Acceptability of the app features was also assessed using qualitative feedback data. Adherence to sessions with MHCs was compared between app feature users (n=1042, 85.9%) and nonfeature users (n=171, 14.1%) using inferential statistics. Subgroup analysis was not feasible in the absence of demographic and clinical user data, potentially limiting the generalizability of the findings.</p><p><strong>Results: </strong>Findings demonstrated high use of the adjunct app features, which allowed communication with the MHCs in between sessions. The thematic analysis captures user experiences of helpfulness within the app and with the MHCs. The Mann-Whitney U test indicated that users who accessed one or more features completed significantly more sessions compared with users who did not use any feature (Mann-Whitney U=154,085.0; P<.001; rB=0.73) with a large effect size. The odds ratio analysis indicated that users were almost thrice as likely to complete sessions after using the adjunct app features (odds ratio 2.91, 95% CI 2.24-3.38; P<.001).</p><p><strong>Conclusions: </strong>Inclusion of adjunct app features enhances continuity in care delivery between sessions with MHCs and is associated with improved engagement with DMHIs. Further efforts are needed to assess the impact of this approach in DMHIs on clinical mental health outcomes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e73033"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052013","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}
Background: Health care delivery is often fragmented, with different services being delivered by different organizations. Various forms of teamwork are often used in health care, aiming to mitigate the challenges related to this fragmentation. One example of teamwork in mental health is Flexible Assertive Community Treatment (FACT). FACT is a model for comprehensive and integrated care for patients with long-term, serious mental illness. FACT teams deliver services using assertive outreach to treat patients who can be hard to reach by health care services. However, Norwegian FACT teams have issues with the current eHealth solutions related to the fragmentation of health care.
Objective: This study aimed to identify requirements and develop use cases and use case diagrams for eHealth solutions that support effective teamwork within FACT teams, using them in a case study for collaborative health care delivery.
Methods: A design science framework was used to explicate the problems of eHealth solutions in FACT teams. This included performing the subactivities of defining the problem precisely, positioning and justifying the problem, and finding root causes. Based on this explication, we derived a set of requirements, use cases, and use case diagrams for FACT teams.
Results: We present the explication of the problems of eHealth in Norwegian FACT teams. Building on the results, we present functional and nonfunctional requirements for electronic health records, electronic whiteboards, video conference solutions, and digital questionnaires. Improved integration across these systems was identified as a recurring need. We also provide use cases and diagrams illustrating system use in practice.
Conclusions: FACT teams in Norway require more integrated and tailored eHealth solutions. The requirements and use cases presented in this study offer a foundation for developing tools that better support the collaborative and mobile nature of FACT team operations.
{"title":"Requirements and Use Cases for eHealth Solutions in Flexible Assertive Community Treatment Teams: Design Science Study.","authors":"Erlend Bønes, Conceição Granja, Terje Solvoll","doi":"10.2196/77354","DOIUrl":"10.2196/77354","url":null,"abstract":"<p><strong>Background: </strong>Health care delivery is often fragmented, with different services being delivered by different organizations. Various forms of teamwork are often used in health care, aiming to mitigate the challenges related to this fragmentation. One example of teamwork in mental health is Flexible Assertive Community Treatment (FACT). FACT is a model for comprehensive and integrated care for patients with long-term, serious mental illness. FACT teams deliver services using assertive outreach to treat patients who can be hard to reach by health care services. However, Norwegian FACT teams have issues with the current eHealth solutions related to the fragmentation of health care.</p><p><strong>Objective: </strong>This study aimed to identify requirements and develop use cases and use case diagrams for eHealth solutions that support effective teamwork within FACT teams, using them in a case study for collaborative health care delivery.</p><p><strong>Methods: </strong>A design science framework was used to explicate the problems of eHealth solutions in FACT teams. This included performing the subactivities of defining the problem precisely, positioning and justifying the problem, and finding root causes. Based on this explication, we derived a set of requirements, use cases, and use case diagrams for FACT teams.</p><p><strong>Results: </strong>We present the explication of the problems of eHealth in Norwegian FACT teams. Building on the results, we present functional and nonfunctional requirements for electronic health records, electronic whiteboards, video conference solutions, and digital questionnaires. Improved integration across these systems was identified as a recurring need. We also provide use cases and diagrams illustrating system use in practice.</p><p><strong>Conclusions: </strong>FACT teams in Norway require more integrated and tailored eHealth solutions. The requirements and use cases presented in this study offer a foundation for developing tools that better support the collaborative and mobile nature of FACT team operations.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e77354"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052098","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}
Chuanying Huang, Rong Yang, Lidi Liu, Yu Jia, Xiaoyang Liao
Background: Informational support has been demonstrated to enhance patients' treatment adherence. However, which specific mode of informational support is more effective for patients with hypertension remains undetermined.
