Moayad Alshawmar, Bengisu Tulu, E Vance Wilson, Adrienne Hall-Phillips
{"title":"Personality-Driven Variations in Fitness App Affordance Actualization Among Adults: Quantitative Survey Study.","authors":"Moayad Alshawmar, Bengisu Tulu, E Vance Wilson, Adrienne Hall-Phillips","doi":"10.2196/72691","DOIUrl":"10.2196/72691","url":null,"abstract":"<p><strong>Background: </strong>Fitness apps aim to advance individuals' health and wellness by encouraging consistent healthy habits. Despite their widespread use, sustaining user engagement remains a challenge. Research studies on fitness apps have identified app affordances as one of the key factors that influence user engagement. Some affordances, such as exercise guidance and activity status updates, are shown to support users in achieving their health goals if the users actualize them. However, these affordances need to be actualized by the users to seize these benefits. While identifying these app affordances can deepen our insight into user-app interactions, the impact of personality traits on the actualization of these affordances remains underexplored.</p><p><strong>Objective: </strong>This study aims to examine the influence of personality traits on the actualization of fitness app affordances.</p><p><strong>Methods: </strong>Building on affordance actualization theory and the Big Five personality framework, we hypothesized about certain personality traits influencing the actualization of certain app affordances. We tested these hypotheses using a survey of adult Fitbit app (Google LLC) users (N=442). We used validated measures from the literature to assess these variables. We analyzed the survey data using covariance-based structural equation modeling.</p><p><strong>Results: </strong>Our findings reveal distinct affordance actualization patterns based on users' personality traits. Users with the conscientious personality trait primarily actualize the updating affordance (β=0.136, P=.01), while the influence of the conscientious trait on actualization of rewards (β=-0.154, P=.06), competing (β=-0.118, P=.18), comparing (β=-0.084, P=.33), reminding (β=-0.060, P=.44), or guidance (β=-0.006, P=.95) affordances was not significant. The openness to experience trait showed a significant positive effect on actualization of updating affordances (β=0.227, P=.001), but did not significantly influence actualization of searching (β=-0.172, P=.11), watching others (β=-0.077, P=.50), or guidance (β=-0.005, P=.96) affordances. Users with the agreeableness trait actualized comparison (β=0.213, P=.02), guidance (β=0.259, P=.003), and encouragement (β=0.244, P=.01) affordances, while the effect of the agreeableness trait on actualization of watching others was not significant (β=0.143, P=.13). Extravert users actualized recognition (β=0.191, P<.001), self-presentation (β=0.165, P=.002), and watching others (β=0.167, P=.003) affordances, but did not actualize updating affordances (β=0.001, P=.98). Finally, a lower emotional stability trait did not significantly influence any of the hypothesized affordances, with nonsignificant effects on guidance (β=-0.083, P=.30), reminding (β=-0.093, P=.21), and updates (β=-0.036, P=.49).</p><p><strong>Conclusions: </strong>Our study shows that certain personality traits are associated with the actualization of specifi","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e72691"},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145053534","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}
Rendong Zhang, Sophie Chiron, Regina Tyree, Kate Carson, Larry Raber, Karthik Ramadass, Chenyu Gao, Michael E Kim, Lianrui Zuo, Yike Li, Zhiyu Wan, Paul A Harris, Qi Liu, Ken S Lau, Lori A Coburn, Keith T Wilson, Yuankai Huo, Bennett A Landman, Shunxing Bao
{"title":"Enhancing Clinical Data Management Through Barcode Integration and Research Electronic Data Capture: Scalable and Adaptable Implementation Study.","authors":"Rendong Zhang, Sophie Chiron, Regina Tyree, Kate Carson, Larry Raber, Karthik Ramadass, Chenyu Gao, Michael E Kim, Lianrui Zuo, Yike Li, Zhiyu Wan, Paul A Harris, Qi Liu, Ken S Lau, Lori A Coburn, Keith T Wilson, Yuankai Huo, Bennett A Landman, Shunxing Bao","doi":"10.2196/70016","DOIUrl":"10.2196/70016","url":null,"abstract":"<p><strong>Background: </strong>Effective data management is crucial in clinical studies for precise tracking, secure storage, and reliable analysis of samples. Traditional systems often encounter challenges like barcode recognition errors, inadequate data details, and diminished performance under heavy workloads.</p><p><strong>Objective: </strong>This paper aims to enhance clinical data management by improving barcode robustness, increasing data granularity, and boosting system throughput. These improvements address key challenges in barcode informatics systems, as highlighted in previous studies, to better support real clinical applications. In addition, we aim to validate the design criteria on various gastrointestinal-related studies, ensuring it can be easily integrated into other clinical data management workflows.