Effects of 12 Weeks of At-Home, Application-Based Exercise on Health Care Workers’ Depressive Symptoms, Burnout, and Absenteeism
Notice bibliographique
Résumé
Importance: During the COVID-19 pandemic, health care workers (HCWs) reported a significant decline in their mental health. One potential health behavior intervention that has been shown to be effective for improving mental health is exercise, which may be facilitated by taking advantage of mobile application (app) technologies. Objective: To determine the extent to which a 12-week app-based exercise intervention can reduce depressive symptoms, burnout, and absenteeism in HCWs, compared with a wait list control condition. Design, Setting, and Participants: A 2-group randomized clinical trial was conducted, with participants screened from April 6 to July 4, 2022. Participants were recruited from an urban health care organization in British Columbia, Canada. Participants completed measures before randomization and every 2 weeks thereafter. Interventions: Exercise condition participants were asked to complete four 20-minute sessions per week using a suite of body weight interval training, yoga, barre, and running apps. Wait-listed control participants received the apps at the end of the trial. Main Outcomes and Measures: The primary outcome consisted of the between-group difference in depressive symptoms measured with the 10-item Center for Epidemiological Studies Depression Scale. The secondary outcomes corresponded to 3 subfacets of burnout (cynicism, emotional exhaustion, and professional efficacy) and absenteeism. Intention-to-treat analyses were completed with multilevel structural equation modeling, and Feingold effect sizes (ES) were estimated every 2 weeks. Results: A total of 288 participants (mean [SD] age, 41.0 [10.8] years; 246 [85.4%] women) were randomized to either exercise (n = 142) or wait list control (n = 146) conditions. Results revealed that ESs for depressive symptoms were in the small to medium range by trial's end (week 12, -0.41 [95% CI, -0.69 to -0.13]). Significant and consistent treatment effects were revealed for 2 facets of burnout, namely cynicism (week 12 ES, -0.33 [95% CI, -0.53 to -0.13]) and emotional exhaustion (week 12 ES, -0.39 [95% CI, -0.64 to -0.14]), as well as absenteeism (r = 0.15 [95% CI, 0.03-0.26]). Adherence to the 80 minutes per week of exercise decreased from 78 (54.9%) to 33 (23.2%) participants between weeks 2 and 12. Conclusions and Relevance: Although exercise was able to reduce depressive symptoms among HCWs, adherence was low toward the end of the trial. Optimizing adherence to exercise programming represents an important challenge to help maintain improvements in mental health among HCWs. Trial Registration: ClinicalTrials.gov Identifier: NCT05271006.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».