Prevalence and Impact of Postexertional Malaise on Recovery in Adults With Post-COVID-19 Condition: A Systematic Review With Meta-analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,025 | 0,047 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».