Exploring the Use of Multiple Mental Health Apps Within a Platform: Secondary Analysis of the IntelliCare Field Trial
Notice bibliographique
Résumé
BACKGROUND: IntelliCare is a mental health app platform with 14 apps that are elemental, simple and brief to use, and eclectic. Although a variety of apps may improve engagement, leading to better outcomes, they may require navigation aids such as recommender systems that can quickly direct a person to a useful app. OBJECTIVE: As the first step toward developing navigation and recommender tools, this study explored app-use patterns across the IntelliCare platform and their relationship with depression and anxiety outcomes. METHODS: This is a secondary analysis of the IntelliCare Field Trial, which recruited people with depression or anxiety. Participants of the trial received 8 weeks of coaching, primarily by text, and weekly random recommendations for apps. App-use metrics included frequency and lifetime use. Depression and anxiety, measured using the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively, were assessed at baseline and end of treatment. Cluster analysis was utilized to determine patterns of app use; ordinal logistic regression models and log-rank tests were used to determine if these use metrics alone, or in combination, predicted improvement or remission in depression or anxiety. RESULTS: The analysis included 96 people who generally followed recommendations to download and try new apps each week. Apps were clustered into 5 groups: Thinking (apps that targeted or relied on thinking), Calming (relaxation and insomnia), Checklists (apps that used checklists), Activity (behavioral activation and activity), and Other. Both overall frequency of use and lifetime use predicted response for depression and anxiety. The Thinking, Calming, and Checklist clusters were associated with improvement in depression and anxiety, and the Activity cluster was associated with improvement in Anxiety only. However, the use of clusters was less strongly associated with improvement than individual app use. CONCLUSIONS: Participants in the field trial remained engaged with a suite of apps for the full 8 weeks of the trial. App-use patterns did fall into clusters, suggesting that some knowledge about the use of one app may be useful in selecting another app that the person is more likely to use and may help suggest apps based on baseline symptomology and personal preference.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».