Mental Health Apps Available in App Stores for Indian Users: Protocol for a Systematic Review
Notice bibliographique
Résumé
BACKGROUND: There has been a surge in mental health apps over the past few years. While these have great potential to address the unmet mental health needs of the population, the recent proliferation of mental health apps in the commercial marketplace has raised several concerns, such as privacy, evidence-based, and quality. Although there is mounting research on the effectiveness of mental health apps, the majority of these are not accessible to the public and most of those available have not been researched. Despite the rapid growth of the digital health market in India, there are no comprehensive reviews of publicly available mental health apps for Indian users. Hence it becomes important to review mental health apps freely available to potential end users in terms of their scope, functions, and quality. OBJECTIVE: This study aims to systematically evaluate mental health apps available to Indian users in app stores. METHODS: This systematic review of mental health apps will be performed following the Target user, Evaluation focus, Connectedness and Health domain approach and the PASSR (Protocol for App Store Systematic Reviews) checklist. Fifteen key search terms covering various mental health conditions and therapies will be used on the Android and iOS stores. The identified apps will be further screened and reviewed based on the inclusion and exclusion criteria. The pool of eligible apps will be downloaded for detailed review. The following steps will be adopted to streamline the review process and interrater consistency. Six apps will be randomly selected from the downloaded apps, for joint discussion and review by a team of 4 primary reviewers and 2 mentors. Following this, a new set of 6 randomly selected apps will be rated independently by the primary reviewers and the differences in ratings will be jointly discussed for generating consensus. Subsequently, the primary reviewers will individually review the remaining apps in the list. Data will be extracted based on predecided parameters such as privacy policy, basic purpose, type of developer, nature of intervention strategies, and guided versus unguided nature. Additionally, the apps will be reviewed for quality using the Mobile Application Rating Scale. The data analysis and synthesis strategy will incorporate descriptive statistics based on quality evaluation using the Mobile Application Rating Scale and examining the content of the apps for generating descriptive information. RESULTS: The initial screening of mental health apps available for Indian users on the Google Play Store and Apple App Store was initiated in October 2024. We expect to complete the detailed systematic review by April 2025. CONCLUSIONS: This study will offer a comprehensive review of mental health apps available in digital marketplaces for Indian users and has implications for end users, policy makers, developers, and mental health professionals. TRIAL REGISTRATION: International Platform of Registered Systematic Review and Meta-analysis Protocols INPLASY2024100035; https://inplasy.com/inplasy-2024-10-0035/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71071.
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 | 0,084 | 0,115 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,006 |
| Méta-épidémiologie (sens large) | 0,019 | 0,015 |
| Bibliométrie | 0,015 | 0,012 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,063 | 0,009 |
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 ».