Developing eHealth Interventions to Improve Diabetes Management in Emerging Adulthood: Qualitative Formative Study
Bibliographic record
Abstract
Background: Emerging adulthood is a high-risk period during which many with type 1 diabetes demonstrate suboptimal diabetes management and glycemic control. There is a need for effective, scalable interventions designed specifically for this population. Technology-based approaches are readily accessible to this age group. Furthermore, interventions consistent with self-determination theory-which posits that the fulfillment of psychological needs for autonomy, self-efficacy, and relatedness promotes intrinsic motivation for change-may resonate well with emerging adults' developmental needs for establishing independence and autonomy, and growing their social network. Objective: This study aimed to enhance the potential relevance, sustainability, and efficacy of 3 self-determination theory-informed mobile health intervention components and content for emerging adults with type 1 diabetes. Key areas of interest included emerging adults' perspectives on the use of cultural tailoring, developmental relevance of content, and delivery preferences. Methods: In this qualitative formative study, 20 emerging adults reviewed and provided feedback on 3 newly developed intervention components via individual interviews. Ten reviewed the motivation enhancement system, a 2-session counseling intervention grounded in motivational interviewing and designed to enhance emerging adults' autonomy and self-efficacy for diabetes self-management. Ten reviewed the SMS text messaging reminder intervention (one-way text message reminders to complete diabetes care) and the question prompt list (a list of questions related to diabetes care designed to increase patients' active participation during medical visits). Interviews were analyzed using framework matrix analysis, an efficient approach to inductive thematic analysis. Results: Emerging adults found all 3 interventions acceptable and helpful. They noted the interventions' integration into the technology they already use as a strength. Across interventions, emerging adults also expressed a preference for culturally tailored intervention content, including intervention examples, actors, and language representing their illness experience, identity, and personal preferences. Intervention-specific feedback suggested emerging adults liked motivation enhancement system intervention elements that were engaging (videos) and relatable (peer testimonials), and supported their growing autonomy and independence. For SMS text messaging reminders, emerging adults appreciated the straightforward nature of the reminders and recommended more directive messages. They appreciated the range of topics and variety of messages. Suggestions included making the messages more impactful (eg, direct, personalized, and engaging, such as using emojis). Emerging adults saw the question prompt list content areas as relevant and well-aligned with their concerns highlighting the topic of transitioning to adult life with diabetes as particularly salient. Conclusions: Emerging adult feedback supports the acceptability and use of these intervention components and will be used to refine the interventions. Feedback was especially positive regarding cultural and other tailoring efforts, as well as content directed at their pending transition to full independence. At the same time, their input suggests the need for multiple specific modifications, highlighting the importance of intensive and detailed feedback from end users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".