</p><p><strong>Methods: </strong>We evaluated the robustness of various barcode technologies under significant blurring conditions, implemented a dynamic organ-specific archive in the REDCap (Research Electronic Data Capture) database for various clinical study data collection criteria, and used Docker to containerize the informatics software for different studies. In addition, we proposed a local cache system to reduce interaction times with REDCap for large-scale data records. Experimental setups include assessing barcode recognition accuracy under various levels of image blurring, showcasing different study types managed with the organ-specific archive, and measuring system throughput and response times with and without the proposed local cache system.</p><p><strong>Results: </strong>Our findings demonstrate that the DataMatrix barcode exhibits superior resilience, maintaining high recognition accuracy under blurred conditions. The dynamic organ-specific archive in REDCap enabled precise tracking of sample origins, improving data granularity. Docker containerization streamlines software deployment and ensures consistency across studies. The local cache system significantly reduces interaction times with REDCap, decreasing operating time by nearly eightfold compared to the naïve strategy when handling large patient datasets.</p><p><strong>Conclusions: </strong>The proposed enhancements significantly improve barcode robustness, data granularity, and system throughput in the informatics system, addressing key limitations identified in previous studies. These optimizations ensure efficient data management and robust support for diverse clinical research needs.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e70016"},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12434633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145064471","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":"Acceptability of Active and Passive Data Collection Methods for Mobile Health Research: Cross-Sectional Survey of an Online Adult Sample in the United States.","authors":"Nelson Roque, John Felt","doi":"10.2196/64082","DOIUrl":"10.2196/64082","url":null,"abstract":"<p><strong>Background: </strong>Digital health technologies, including wearable devices and app-based cognitive and health assessments, are pervasive and crucial to better understanding important public health problems (eg, Alzheimer's disease and related dementias). Central to understanding mechanisms driving individuals' willingness to share various data streams are concerns regarding data privacy, security, and control over generated data.</p><p><strong>Objective: </strong>This survey was designed to learn more about attitudes and opinions related to digital health technologies and the sharing of associated data.</p><p><strong>Methods: </strong>A total of 1509 adults were recruited from Prolific to complete an online survey via Qualtrics. Of these, 1489 participants provided valid data for analyses. Participants completed a structured survey consisting of multiple modules after informed consent was provided. These included: (1) demographic characteristics; (2) prior research experience; (3) mobility factors (eg, use of mobility aids, driving frequency); (4) technology ownership (eg, smartphones, tablets, home Wi-Fi); (5) social media use (eg, frequency of engagement with platforms such as Facebook, Instagram, and TikTok); (6) willingness to contribute different types of data across categories, including activities, sensors, and metadata; (7) opinions about data control and privacy options (eg, data deletion, stream-specific control); and (8) willingness to interact with assistive technologies such as robots, for Instrumental Activities of Daily Living.</p><p><strong>Results: </strong>The final cohort (N=1489) had a mean age of 35.5 years (SD 12.0), was 44% female (n=652), and predominantly identified as White (76%, n=1134), with high rates of smartphone ownership (99%, n=1479) and home Wi-Fi access (98%, n=1464). Participants were most willing to share data streams with clear health implications and least willing to share data streams with greater privacy or reidentification potential (eg, GPS location, in-vehicle dashcam footage). On average, people were willing to complete ambulatory cognitive assessments for 56.7 (SD 36.2) days, air quality monitoring for 58.1 (SD 37.7) days, and GPS location monitoring for 37 (SD 39.0) days. People expected control over their data, including the ability to delete all or specific streams of the data contributed for research. Most participants prioritized control over their data, with 71% (n=1061) favoring the ability to delete all data contributed for research purposes. Stream-specific data deletion (65%, n=960) and time-specific deletion (44%, n=653) were also valued; interest in sharing data with insurance providers (30%, n=453) or caregivers (26%, n=384) was notably lower.</p><p><strong>Conclusions: </strong>Findings have implications for the design of digital health technologies and education-related to the use and implications of collected data.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e64082"},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145053513","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}
