Understanding Engagement With Digital Mental Health Technology in Mental Health Services: Multicenter Observational Study
Notice bibliographique
Résumé
BACKGROUND: Digital technologies can substantially improve mental health care by facilitating measurement-based care through routine outcome monitoring. However, their effectiveness is constrained by the extent to which these technologies are used by services, clinicians, and clients. OBJECTIVE: This study aims to investigate engagement with the Innowell platform, a measurement-based digital mental health technology (DMHT), to gain insights into the individual- and service-level factors influencing engagement. METHODS: Participants were 2682 help-seeking clients from 12 Australian mental health services (11 headspace centers and 1 private practice, Mind Plasticity), wherein the Innowell platform was implemented. Although the initial implementation was standardized, services varied in their practical and continued use of the platform, as well as in the resources allocated to foster engagement. All participants completed an initial assessment during onboarding. Engagement was defined as their ensuing completion of the summary questionnaire, designed for routine outcome monitoring. Participants were classified as "initial assessment only," "single use" (1 completion of the summary questionnaire), or "≥2 uses" (≥2 completions). We analyzed engagement differences across services and associations between engagement and initial assessment scores. RESULTS: Of the 2682 help-seeking clients, 75.43% (n=2023) completed the initial assessment only, 11.56% (n=310) had 1 completion of the summary questionnaire, and 13.01% (n=349) had 2 or more completions. The service center was the strongest predictor of engagement, with Mind Plasticity participants showing >8 times higher engagement than other centers. At the individual level, higher scores in depression (P=.002), mania-like experiences (P=.047), suicide ideation (P=.004), hospitalization history for mental illness (P=.01), and physical activity (P<.001) were associated with increased engagement. In contrast, higher levels of anxiety symptoms (P=.01), alcohol use (P<.001), self-reported mental illness severity (P=.02), and social support (P=.047) predicted lower engagement. Age and several other clinical variables were not significant predictors when controlling for service-level factors. CONCLUSIONS: This study reveals that both individual- and service-level factors significantly influence DMHT engagement, with the service center being the strongest predictor. This highlights the importance of service-level technology integration and support roles, such as digital navigators, in fostering engagement. Significant variation in engagement among user groups indicates the need for a nuanced approach to measurement-based care. While mental illness generally did not impede engagement, self-perceived severity and anxiety symptoms were barriers. These findings underscore the critical importance of systemic factors and service-level integration strategies in driving DMHT engagement. User-centered designs remain important, but effective integration of DMHTs into existing mental health services is paramount for improving engagement across diverse user groups and clinical presentations. This multilevel approach, encompassing individual, service, and system-wide considerations, is essential for realizing DMHTs' full potential in delivering effective measurement-based care.
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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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».