Defining and Measuring Engagement and Adherence in Digital Mental Health Interventions: Protocol for an Umbrella Review
Notice bibliographique
Résumé
BACKGROUND: Digital mental health interventions (DMHIs) offer scalable solutions to address mental health needs, particularly among marginalized populations. However, engagement and adherence rates in DMHIs are often suboptimal, limiting their potential impact. Despite the growing body of literature on DMHI engagement, there is no consensus on how engagement and adherence are defined and measured across studies. Understanding these variations is crucial to improving DMHI design, evaluation, and outcomes. OBJECTIVE: Using the population, concept, context framework to frame the objectives, this umbrella review aims to synthesize existing systematic reviews, meta-analyses, and scoping reviews to identify how engagement and adherence are defined and measured in DMHIs. Additionally, this review seeks to explore factors that may influence DMHI engagement and adherence. METHODS: A systematic search of peer-reviewed literature will be conducted across major electronic databases following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Eligible studies will include systematic reviews, meta-analyses, and scoping reviews published in English in the past 10 years that examine engagement and/or adherence in DMHIs. Data will be extracted and synthesized to identify definitions, measurement methods, and influencing factors. Risk of bias will be assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist for systematic reviews and research syntheses. Findings will be presented using a mixed methods convergent integrated approach, identifying and synthesizing themes across the included quantitative and qualitative study results. RESULTS: This study is expected to be conducted over a 6-month period. The search, conducted in early March 2025, initially identified 5087 papers. An additional 35 papers were found through manual handsearching of BMC Digital Health. These totals were recorded prior to the removal of duplicates. This umbrella review is expected to be conducted with screening, quality assessment, and data extraction streamlined through the Covidence platform. The screening and selection of studies will be performed in month 1, followed by data extraction and quality appraisal in months 2 and 3. Data synthesis and integration will take place in months 4 and 5, and writing conclusions and preparing the manuscript will occur in month 6. This review will provide a comprehensive summary of how engagement and adherence are operationalized across existing literature. It will highlight commonalities, inconsistencies, and gaps in definitions and measurement methods. Additionally, this review will outline the key factors that influence engagement and adherence, including individual, technological, and contextual elements. CONCLUSIONS: This umbrella review will contribute to a more nuanced understanding of engagement and adherence in DMHIs, informing future intervention design and evaluation. The findings will support the development of standardized definitions and measurement frameworks, ultimately enhancing the effectiveness and inclusivity of DMHIs. TRIAL REGISTRATION: PROSPERO CRD42025637603; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025637603. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73438.
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 | 0,121 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,006 |
| Méta-épidémiologie (sens large) | 0,011 | 0,021 |
| Bibliométrie | 0,013 | 0,014 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,085 | 0,020 |
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 ».