Anna Gomes, Angeliki Tsiouris, Florian Jung, Laura Rebecca Klein, Adina Kreis, Manfred E Beutel, Bernhard Strauss, Madita Hoy, Rüdiger Zwerenz
{"title":"Needs and Expectations Associated With an e-Mental Health Intervention for Reducing Somatoform, Anxiety, and Depressive Symptoms in Sexual and Gender Minority Adults: Qualitative Participative Study.","authors":"Anna Gomes, Angeliki Tsiouris, Florian Jung, Laura Rebecca Klein, Adina Kreis, Manfred E Beutel, Bernhard Strauss, Madita Hoy, Rüdiger Zwerenz","doi":"10.2196/65834","DOIUrl":"10.2196/65834","url":null,"abstract":"<p><strong>Background: </strong>Sexual and gender minority individuals experience heightened risks of mental health disorders due to marginalization, discrimination, and inadequacies in health care.</p><p><strong>Objective: </strong>This study aims to identify the needs and expectations concerning an e-mental health intervention designed for people who are lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual, or have other sexual orientation and gender identities (LGBTQIA+) to reduce somatoform, anxiety and depressive (SAD) symptoms.</p><p><strong>Methods: </strong>A qualitative participative study was conducted, involving semistructured interviews (face-to-face and online) with 10 sexual and gender minority individuals experiencing SAD symptoms. Telephone interviews were conducted with 10 health care professionals (HCPs). This study was part of a participatory project, emphasizing cooperation with the LGBTQIA+ community. Data were analyzed through a deductive-inductive content analysis to derive categories of needs and expectations relevant for the development of an e-mental health intervention.</p><p><strong>Results: </strong>Participants expressed a strong desire for the intervention to be inclusive, validating, and sensitive to the unique challenges faced by LGBTQIA+ people. Key themes included the need for information on the relationship between being queer and mental health; representation through case stories; psychoeducation; and exercises tailored to address minority stress, identity affirmation, and coping strategies. HCPs emphasized the importance of addressing the coming-out process, managing rejection, fostering self-acceptance, and including content on minority stress and its impact on mental health. Results of both interview groups highlighted the need for direct interaction with therapists or peer support, including both synchronous and asynchronous elements (eg, video calls and chat) based on nonheteronormative, sensitive therapeutic support, for example, avoiding preassumptions, using sensitive language, and reflecting possible trigger points.</p><p><strong>Conclusions: </strong>This study underscores the need for e-mental health interventions tailored to a queer-sensitive and participatory approach. Interventions should incorporate comprehensive psychoeducation, interactive elements, content reflecting the lived experiences of LGBTQIA+ individuals with SAD symptoms, and the possibility to connect and exchange experiences with others facing similar challenges. Engaging with both LGBTQIA+ people and HCPs in the development process is essential to ensure the intervention's relevance, effectiveness, and acceptability.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e65834"},"PeriodicalIF":2.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040126","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}
Elizaveta Kopacheva, Aron Henriksson, Hercules Dalianis, Tora Hammar, Alisa Lincke
{"title":"Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.","authors":"Elizaveta Kopacheva, Aron Henriksson, Hercules Dalianis, Tora Hammar, Alisa Lincke","doi":"10.2196/71949","DOIUrl":"10.2196/71949","url":null,"abstract":"<p><strong>Background: </strong>Medications are essential for health care but can cause adverse drug events (ADEs), which are harmful and sometimes fatal. Detecting ADEs is a challenging task because they are often not documented in the structured data of electronic health records (EHRs). There is a need for automatically extracting ADE-related information from clinical notes, as manual review is labor-intensive and time-consuming.