Physical activity is good for older adults—but is programme implementation being overlooked? A systematic review of intervention studies that reported frameworks or measures of implementation
Bibliographic record
Abstract
OBJECTIVE: To examine older adult physical activity (PA) intervention studies that evaluated implementation and/or scale-up. Research question 1: What implementation and/or scale-up indicators (specific, observable and measurable characteristics that show the progress of implementation) were reported? Research question 2: What implementation and/or scale-up frameworks were reported? Research question 3: Did studies evaluate the relationship between implementation or scale-up of the intervention and individual level health/behaviour outcomes? If yes, how? DESIGN: Systematic review. DATA SOURCES: Publications from electronic databases and hand searches (2000 to December 2019). ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Any PA intervention studies with community-dwelling older adult participants (mean age ≥60 years). Required indicators: (a) Must report amount of PA as an outcome, with validated self-report or objective measures, and (b) Must have reported at least one implementation or scale-up framework and/or one implementation or scale-up indicator. RESULTS: 137 studies were included for research question 1, 11 for question 2 and 22 for question 3. 137 studies reported an implementation indicator: 14 unique indicators. None were specified as indicators for scale-up evaluation. 11 studies were guided by an implementation or scale-up framework. 22 studies described a relationship between an implementation indicator and an individual-level health outcome. CONCLUSION: There is need for implementation research that extends beyond analysis at the individual level, includes clearly defined indicators and provides a guiding framework to support PA initiatives in older adults. Such implementation studies should evaluate factors in the broader context (eg,political, environmental) that influence scale-up. PROSPERO REGISTRATION: CRD42018091839.
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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 | 0.091 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".