Insights Into How Digital Health Interventions Shape Outcomes for Emerging Adults Living With Type 1 Diabetes: Qualitative Realist Process Evaluation
Bibliographic record
Abstract
BACKGROUND: Emerging adults living with type 1 diabetes (T1D) need targeted support to equip them with the knowledge and motivation required for self-management, particularly as they transition from pediatric to adult care. While multicomponent digital health interventions have shown promise in addressing their multifaceted needs, traditional effectiveness studies provide little, if any, insights into which components work effectively, how they function, and for whom. OBJECTIVE: This study aims to explore the implementation of a multicomponent, text message-based digital intervention (Keeping in Touch; KiT) to provide early insights into which components may shape participants' transition experiences and how. The secondary objective was to explore which subgroups, defined by individual characteristics, may benefit most from the intervention. METHODS: Embedded within a broader randomized controlled trial, we conducted a qualitative realist evaluation with intervention-arm participants who had engaged with KiT for a minimum of 3 months. One-on-one semistructured realist interviews were conducted in a teacher-learner cycle to test the initial program theory. The initial program theory included several pathways through which the 5 intervention components (ie, T1D self-management information and suggestions, transition support information, problem-solving support, stress management strategies, and transition reminders) were hypothesized to influence a range of theorized outcomes. RESULTS: A total of 16 interviews were completed with intervention participants. All 5 KiT intervention components were reported to shape participants' transition experiences positively but to varying degrees. T1D self-management information and suggestions presented a universal positive impact across all participants. However, the effectiveness of problem-solving support and stress management strategies varied depending on participants' individual characteristics (eg, duration of diabetes, perceived access to information, and baseline diabetes distress). Rather than acting through parallel independent mechanisms, KiT appeared to support participants' transition experiences via multiple chains of interconnected mechanisms, often beginning with knowledge or reinforcement and contributing to changes in motivation (eg, self-efficacy and diabetes distress). Interview participants described tangible improvement in mechanisms and proximal outcomes (eg, diabetes knowledge and self-efficacy). CONCLUSIONS: A multicomponent, text message-based digital intervention could support emerging adults living with T1D during their transition to adult care by enhancing their knowledge and motivation for self-management. Participant subgroups responded differently to various intervention components, which highlights that one-size-fits-all approaches are likely inadequate. Digital interventions should be developed and studied in a variety of subgroups and contexts to optimize their reach. Interventions for emerging adults living with T1D might benefit from targeting those who are more recently diagnosed with relatively lower baseline levels of diabetes knowledge and self-efficacy or higher levels of diabetes distress. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/46115.
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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 | 0.075 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".