Mobile Apps That Promote Emotion Regulation, Positive Mental Health, and Well-being in the General Population: Systematic Review and Meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Among the general public, there appears to be a growing need and interest in receiving digital mental health and well-being support. In response to this, mental health apps (MHapps) are becoming available for monitoring, managing, and promoting positive mental health and well-being. Thus far, evidence supports favorable outcomes when users engage with MHapps, yet there is a relative paucity of reviews on apps that support positive mental health and well-being. OBJECTIVE: We aimed to systematically review the available research on MHapps that promote emotion regulation, positive mental health, and well-being in the general population aged 18-45 years. More specifically, the review aimed at providing a systematic description of the theoretical background and features of MHapps while evaluating any potential effectiveness. METHODS: A comprehensive literature search of key databases, including MEDLINE (via Ovid), EMBASE (via Ovid), PsycINFO (via Ovid), Web of Science, and the Cochrane Register of Controlled Trials (CENTRAL), was performed until January 2021. Studies were included if they described standalone mental health and well-being apps for adults without a formal mental health diagnosis. The quality of all studies was assessed against the Mixed Methods Appraisal Tool. In addition, the Cochrane Risk-of-Bias tool (RoB-2) was used to assess randomized control trials (RCTs). Data were extracted using a modified extraction form from the Cochrane Handbook of Systematic Reviews. A narrative synthesis and meta-analysis were then undertaken to address the review aims. RESULTS: In total, 3156 abstracts were identified. Of these, 52 publications describing 48 MHapps met the inclusion criteria. Together, the studies evaluated interventions across 15 countries. Thirty-nine RCTs were identified suggesting some support for the role of individual MHapps in improving and promoting mental health and well-being. Regarding the pooled effect, MHapps, when compared to controls, showed a small effect for reducing mental health symptoms (k=19, Hedges g=-0.24, 95% CI -0.34 to -0.14; P<.001) and improving well-being (k=13, g=0.17, 95% CI 0.05-0.29, P=.004), and a medium effect for emotion regulation (k=6, g=0.49, 95% CI 0.23-0.74, P<.001). There is also a wide knowledge base of creative and innovative ways to engage users in techniques such as mood monitoring and guided exercises. Studies were generally assessed to contribute unclear or a high risk of bias, or to be of medium to low methodological quality. CONCLUSIONS: The emerging evidence for MHapps that promote positive mental health and well-being suggests promising outcomes. Despite a wide range of MHapps, few apps specifically promote emotion regulation. However, our findings may position emotion regulation as an important mechanism for inclusion in future MHapps. A fair proportion of the included studies were pilot or feasibility trials (k=17, 33%), and full-scale RCTs reported high attrition rates and nondiverse samples. Given the number and pace at which MHapps are being released, further robust research is warranted to inform the development and testing of evidence-based programs.
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 | 0,013 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,029 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».