Objective: The primary objective of this study was to conduct a feasibility exploration of personalized informational support in patients with hypertension using a single-arm pretest-posttest design.
Methods: A prospective, single-center, pretest-posttest study was used to investigate the feasibility of providing an informational support intervention to patients with hypertension attending a community health facility in Chengdu, China. The intervention combined in-person follow-ups and telephone counseling. Adherence and clinical outcomes (blood pressure, ambulatory blood pressure, and laboratory tests) were measured at baseline and the postintervention time point. Patients' health behaviors were assessed at baseline and the postintervention time point using validated structured questionnaires. Descriptive statistics and effect sizes were calculated to determine clinically important changes relative to baseline.
Results: Significant improvements were observed: medication adherence scores increased by 0.65 points (95% CI 0.38-0.91; P<.001). Nutrition scores increased by 1.31 points (95% CI 0.53-2.09; P<.001), interpersonal relationship scores increased by 1.17 points (95% CI 1.03-2.02; P=.007), health responsibility scores increased by 2.42 points (95% CI 0.33-3.80; P=.001), and the total Health-Promoting Lifestyle Profile II-Revised score significantly increased by 6.81 points (95% CI 3.01-10.61; P=.001). Nighttime systolic blood pressure decreased significantly by 5.07 mm Hg (95% CI -8.12 to -2.01; P=.001), and nighttime diastolic blood pressure decreased significantly by 3.39 mm Hg (95% CI -5.12 to -1.67; P<.001).
Conclusions: This feasibility study found that a structured informational support intervention was well accepted (93/100, 93% retention) and was associated with preliminary improvements in medication adherence and nocturnal blood pressure. These findings suggest potential benefits and support the need for a definitive randomized controlled trial to establish efficacy.
背景：信息支持已被证明可以提高患者的治疗依从性。然而，哪种特定的信息支持模式对高血压患者更有效仍未确定。目的：本研究的主要目的是采用单臂前测后测设计对高血压患者进行个性化信息支持的可行性探索。方法：采用一项前瞻性、单中心、前测后测研究，探讨在中国成都一家社区卫生机构为高血压患者提供信息支持干预的可行性。干预结合了面对面的随访和电话咨询。在基线和干预后时间点测量依从性和临床结果（血压、动态血压和实验室检查）。在基线和干预后时间点使用有效的结构化问卷评估患者的健康行为。计算描述性统计和效应量，以确定相对于基线的临床重要变化。结果：观察到显著改善：药物依从性评分提高0.65分（95% CI 0.38-0.91）；结论：本可行性研究发现，结构化信息支持干预被很好地接受（93/100，保留率93%），并与药物依从性和夜间血压的初步改善相关。这些发现提示了潜在的益处，并支持需要一个明确的随机对照试验来确定疗效。
{"title":"Personalized Informational Support for Patients With Hypertension: Single-Arm Pretest-Posttest Study.","authors":"Chuanying Huang, Rong Yang, Lidi Liu, Yu Jia, Xiaoyang Liao","doi":"10.2196/82147","DOIUrl":"10.2196/82147","url":null,"abstract":"<p><strong>Background: </strong>Informational support has been demonstrated to enhance patients' treatment adherence. However, which specific mode of informational support is more effective for patients with hypertension remains undetermined.</p><p><strong>Objective: </strong>The primary objective of this study was to conduct a feasibility exploration of personalized informational support in patients with hypertension using a single-arm pretest-posttest design.