@Article{info:doi/10.2196/68093, author="Harry, Christiana and Goodday, Sarah and Chapman, Carol and Karlin, Emma and Damian, Joy April and Brooks, Alexa and Boch, Adrien and Lugo, Nelly and McMillan, Rebecca and Tempero, Jonell and Swanson, Ella and Peabody, Shannon and McKenzie, Diane and Friend, Stephen", title="Using Social Media to Engage and Enroll Underrepresented Populations: Longitudinal Digital Health Research", journal="JMIR Form Res", year="2025", month="Apr", day="15", volume="9", pages="e68093", keywords="digital health research", keywords="digital health technology", keywords="recruitment", keywords="research subject", keywords="participant", keywords="pregnancy", keywords="maternal health", keywords="underrepresented populations", keywords="health equity", keywords="diversity", keywords="marginalized", keywords="advertisement", keywords="social media", keywords="retention", keywords="attrition", keywords="dropout", abstract="Background: Emerging digital health research poses roadblocks to the inclusion of historically marginalized populations in research. Exclusion of underresourced communities in digital health research is a result of multiple factors (eg, limited technology access, decreased digital literacy, language barriers, and historical mistrust of research and research institutions). Alternative methods of access and engagement may aid in achieving long-term sustainability of diversified participation in digital health research, ensuring that developed technologies and research outcomes are effective and equitable. Objective: This study aims to (1) characterize socioeconomic and demographic differences in individuals who enrolled and engaged with different remote, digital, and traditional recruitment methods in a digital health pregnancy study and (2) determine whether social media outreach is an efficient way of recruiting and retaining specific underrepresented populations (URPs) in digital health research. Methods: The Better Understanding the Metamorphosis of Pregnancy (BUMP) study was used as a case example. This is a prospective, observational, cohort study using digital health technology to increase understanding of pregnancy among 524 women, aged 18-40 years, in the United States. The study used different recruitment strategies: patient portal for genetic testing results, paid/unpaid social media ads, and a community health organization providing care to pregnant women (Moses/Weitzman Health System). Results: Social media as a recruitment tool to engage URPs in a digital health study was overall effective, with a 23.6\% (140/594) enrollment rate of those completing study interest forms across 25 weeks. Community-based partnerships were less successful, however, resulting in 53.3\% (57/107) engagement with recruitment material and only 8.8\% (5/57) ultimately enrolling in the study. Paid social media ads provided access to and enrollment of a diverse potential participant pool of race- or ethnicity-based URPs in comparison to other digital recruitment channels. Of those that engaged with study materials, paid recruitment had the highest percentage of non-White (non-Hispanic) respondents (85/321, 26.5\%), in comparison to unpaid ads (Facebook and Reddit; 37/167, 22.2\%). Of the enrolled participants, paid ads also had the highest percentage of non-White (non-Hispanic) participants (14/70, 20\%), compared to unpaid ads (8/52, 15.4\%) and genetic testing service subscribers (72/384, 18.8\%). Recruitment completed via paid ads (Instagram) had the highest study retention rate (52/70, 74.3\%) across outreach methods, whereas recruitment via community-based partnerships had the lowest (2/5, 40\%). Retention of non-White (non-Hispanic) participants was low across recruitment methods: paid (8/52, 15.4\%), unpaid (3/35, 14.3\%), and genetic testing service subscribers (50/281, 17.8\%). Conclusions: Social media recruitment (paid/unpaid) provides access to URPs and facilitates sustained retention similar to other methods, but with varying strengths and weaknesses. URPs showed lower retention rates than their White counterparts across outreach methods. Community-based recruitment showed lower engagement, enrollment, and retention. These findings highlight social media's potential for URP engagement and enrollment, illuminate potential roadblocks of traditional methods, and underscore the need for tailored research to improve URP enrollment and retention. ", doi="10.2196/68093", url="https://formative.jmir.org/2025/1/e68093" } @Article{info:doi/10.2196/69013, author="Laestadius, Linnea and Hamad, Fridarose and Le, Leena and Buchtel, Rosemary and Campos-Castillo, Celeste", title="Amplifying the Voices of Youth for Equity in Wellness and Technology Research: Reflections on the Midwest Youth Wellness Initiative on Technology (MYWIT) Youth Advisory Board", journal="JMIR Public Health Surveill", year="2025", month="Apr", day="10", volume="11", pages="e69013", keywords="advisory boards", keywords="adolescents", keywords="social media", keywords="qualitative research", keywords="community engagement", doi="10.2196/69013", url="https://publichealth.jmir.org/2025/1/e69013" } @Article{info:doi/10.2196/67914, author="Liu, Darren and Hu, Xiao and Xiao, Canhua and Bai, Jinbing and Barandouzi, A. Zahra and Lee, Stephanie and Webster, Caitlin and Brock, La-Urshalar and Lee, Lindsay and Bold, Delgersuren and Lin, Yufen", title="Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis", journal="JMIR