The use of co-design in developing physical activity interventions for older adults: a scoping review
Bibliographic record
Abstract
BACKGROUND: Promoting physical activity (PA) participation in older adults is important for preserving quality of life and functional independence. Co-design has been shown to increase engagement of end-users in health-related policies and interventions. This scoping review aimed to examine how co-design has been used to develop PA interventions for older adults. METHODS: We searched MEDLINE, EMBASE, AMED, and CINAHL. Peer-reviewed primary research studies that met the following criteria were included: had at least one participant aged ≥60 years involved in the co-design process and the intervention was delivered to individuals whose mean age was ≥60, used co-design methodologies, and any form of PA. After duplicate removal, two or more independent reviewers completed title and abstract and full text screening. Data were extracted from the included studies according to study aims. RESULTS: Of the 29 included studies, 12 different terms were used to describe co-design with variable operational definitions that we consolidated into five proposed components. Fifteen studies engaged users in a consultative way, 13 studies using collaboration, and one study engaged end-users in consumer-control. No studies involved end-users in the dissemination phase. Further, no studies directly measured the effectiveness of the co-design process. Five categories of barriers and facilitators to co-design were identified including frameworks and methodologies, logistics, relationships, participation, and generalizability. CONCLUSIONS: There is a large degree of variability in how co-design is used to develop PA interventions for older adults. Our findings can be used by researchers to improve rigor and standardization in this emerging field. TRIAL REGISTRATION: osf.io/vsw2m.
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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 | 0.110 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| 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".