Consumer Involvement in the Co-Design of Diabetes Self-Management Smartphone Apps: A Scoping Review
Bibliographic record
Abstract
Consumer involvement in the co-design of diabetes self-management smartphone apps is vital. This scoping review explored how consumers are involved in the co-design processes and methods and approaches guiding this research.Our review was guided by Arksey and O'Malley's five-stage framework, PRISMA-ScR guidelines, and Witteman and colleagues' 11-item user-centered design (UCD-11) framework. We searched literature across five databases and examined types of consumer involvement in co-design and frequency of methods and approaches (i.e., co-design approaches, behavioral theories, and other frameworks), synthesizing findings in SPSS and Excel.Of the 14,206 initial items, 283 articles were included. Most studies were conducted in Asia (33.2%) and focused on type 2 diabetes (43.1%). All articles addressed at least one UCD principle, and prototype evaluation (UCD-3) was the most frequent (82.3%); 85.2% addressed iterative responsiveness (factor 2). Most articles (66.8%) did not report a particular method or approach; 20.5% used design-related approaches, with user-centered design being the most common (7.4%). Few articles (3.9%) utilized social cognitive theory.Overall, co-design activities were isolated by phase. Consumers were primarily involved in evaluating prototypes and had limited engagement in the early stages. Iterative responsiveness factor activities were underreported or limited in scope. The use of approaches, theories, and frameworks was inconsistent. Consumer involvement in the co-design of diabetes self-management apps is often limited to later phases, with minimal engagement during the critical preprototype phase. To enhance the relevance, effectiveness, and adoption of diabetes self-management apps, app designers should improve the reporting of co-design activities and engage consumers across all co-design phases.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".