Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Digital health tools such as mobile apps and patient portals continue to be embedded in clinical care pathways to enhance mental health care delivery and achieve the quintuple aim of improving patient experience, population health, care team well-being, health care costs, and equity. However, a key issue that has greatly hindered the value of these tools is the suboptimal user engagement by patients and families. With only a small fraction of users staying engaged over time, there is a great need to better understand the factors that influence user engagement with digital mental health tools in clinical care settings. OBJECTIVE: This review aims to identify the factors relevant to user engagement with digital mental health tools in clinical care settings using a sociotechnical approach. METHODS: A scoping review methodology was used to identify the relevant factors from the literature. Five academic databases (MEDLINE, Embase, CINAHL, Web of Science, and PsycINFO) were searched to identify pertinent articles using key terms related to user engagement, mental health, and digital health tools. The abstracts were screened independently by 2 reviewers, and data were extracted using a standardized data extraction form. Articles were included if the digital mental health tool had at least 1 patient-facing component and 1 clinician-facing component, and at least one of the objectives of the article was to examine user engagement with the tool. An established sociotechnical framework developed by Sittig and Singh was used to inform the mapping and analysis of the factors. RESULTS: The database search identified 136 articles for inclusion in the analysis. Of these 136 articles, 84 (61.8%) were published in the last 5 years, 47 (34.6%) were from the United States, and 23 (16.9%) were from the United Kingdom. With regard to examining user engagement, the majority of the articles (95/136, 69.9%) used a qualitative approach to understand engagement. From these articles, 26 factors were identified across 7 categories of the established sociotechnical framework. These ranged from technology-focused factors (eg, the modality of the tool) and the clinical environment (eg, alignment with clinical workflows) to system-level issues (eg, reimbursement for physician use of the digital tool with patients). CONCLUSIONS: On the basis of the factors identified in this review, we have uncovered how the tool, individuals, the clinical environment, and the health system may influence user engagement with digital mental health tools for clinical care. Future work should focus on validating and identifying a core set of essential factors for user engagement with digital mental health tools in clinical care environments. Moreover, exploring strategies for improving user engagement through these factors would be useful for health care leaders and clinicians interested in using digital health tools in care.
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 | 0,069 | 0,151 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,052 | 0,036 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,013 | 0,011 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».