</p><p><strong>Objective: </strong>This study aims to fine-tune the pretrained clinical language model, Swedish Deidentified Clinical Bidirectional Encoder Representations from Transformers (SweDeClin-BERT), for medical named entity recognition (NER) and relation extraction (RE) tasks, and to implement an integrated NER-RE approach to more effectively identify ADEs in clinical notes from clinical units in Sweden. The performance of this approach is compared with our previous machine learning method, which used conditional random fields (CRFs) and random forest (RF).</p><p><strong>Methods: </strong>A subset of clinical notes from the Stockholm EPR (Electronic Patient Record) Corpus, dated 2009-2010, containing suspected ADEs based on International Classification of Diseases, 10th Revision (ICD-10) codes in the A.1 and A.2 categories was randomly sampled. These notes were annotated by a physician with ADE-related entities and relations following the ADE annotation guidelines. We fine-tuned the SweDeClin-BERT model for the NER and RE tasks and implemented an integrated NER-RE pipeline to extract entities and relationships from clinical notes. The models were evaluated using 395 clinical notes from clinical units in Sweden. The NER-RE pipeline was then applied to classify the clinical notes as containing or not containing ADEs. In addition, we conducted an error analysis to better understand the model's behavior and to identify potential areas for improvement.</p><p><strong>Results: </strong>In total, 62% of notes contained an explicit description of an ADE, indicating that an ADE-related ICD-10 code alone does not ensure detailed event documentation. The fine-tuned SweDeClin-BERT model achieved an F1-score of 0.845 for NER and 0.81 for RE task, outperforming the baseline models (CRFs for NER and random forests for RE). In particular, the RE task showed a 53% improvement in macro-average F1-score compared to the baseline. The integrated NER-RE pipeline achieved an overall F1-score of 0.81.</p><p><strong>Conclusions: </strong>Using a domain-specific language model like SweDeClin-BERT for detecting ADEs in clinical notes demonstrates improved classification performance (0.77 in strict and 0.81 in relaxed mode) compared to conventional machine learning models like CRFs and RF. The proposed fine-tuned ADE model requires further refinement and evaluation on annotated clinical notes from another hospital to evaluate the model's generalizability. In addition, the annotation guidelines should be revised, as there is an overlap of w","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e71949"},"PeriodicalIF":2.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040213","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}
Kendra Nelson Ferguson, Kyla Christianson, Adrian Delgado, Gina Martin, Stephanie E Coen, Laura Struik
{"title":"A Youth-Centered Digital Infographic on Vaping Risks (What's in a Vape?): Mixed Methods Study.","authors":"Kendra Nelson Ferguson, Kyla Christianson, Adrian Delgado, Gina Martin, Stephanie E Coen, Laura Struik","doi":"10.2196/75694","DOIUrl":"10.2196/75694","url":null,"abstract":"<p><strong>Background: </strong>As youth engagement with traditional public health warnings declines, innovative strategies are needed. Visually compelling, youth-driven digital content such as interactive infographics may help bridge knowledge gaps, enhance risk perception, and support more informed decision-making. Despite this potential, limited research has assessed its effectiveness in conveying vaping-related harms to youth.</p><p><strong>Objective: </strong>To address this gap, this study evaluated the impact of a codeveloped, youth-informed digital infographic (What's in a Vape?) on enhancing vaping education and improving youth understanding of vaping-related harms.</p><p><strong>Methods: </strong>A convergent parallel mixed methods design was used to assess the impact of a youth-informed digital infographic. The infographic was created in collaboration with youth coresearchers and youth advisory councils to ensure relevance. Participants were recruited through community partners, school boards, and youth networks. By May 2024, we had enrolled 63 high school students aged 14 to 19 years (mean age 16.5, SD 1.2 years) primarily from Ontario and British Columbia. The survey evaluated baseline knowledge of vaping, engagement with the infographic, and postexposure perceptions on whether the content contributed to increased awareness or understanding of vaping.</p><p><strong>Results: </strong>Data collection took place between April 2024 and May 2024. Quantitative analysis showed that 87% (55/63) of participants agreed that the infographic effectively communicated key information, and 86% (54/63) gained new knowledge about vaping. In addition, 73% (46/63) found that the infographic was presented in an easy and meaningful way, whereas 52% (33/63) indicated that they would definitely share it with others, reflecting strong engagement. However, over half (33/63, 52%) also found the amount of information excessive, and 17% (11/63) found it difficult to digest, indicating variation in youth information preferences. Thematic analysis of qualitative feedback revealed four key themes: (1) the visual content enabled gaining new insights into and knowledge of vaping, (2) the visual design had a positive impact on engagement with information, (3) sourced information enhanced the credibility of the infographic information, and (4) the digital design of the infographic made complex information more understandable. Qualitative insights contextualized and supported the quantitative findings, highlighting both benefits and areas for improvement.