JMIR Formative Research最新文献

{"title":"The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.","authors":"Weipeng Zhou, Amy Youngbloom, Xinyang Ren, Brian E Saelens, Sean D Mooney, Stephen J Mooney","doi":"10.2196/56510","DOIUrl":"10.2196/56510","url":null,"abstract":"<p><strong>Background: </strong>The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).</p><p><strong>Objective: </strong>The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.</p><p><strong>Methods: </strong>We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State.</p><p><strong>Results: </strong>In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status.</p><p><strong>Conclusions: </strong>The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11489801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.","authors":"Reshma Aziz Merchant, Bernard Loke, Yiong Huak Chan","doi":"10.2196/58110","DOIUrl":"10.2196/58110","url":null,"abstract":"<p><strong>Background: </strong>Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously.</p><p><strong>Objective: </strong>The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls.</p><p><strong>Methods: </strong>This is a substudy of 156 community-dwelling older adults ≥60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness).</p><p><strong>Results: </strong>The participants' mean age was 74.6 years. Of the 22 participants, 9 (41%) were robust, 10 (46%) were prefrail, and 3 (14%) were frail. In addition, 8 of 22 (36%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83.</p><p><strong>Conclusions: </strong>Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The WeThrive App and Its Impact on Adolescents Who Menstruate: Qualitative Study.","authors":"Nora MacNeil, Victoria Price, Meghan Pike","doi":"10.2196/57936","DOIUrl":"10.2196/57936","url":null,"abstract":"<p><strong>Background: </strong>Heavy menstrual bleeding (HMB) affects up to 37% of adolescents. Without recognition, HMB can lead to other medical conditions resulting in diminished health-related quality of life. WeThrive, a new mobile health (mHealth) app, implements the pictorial bleeding assessment chart to identify HMB, and the adolescent Menstrual Bleeding Questionnaire to measure the effects of HMB on adolescents' health-related quality of life. If HMB is identified, WeThrive will connect users to local clinics for further assessment of their menstrual bleeding with a health care provider.</p><p><strong>Objective: </strong>This study aimed to describe adolescents' experiences using WeThrive app.</p><p><strong>Methods: </strong>This qualitative study was approved by the local Research Ethics Board in Halifax, Nova Scotia, and informed consent was provided by all participants. Individual semistructured interviews were held via videoconference with adolescents younger than 18 years, who had at least 1 menstrual period and had used WeThrive at least once. Interview transcripts were thematically analyzed by 2 investigators (MP and NMN) independently, and the κ statistic was calculated to determine the strength of correlation in themes.</p><p><strong>Results: </strong>Five adolescents (mean age 15.5, range 13-18 years), participated in the interviews. All participants stated that WeThrive helps them better understand their menstrual periods by predicting period onset, recognizing menstrual symptoms, and identifying HMB. Four themes were identified: (1) the importance of visual features and usability, (2) newly obtained knowledge using WeThrive, (3) feature use depends on menstrual health, and (4) trustworthiness. There was substantial agreement on the identified themes (κ=0.73).</p><p><strong>Conclusions: </strong>WeThrive is visually appealing, and trustworthy, and helps users better understand their menstrual periods, including identifying HMB. By identifying HMB early, WeThrive has the potential to improve the recognition of bleeding disorders and iron deficiency in adolescents. WeThrive is a useful tool to help adolescents better understand their menstrual periods.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing ChatGPT's Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study.","authors":"Yong K Choi, Shih-Yin Lin, Donna Marie Fick, Richard W Shulman, Sangil Lee, Priyanka Shrestha, Kate Santoso","doi":"10.2196/51383","DOIUrl":"10.2196/51383","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative artificial intelligence (AI) and large language models, such as OpenAI's ChatGPT, have shown promising potential in supporting medical education and clinical decision-making, given their vast knowledge base and natural language processing capabilities. As a general purpose AI system, ChatGPT can complete a wide range of tasks, including differential diagnosis without additional training. However, the specific application of ChatGPT in learning and applying a series of specialized, context-specific tasks mimicking the workflow of a human assessor, such as administering a standardized assessment questionnaire, followed by inputting assessment results in a standardized form, and interpretating assessment results strictly following credible, published scoring criteria, have not been thoroughly studied.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This exploratory study aims to evaluate and optimize ChatGPT's capabilities in administering and interpreting the Sour Seven Questionnaire, an informant-based delirium assessment tool. Specifically, the objectives were to train ChatGPT-3.5 and ChatGPT-4 to understand and correctly apply the Sour Seven Questionnaire to clinical vignettes using prompt engineering, assess the performance of these AI models in identifying and scoring delirium symptoms against scores from human experts, and refine and enhance the models' interpretation and reporting accuracy through iterative prompt optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used prompt engineering to train ChatGPT-3.5 and ChatGPT-4 models on the Sour Seven Questionnaire, a tool for assessing delirium through caregiver input. Prompt engineering is a methodology used to enhance the AI's processing of inputs by meticulously structuring the prompts to improve accuracy and consistency in outputs. In this study, prompt engineering involved creating specific, structured commands that guided the AI models in understanding and applying the assessment tool's criteria accurately to clinical vignettes. This approach also included designing prompts to explicitly instruct the AI on how to format its responses, ensuring they were consistent with clinical documentation standards.