Meeting the Needs of Emerging Adults With Type 1 Diabetes Living in a Rural Area With Mobile Health Interventions: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Emerging adults (EAs; age 18-30 years) with type 1 diabetes (T1D) have more challenges with diabetes management and glycemic control than other age groups. Living in a rural community introduces additional unique diabetes care challenges due to limited access to specialty care and ancillary support services. Yet, few interventions have been developed to improve diabetes management in rural-dwelling EAs with T1D. OBJECTIVE: This study aimed to understand the diabetes management experiences of older adolescents and EAs (age 16-25 years) with T1D living in a rural area and to assess their perceptions of the acceptability of 4 fully automated mobile health (mHealth) interventions to support diabetes management. METHODS: EAs were identified by clinical staff through convenience sampling. In total, 8 EAs participated in 1 focus group and 1 EA completed an individual interview; all data were collected over Zoom. Facilitators explored EAs' experiences living in a rural community with T1D and discussed EAs' impressions of, feedback on, and recommendations for improving 4 mHealth interventions to meet the specific needs of EAs with T1D living in rural communities. Discussions were transcribed and analyzed using conventional content analysis. RESULTS: In total, 9 EAs (aged 18.8, SD 2.7 years; 5, 56% men; 8, 89% White) with a duration of diabetes of 8.6 (SD 4.3) years participated. They described experiences with diabetes stigma (attributing diabetes to poor lifestyle choices) and feelings of self-consciousness (hyperawareness) in their rural communities. They attributed these experiences to the small size of their communities ("everyone knows") and community members' lack of knowledge about diabetes (unable to differentiate between type 1 and type 2 diabetes). In contrast, EAs reported high levels of social support for diabetes and diabetes care from family, friends, and other community members, but low support for medical needs. The location of their diabetes care providers and the limited accessibility of diabetes-specific and general medical care services in their local community created a challenging medical care context. Overall, EAs found mHealth interventions appealing due to their digital delivery and highlighted features that increased accessibility (voiceovers and simple, jargon-free language), individualization (ability to tailor intervention content and delivery), and applicability to their own lives and other EAs with T1D (relatability of vignettes and other content). EAs suggestions for improving the interventions included more opportunities to tailor the interventions to their preferences (greater frequency and duration, ability to adapt content to emerging needs), increasing opportunities for peer support within the interventions (friend and significant other as identified support person, connecting with peers beyond their local community), and making the tone of intervention components more casual and engaging. CONCLUSIONS: mHealth interventions aligned with EAs' needs and preferences are a promising strategy to support EAs in communities where social support and resources might be limited. TRIAL REGISTRATION: N/A, not a clinical trial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".