</p><p><strong>Conclusions: </strong>This study demonstrates that youth-driven digital infographics may serve as useful health communication tools. Findings highlight the importance of peer-led design; evidence-based content; and interactive, visually compelling formats in enhancing youth comprehension and receptiveness to health messaging. By integrating youth feedback into development and prioritizing digital engagem","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e75694"},"PeriodicalIF":2.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040193","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}
Sanskriti Varma, Alicen Black, Erin Commons, Pen-Che Ho, Dena M Bravata, Hau Liu
{"title":"The Impact of a Digital Digestive Health Program and Telehealth Visits in Socially Vulnerable Populations: Cohort Evaluation.","authors":"Sanskriti Varma, Alicen Black, Erin Commons, Pen-Che Ho, Dena M Bravata, Hau Liu","doi":"10.2196/70748","DOIUrl":"10.2196/70748","url":null,"abstract":"<p><strong>Background: </strong>Socially vulnerable populations have less access to quality gastrointestinal (GI) care. Digital telehealth services provided by GI-focused registered dietitian nutritionists (RDNs) and digestive health coaches (HCs) may improve digestive health outcomes by facilitating access to GI care and thereby reduce health care disparities among vulnerable populations.</p><p><strong>Objective: </strong>The objectives of this study were to (1) evaluate the impact of a digital digestive health program on reducing GI symptoms among socially vulnerable populations and (2) assess whether telehealth visits with digital app use provide additional benefits in symptom reduction compared to digital app use alone among socially vulnerable populations.</p><p><strong>Methods: </strong>A comprehensive digital digestive care program with optional telehealth visits with RDNs and HCs was provided to US employees of participating companies via their employee benefits. We enrolled participants in the program between 2022 and 2023 who tracked digestive symptoms multiple times at baseline and then over 90 days. We measured changes in GI symptoms from baseline to up to 3 months, comparing those who opted for telehealth visits with those who used the app only. We stratified participants by the median Social Vulnerability Index (SVI) to evaluate symptom improvement across socially vulnerable populations. Multivariable regressions adjusted for age, gender, race, BMI, and preexisting GI conditions.</p><p><strong>Results: </strong>A total of 1656 participants met the inclusion criteria, of which 1362 (82%) scheduled at least one telehealth visit and 294 (18%) used only app-based resources. The majority (n=1417 86%) of participants saw GI symptom improvement, with an average reduction of 60% in symptom burden (P<.001). Participants who used telehealth services and the app had a reduction in symptoms 16% greater than that of app-only users (P=.01). High-SVI participants (ie, those with an SVI score above the median of 0.4, indicating greater social vulnerability) had a 22% greater reduction in GI symptom severity score than app-only high-SVI participants (P=.04).</p><p><strong>Conclusions: </strong>Digital health solutions may be an important resource in advancing equitable access to quality GI care and addressing disparities among populations with high social vulnerability. Virtual telehealth visits with RDNs and HCs appear to be particularly beneficial in improving digestive symptoms in such populations.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e70748"},"PeriodicalIF":2.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040149","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}
Marco van Zwetselaar, Jan Ostermann, Melkiory Beti, Joy Noel Baumgartner, Sayoki Mfinanga, Esther Ngadaya, Lavanya Vasudevan, Nathan Thielman