Cancer", year="2025", month="Apr", day="7", volume="11", pages="e67914", keywords="large language models", keywords="GPT-4", keywords="cancer survivors", keywords="caregivers", keywords="education", keywords="health equity", abstract="Background: Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing symptom burdens from cancer and its treatments. Large language models (LLMs) offer a promising avenue for generating concise, linguistically appropriate, and accessible educational materials tailored to these populations. However, there is limited research evaluating how effectively LLMs perform in creating targeted content for individuals with diverse literacy and language needs. Objective: This study aimed to evaluate the overall performance of LLMs in generating tailored educational content for cancer survivors and their caregivers with limited health literacy or language barriers, compare the performances of 3 Generative Pretrained Transformer (GPT) models (ie, GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo; OpenAI), and examine how different prompting approaches influence the quality of the generated content. Methods: We selected 30 topics from national guidelines on cancer care and education. GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo were used to generate tailored content of up to 250 words at a 6th-grade reading level, with translations into Spanish and Chinese for each topic. Two distinct prompting approaches (textual and bulleted) were applied and evaluated. Nine oncology experts evaluated 360 generated responses based on predetermined criteria: word limit, reading level, and quality assessment (ie, clarity, accuracy, relevance, completeness, and comprehensibility). ANOVA (analysis of variance) or chi-square analyses were used to compare differences among the various GPT models and prompts. Results: Overall, LLMs showed excellent performance in tailoring educational content, with 74.2\% (267/360) adhering to the specified word limit and achieving an average quality assessment score of 8.933 out of 10. However, LLMs showed moderate performance in reading level, with 41.1\% (148/360) of content failing to meet the sixth-grade reading level. LLMs demonstrated strong translation capabilities, achieving an accuracy of 96.7\% (87/90) for Spanish and 81.1\% (73/90) for Chinese translations. Common errors included imprecise scopes, inaccuracies in definitions, and content that lacked actionable recommendations. The more advanced GPT-4 family models showed better overall performance compared to GPT-3.5 Turbo. Prompting GPTs to produce bulleted-format content was likely to result in better educational content compared with textual-format content. Conclusions: All 3 LLMs demonstrated high potential for delivering multilingual, concise, and low health literacy educational content for cancer survivors and caregivers who face limited literacy or language barriers. GPT-4 family models were notably more robust. While further refinement is required to ensure simpler reading levels and fully comprehensive information, these findings highlight LLMs as an emerging tool for bridging gaps in cancer education and advancing health equity. Future research should integrate expert feedback, additional prompt engineering strategies, and specialized training data to optimize content accuracy and accessibility. International Registered Report Identifier (IRRID): RR2-10.2196/48499 ", doi="10.2196/67914", url="https://cancer.jmir.org/2025/1/e67914" } @Article{info:doi/10.2196/64707, author="Pettersson, Linda and Johansson, Stefan and Demmelmaier, Ingrid and von Koch, Lena and Gulliksen, Jan and Hedvall, Per-Olof and Gummesson, Karl and Gustavsson, Catharina", title="Accessibility of eHealth Before and During the COVID-19 Pandemic Among People With and People Without Impairment: Repeated Cross-Sectional Survey", journal="JMIR Public Health Surveill", year="2025", month="Mar", day="28", volume="11", pages="e64707", keywords="eHealth", keywords="impairment", keywords="accessibility", keywords="digital inclusion", keywords="universal design", keywords="disability", keywords="digital divide", keywords="electronic health", keywords="COVID-19", keywords="pandemic", keywords="cross-sectional study", keywords="Sweden", keywords="online booking", keywords="digital identification", keywords="web portal", keywords="health information", keywords="control group", keywords="public health", keywords="digital health", keywords="digital literacy", keywords="health informatics", keywords="mobile phone", abstract="Background: The adoption of eHealth accelerated during the COVID-19 pandemic. Inequalities in the adoption of eHealth during the COVID-19 pandemic have been reported, but there are few such studies among people with impairment. Objectives: This study aimed to investigate self-reported use and difficulty in the use of eHealth before the COVID-19 pandemic compared to during late social distancing restrictions in Sweden, among people with and without impairment, as well as between different types of impairment. Methods: A cross-sectional survey was distributed twice by snowball sampling to people with self-reported impairment and a general population matched by age, gender, and county. Use and difficulty in the use of six eHealth services were compared between groups using chi-square test and logistic regression with year interaction terms, reported as odds ratio adjusted (aOR) for gender and age with 95\% CI. Results: The surveys included 1631 (in 2019) and 1410 (in 2021) participants with impairment, and 1084 (in 2019) and 1223 (in 2021) participants without. Participants with impairment, compared to those without impairment, reported less use and more difficulty in booking health care appointments online, digital identification, and the Swedish national web portal for health information and eHealth services (1177.se), both before and during the pandemic (P=.003 or lower). Video health care appointments were the exception to this disability digital divide in eHealth as video appointment adoption was the most likely among participants with attention, executive, and memory impairments (interaction term aOR 2.10, 95\% CI 1.30?3.39). Nonuse and difficulty in the use of eHealth were consistently associated with language impairments and intellectual impairments. For example, language impairments were inversely associated with use of the logged-in eHealth services in 1177.se in 2021 (aOR 0.49, 95\% CI 0.36?0.67) and were associated with difficulty in the use of 1177.se in 2019 (aOR 2.24, 95\% CI 1.50?3.36) and the logged-in eHealth services in 1177.se in 2021 (aOR 1.89, 95\% CI 1.32?2.70). Intellectual impairments were inversely associated with the use of the logged-in eHealth services in 1177.se in 2021 (aOR 0.19, 95\% CI 0.13?0.27). Conclusions: This repeated cross-sectional survey study, including participants with diverse types of impairment and a control group without impairment, reveals persisting disability digital divides, despite an accelerated adoption of eHealth across the pandemic. eHealth services were not accessible to some groups of people who were identified as being at risk of severe disease during the COVID-19 pandemic. This implies that all people could not use eHealth as a measure of infection protection. ", doi="10.2196/64707", url="https://publichealth.jmir.org/2025/1/e64707" } @Article{info:doi/10.2196/63671, author="Yu, Yao and Liang, Zhenning and Zhou, Qingping and Tuersun, Yusupujiang and Liu, Siyuan and Wang, Chenxi and Xie, Yuying and Wang, Xinyu and Wu, Zhuotong and Qian, Yi", title="Decomposition and Comparative Analysis of Urban-Rural Disparities in eHealth Literacy Among Chinese University Students: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="26", volume="27", pages="e63671", keywords="university students", keywords="eHealth literacy", keywords="urban-rural disparities", keywords="Fairlie decomposition model", keywords="health equity", abstract="Background: Mobile health care is rapidly expanding in China, making the enhancement of eHealth literacy a crucial strategy for improving public health. However, the persistent urban-rural divide may contribute to disparities in eHealth literacy between urban and rural university students, potentially affecting their health-related behaviors and outcomes. Objective: This study aims to examine disparities in eHealth literacy between university students in urban and rural China, identifying key influencing factors and their contributions. The findings will help bridge these gaps, promote social equity, enhance overall health and well-being, and inform future advancements in the digital health era. Methods: The eHealth Literacy Scale (eHEALS) was used to assess eHealth literacy levels among 7230 university students from diverse schools and majors across 10 regions, including Guangdong Province, Shanghai Municipality, and Jiangsu Province. Descriptive statistics summarized demographic, sociological, and lifestyle characteristics. Chi-square tests examined the distribution of eHealth literacy between urban and rural students. A binary logistic regression model identified key influencing factors, while a Fairlie decomposition model quantified their contributions to the observed disparities. Results: The average eHealth literacy score among Chinese university students was 29.22 (SD 6.68), with 4135 out of 7230 (57.19\%) scoring below the passing mark. Rural students had a significantly higher proportion of inadequate eHealth literacy (2837/4510, 62.90\%) compared with urban students (1298/2720, 47.72\%; P<.001). The Fairlie decomposition analysis showed that 71.4\% of the disparity in eHealth literacy was attributable to urban-rural factors and unobserved variables, while 28.6\% resulted from observed factors. The primary contributors were monthly per capita household income (13.4\%), exercise habits (11.7\%), and 9-item Patient Health Questionnaire (PHQ-9) scores (2.1\%). Conclusions: Rural university students exhibit lower eHealth literacy levels than their urban counterparts, a disparity influenced by differences in socioeconomic status, individual lifestyles, and personal health status. These findings highlight the need for targeted intervention strategies, including (1) improving access to eHealth resources in rural and underserved areas; (2) fostering an environment that encourages physical activity to promote healthy behaviors; (3) expanding school-based mental health services to enhance health information processing capacity; and (4) implementing systematic eHealth literacy training with ongoing evaluation. These strategies will support equitable access to and utilization of eHealth resources for all students, regardless of their geographic location. ", doi="10.2196/63671", url="https://www.jmir.org/2025/1/e63671" } @Article{info:doi/10.2196/64591, author="Ye, Qin and Wang, Wei and Zeng, Xuan and Kuang, Yuxian and Geng, Bingbing and Zhou, Song and Liu, Ning", title="Development and Validation of the Digital