Predictors of adherence to prescribed exercise programs for older adults with non-musculoskeletal indications for exercise: a systematic review
Bibliographic record
Abstract
Abstract BACKGROUND AND OBJECTIVES: Prescribed exercise to treat medical conditions and to prepare for surgery is a promising intervention to prevent adverse health outcomes for older adults; however, adherence to exercise programs may be low. Our objective was to identify and grade the quality of predictors of adherence to prescribed exercise in older adults.METHODS: After registration (CRD42018108242), prospective experimental studies were identified using a peer-reviewed search strategy applied to MEDLINE, EMBASE, Cochrane and CINAHL from inception until April 23, 2019. Following independent and duplicate review of titles, abstracts and full texts, we included prospective studies with an average population age >65 years, where exercise was formally prescribed for a medical or surgical condition. We excluded studies where exercise was prescribed for a chronic musculoskeletal condition. Risk of bias was assessed using the Quality in Prognostic studies tool or Cochrane risk of bias tool, as appropriate. Predictors of adherence were identified, pooled, and graded for quality using an adaptation of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework for predictor studies.RESULTS: We included 19 observational studies and 4 randomized controlled trials (n=5785) Indications for exercise included cardiac (n=6), pulmonary rehabilitation (n=7), or other (n=10; surgical, medical, and neurologic). Overall adherence rate was reported in 20 studies (range 21%-93%; mean 68%, standard deviation 23%). Moderate-quality evidence suggested that positive predictors of adherence were self-efficacy and good self-rated mental health; negative predictors were depression (high quality) and distance from the exercise facility. Moderate-quality evidence suggested that comorbidity and age were not predictive of adherence.CONCLUSIONS: These findings can inform design of future exercise programs as well as identification of individuals who may require extra support to benefit from prescribed exercise.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".