An umbrella review on how digital health intervention co-design is conducted and described
Bibliographic record
Abstract
Co-design has been suggested to improve intervention effectiveness and sustainability. However, digital health intervention co-design is inconsistently reported. This umbrella review aims to synthesize what is known about co-design of digital health interventions. We searched five databases from inception. Reviews which reported on co-design methodologies used in digital health were eligible. Information on review type, health conditions, and reported specifics of co-design were extracted and synthesized. Methodological quality was assessed using the AMSTAR2 tool. We included 21 reviews published between 2015 and 2023. Co-design participants included patients, caregivers and healthcare professionals. The frequency and breadth of participant involvement in co-design activities were reported in less than half of reviews. Participants evaluated intervention co-design as a positive process. All reviews were rated as critically low quality. This umbrella review highlights the inconsistent reporting of co-design in digital health. Here, we emphasize the importance of creating guidelines to direct co-design activities.
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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 | 0.046 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".