Prevalence and Impact of Postexertional Malaise on Recovery in Adults With Post-COVID-19 Condition: A Systematic Review With Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of postexertional malaise (PEM) in people with post-COVID-19 condition (PCC); and the change in prevalence of PEM after rehabilitation interventions in people with PCC. DATA SOURCES: We searched MEDLINE, Embase, CENTRAL, CINAHL, PsychINFO, and clinical trial registries from inception until February 11, 2025. STUDY SELECTION: We included observational studies that measured the prevalence of PEM in adults with PCC and interventional studies that measured the change in prevalence of PEM after rehabilitation interventions in adults with PCC. Two independent researchers screened titles and abstracts. Any discrepancies underwent full text review. Two independent researchers screened the articles included at the full text level. DATA EXTRACTION: Two independent researchers extracted data from eligible studies. We extracted point prevalence from the cross-sectional studies; and period prevalence from the longitudinal studies. Two independent reviewers assessed the risk of bias. Discrepancies were resolved with a senior research team member. For the prevalence studies we used the ROBINS-E tool. For randomized controlled trials we used the RoB2 tool. For non-randomized interventional studies we used the ROBINS-I tool to assess the non-randomized studies. We used the GRADE system to assess the certainty of the evidence. DATA SYNTHESIS: We performed a single-arm proportional meta-analysis to synthesize prevalence estimates using logit transformation. We conducted a sensitivity analysis using multilevel-mixed-effects logistic regression. We used a random effects model. Results were reported as proportions with corresponding 95% confidence intervals (95% CI) or presented descriptively when statistical analysis was not applied. This study is registered with PROSPERO (CRD42024516682). The prevalence of PEM in community-dwelling adults living with PCC was 25% (95% CI: 0.17-0.36; 10 studies; 4,076 low certainty after the word participants). Five of the included studies (193 patients) found a decrease in the frequency and intensity of PEM episodes in adults with PCC after a tailored rehabilitation program centered on integrating pacing approaches. Eight studies (1080 patients) measured PEM as an adverse event following an individually tailored rehabilitation intervention with a therapeutic exercise component. Seven of these studies did not find indications of post exertional symptom exacerbation related to the exercise component of the intervention. All of the studies had high to very high risk of bias. CONCLUSIONS: Our research confirms that there is a large burden of PEM in adults living with PCC, highlighting a critical challenge for health care systems and an urgent need for more inclusive and rigorous research, to offer safe and effective therapeutic solutions and meet the variable needs of people with PCC that experience PEM. There is a subgroup of patients with PCC who do not experience PEM; and there is limited evidence that supervised, individually tailored, symptom-titrated rehabilitation interventions with active exercise components may not trigger PEM in this subgroup of people with PCC. Our results are limited by the insufficient reporting of the percentage of PEM in the baseline before enrolling patients in the rehabilitation programs, and the large number of studies using nonvalidated, unstandardized tools to measure PEM in people with PCC; hence, there is an urgent need to strengthen the methods of future trials.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.047 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".