Mental health INtervention with Digital APPlications (MIND-APP): Protocol for a Randomized Controlled Researcher Blinded Trial Evaluating the Effectiveness of the Tankevirus and Grubl Mental Health Apps Compared to a Placebo App (Preprint)
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression impose substantial clinical and economic burdens worldwide, with high prevalence, impaired functioning, and elevated health care costs. Digital self-help interventions offer scalable and potentially cost-effective strategies; however, evidence from rigorously controlled economic evaluations remains sparse. OBJECTIVE: This trial aims to evaluate the effectiveness and cost-effectiveness of 2 Norwegian mental health apps: Tankevirus (cognitive behavioral therapy-based) and Grubl (metacognitive therapy-based), compared with a digital placebo in reducing anxiety and depression symptoms, improving health-related quality of life, and generating quality-adjusted life years. METHODS: The Mental Health Intervention With Digital Applications (MIND-APP) trial is a 3-arm randomized controlled trial (1:1:1 allocation) conducted fully remotely via a bespoke smartphone research platform. A total of 1000 Norwegian residents aged 16 years or older with mild to moderate symptoms of anxiety and/or depression will be recruited through national digital outreach. Coprimary outcomes are changes in anxiety (Generalized Anxiety Disorder-7) and depression (Patient Health Questionnaire-9) scores from baseline to postintervention (2-4 weeks). Secondary outcomes include health-related quality of life (EQ-5D-5L), quality-adjusted life years accrued over 6 months, functional impairment (Work and Social Adjustment Scale), health care resource use, and adverse events. Incremental cost-effectiveness ratios for Tankevirus and Grubl relative to placebo will be estimated from the perspective of public health services. RESULTS: Funding was secured in April 2025, with ethical approvals, licensing, and app development planned through 2026. Recruitment will commence in 2027, with follow-up through 2027 and early 2028. An extension of the timetable has been approved by the funding agent to allow inclusion of an updated version of the Tankevirus app, which will be ready for testing around May 2027. Results are expected to be published in autumn 2028 and will provide robust evidence on the clinical and economic value of scalable app-based interventions for common mental health disorders. CONCLUSIONS: This trial will be among the first large-scale registered reports to combine rigorous clinical and economic evaluation of digital mental health interventions. Findings will inform health policy and resource allocation by determining whether low-cost, app-based programs represent cost-effective solutions for reducing the burden of anxiety and depression. TRIAL REGISTRATION: ClinicalTrials.gov NCT07627204; https://clinicaltrials.gov/study/NCT07627204. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/84096.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.009 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.077 | 0.015 |
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".