</p><p><strong>Methods: </strong>A prospective, single-center, pretest-posttest study was used to investigate the feasibility of providing an informational support intervention to patients with hypertension attending a community health facility in Chengdu, China. The intervention combined in-person follow-ups and telephone counseling. Adherence and clinical outcomes (blood pressure, ambulatory blood pressure, and laboratory tests) were measured at baseline and the postintervention time point. Patients' health behaviors were assessed at baseline and the postintervention time point using validated structured questionnaires. Descriptive statistics and effect sizes were calculated to determine clinically important changes relative to baseline.</p><p><strong>Results: </strong>Significant improvements were observed: medication adherence scores increased by 0.65 points (95% CI 0.38-0.91; P<.001). Nutrition scores increased by 1.31 points (95% CI 0.53-2.09; P<.001), interpersonal relationship scores increased by 1.17 points (95% CI 1.03-2.02; P=.007), health responsibility scores increased by 2.42 points (95% CI 0.33-3.80; P=.001), and the total Health-Promoting Lifestyle Profile II-Revised score significantly increased by 6.81 points (95% CI 3.01-10.61; P=.001). Nighttime systolic blood pressure decreased significantly by 5.07 mm Hg (95% CI -8.12 to -2.01; P=.001), and nighttime diastolic blood pressure decreased significantly by 3.39 mm Hg (95% CI -5.12 to -1.67; P<.001).</p><p><strong>Conclusions: </strong>This feasibility study found that a structured informational support intervention was well accepted (93/100, 93% retention) and was associated with preliminary improvements in medication adherence and nocturnal blood pressure. These findings suggest potential benefits and support the need for a definitive randomized controlled trial to establish efficacy.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e82147"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052051","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}
Bibina Tuty Umaira Hj Abd Hamid, Ronald Wihal Oei, Norhayati Kassim, Ryutaro Oikawa, Norzawani Ishak, Si Yee Chan, Jane Tey, Pijika Watcharapichat, Joshua Lam, Pg Dr Noor Azmi Mohammad
Background: The World Health Organization reported that noncommunicable diseases (NCDs) contribute to around 74% of deaths worldwide. A similar phenomenon can also be observed in Brunei Darussalam. One of the most cost-effective approaches to control the growing burden of NCDs is to reduce related modifiable risk factors.
Objective: This study aims to propose a composite health score called Health Index, inspired by the 6 pillars of lifestyle medicine, which acts as a measure of health and can show how health changes over time at an individual and national level.
Methods: Health Index is a series of questionnaires that captures users' health status on several domains of health and, upon completion, the users are categorized as either healthy, at risk, or in poor health. Users will also be able to view health advice based on their answers to the questionnaires.
Results: The field testing results show Health Index as a promising population health management tool. 13.8% (166/1200) of the targeted users completed Health Index within 1 month, with 85% (1019/1200) of them in the "At Risk" category. We also identified diet as the most prominent health issue.
Conclusions: In conclusion, the Health Index potentially enables early detection and management of NCD risk factors to mitigate the high cost of advanced disease and complications. In the future, we aim to retrospectively and prospectively validate the Health Index through several statistical analyses.
