Mobile Technologies for Supporting Mental Health in Youths: Scoping Review of Effectiveness, Limitations, and Inclusivity
Notice bibliographique
Résumé
BACKGROUND: Over the past decade, there has been growing support for the use of mobile health (mHealth) technologies to improve the availability of mental health interventions. While mHealth is a promising tool for improving access to interventions, research on the effectiveness and efficacy of mHealth apps for youths is limited, particularly for underrepresented populations, including youths of color and economically marginalized youths. OBJECTIVE: This scoping review study sought to evaluate the following research questions: (1) What is the extent of the current literature on mHealth apps that provide intervention for mental health problems in children and adolescents? (2) What is known from the existing literature about the effectiveness or efficacy of delivering mental health services via mHealth apps? (3) What are the gaps in the knowledge base in the fields of technology and mental health? (4) Do the reviewed mHealth apps address issues of cultural sensitivity or have they been tested with underrepresented groups (ie, youths of color or economically marginalized groups)? METHODS: An electronic database search was conducted using relevant search terms. Seven independent reviewers screened identified studies, including title and abstract review to determine if studies met the following inclusion criteria: (1) targeted samples with mental health symptomology or disorders, (2) studied youth participants aged 6-17 years, and (3) examined the use of a mobile app-based platform for intervention. Relevant studies were subjected to full-text review to extract and chart relevant data based on a priori research questions. RESULTS: The initial database search yielded 304 papers published from 2010 to 2021. After screening and selection, the final review included 10 papers on the effectiveness and efficacy of mental health intervention apps for youths aged 8 to 17 years. Identified apps targeted a broad range of mental health challenges in youths (ie, depression, self-harm, autism spectrum disorder, anxiety, and obsessive-compulsive disorder). Results identified only a small number of studies suggesting that current effectiveness and efficacy research in this area are limited. While some studies provided general support for the effectiveness of mHealth apps in improving mental health outcomes in youths, several notable limitations were present across the literature, reducing the generalizability of findings. Additionally, considerations around racial, ethnic, and socioeconomic diversity were scarce across studies. CONCLUSIONS: Although some studies cited in this scoping review provide support for the effectiveness and efficacy of mHealth apps targeting mental health concerns in youths, the overall body of literature remains quite limited. Moreover, mHealth apps expressly developed to be culturally responsive are almost nonexistent. Further efforts are needed to recruit youths who are typically underrepresented in research and invite stakeholder participation and collaborative input in the early stages of the mHealth app development process.
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 | 0,002 | 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 ».