Learning experience design of an mHealth self-management intervention for adolescents with type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is a lifelong and chronic condition that can cause severely compromised health. The T1D treatment regimen is complex, and is a particular challenge for adolescents, who frequently experience a number of treatment adherence barriers (e.g., forgetfulness, planning and organizational challenges, stress). Diabetes Journey is a gamified mHealth program designed to improve T1D self-management through a specific focus on decreasing adherence barriers and improving executive functioning skills for adolescents. Grounded in situativity theory and guided by a sociotechnical-pedagogical usability framework, Diabetes Journey was designed, developed, and evaluated using a learning experience design approach. This approach applied design thinking methods within a Successive Approximation Model design process. Iterative design and formative evaluation were conducted across three design phases, and improvements were implemented following each phase. Findings from the user testing phase indicate Diabetes Journey is a user-friendly mHealth program with high usability that holds promise for enhancing adolescents' T1D self-management. Implications for future designers and researchers are discussed regarding the social dimension of the sociotechnical-pedagogical usability framework. An extension to the framework is proposed to extend the social dimension to include socio-cultural and contextual considerations when designing mHealth applications. Consideration of the pedagogical and sociocultural dimensions of learning is imperative when developing psychoeducational interventions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".