{"title":"An Innovative Population Health Tool for Overall Health Status Assessment: Prospective Observational Study.","authors":"Bibina Tuty Umaira Hj Abd Hamid, Ronald Wihal Oei, Norhayati Kassim, Ryutaro Oikawa, Norzawani Ishak, Si Yee Chan, Jane Tey, Pijika Watcharapichat, Joshua Lam, Pg Dr Noor Azmi Mohammad","doi":"10.2196/74101","DOIUrl":"10.2196/74101","url":null,"abstract":"<p><strong>Background: </strong>The World Health Organization reported that noncommunicable diseases (NCDs) contribute to around 74% of deaths worldwide. A similar phenomenon can also be observed in Brunei Darussalam. One of the most cost-effective approaches to control the growing burden of NCDs is to reduce related modifiable risk factors.</p><p><strong>Objective: </strong>This study aims to propose a composite health score called Health Index, inspired by the 6 pillars of lifestyle medicine, which acts as a measure of health and can show how health changes over time at an individual and national level.</p><p><strong>Methods: </strong>Health Index is a series of questionnaires that captures users' health status on several domains of health and, upon completion, the users are categorized as either healthy, at risk, or in poor health. Users will also be able to view health advice based on their answers to the questionnaires.</p><p><strong>Results: </strong>The field testing results show Health Index as a promising population health management tool. 13.8% (166/1200) of the targeted users completed Health Index within 1 month, with 85% (1019/1200) of them in the \"At Risk\" category. We also identified diet as the most prominent health issue.</p><p><strong>Conclusions: </strong>In conclusion, the Health Index potentially enables early detection and management of NCD risk factors to mitigate the high cost of advanced disease and complications. In the future, we aim to retrospectively and prospectively validate the Health Index through several statistical analyses.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e74101"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12834551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052034","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}
Szilvia Vincze, Antal Bugán, Karolina Kósa, Zoltán Bács
<p><strong>Background: </strong>University students, in the life stage of emerging adulthood, struggle with a number of mental health problems around the world, mostly due to difficulties related to their studies and social relations. Though most students are aware of their problems, health-seeking behavior tends to lag behind. The COVID-19 pandemic aggravated mental health problems among students. In response, the University of Debrecen developed an integrated, multilevel model system aimed at the screening, prevention, and treatment of students' mental health problems.</p><p><strong>Objective: </strong>This paper describes the testing of the integrated, multilevel model system aimed at the screening, prevention, and treatment of students' mental health problems.</p><p><strong>Methods: </strong>The new system consists of data collection and intervention or service functions with 2 levels of informal digital, and 3 with partly digital, partly personal service modalities. Students access the system through a dedicated smartphone app that offers other university-related functions requiring personal login. One function of the app involves a mood report with 3 categories (awful, acceptable, and good), of which one per day can be submitted by students. Based on this report, further services are offered. According to the weekly patterns of the mood report, responding students may be directed to the second level, at which screening for depression or willingness to participate in peer groups is assessed. Depending on the responses, students are referred to personal (face-to-face) services at the secondary level for intervention. Aggregated reporting for leadership on the use of functions is available at all levels, which can be used to make decisions regarding the expansion of services or creating new ones.</p><p><strong>Results: </strong>The model was launched in September 2020 and was tested for 45 months. After an initial increase in use, approximately 29% (8673/29,045) of all students provided mood reports (the University of Debrecen student population on October 15, 2024, was 29,045 students; the student population using the mobile app mood report was 8673 students). The percentage of students reporting a bad mood varied between 8.9% (26,465/297,372) and 12.2% (36,280/297,372) in the test period, while 50% (151,548/297,372 reports) of students reported being in a good mood. There was a marked pattern of increased use of mood reporting during the fall and spring study periods, while usage prominently decreased during the examination period and summer recess.</p><p><strong>Conclusions: </strong>The 4-year trial period demonstrated that the mood report embedded in the mobile app can identify students with a potentially increased risk of mental health problems in need of support without stigmatization. The unique feature of our model seems to be its app-based screening at the first level and its hierarchy integrating digital and personal services. The s
