Enhancing Engagement, Practice Integration, and Skill Learning in Mobile Technology–Delivered Interventions Using Human Support: Randomized Controlled Trial With Depressed College Students
Notice bibliographique
Résumé
Background Among evidence-based mobile technology–delivered interventions (mTDIs), mindfulness apps such as Headspace have demonstrated numerous benefits. These benefits are particularly important for college students, who continue to face high rates of depression and psychological distress that are paired with insufficient mental health services to meet these needs. mTDIs offer scalable solutions to ameliorating mental health symptoms and may be able to help address this gap in limited access to mental health services for all populations. While mTDIs have great promise for maximizing reach, their utility can be hamstrung by low rates of user engagement and uptake. Thus, this study implemented 2 human support enhancements designed to boost user in-app engagement, practice integration into daily life (ie, sustainability), and app-related skill learning (ie, perceived benefits) in a sample of college students with depression who were granted full access to an mTDI (Headspace). Objective This randomized controlled trial evaluated the impact of two human support enhancements—(1) a one-time face-to-face orientation with or without (2) placement in a peer supportive accountability group—on self-reported and objectively captured mTDI engagement, practice integration, and skill learning among a sample of college students with depression. Methods Participants (n=123) authorized access to their recorded app use data, provided by Headspace. In addition, at the midpoint (1 mo), postintervention (2 mo), and follow-up (3 mo) assessments, participants self-reported on the extent to which they had used the app, how likely they were to continue using the app and related skills in the future, and the extent to which they learned skills and practiced these skills in their daily lives. Results Compared to participants who were simply given access to the app (37/123, 30.1%) without these enhancements, those who attended the orientation (86/123, 69.9%), regardless of additional random allocation to the peer supportive accountability group (48/123, 39%), demonstrated significantly greater mTDI engagement (ie, more minutes meditated [F2,117=11.20; P<.001] and more sessions overall [F2,117=15.00; P<.001]) and rated more favorably multiple aspects of practice integration (ie, more everyday mindfulness practice [F2,72=6.20; P=.003] and greater likelihood of future mindfulness [F2,71=7.42; P<.001]) and skill learning (ie, learning about mindfulness [F2,73=6.02; P=.004], learning mindfulness skills [F2,72=11.01; P<.001], and an increased awareness of thoughts and feelings [F2,73=6.05; P=.004]), indicating potential implications for amplifying the benefits of mTDIs through increased user engagement. Conclusions The results of this study illustrate that an initial face-to-face orientation boosts mTDI engagement, enhances the integration of intervention skills into everyday life, and increases learning. Future work is needed to determine the active ingredients of the orientation as well as to narrow in on the optimal implementation of supportive accountability that might drive increased levels of engagement and the associated positive intervention benefits. Trial Registration Open Science Foundation (OSF) 3trzk; https://osf.io/3trzk
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| 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 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».