Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Digital health interventions (DHIs) are a central focus of health care transformation efforts, yet their uptake in practice continues to fall short of their potential. In order to achieve their desired outcomes and impact, DHIs need to reach their target population and need to be used. Many factors can rapidly intersect between this dynamic of users and interventions. The application of theories, models, and frameworks (TMFs) can facilitate the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions. There remains a gap in our understanding of how TMFs have been applied to guide the evaluation of DHIs with real-world health system operations. OBJECTIVE: This study aims to map TMFs used in studies to guide the evaluation of DHIs. The objectives are to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been prospectively used (ie, their roles) in primary studies to evaluate DHIs, and (3) to reflect on the relevance and utility of our findings for knowledge users. METHODS: This scoping review was conducted in partnership with knowledge users using an integrated knowledge translation approach. We included papers (eg, reports; empirical quantitative, qualitative, and mixed methods studies; conference proceedings; and dissertations) if primary insights resulting from the application of TMFs were presented. Any type of DHI was eligible. Papers published from 2000 and onward were mainly identified from the following databases: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid). RESULTS: A total of 156 studies published between 2000 and 2022 were included. A total of 68 distinct TMFs were identified across 85 individual studies. In more than half (85/156, 55%) of the included studies, 1 of following 6 prevailing TMFs were reported: Consolidated Framework for Implementation Research (n=39); the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (n=17); the Technology of Acceptance Model (n=16); the Unified Theory on Acceptance and Use of Technology (n=12); the Diffusion of Innovation Theory (n=10); and Normalization Process Theory (n=9). The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), and to identify key constructs that may serve as barriers and facilitators (n=52). CONCLUSIONS: As TMFs are most often reported to be applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. There is also a need to adapt or develop guidelines for better reporting DHIs and the use of TMFs to guide evaluation. Hence, it would contribute to ensuring ongoing technology transformation efforts are evidence and theory informed rather than anecdotally driven.
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 | 0,019 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».