The Effect of Mental Health App Customization on Depressive Symptoms in College Students: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Mental health apps have shown promise in improving mental health symptoms, including depressive symptoms. However, limited research has been aimed at understanding how specific app features and designs can optimize the therapeutic benefits and adherence to such mental health apps. OBJECTIVE: The primary purpose of this study is to investigate the effect of avatar customization on depressive symptoms and adherence to use a novel cognitive behavioral therapy (CBT)-based mental health app. The secondary aim is to examine whether specific app features, including journaling, mood tracking, and reminders, affect the usability of the mental health app. METHODS: College students were recruited from a university study recruitment pool website and via flyer advertisements throughout campus. A total of 94 participants completed a randomized controlled trial in which they were randomized to either customization or no customization version of the app. Customization involved personalizing a virtual avatar and a travel vehicle to one's own preferences and use of one's name throughout the app. Participants completed a 14-day trial using a novel CBT-based mental health app called AirHeart. Self-report scores for depressive symptoms, anxiety, and stress were measured at baseline and after the intervention. Postintervention survey measures also included usability and avatar identification questionnaires. RESULTS: Of the 94 enrolled participants, 83 (88%) completed the intervention and postintervention assessments. AirHeart app use significantly reduced symptoms of depression (P=.006) from baseline to the end of the 2-week intervention period for all participants, regardless of the customization condition. However, no differences in depressive symptoms (P=.17) or adherence (P=.80) were observed between the customization (39/83, 47%) and no customization (44/83, 53%) conditions. The frequency of journaling, usefulness of mood tracking, and helpfulness of reminders were not associated with changes in depressive symptoms or adherence (P>.05). Exploratory analyses showed that there were 3 moderate positive correlations between avatar identification and depressive symptoms (identification: r=-0.312, P=.02; connection: r=-0.305, P=.02; and lack of relatability: r=0.338, P=.01). CONCLUSIONS: These results indicate that CBT mental health apps, such as AirHeart, have the potential to reduce depressive symptoms over a short intervention period. The randomized controlled trial results demonstrated that customization of app features, such as avatars, does not further reduce depressive symptoms over and above the CBT modules and standard app features, including journal, reminders, and mood tracking. However, further research elucidating the relationship between virtual avatar identification and mental health systems is needed as society becomes increasingly more digitized. These findings have potential implications for improving the optimization of mental health app designs. TRIAL REGISTRATION: Open Science Framework t28gm; https://osf.io/t28gm.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».