{"title":"Design of an Automated Mobile Phone-Based Reminder and Incentive System: Application in a Quasi-Randomized Controlled Trial to Improve the Timeliness of Childhood Vaccinations in Tanzania.","authors":"Marco van Zwetselaar, Jan Ostermann, Melkiory Beti, Joy Noel Baumgartner, Sayoki Mfinanga, Esther Ngadaya, Lavanya Vasudevan, Nathan Thielman","doi":"10.2196/65150","DOIUrl":"10.2196/65150","url":null,"abstract":"<p><strong>Background: </strong>The global penetration of mobile phones has offered novel opportunities for communicating health-related information to individuals. A low-cost system that facilitates autonomous communication with individuals via mobile phones holds potential for expanding the reach of health messaging in settings with human resource and infrastructure limitations.</p><p><strong>Objective: </strong>We sought to design a flexible, low-code system using open-source software that could be adapted to different contexts and technical environments and accommodate a wide range of automation needs. We report on key details of the mobile phone-based appointment reminder and incentive system (mParis), document its use, review implementation challenges and adaptations to address these challenges in the context of a quasi-randomized trial of mobile phone-based reminders and incentives as means of increasing the timeliness of childhood vaccinations in Tanzania, and outline other use cases that highlight the versatility of the system.</p><p><strong>Methods: </strong>The mParis instance described in this paper, which is hosted in Tanzania, sent automated, individualized vaccination reminders in the form of SMS text messages to the mobile phones of mothers of young children. Process workflows, based on the national vaccination schedule of Tanzania, were programmed into mParis. Reminders for vaccinations due at ages 6, 10, and 14 weeks were sent 7 days and 1 day before and 14 days after each vaccination due date. A subset of messages included financial incentive offers to mothers for the timely vaccination of their children. We report on implementation outcomes, challenges, and adaptations to address these challenges.</p><p><strong>Results: </strong>Between August and December 2017, a total of 412 pregnant women were enrolled in the trial. After mothers reported the birth of their children, individualized vaccination reminder messages were sent for vaccination due dates between January and July 2018. From March 2018, messages contained financial incentive offers. Of 1397 messages sent, 1122 (80.3%) messages were recorded as delivered, 249 (18.8%) as expired and resent; 23 (1.6%) as failed, and 3 (0.2%) as sent but lacking a delivery confirmation. In total, 633 (45.3%) messages contained incentive offers. Of 173 women who received at least 1 message, 67 (38.7%) were sent reminders only; 106 (61.3%) women were sent at least 1 incentivized message. Numerous challenges were encountered during the system's implementation, despite its deliberate design to accommodate basic problems, such as intermittent internet access and power failures. Continuous adaptation to increase the resilience of the system resulted in a successful deployment.</p><p><strong>Conclusions: </strong>mParis' open-source nature, auditability, and ability to autonomously execute algorithms in a low-resource setting with frequent infrastructure challenges suggest favorable prospects t","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e65150"},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033414","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":"Quantifying Spatial Shadow Zones and Their Association With Hospital Falls in Acute Care Unit: Real-Time Location System Observational Study.","authors":"Yen-Pin Chen, Yi-Chun Chen, Chen-Liang Lin, Chien-Yu Chi, Yi-Ying Chen, Bey-Jing Yang, Chien-Hua Huang","doi":"10.2196/75697","DOIUrl":"10.2196/75697","url":null,"abstract":"<p><strong>Background: </strong>Hospital falls represent a persistent and significant threat to safety within health care systems worldwide, impacting both patient well-being and the occupational health of health care staff. While patient falls are a primary concern, addressing fall risks for all individuals within the health care environment remains a key objective. Caregiver visibility and spatial monitoring are recognized as crucial considerations in mitigating fall-related incidents.</p><p><strong>Objective: </strong>This study aimed to investigate the association between the percentage of spatial shadow zone, defined as areas within an acute care unit unvisited by mobile workstations for prolonged periods, and the incidence of hospital falls and intensive care unit (ICU) transfers.</p><p><strong>Methods: </strong>This retrospective observational study was conducted in a 400-square-meter acute care unit of a tertiary hospital for over 210 days. An ultrawideband real-time location system was deployed to continuously track mobile workstations' spatial coverage. Spatial shadow zones were defined as areas unvisited by mobile workstations for 60 continuous minutes. The primary outcome was hospital falls; the secondary outcome was ICU transfers. Multivariable logistic regression analysis, adjusted for patient-to-nurse ratio and day of week, was used to examine the association between the percentage of spatial shadow zone and these outcomes. Sensitivity analyses were performed by varying the spatial dilation distance (1-4 meters) and temporal shadow zone thresholds (15-90 minutes).