Health Literacy Questionnaire for Stroke Survivors: Exploratory Sequential Mixed Methods Study", journal="J Med Internet Res", year="2025", month="Mar", day="25", volume="27", pages="e64591", keywords="stroke survivors", keywords="digital health literacy", keywords="validation", keywords="reliability", keywords="mixed methods study", abstract="Background: In China, there is limited research on digital health literacy (DHL) among patients with stroke. This is mainly due to the lack of validated tools, which hinders the precision and sustainability of our country's digital transformation. Objective: This study aimed to develop and validate a DHL scale specifically for stroke survivors in China. Methods: We used a sequential, exploratory, mixed methods approach to develop a DHL questionnaire for stroke survivors. This study comprised 418 patients with stroke aged 18 years and older. To evaluate the questionnaire's psychometric qualities, we randomly assigned individuals to 2 groups (subsample 1: n=118, subsample 2: n=300). Construct validity was evaluated through internal consistency analysis, exploratory and confirmatory factor analyses, hypothesis testing for structural validity, measurement invariance assessments using the eHealth Literacy Scale, and Rasch analyses to determine the questionnaire's validity and reliability. Results: This study underwent 4 stages of systematic development. The initial pool of items contained 25 items, 5 of which were eliminated after content validity testing; 19 items were subsequently retained through cognitive interviews. After an interitem correlation analysis, 2 more items were excluded, leaving 17 items for exploratory factor analysis. Finally, 2 items were excluded by Rasch analysis, resulting in a final version of the questionnaire containing 15 items. The total score range of the scale was 15-75, with higher scores indicating greater DHL competence. Results showed that principal component analysis confirmed the theoretical structure of the questionnaire (69.212\% explained variance). The factor model fit was good with $\chi$24=1.669; root mean square error of approximation=0.047; Tucker-Lewis Index=0.973; and Comparative Fit Index=0.977. In addition, hypothesis-testing construct validity with the eHealth Literacy Scale revealed a strong correlation (r=0.853). The internal consistency (Cronbach $\alpha$) coefficient was 0.937. The retest reliability coefficient was 0.941. Rasch analysis demonstrated the item separation index was 3.81 (reliability 0.94) and the individual separation index was 2.91 (reliability 0.89). Conclusions: The DHL Questionnaire for Stroke Survivors is a reliable and valid measure to assess DHL among stroke survivors in China. ", doi="10.2196/64591", url="https://www.jmir.org/2025/1/e64591" } @Article{info:doi/10.2196/67293, author="Whittemore, Robin and Jeon, Sangchoon and Akyirem, Samuel and Chen, C. Helen N. and Lipson, Joanna and Minchala, Maritza and Wagner, Julie", title="Multilevel Intervention to Increase Patient Portal Use in Adults With Type 2 Diabetes Who Access Health Care at Community Health Centers: Single Arm, Pre-Post Pilot Study", journal="JMIR Form Res", year="2025", month="Mar", day="25", volume="9", pages="e67293", keywords="patient portal", keywords="mobile phone", keywords="diabetes", keywords="community health center", keywords="adults", keywords="diabetic", keywords="DM", keywords="diabetes mellitus", keywords="Type 2 diabetes", keywords="T2D", keywords="community health centers", keywords="CHCs", keywords="pilot study", keywords="feasibility", keywords="self-management", keywords="glycemic control", keywords="patient portals", keywords="social determinants of health", keywords="primary outcome", keywords="digital health", keywords="digital health literacy", keywords="health technology", keywords="health technologies", keywords="psychosocial", keywords="efficacy", abstract="Background: Diabetes self-management education and support (DSMS) delivered via patient portals significantly improves glycemic control. Yet, disparities in patient portal use persist. Community health centers (CHCs) deliver care to anyone who needs it, regardless of income or insurance status. Objective: This study aimed to evaluate the feasibility, acceptability, and preliminary efficacy of a multilevel intervention to increase access and use of portals (MAP) among people with type 2 diabetes (T2D) receiving health care at CHCs. Methods: A within-subjects, pre-post design was used. Adults with T2D who were portal naive were recruited from 2 CHCs. After informed consent, participants met with a community health worker for referrals for social determinants of health, provision of a tablet with cell service, and individualized training on use of the tablet and portal. Next, a nurse met individually with participants to develop a DSMS plan and then communicated with patients via the portal at least twice weekly during the first 3 months and weekly for the latter 3 months. Data were collected at baseline, 3 months and 6 months. The primary outcome was patient activation and engagement with the portal. Secondary outcomes included technology attitudes, digital health literacy, health-related outcomes and psychosocial function. Results: In total, 26 patients were eligible, 23 received the intervention, and one was lost to follow up. The sample was predominately Latino or Hispanic (17/22, 77\%) and reported low income (19/22, 86\%< US \$40,000/year), low education (13/22, 59\%