Piloting Digital Navigators to Promote Acceptance and Engagement With Digital Mental Health Apps in German Outpatient Care: Protocol for a Multicenter, Single-Group, Observational, Mixed Methods Interventional Study (DigiNavi)
Notice bibliographique
Résumé
BACKGROUND: In Germany, patients often have to wait several months for psychotherapeutic treatment. Digital therapeutics (DTx) offer a promising approach for timely mental health support, but their use remains limited. Digital navigators (DNs) are specially trained medical assistants who support other health care professionals (HCPs) in selecting and using DTx. This can improve digital health literacy, increase engagement, and reduce the burden on HCPs. OBJECTIVE: The DigiNavi study is the first pilot study that aims to test the feasibility of implementing DNs in general practice and outpatient psychiatric care in Germany. METHODS: This mixed methods study took place at six study sites (three psychiatric outpatient clinics, three general practices) in Germany. In the prestudy, patients and HCPs participated in semistructured interviews and focus groups concerning their acceptance and expectations of DNs (phase I). The Harvard Digital Navigator Training (HDNT) was adapted, and medical assistants were trained as DNs (phase II). During the intervention, 8 patients per site (N=48) diagnosed with a mental disorder were recruited via convenience sampling and supported by DNs in using DTx for mental health for 12 weeks (phase III). Patients' (N=48) and HCPs' (N=18) digital health literacy, digital and technical literacy, readiness and ability to change, and clinical symptom severity were assessed before and after 12 weeks of DTx prescription and support by DNs. Patient engagement with the DiGAs (usage duration and intensity) was measured after the intervention. Quantitative data were analyzed using a pre-post design. Finally, qualitative interviews were conducted with HCPs, patients, and DNs to explore their experiences with DNs, including perceived implementation barriers. RESULTS: The study received funding in July 2024. The prestudy including 35 participants was conducted from August to October 2024. HDNT adaptation and DN training were conducted from October to December 2024. Recruitment and quantitative baseline data collection started in December 2024, and 48 participants were enrolled by the end of March 2025. The intervention study ended in June 2025. Result dissemination and the development of strategies for the long-term implementation of DNs into the German health care system are planned until September 2025. We hypothesize that the provision of support by DNs will enhance patients' and HCP' digital and technical literacy, patient engagement with DiGAs, and readiness and ability to change. In addition, patients' mental health is expected to improve after the end of the intervention. CONCLUSIONS: This is the first study to examine the feasibility and effects of DNs in German health care. The study will provide significant insights into the acceptability and feasibility of human-facilitated competency development for mental health apps in multiprofessional health care teams and their patients. The successful implementation of DNs can promote the use of DTx in Germany and thus enhance access to and the provision of health care for individuals affected by a mental disorder. TRIAL REGISTRATION: German Clinical Trial Register DRKS00034327; https://drks.de/search/en/trial/DRKS00034327; ClinicalTrials.gov NCT06575582; https://clinicaltrials.gov/study/NCT06575582. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67655.
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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 | 0,026 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,004 |
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 ».