Effect of Physical Exercise–Based Rehabilitation on Long COVID: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
PURPOSE: The number of persons living with post-coronavirus disease 2019 (COVID-19) conditions or long COVID continues to rise worldwide; however, the etiology and the treatment of long COVID remain nebulous. Therefore, efficient, feasible, and cost-effective therapeutic strategies for a large population with long COVID remain warranted. Physical exercise-based rehabilitation is a promising strategy for long COVID, although its therapeutic effects remain to be determined. This systematic review and meta-analysis aimed to examine the effects of physical exercise-based rehabilitation on long COVID. METHODS: The electronic databases Medline, Embase, Global Health (Ovid), CINAHL (EBSCO), Web of Science, WHO Global Research Database on COVID-19, LitCovid, and Google Scholar were searched from their inception to November 2022. The identified articles were independently screened by three reviewers, and a random-effects model was used to determine the mean differences in the meta-analysis. RESULTS: Twenty-three studies involving 1579 individuals who had COVID-19 (752 women) were included. Physical exercise-based rehabilitation showed beneficial effects on long COVID-related symptoms characterized by dyspnea, fatigue, and depression, as well as on the 6-min walk test, forced expiratory volume in 1 s/forced vital capacity, and quality of life in people who had COVID-19. CONCLUSIONS: Physical exercise-based rehabilitation is a potential therapeutic strategy against long COVID and can be applied as a routine clinical practice in people who have recovered from COVID-19. However, customized physical exercise-based rehabilitation programs and their effects on specific types of long COVID require future large-scale studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.003 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".