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Both ChatGPT models demonstrated promising proficiency in applying the Sour Seven Questionnaire to the vignettes, despite initial inconsistencies and errors. Performance notably improved through iterative prompt engineering, enhancing the models' capacity to detect delirium symptoms and assign scores. Prompt optimizations included adjusting the scoring methodology to accept only definitive \"Yes\" or \"No\" responses, revising the evaluation prompt to mandate responses in a tabular format, and guiding the models to adhere to the 2 recommended actions specified in the Sour Seven Questionnaire.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings provide preliminary evidence supporting the potential utility of AI models such as ChatGPT in admi","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study.","authors":"Yuhan Liu, Anna Fang, Glen Moriarty, Cristopher Firman, Robert E Kraut, Haiyi Zhu","doi":"10.2196/58241","DOIUrl":"10.2196/58241","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;In this study, we collaborated with one of the world's largest OMHCs; our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a \"sandbox\" to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker-counselor matches occurred using a \"first come, first served\" protocol, whereas relatively fewer matches occurred using a \"last come, first served\" protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group revealed disparities-underaged and gender minority groups had lower average chat ratings and higher blocking rates on the site when compared to their majority counterparts, indicating the potential benefits of algorithmically matching them. We found that some protocols, such as a \"filter\"-based approach that matched vulnerable support seekers only with a counselor of their same demographic, led to improvements for these groups but resulted in lower satisfaction (-12%) among the overall population. However, this trade-off between minority and majority groups was not observed when using \"topic\" as a matching criterion. Topic-based matching actually outperformed the filter-based protocol among underaged people and led to significant improvements over the status quo among all minority and majority groups-specifically, a 6% average chat rating improvement and a decrease in blocking incidents from 5.86% to 4.","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study.","authors":"Youyou Tao, Ruilin Zhu, Dezhi Wu","doi":"10.2196/51198","DOIUrl":"10.2196/51198","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of -0.0352 (P=.003), -0.0520 (P&lt;.001), and -0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients' disease types.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers pra","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Care Program as a Tool for Alleviating Anxiety and Loneliness and Promoting Satisfaction With Life in High School Students and Staff: Randomized Survey Study.","authors":"Priya Iyer, Lina Iyer, Nicole Carter, Ranjani Iyer, Amy Stirling, Lakshmi Priya, Ushma Sriraman","doi":"10.2196/56355","DOIUrl":"10.2196/56355","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The COVID-19 global pandemic has led to a marked increase in anxiety levels, significantly affecting the well-being of individuals worldwide. In response to this growing concern, interventions aimed at enhancing social-emotional skills and promoting mental health are more crucial than ever.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This global study aimed to examine the effectiveness of a self-care program on anxiety, loneliness, and satisfaction with life in high school students and staff in a randomized, waitlist control trial with baseline and postintervention assessments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The 4-week web-based self-care program, offered by the Heartfulness Institute, is designed to develop social-emotional skills through stress management and self-observation. The web-based program was a positive intervention that offered support to the students and staff to build specific skills, such as reflection, observation, positivity, time management, and goal setting. In this study, the sample consisted of a total of 203 high school students and staff randomized into a control waitlisted group (students: n=57 and staff: n=45) and a Heartfulness group (students: n=57 and staff: n=44) from 3 schools. Both the groups completed web-based surveys at weeks 0, 4, and 8, assessing their anxiety, loneliness, and satisfaction with life scores using Generalized Anxiety Disorder-7 Scale (GAD-7 and Severity Measure for Generalized Anxiety Disorder-Child Age 11-17), Satisfaction With Life scale (SWLS) and Satisfaction With Life Scale-Child (SWLS-C), and the University of California, Los Angeles (UCLA) Loneliness Scale. Survey responses were each individually analyzed using repeated measures ANOVA.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The study received institutional review board approval on February 3, 2022. Participant recruitment lasted from the approval date until March 30, 2022. The 4-week program for the Heartfulness group started on April 4, 2024. There was a significant 3-way interaction among time, group, and school showing a decrease in anxiety and loneliness scores and an increase in satisfaction-with-life scores (P&lt;.05). In students in the Heartfulness group, there was strong evidence to suggest a significant mean difference in GAD-7, SWLS, and UCLA scores between week 0 and week 4 at all schools (P&lt;.001). In staff in the Heartfulness group, there was strong evidence to suggest a significant mean difference in GAD-7, SWLS, and UCLA scores between week 0 and week 4 at all schools (P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The pandemic brought severe educational and social changes that triggered a decline in mental health in schools. This study showed the effectiveness of noninvasive self-care tools used digitally to significantly decrease anxiety and loneliness scores and increase satisfaction of life scores in the participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Trial registration: &lt;/strong&gt;ClinicalTrials.gov NCT05874232; https://clinicalt","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation and Future Challenges in a Self-Guided Web-Based Intervention With and Without Chat Support for Depression and Anxiety Symptoms During the COVID-19 Pandemic: Randomized Controlled Trial.","authors":"Alejandro Dominguez-Rodriguez, Sergio Sanz-Gomez, Leivy Patricia González Ramírez, Paulina Erika Herdoiza-Arroyo, Lorena Edith Trevino Garcia, Anabel de la Rosa-Gómez, Joel Omar González-Cantero, Valeria Macias-Aguinaga, Paulina Arenas Landgrave, Sarah Margarita Chávez-Valdez","doi":"10.2196/53767","DOIUrl":"10.2196/53767","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The COVID-19 pandemic has had an impact on mental health worldwide. Low- and middle-income countries were largely affected by it. Mexico was one of the most affected countries. Extended periods of lockdowns, isolation, and social distancing, among other factors, highlighted the need to introduce web-based psychological interventions to the Mexican population. In this context, Mental Health COVID-19 emerged as a self-guided web-based intervention (SGWI) aimed at adults to improve mental health during the COVID-19 pandemic.