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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".