Cognitive Behavioral Immersion for Depression: Protocol for a Three-Arm Randomized Controlled Trial of Peer-Based Coaching in the Metaverse
Notice bibliographique
Résumé
BACKGROUND: Depression and anxiety are among the most common mental health concerns globally. Efficacious treatments such as cognitive behavioral therapy exist but remain difficult to access and scale. Cognitive behavioral immersion (CBI)-a cognitive behavioral skills training intervention delivered by peer coaches in the metaverse-has been developed to address these barriers. CBI can be used through a virtual reality headset or via flat-screen devices such as phones, tablets, or computers. Pilot data have established its usability among participants with clinical levels of depression and anxiety. However, more research is needed to determine whether CBI causes decreases in these symptoms and how delivery via virtual reality compares to flat-screen devices. OBJECTIVE: This protocol aims to conduct a randomized controlled trial evaluating the efficacy of immersive CBI accessed via a virtual reality headset (CBI-VR) as compared to a less immersive (but more accessible) CBI condition accessed via a flat-screen device (CBI-FS) and each to a delayed access control (DAC). METHODS: A total of 306 adults experiencing clinical levels of depressive symptoms are being recruited nationally to participate in this web-based trial. Participants will be randomized according to a 1:1:1 ratio to one of three conditions: (1) CBI-VR, (2) CBI-FS, and (3) DAC. The CBI program consists of eight weekly 60-minute group sessions led by trained peer coaches who teach cognitive behavioral skills. The acute period of each condition will last 8 weeks with a follow-up period of 6 months. The primary outcome is depressive symptoms; secondary outcomes are anxiety symptoms and quality of life. Outcomes will be assessed once at baseline, weekly during the course of the intervention, and monthly during follow-ups. We will use hierarchical linear models to assess differences in the rate of symptom change among conditions. We will also explore potential prognostic (demographics and immersion) and prescriptive (cognitive behavioral skills, group alliance, and program engagement) predictors, as well as potential mechanisms (cognitive change and social support) of response. RESULTS: We hypothesize that participants randomized to either CBI group will experience greater symptom improvement than those in DAC and that CBI-VR participants will improve more than CBI-FS participants. This study was funded in September 2023. Data collection began in February 2024. As of January 2025, all 306 participants have been enrolled. Data collection should conclude by September 2025. Data have not yet been analyzed. Expected results to be submitted for publication in the winter of 2025. CONCLUSIONS: This trial will determine if CBI via either device is efficacious as compared to DAC and whether virtual reality enhances outcomes. Findings will contribute to the literature on using the metaverse and virtual reality to facilitate effective accessible mental health interventions, particularly for depression. TRIAL REGISTRATION: ClinicalTrials.gov NCT06418997; https://clinicaltrials.gov/study/NCT06418997. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65970.
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 | 0,022 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,009 |
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 ».