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to assess the efficacy of 2 modalities of a self-guided intervention (with and without chat support) in reducing depression symptoms, generalized anxiety, community posttraumatic stress, widespread fear, anxiety, sleep quality, physiological and affective coping, and suicide ideation. In addition, it aimed to compare the moderating role of coping strategies, acceptance, and satisfaction in participants' symptom reduction. We hypothesize that the self-guided, chat-supported modality will show higher efficacy than the modality without chat support in achieving clinical change and better performance as a moderator of depression symptoms, generalized anxiety, community posttraumatic stress, widespread fear, anxiety, sleep quality, physiological and affective coping, and suicide ideation, as well as an increase in participants' satisfaction and acceptability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A randomized controlled trial was conducted. Data were collected from May 2020 to June 2022. We performed intrasubject measures at 4 evaluation periods: pretest, posttest, and follow-up measurements at 3 and 6 months. Differences between intervention groups were assessed through the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables. Changes due to intervention were analyzed using Wilcoxon W test. Moderated regression analysis was performed to test the hypothesized moderating role of coping strategies, usability, and opinion about treatment on clinical change.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 36 participants completed the intervention; of these, 5 (14%) were part of the SGWI group, and 31 (86%) were on the SGWI plus chat support (SGWI+C) group, which included a chat service with therapists. The perceived high complexity of the system for the SGWI group had a moderating effect associated with a lack of efficacy of the intervention regarding depression, but not when controlled for sociodemographic variables. A perception of lower helpfulness of the intervention was associated with poorer outcomes. Coping strategies did not show moderating effects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Enhancing the utility of web-based interventions for reducing clinical symptoms by incorporating a support chat to boost treatment adherence seemed to improve the perception of the intervention's usefulness. Web-based interventions face ","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mHealth Apps for Hypertension Self-Management: Interview Study Among Patient-Users.","authors":"Felix Muehlensiepen, Dunja Bruch, Frances Seifert, Eileen Wengemuth, Martin Heinze, Sebastian Spethmann, Susann May","doi":"10.2196/56162","DOIUrl":"10.2196/56162","url":null,"abstract":"<p><strong>Background: </strong>Hypertension is a major risk factor for cardiovascular disease, affecting over a billion people worldwide. Mobile health (mHealth) apps have emerged as effective tools for managing hypertension, offering capabilities for monitoring blood pressure, fostering lifestyle changes, and improving treatment adherence.</p><p><strong>Objective: </strong>This study aimed to explore patient-users' perspectives on the hypertension care mHealth app Hypertension.APP, focusing on its accessibility, expected benefits, potential risks, and role in hypertension management in Germany.</p><p><strong>Methods: </strong>A qualitative study was conducted involving semistructured interviews with 20 patient-users of a hypertension care mHealth app, Hypertension.APP. Participants were recruited between January and June 2023 using purposive sampling. Verbatim transcripts were analyzed using qualitative content analysis.</p><p><strong>Results: </strong>Participants primarily discovered the app independently, driven by recent hypertension diagnoses and insufficient information from health care professionals regarding effective self-management strategies for their blood pressure. They valued the app for its continuous monitoring and feedback capabilities, aiding in understanding their condition and making lifestyle adjustments. Risks were perceived as minimal, mainly concerning data privacy and potential overreliance on the app. The app became integral to patient-users' hypertension management by offering consistent information and support. The integration into formal health care was limited, as patient-users felt that health care professionals did not accept the use of the technology or might have even felt intimidated to use it.</p><p><strong>Conclusions: </strong>Among the sample studied, mHealth apps like Hypertension.APP were valued for their continuous monitoring and educational content, aiding in hypertension management. The findings suggest potential benefits of mHealth apps for effective hypertension care among patients who are health- and digitally literate as well as self-effective. There is a critical need for better integration of these apps into routine health care practices, as perceived by the app users. Given the small and specific sample of this qualitative study, further quantitative research with a broader and more varied participant group is necessary to validate these findings.</p><p><strong>Trial registration: </strong>Deutsches Register Klinischer Studien DRKS00029761; https://tinyurl.com/r33ru22s.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.","authors":"Sachiko Lim, Paul Johannesson","doi":"10.2196/53711","DOIUrl":"10.2196/53711","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The ","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347078","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}