{"title":"An Integrated Multilevel Mental Health Support System for University Students: 4-Year Longitudinal Observational Study.","authors":"Szilvia Vincze, Antal Bugán, Karolina Kósa, Zoltán Bács","doi":"10.2196/67089","DOIUrl":"https://doi.org/10.2196/67089","url":null,"abstract":"<p><strong>Background: </strong>University students, in the life stage of emerging adulthood, struggle with a number of mental health problems around the world, mostly due to difficulties related to their studies and social relations. Though most students are aware of their problems, health-seeking behavior tends to lag behind. The COVID-19 pandemic aggravated mental health problems among students. In response, the University of Debrecen developed an integrated, multilevel model system aimed at the screening, prevention, and treatment of students' mental health problems.</p><p><strong>Objective: </strong>This paper describes the testing of the integrated, multilevel model system aimed at the screening, prevention, and treatment of students' mental health problems.</p><p><strong>Methods: </strong>The new system consists of data collection and intervention or service functions with 2 levels of informal digital, and 3 with partly digital, partly personal service modalities. Students access the system through a dedicated smartphone app that offers other university-related functions requiring personal login. One function of the app involves a mood report with 3 categories (awful, acceptable, and good), of which one per day can be submitted by students. Based on this report, further services are offered. According to the weekly patterns of the mood report, responding students may be directed to the second level, at which screening for depression or willingness to participate in peer groups is assessed. Depending on the responses, students are referred to personal (face-to-face) services at the secondary level for intervention. Aggregated reporting for leadership on the use of functions is available at all levels, which can be used to make decisions regarding the expansion of services or creating new ones.</p><p><strong>Results: </strong>The model was launched in September 2020 and was tested for 45 months. After an initial increase in use, approximately 29% (8673/29,045) of all students provided mood reports (the University of Debrecen student population on October 15, 2024, was 29,045 students; the student population using the mobile app mood report was 8673 students). The percentage of students reporting a bad mood varied between 8.9% (26,465/297,372) and 12.2% (36,280/297,372) in the test period, while 50% (151,548/297,372 reports) of students reported being in a good mood. There was a marked pattern of increased use of mood reporting during the fall and spring study periods, while usage prominently decreased during the examination period and summer recess.</p><p><strong>Conclusions: </strong>The 4-year trial period demonstrated that the mood report embedded in the mobile app can identify students with a potentially increased risk of mental health problems in need of support without stigmatization. The unique feature of our model seems to be its app-based screening at the first level and its hierarchy integrating digital and personal services. The s","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e67089"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052044","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}
Athina Servi, Emily Gardner-Bougaard, Saida Mohamed, Aaron McDermott, Rachel Rodrigues, Ben Aveyard, Nejra Van Zalk, Adam Hampshire, Lindsay Dewa, Martina Di Simplicio
<p><strong>Background: </strong>Self-harm (SH) affects around 20% of all young people in the United Kingdom. Treatment options for SH remain limited and those available are long and costly and may not suit all young people. There is an urgent need to develop new scalable interventions to address this gap. IMAGINATOR is a novel imagery-based intervention targeting SH initially developed for individuals aged 16 to 25 years. It is a blended digital intervention delivering functional imagery training via therapy sessions and a smartphone app.</p><p><strong>Objective: </strong>This study aimed to pilot a new version of the app, IMAGINATOR 2.0, extended to adolescents from the age of 12 years and coproduced with a diverse group of young people with lived experience. Our aim was also to test the feasibility and acceptability of delivering IMAGINATOR 2.0 in secondary mental health services.</p><p><strong>Methods: </strong>A total of 4 co-design workshops were conducted online with UK-based lived-experience co-designers aged 14-25 years to develop the IMAGINATOR 2.0 app. The intervention was then piloted with participants recruited from West London NHS Trust Tier 2 Child and Adolescent Mental Health Services and adult Mental Health Integrated Network Teams. Participants received 3 face-to-face functional imagery training sessions in which the app was introduced and 5 brief phone support sessions. Outcome assessments were conducted after completing therapy, approximately 3 months post baseline. Two focus groups gathered the therapists' perspectives on IMAGINATOR 2.0's acceptability and means of improvement. For quantitative data, descriptives are reported. Qualitative data were analyzed using a coproduced thematic analysis method with young people with lived experiences.