JMIR Formative Research最新文献

Background: Access to care that affirms one's entire self is essential, especially for gender-diverse individuals. Gender-affirming care includes medical, social, and nonmedical supports to affirm gender identity.

Objective: This study qualitatively examined the importance of social and nonmedical gender-affirming services as described by gender-diverse community members.

Methods: Thematic analysis was conducted on qualitative data from 5 participants (3 rural and 2 urban; 2 with doctoral-level education and 3 health professionals) with experiences accessing gender-affirming care in Nova Scotia, Canada, between October 2023 and November 2023.

Results: Participants included transgender and/or nonbinary individuals who highlighted the significance of social and nonmedical gender-affirming care over traditional medical interventions. Themes included the centrality of belonging, the use of online spaces such as TikTok for gender affirmation, and the emotional impact of barriers such as cost and safety concerns. Four of 5 participants emphasized the importance of social and nonmedical gender-affirming care over medical interventions. Participants stressed the importance of fostering a sense of belonging and accessing supportive communities, which is crucial in navigating transphobic environments without support. Many felt abandoned by public systems and resorted to passing as cisgender due to barriers such as cost in accessing gender-affirming resources. Internet platforms such as TikTok provided valuable guidance, supplementing limited access to medical gender-affirming care. Participants emphasized a crucial need for health care providers to understand basic gender-affirming care, including respect for preferred pronouns and gender identities.

Conclusions: This study found that members of the gender-diverse community significantly value social and nonmedical gender-affirming care services with respect to their well-being. The findings underscore the complex interplay among social support, health care access, and resilience in transgender and/or nonbinary individuals' lives. This work can aid in exploring how best to educate health care providers in gender-inclusive care and enable increased access to all forms of care that can help affirm an individual's gender.

Background: Adolescent obesity remains a pressing public health challenge, particularly among socioeconomically disadvantaged populations. Artificial intelligence (AI) holds the promise for supporting students in managing daily health behaviors, but few existing studies used AI-based interventions in naturalistic settings such as schools.

Objective: This study evaluated the feasibility and preliminary impact of ProudMe Tech (Louisiana State University), an AI-assisted web app designed to help students manage 4 obesity-related behaviors: physical activity, screen time, diet, and sleep.

Methods: The 8-week, 1-arm pilot intervention study recruited 172 participants from 5 middle schools in Louisiana and used the ProudMe Tech to set behavior goals, track behaviors, record reflections, and receive AI-generated feedback. Both engagement (primary focus) and behavior impacts (secondary focus) were examined.

Results: Engagement metrics indicated varying levels of usage, averaging 8.9 (SD 7.6) behavior entries and 30.0 (SD 28.3) reflections per student, and receiving 33.5 (SD 29.7) AI feedback messages. Overall, participants recorded 6164 valid daily goals, of which 3934 (63.8%) were achieved. Natural language processing of the reflections and AI feedback messages revealed an overall neutral to positive sentiment. Pre- to postcomparisons showed (1) a significant reduction in screen time from 4.3 (SD 2.6) to 3.4 (SD 2.5) hours per day (21.6% decrease; t164=6.18, P<.001), (2) a small but significant decrease in fruit and vegetable intake from 5.7 (SD 3.8) to 5.2 (SD 3.5) servings per day (8.9% decrease; t169=2.27, P=.46), and (3) no significant changes in physical activity and sleep.

Conclusions: These findings suggest that ProudMe Tech is a feasible AI chatbot that can engage adolescents in health behavior management, but more adaptation is needed to effectively elicit improvements in health behaviors and lower the obesity risk in middle school students.

Background: Bipolar disorder requires immediate and frequent daily symptom monitoring due to its extreme mood fluctuations. Ecological momentary assessment (EMA) technology uses high-frequency data collection to achieve ecologically valid capture of patient symptoms. Investigating EMA compliance among Chinese patients with bipolar disorder and its influencing factors is essential for developing more feasible daily symptom monitoring protocols.

Objective: This study aimed to investigate the 14-day compliance rate of EMA among Chinese individuals with bipolar disorder and to examine the demographic and clinical characteristics associated with that compliance.

Methods: A total of 100 adults (63 female individuals) with bipolar disorder across mood states (depressive episode, n=29, 29%; hypomanic or manic episode, n=17, 17%; euthymic state, n=54, 54%) completed self-monitoring via the WeChat Mini Program "Xunkang Assessment System" 3 times daily for 14 days. The compliance rate was calculated as the percentage of completed questionnaires out of the total required over 2 weeks. Multivariate ordinal logistic regression was used to explore the factors associated with the compliance rate.

Results: The median compliance rate was 75% (IQR 35.7%-90.4%). Compliance did not differ significantly across mood states (P=.15). In multivariable models, higher Bech-Rafaelsen Mania Scale scores and lower Functioning Assessment Short Test scores were independently associated with better compliance (Bech-Rafaelsen Mania Scale: B=0.11; P=.03 and Functioning Assessment Short Test: B=-0.06; P=.01).

Conclusions: Two-week EMA monitoring via the WeChat Mini Program is feasible among Chinese individuals with bipolar disorder across mood states. Manic symptom severity and functional impairment were associated with EMA adherence and should be considered in study design and interpretation.

Background: Although sexual exploration is normative during adolescence, sexual activities that are unprotected and occur under the influence of substances can pose significant risks to young people. Youth exposed to adversity are among the groups most vulnerable to sexual risk-taking in adolescence. Selective interventions that consider lived experiences and the local context may help reduce sexual risk-taking among this population.

Objective: This pilot study assessed the feasibility of participant recruitment and retention as well as participant engagement with an adapted version of Focus on Youth with Informed Parents and Children Together for Black youth exposed to household challenges.

