Efficacy of a Self-Guided Transdiagnostic Intervention for Adults With Anxiety and Depression: Randomized Controlled Trial
Notice bibliographique
Résumé
Background Anxiety and depressive disorders affect hundreds of millions globally, with substantial barriers limiting access to evidence-based treatments like cognitive behavioral therapy. Digital mental health interventions offer potential solutions to improve access to support. However, evidence of efficacy through randomized controlled trials is essential for clinical adoption. Objective This study evaluated the efficacy of the Headspace Anxiety and Depression Program, a digitally delivered 21-session intervention grounded in the Unified Protocol that integrates cognitive behavioral and mindfulness-based strategies to target common drivers of emotional disorders, compared to a waitlist control group. Primary aims were to assess reductions in anxiety (Generalized Anxiety Disorder-7 [GAD-7]) and depression (Patient Health Questionnaire-8 [PHQ-8]) symptoms. Secondary aims examined improvements in sleep quality (Pittsburgh Sleep Quality Index), perceived stress (Perceived Stress Scale-10), mindfulness (Mindful Attention Awareness Scale), and overall well-being (Warwick-Edinburgh Mental Well-Being Scale), as well as clinical remission and treatment response rates. Methods A fully remote, 2-arm parallel-group randomized controlled trial was conducted with 168 participants (aged ≥18 years) who had clinically significant anxiety (GAD-7≥10) or depression symptoms (PHQ-8≥10). Participants were randomized to either the Headspace Anxiety and Depression Program (n=84) or waitlist control (n=84). The intervention consisted of 21 daily sessions (5-10 minutes each) delivered via the Headspace app. Primary (GAD-7 and PHQ-8) and secondary outcomes (Pittsburgh Sleep Quality Index, Perceived Stress Scale-10, Mindful Attention Awareness Scale, and Warwick-Edinburgh Mental Well-Being Scale) were assessed at baseline, postintervention, and a 3-week follow-up using intention-to-treat analysis with mixed-model ANOVAs. Results Study retention was high, with the majority (154/168, 91.7%) of participants completing the 3-week follow-up visit, and no serious adverse events were reported. Intervention adherence was high, with 82.1% (69/84) in the intervention group completing all 21 sessions. Significant group×time interactions were observed for both primary outcomes (P<.001). The Headspace group showed substantial reductions in anxiety symptoms (GAD-7: 34.5% reduction from baseline: mean 11.9, SD 2.8 to follow-up: mean 7.8, SD 2.3; η2p=0.350) and depression symptoms (PHQ-8: 33.9% reduction from baseline: mean 12.1, SD 2.8 to follow-up: mean 8.0, SD 2.0; η2p=0.370), while the control group did not show significant reductions. Combined anxiety and depression remission rates were significantly higher in the Headspace group (18/84, 21.4%) versus controls (7/84, 8.3%; P=.03), as were treatment response rates (23/84, 27.3% vs 2/84, 2.3%; P=.03). Participants in the Headspace group also demonstrated significant improvements in secondary outcomes, including sleep quality (30.2% improvement), perceived stress (13.2% reduction), mindfulness (10.3% increase), and mental well-being (10.7% increase). Conclusions The Headspace Anxiety and Depression Program demonstrated significant efficacy in reducing anxiety and depression symptoms comparable to established treatments. Benefits were sustained at the 3-week follow-up. These findings support the potential of evidence-based transdiagnostic digital mental health interventions to address critical gaps in mental health care delivery. Trial Registration ClinicalTrials.gov NCT06434883; https://clinicaltrials.gov/study/NCT06434883
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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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,003 | 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 ».