</p><p><strong>Results: </strong>Overall, 83 participants were referred, and 29 (gender: n=28 women, n=1 transgender; mean age 18.9, SD 3.74 years) were eligible and completed screening. Of the 27 participants who started, 59% (n=16) completed therapy per protocol, while only 15 (55.6%) completed the quantitative outcome assessment. There was an overall reduction in the number of SH episodes over 3 months from pre- to postintervention (baseline: median 7, IQR 3.5-21.5 months; postintervention: median 0, IQR 0-7 months; median difference=-6.5; r=0.69). Six themes were identified through thematic analysis of therapists' feedback, including mental imagery's potential and boundaries, therapy expectations, experience and effectiveness, accessibility of digital support, and adaptation of the IMAGINATOR 2.0 app to complement care pathways. The app was valued by therapists who highlighted the need for an intervention like IMAGINATOR 2.0 in their services.</p><p><strong>Conclusions: </strong>IMAGINATOR 2.0 shows initial promise as an acceptable brief intervention targeting SH in young people under adolescent and adult mental health services. Challenges with attrition need to be addressed for a defin
{"title":"Early Evaluation of IMAGINATOR 2.0 Intervention Targeting Self-Harm in Young People: Single-Arm Feasibility Trial.","authors":"Athina Servi, Emily Gardner-Bougaard, Saida Mohamed, Aaron McDermott, Rachel Rodrigues, Ben Aveyard, Nejra Van Zalk, Adam Hampshire, Lindsay Dewa, Martina Di Simplicio","doi":"10.2196/79496","DOIUrl":"https://doi.org/10.2196/79496","url":null,"abstract":"<p><strong>Background: </strong>Self-harm (SH) affects around 20% of all young people in the United Kingdom. Treatment options for SH remain limited and those available are long and costly and may not suit all young people. There is an urgent need to develop new scalable interventions to address this gap. IMAGINATOR is a novel imagery-based intervention targeting SH initially developed for individuals aged 16 to 25 years. It is a blended digital intervention delivering functional imagery training via therapy sessions and a smartphone app.</p><p><strong>Objective: </strong>This study aimed to pilot a new version of the app, IMAGINATOR 2.0, extended to adolescents from the age of 12 years and coproduced with a diverse group of young people with lived experience. Our aim was also to test the feasibility and acceptability of delivering IMAGINATOR 2.0 in secondary mental health services.</p><p><strong>Methods: </strong>A total of 4 co-design workshops were conducted online with UK-based lived-experience co-designers aged 14-25 years to develop the IMAGINATOR 2.0 app. The intervention was then piloted with participants recruited from West London NHS Trust Tier 2 Child and Adolescent Mental Health Services and adult Mental Health Integrated Network Teams. Participants received 3 face-to-face functional imagery training sessions in which the app was introduced and 5 brief phone support sessions. Outcome assessments were conducted after completing therapy, approximately 3 months post baseline. Two focus groups gathered the therapists' perspectives on IMAGINATOR 2.0's acceptability and means of improvement. For quantitative data, descriptives are reported. Qualitative data were analyzed using a coproduced thematic analysis method with young people with lived experiences.</p><p><strong>Results: </strong>Overall, 83 participants were referred, and 29 (gender: n=28 women, n=1 transgender; mean age 18.9, SD 3.74 years) were eligible and completed screening. Of the 27 participants who started, 59% (n=16) completed therapy per protocol, while only 15 (55.6%) completed the quantitative outcome assessment. There was an overall reduction in the number of SH episodes over 3 months from pre- to postintervention (baseline: median 7, IQR 3.5-21.5 months; postintervention: median 0, IQR 0-7 months; median difference=-6.5; r=0.69). Six themes were identified through thematic analysis of therapists' feedback, including mental imagery's potential and boundaries, therapy expectations, experience and effectiveness, accessibility of digital support, and adaptation of the IMAGINATOR 2.0 app to complement care pathways. The app was valued by therapists who highlighted the need for an intervention like IMAGINATOR 2.0 in their services.</p><p><strong>Conclusions: </strong>IMAGINATOR 2.0 shows initial promise as an acceptable brief intervention targeting SH in young people under adolescent and adult mental health services. Challenges with attrition need to be addressed for a defin","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"10 ","pages":"e79496"},"PeriodicalIF":2.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052066","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}
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