Background: Uncontrolled diabetes contributes to serious comorbidities and mortality. Effective self-management can improve outcomes, though barriers such as limited education and support often prevent patients from engaging in such behaviors. Automated texting systems show promise to deliver diabetes self-management education as they are accessible and scalable. Furthermore, customizing these systems may further enhance patient engagement compared to standard, one-size-fits-all approaches. However, such customization is more resource-intensive, and it remains unclear whether the added effort meaningfully enhances diabetes self-management and outcomes.
Objective: This study aimed to describe the development of 2 versions of an automated texting system intervention for diabetes self-management: (1) a standard, education-only intervention (Diabetes Self-Management Support; DSMS) and (2) an interactive, customizable intervention (Diabetes Self-Management Support + Interactive and Customizable Messages; DSMS+).
Methods: Two versions of an automated texting system intervention were developed using a participatory design approach that incorporated input from veterans and expert clinicians. Message content was refined through feedback from a multidisciplinary team, veteran coinvestigators, national surveys, interviews, clinical expert panel reviews, and beta testing. Surveys were mailed to 1000 potential participants, oversampling rural, low-income, minority, and female participants. Respondents rated message relevance and provided preferences for content, timing, and frequency. Interviews provided customization preferences. A clinical expert panel reviewed all messages for safety and appropriateness. Beta testing informed final refinements.
Results: Ninety-two surveys were completed (9.2% response rate). Respondents rated 62% of the messages as personally relevant and 61% confidence-enhancing. Interviews with 23 respondents revealed a preference for 1-2 texts per day, emphasizing topics such as healthy eating and weight management. The clinical expert panel reviewed 536 messages, flagging 81 for revision. Beta testing confirmed feasibility and informed refinements to clarity and timing. The 2 resulting interventions were built in the US Department of Veterans Affairs' automated texting system, Annie.
Conclusions: Two text messaging interventions, DSMS and DSMS+, were developed to support diabetes self-management among US veterans. DSMS delivers standard educational content, while DSMS+ incorporates interactive features and personalization. The subsequent clinical trial will assess whether customization enhances engagement and improves diabetes outcomes, providing insights into the potential of tailored mobile health interventions for chronic disease management.
Background: Methadone is a first-line treatment for opioid use disorder, which is delivered in federally regulated opioid treatment programs (OTPs). Federal policies require directly observed dosing of methadone followed by graduated provision of nonobserved doses to take at home (ie, "take-home" dosing) after demonstrated stability is achieved. Policy changes since the COVID-19 pandemic have greatly expanded take-home dosing. Video directly observed treatment (video DOT) is an approach in which patients submit videos of themselves taking medications, which are asynchronously reviewed to verify adherence.
Objective: In preparation for an implementation trial evaluating the adoption of video DOT in OTP settings, we conducted a rapid needs assessment with multidisciplinary stakeholders to assess acceptability, perceived benefits, and needed support for video DOT to monitor take-home methadone dosing.
Methods: In our rapid needs assessment, we explored perspectives of multidisciplinary stakeholders (N=20) at 3 clinical sites within a single OTP in western Washington state. Trained qualitative researchers took ethnographic field notes during meetings with organizational leadership and in-person site visits with clinical and administrative staff. Field notes were analyzed via a team-based rapid assessment process using coding templates informed by the Consolidated Framework for Implementation Research. Summaries of qualitative data were iteratively reviewed by the study team and further confirmed with site stakeholders.
Results: Stakeholders included leadership (n=6, 30%), medical providers (n=4, 20%), substance use disorder counselors (n=7, 35%), and clinic managers and support staff (n=3, 15%). Stakeholders perceived that video DOT could lessen the barriers patients face, including travel burden (eg, time and cost) and stigma. They also identified that video DOT could have important impacts on early care retention, given expansions of take-home dosing. However, stakeholders anticipated an added burden for clinical staff and emphasized the need for implementation supports that would limit burden, such as additional staff support for video submission review and clear communication pathways when video submissions require additional clinical input.
Conclusions: A rapid needs assessment of OTP sites for a future implementation study suggested that stakeholders saw potential benefits for patients receiving video DOT, but there were concerns that this would add to their work burden. Learnings informed the subsequent tailoring of clinical use cases and implementation supports.
When given a sample of 100 emergency department discharge instructions, Claude Sonnet, a large language model, produced accurate Spanish translations as evaluated by Spanish-speaking physicians and medical interpreters.
Background: Mobile health (mHealth) apps can innovatively diagnose, prevent, and treat many diseases. The increasing use of mHealth apps necessitates an appropriate assessment standard.
Objective: This study aimed to translate the User Version of the Mobile Application Rating Scale (uMARS) into Polish, followed by validation, cultural adaptation, and examination of its reliability and validity.
Methods: The Polish version of uMARS was adapted, translated, and validated based on the free STOP COVID ProteGO Safe app available for Android and iOS platforms. A total of 117 participants rated the app using the translated scale and rerated it 1 week later.
Results: The conceptual equivalence of all items and subscales of the original uMARS and its Polish version was confirmed. The translated uMARS scale exhibited high reliability (Cronbach α=0.95). The perceived test-retest reliability and total influence score were acceptable, with intraclass correlation coefficient values of 0.59 and 0.65, respectively.
Conclusions: The translated scale matched the reliability of the original scale. It can help users choose the best mHealth apps available in Poland and report their quality. The Polish version of uMARS was cross-culturally validated and was found to be as reliable as the original uMARS. The translated and validated uMARS tool can be used to evaluate mHealth apps in various aspects. App developers can reliably assess app components and determine areas that require further improvement and development to deliver higher-quality apps. The Polish version of the uMARS can become a standard tool for evaluating the quality of mHealth apps in Poland.
Background: Cardiac arrest (CA), characterized by an extremely high mortality rate, remains one of the most pressing global public health challenges. It not only causes a substantial strain on health care systems but also severely impacts individual health outcomes. Clinical evidence demonstrates that early identification of CA significantly reduced the mortality rate. However, the developed CA prediction models exhibit limitations such as low sensitivity and high false alarm rates. Moreover, issues with model generalization remain insufficiently addressed.
Objective: The aim of this study was to develop a real-time prediction method based on clinical vital signs, using patient vital sign data from the past 2 hours to predict whether CA would occur within the next 1 hour at 5-minute intervals, thereby enabling timely and accurate prediction of CA events. Additionally, the eICU-CRD dataset was used for external validation to assess the model's generalization capability.
Methods: We reviewed and analyzed 4063 patients from the MIMIC-III waveform database, extracting 6 features to develop a deep learning-based CA prediction model named TrGRU. To further enhance performance, statistical features based on a sliding window were also constructed. The TrGRU model was developed using a combination of transformer and gated recurrent unit architectures. The primary evaluation metrics for the model included accuracy, sensitivity, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC), with generalization capability validated using the eICU-CRD dataset.
Results: The proposed model yielded an accuracy of 0.904, sensitivity of 0.859, AUROC of 0.957, and AUPRC of 0.949. The results showed that the predictive performance of TrGRU was superior to that of the models reported in previous studies. External validation using the eICU-CRD achieved a sensitivity of 0.813, an AUROC of 0.920, and an AUPRC of 0.848, indicating excellent generalization capability.
Conclusions: The proposed model demonstrates high sensitivity and a low false-alarm rate, enabling clinical health care providers to predict CA events in a more timely and accurate manner. The adopted meta-learning approach effectively enhances the model's generalization capability, showcasing its promising clinical application.
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.
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