Health Care Workers’ Need for Headspace: Findings From a Multisite Definitive Randomized Controlled Trial of an Unguided Digital Mindfulness-Based Self-help App to Reduce Healthcare Worker Stress
Notice bibliographique
Résumé
BACKGROUND: Health care workers experience high stress. Accessible, affordable, and effective approaches to reducing stress are lacking. In-person mindfulness-based interventions can reduce health care worker stress but are not widely available or accessible to busy health care workers. Unguided, digital, mindfulness-based self-help (MBSH) interventions show promise and can be flexibly engaged with. However, their effectiveness in reducing health care worker stress has not yet been explored in a definitive trial. OBJECTIVE: This study aimed to investigate the effectiveness of an unguided digital MBSH app (Headspace) in reducing health care worker stress. METHODS: This was a definitive superiority randomized controlled trial with 2182 National Health Service staff in England recruited on the web and allocated in a 1:1 ratio to fully automated Headspace (n=1095, 50.18%) or active control (Moodzone; n=1087, 49.82%) for 4.5 months. Outcomes were subscales of the Depression, Anxiety, and Stress (primary outcome) Scale short form; Short Warwick Edinburgh Mental Well-being Scale; Maslach Burnout Inventory; 15-item Five-Facet Mindfulness Questionnaire minus Observe items; Self-Compassion Scale-Short Form; Compassionate Love Scale; Penn State Worry Questionnaire; Brooding subscale of the Ruminative Response Scale; and sickness absence. RESULTS: Intention-to-treat analyses found that Headspace led to greater reductions in stress over time than Moodzone (b=-0.31, 95% CI -0.47 to -0.14; P<.001), with small effects. Small effects of Headspace versus Moodzone were found for depression (b=-0.24, 95% CI -0.40 to -0.08; P=.003), anxiety (b=-0.19, 95% CI -0.32 to -0.06; P=.004), well-being (b=0.14, 95% CI 0.05-0.23; P=.002), mindfulness (b=0.22, 95% CI 0.09-0.34; P=.001), self-compassion (b=0.48, 95% CI 0.33-0.64; P<.001), compassion for others (b=0.02, 95% CI 0.00-0.04; P=.04), and worry (b=-0.30, 95% CI -0.51 to -0.09; P=.005) but not for burnout (b=-0.19, -0.04, and 0.13, all 95% CIs >0; P=.65, .67, and .35), ruminative brooding (b=-0.06, 95% CI -0.12 to 0.00; P=.06), or sickness absence (γ=0.09, 95% CI -0.18 to 0.34). Per-protocol effects of Headspace (454/1095, 41.46%) versus Moodzone (283/1087, 26.03%) over time were found for stress, self-compassion, and compassion for others but not for the other outcomes. Engagement (practice days per week) and improvements in self-compassion during the initial 1.5-month intervention period mediated pre- to postintervention improvements in stress. Improvements in mindfulness, rumination, and worry did not mediate pre- to postintervention improvements in stress. No serious adverse events were reported. CONCLUSIONS: An unguided digital MBSH intervention (Headspace) can reduce health care workers' stress. Effect sizes were small but could have population-level benefits. Unguided digital MBSH interventions can be part of the solution to reducing health care worker stress alongside potentially costlier but potentially more effective in-person mindfulness-based interventions, nonmindfulness courses, and organizational-level interventions. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ISRCTN15424185; https://tinyurl.com/rv9en5kc.
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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 | 0,006 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».