Community-based Exercise Programs as a Strategy to Optimize Function in Chronic Disease
Bibliographic record
Abstract
BACKGROUND: Chronic diseases are the leading cause of death and disability worldwide. Preliminary evidence suggests that community-based exercise (CBE) improves functional capacity (FC) and health-related quality of life (HRQL). OBJECTIVE: To describe the structure and delivery of CBE programs for chronic disease populations and compare their impact on FC and HRQL to standard care. RESEARCH DESIGN: Randomized trials examining CBE programs for individuals with stroke, chronic obstructive pulmonary disease, osteoarthritis, diabetes, and cardiovascular disease were identified. Quality was assessed using the Cochrane risk of bias tool. Meta-analyses were conducted using Review Manager 5.1. The protocol was registered on PROSPERO (CRD42012002786). RESULTS: Sixteen studies (2198 individuals, mean age 66.8±4.9 y) were included to describe program structures, which were comparable in their design and components, irrespective of the chronic disease. Aerobic exercise and resistance training were the primary interventions in 85% of studies. Nine studies were included in the meta-analysis. The weighted mean difference for FC, evaluated using the 6-minute walk test, was 41.7 m (95% confidence interval [CI], 20.5-62.8). The standardized mean difference for all FC measures was 0.18 (95% CI, 0.05-0.3). The standardized mean difference for the physical component of HRQL measures was 0.21 (95% CI, 0.05-0.4) and 0.38 (95% CI, 0.04-0.7) for the total score. CONCLUSIONS: CBE programs across chronic disease populations have similar structures. These programs appear superior to standard care with respect to optimizing FC and HRQL in individuals with osteoarthritis; however, the effect beyond this population is unknown. Long-term sustainability of these programs remains to be established.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".