Mobile Behavioral Health Coaching as a Preventive Intervention for Occupational Public Health: Retrospective Longitudinal Study
Notice bibliographique
Résumé
Background Researchers have recently proposed that behavioral health coaching (BHC) is effective in promoting proactive care among employees. However, to qualify as a preventive workplace intervention, more research is needed to evaluate whether BHC can further elevate well-being among moderately mentally healthy employees. Objective Using real-world data, this study evaluates the preliminary effectiveness of app-based BHC against a nonrandomized control group with open access to self-help tools in improving well-being (ie, mood levels and perceived stress). The study also explores the active ingredients of BHC and dose-response associations between the number of BHC sessions and well-being improvements. Methods Employees residing across Asia-Pacific countries (N=1025; mean age 30.85, SD 6.97 y) who reported moderately positive mood and medium levels of perceived stress in their first week of using the mental health app Intellect were included in this study. Users who were given access by their organizations to Intellect’s BHC services were assigned to the “Coaching” condition (512/1025, 49.95%; mean age 31.09, SD 6.87 y), whereas other employees remained as “Control” participants (513/1025, 50.05%; mean age 30.61, SD 7.06 y). To evaluate effectiveness, monthly scores from the validated mood and stress sliders were aggregated into a composite well-being score and further examined using repeated-measure conditional growth models. Postcoaching items on “Perceived Usefulness of the BHC session” and “Working Alliance with my Coach” were examined as active ingredients of BHC using 1-1-1 multilevel mediation models. Finally, 2-way repeated-measure mixed ANOVA models were conducted to examine dose-response effects on well-being improvements between groups (coaching and control) across time. Results Growth curve analyses revealed significant time by group interaction effects for composite well-being, where “Coaching” users reported significantly greater improvements in well-being than “Control” participants across time (composite well-being: F1,391=6.12; ηp2=0.02; P=.01). Among “Coaching” participants, dependent-sample 2-tailed t tests revealed significant improvements in composite well-being from baseline to 11 months (t512=1.98; Cohen d=0.17; P=.049). Improvements in “Usefulness of the BHC session” (β=.078, 95% Cl .043-.118; P<.001) and “Working Alliance” (β=.070, 95% Cl .037-.107; P<.001) fully mediated within-level well-being enhancements over time. Comparing against baseline or first month scores, significant time by group interactions were observed between the second and sixth months, with the largest effect size observed at the fifth month mark (first month vs fifth month: F1,282=15.0; P<.001; ηp2=0.051). Conclusions We found preliminary evidence that BHC is an effective preventive workplace intervention. Mobile-based coaching may be a convenient, cost-effective, and scalable means for organizations and governments to boost public mental health.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
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 tête enseignante, pas un consensus.
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