Methods: Participants were recruited using school and community presentations, digital flyers, and referrals. A total of 121 youth from 3 sites in Baltimore, Maryland, were screened. Participants completed 3 assessments: baseline, posttest, and 3-month follow-up. Participant enrollment, session attendance, and assessment completion were used to determine feasibility and engagement. Sexual health knowledge, pregnancy intentions, partner communication, and sexual behaviors were explored as secondary outcomes.

Results: Funded by the National Institutes of Health, the data for this study were collected between January 2022 and April 2023. A total of 61 youth (aged 13-16 years) were recruited and randomized to either the intervention or the control condition (n=33 and n=28, respectively). In total, 87% (53/61) of the participants completed all 3 assessments. There was high engagement: 80% (48/61) of participants attended at least 3 sessions, and 75.2% (115/153) of after-session responses revealed they would recommend a session to a friend. Among the 18 participants who reported having any sex, all 18 (100%) abstained from alcohol use and 12 (67%) abstained from drug use before sex. The intervention group showed a significant increase in sexual health knowledge. No changes in sexual health behaviors or partner communication were observed.

Conclusions: Findings suggest that recruiting, retaining, and engaging participants in the adapted Focus on Youth with Informed Parents and Children Together intervention is feasible. Additional research is needed to determine the extent to which this intervention can mitigate sexual risk-taking among youth exposed to adversity. The findings will inform the redesign of our assessments to capture additional factors that may affect sexual health behaviors.

Trial registration: ClinicalTrials.gov NCT05033821; https://clinicaltrials.gov/study/NCT05033821.

Background: Biomedical research studies are increasingly using digital tools to enroll, recruit, and collect data from participants. However, variability in digital literacy and technological acceptance can be challenging for recruitment from groups traditionally underrepresented in research, including those served by Federally Qualified Health Centers.

Objective: This study aimed to (1) measure participant accessibility and comfort with digital platforms and (2) examine the interrelation of technology access, digital literacy, and support preferences during enrollment and data submission.

Methods: A cross-sectional analysis was conducted using enrollment data from Federally Qualified Health Centers participating in the All of Us Research Program. Participants had the option of High-Touch (staff-assisted) or Low-Touch (self-directed) support for enrollment and survey completion. Survey items assessed internet access and technology comfort, while support type was recorded by the research staff based on participants' actual selection. Logistic regression models evaluated relationships between technology access, comfort, and enacted support type, while controlling for age, consent language, and education, as well as race and ethnicity.

Results: The analytic sample included 605 participants. The majority reported access to the internet (539/605, 89.1%) and felt comfortable with technology (448/605, 74.1%). In the group requesting High-Touch support (n=346), 14.5% (n=50) reported no internet access, and 31.5% (n=109) felt uncomfortable with technology. In the group requesting Low-Touch support (n=259), 6.2% (n=16) had no access to the internet, and 3.9% (n=10) reported feeling uncomfortable (P<.001). In the adjusted models, much greater comfort with technology was significantly correlated with reduced odds of requesting High-Touch support (comfortable: adjusted odds ratio 0.118, 95% CI 0.055-0.255 and neutral: adjusted odds ratio 0.212, 95% CI 0.077-0.587), but internet access was not significantly correlated.

Conclusions: The strongest predictor for support preference for digital enrollment among the participants was their comfort with technology rather than access alone. These findings illustrate the significance of participant-centric design methods coupling adaptive support paths, mixed enrollment strategies, and individualized onboarding methods aligned with digital confidence to promote equitable engagement in precision health research.

This study demonstrates that GPT-4o outperforms traditional natural language processing methods in accurately analyzing patient sentiment toward atopic dermatitis treatments on Reddit, enabling more nuanced and reliable extraction of real-world patient perspectives from large-scale social media data.

Background: Cardiovascular diseases remain the leading global cause of mortality, yet traditional electrocardiogram (ECG) interpretation shows subjective variability and limited sensitivity to complex pathologies.

Objective: This study aims to address these challenges by proposing the Cardiovascular Multimodal Prediction Network (CaMPNet), a transformer-based multimodal architecture that integrates raw 12-lead ECG waveforms, 9-structured machine-measured ECG features, and demographic data (age and sex) through cross-attention fusion.

Methods: The model was trained on 384,877 records from the Medical Information Mart for Intensive Care IV - Electrocardiogram Matched Subset database and evaluated across 12 cardiovascular disease labels. To further assess temporal robustness, a temporal external validation was performed using the most recent 10% of the data, withheld chronologically from model development.

Results: On the internal test set, the model achieved a mean area under the curve (AUC) of 0.845 (SD 0.04) and area under the precision-recall curve of 0.489, outperforming the residual networks-ECG baseline (AUC=0.848 but F1-score=0.152) and all single-modality variants. Subgroup analyses demonstrated consistent performance across demographics (male AUC= 0.846 vs female=0.843; youngest quartile 0.884 vs oldest 0.811). CaMPNet retained moderate discriminative ability in temporal external validation with a mean AUC of 0.715 (SD 0.03) and area under the precision-recall curve of 0.298, although performance declined due to temporal distribution shifts. Despite this, major disease categories, such as atrial fibrillation, heart failure, and normal rhythm, maintained high AUCs (>0.84). Attention-based visualization revealed clinically interpretable patterns (eg, ST-segment elevations in ST-segment elevation myocardial infarction), and ablation experiments verified the model's tolerance to missing structured inputs.

Conclusions: CaMPNet demonstrates robust and interpretable multimodal ECG-based diagnosis, offering a scalable framework for comorbidity screening and continual learning under real-world temporal dynamics.