Prevalence of Musculoskeletal Conditions in Heart Patients Undergoing Cardiovascular Rehabilitation Programs: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Exercise-based cardiovascular rehabilitation is an essential strategy for managing cardiovascular diseases. However, some factors, such as musculoskeletal conditions, may limit exercise capacity and adherence. Although musculoskeletal conditions can lead to activity limitations and influence the strategies used in cardiovascular rehabilitation programs, their management and evaluation are often under-recognized. Knowledge of these conditions is important for interprofessional providers to adapt therapeutic care for this population. This study aimed to verify, through a systematic review and meta-analysis, the prevalence of musculoskeletal conditions in patients enrolled in cardiovascular rehabilitation programs. The search strategy was updated and last executed in May 2024 in the following electronic databases: MEDLINE, EMBASE, CINAHL, Web of Science, and SCOPUS. Observational studies that showed the prevalence of musculoskeletal conditions in patients with heart disease undergoing cardiovascular rehabilitation programs were eligible. To assess the risk of bias in the studies, we used the Newcastle-Ottawa Scale, and to check the overall quality of the evidence, we used the GRADE system. A total of 2,638 studies were screened, of which eight met the inclusion criteria and were rated as having moderate to high methodological quality. The prevalence of musculoskeletal conditions in patients enrolled in a cardiac rehabilitation program was 57.5%, CI 95%: 40.1-73.3; I²: 98%. Common musculoskeletal conditions were arthritis with a prevalence of 49.0%, CI 95%: 35.7-62.4; I²: 98%, musculoskeletal pain with a prevalence of 40.0%, CI 95%: 23.0-60.0; I²: 98%, and osteoporosis with a prevalence of 10.3%, CI 95%: 8.3-12.7; I²: 1%. In the studies included in this review, we found a high prevalence of musculoskeletal conditions. Future studies will be necessary to scrutinize and analyze whether the type, intensity, frequency, and duration of physical exercises proposed in cardiovascular rehabilitation programs can improve or worsen musculoskeletal conditions. These findings highlight the need to integrate musculoskeletal assessment and management into cardiac rehabilitation practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".