The effectiveness of exercise referral schemes on patient health and their cost: an umbrella review
Bibliographic record
Abstract
Exercise referral schemes (ERS) involving referrals from primary care providers to exercise programs and professionals may be a useful strategy for helping patients lead more active lifestyles. We performed an umbrella review to determine the impact of ERS on patient activity/fitness, clinical outcomes, and their cost-effectiveness. The review was pre-registered in Prospero (CRD42023443094) and conducted in July 2023. PRISMA reporting was followed. Our umbrella review screened 2129 citations with 12 studies meeting our inclusion criteria ( n = 8 meta-analyses) that included 110 unique individual studies of 62 815 unique participants. The average study quality was 8.9 ± 1.9 (out of 11). Studies examined patient physical activity or fitness ( n = 9/12), a clinical outcome ( n = 8/12), and/or cost-effectiveness ( n = 4/12). Earlier reviews observed that physical activity or fitness was unchanged following ERS, but more recent (post-2015), larger sample size studies observe low-to-moderate improvements. Recent reviews reported that ERS lowered blood pressure, body mass index, and depression. ERS cost-effectiveness was conflicting. Altogether our umbrella review of high-quality reviews with a low risk of bias demonstrated that although early work indicated null effects, more up-to-date reviews of ERS observe improved patient activity/fitness, cardiometabolic, and mental health. ERS are an effective strategy to improve patient health.
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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 | 0.042 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".