</p><p><strong>Results: </strong>During this study's period, 8 hospital falls and 89 ICU transfers occurred. Real-time location system validation indicated a mean positional error of 0.346 (SD 0.282) meters. In multivariable regression, a higher percentage of spatial shadow zone was significantly associated with an increased odds of hospital falls (odds ratio 1.02, 95% CI 1.01 to 1.03, P<.001). Conversely, a higher percentage of spatial shadow zone was associated with decreased odds of ICU transfer (odds ratio 0.99, 95% CI 0.99 to 0.99, P<.001). Sensitivity analyses demonstrated consistency of the association between spatial shadow zones and falls across varying parameter settings.</p><p><strong>Conclusions: </strong>This study provides novel evidence for a significant positive association between the percentage of spatial shadow zones and hospital falls, underscoring the critical role of caregiver visibility in fall prevention. The findings suggest that proactively minimizing spatial shadow zones through optimized hospital design, workflow strategies, and technology-enabled monitoring may be a valuable approach to enhance patient safety and reduce hospital falls in acute care settings.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e75697"},"PeriodicalIF":2.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033352","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":"AI Model Based on Diaphragm Ultrasound to Improve the Predictive Performance of Invasive Mechanical Ventilation Weaning: Prospective Cohort Study.","authors":"Feier Song, Huazhang Liu, Huan Ma, Xuanhui Chen, Shouhong Wang, Tiehe Qin, Huiying Liang, Daozheng Huang","doi":"10.2196/72482","DOIUrl":"10.2196/72482","url":null,"abstract":"<p><strong>Background: </strong>Point-of-care ultrasonography has become a valuable tool for assessing diaphragmatic function in critically ill patients receiving invasive mechanical ventilation. However, conventional diaphragm ultrasound assessment remains highly operator-dependent and subjective. Previous research introduced automatic measurement of diaphragmatic excursion and velocity using 2D speckle-tracking technology.</p><p><strong>Objective: </strong>This study aimed to develop an artificial intelligence-multimodal learning framework to improve the prediction of weaning failure and guide individualized weaning strategies.</p><p><strong>Methods: </strong>This prospective study enrolled critically ill patients older than 18 years who received mechanical ventilation for more than 48 hours and were eligible for a spontaneous breathing trial in 2 intensive care units in Guangzhou, China. Before the spontaneous breathing trial, diaphragm ultrasound videos were collected using a standardized protocol, and automatic measurements of excursion and velocity were obtained. A total of 88 patients were included, with 50 successfully weaned and 38 experiencing weaning failure. Each patient record included 27 clinical and 6 diaphragmatic indicators, selected based on previous literature and phenotyping studies. Clinical variables were preprocessed using OneHotEncoder, normalization, and scaling. Ultrasound videos were interpolated to a uniform resolution of 224×224×96. Artificial intelligence-multimodal learning based on clinical characteristics, laboratory parameters, and diaphragm ultrasonic videos was established. Four experiments were conducted in an ablation setting to evaluate model performance using different combinations of input data: (1) diaphragmatic excursion only, (2) clinical and diaphragmatic indicators, (3) ultrasound videos only, and (4) all modalities combined (multimodal). Metrics for evaluation included classification accuracy, area under the receiver operating characteristic curve (AUC), average precision in the precision-recall curve, and calibration curve. Variable importance was assessed using SHAP (Shapley Additive Explanation) to interpret feature contributions and understand model predictions.</p><p><strong>Results: </strong>The multimodal co-learning model outperformed all single-modal approaches. The accuracy improved when predicted through diaphragm ultrasound video data using Video Vision Transformer (accuracy=0.8095, AUC=0.852), clinical or ultrasound indicators (accuracy=0.7381, AUC=0.746), and the multimodal co-learning (accuracy=0.8331, AUC=0.894). The proposed co-learning model achieved the highest score (average precision=0.91) among the 4 experiments. Furthermore, calibration curve analysis demonstrated that the proposed colearning model was well calibrated, as the curve was closest to the perfectly calibrated line.</p><p><strong>Conclusions: </strong>Combining ultrasound and clinical data for colearning improved the","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e72482"},"PeriodicalIF":2.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023361","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}