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)
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".