Barriers and facilitators to exercise-based rehabilitation in people with musculoskeletal conditions: A systematic review
Bibliographic record
Abstract
BACKGROUND: Exercise-based rehabilitation is the first line of treatment for people with musculoskeletal conditions. However, uptake and adherence are suboptimal, compromising the success of rehabilitation. OBJECTIVES: To identify the barriers and facilitators that influence adherence to exercise-based rehabilitation in people with musculoskeletal conditions. Additionally, to identify the methods and instruments used to determine these barriers and facilitators. DESIGN: Systematic review METHODS: Five databases from inception to May 2024 using terms related to exercise, musculoskeletal conditions, methods, barriers and facilitators. Risk of bias was assessed using either the Newcastle-Ottawa Scale or Cochrane risk of bias tool. Using an inductive thematic approach, barriers and facilitators were grouped into intrapersonal, interpersonal and community factors. Methods/instruments were categorized into three groups, being questionnaires, interviews and focus-groups. RESULTS: Eighty-one of 8380 studies were included. The majority of studies were of good or fair quality (95%). The most frequently identified barriers were lack of time (53%), pain (45%) and health (40%). The most frequent facilitators were self-efficacy (42%), perceived health benefits (32%) and previous experiences (30%). The methods used were interviews (n = 53), questionnaires (n = 44) and focus groups (n = 10). CONCLUSION: Most barriers and facilitators to exercise were related to intrapersonal factors. Although there is a lack of consistency in instruments used, the reported barriers and facilitators were similar across studies. Clinicians and researchers should consider intrapersonal factors when promoting exercise-based rehabilitation programs. Applying a theoretical framework to investigate barriers and facilitators to exercise-based rehabilitation in people with musculoskeletal conditions might assist practitioners to prioritize their practice.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".