A Gamified Smartphone-Based Intervention for Depression: Randomized Controlled Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Available smartphone-based interventions for depression predominantly use evidence-based strategies from cognitive-behavioral therapy (CBT), but patient engagement and reported effect sizes are small. Recently, studies have demonstrated that smartphone-based interventions combining CBT with gamified approach-avoidance bias modification training (AAMT) can foster patient engagement and reduce symptoms of several mental health problems. OBJECTIVE: Based on these findings, we developed a gamified smartphone-based intervention, mentalis Phoenix (MT-Phoenix), and hypothesized the program would both engage patients and produce preliminary evidence for the reduction of depressive symptoms. METHODS: To test this hypothesis, we evaluated MT-Phoenix in a randomized controlled pilot trial including 77 individuals with elevated depression scores (Patient Health Questionnaire-9 scores ≥5). Participants were either instructed to train for 14 days with MT-Phoenix or assigned to a waitlist control condition. Engagement with the intervention was measured by assessing usage data. The primary outcome was reduction in depressive symptom severity at postassessment. RESULTS: =19.34; P=.001), with a large effect size (d=1.02). Effects were sustained at a 3-month follow-up. CONCLUSIONS: A gamified smartphone-based intervention combining CBT with AAMT may foster patient engagement and effectively target depressive symptoms. Future studies should evaluate the effectiveness of this intervention in a phase 3 trial using clinical samples. Moreover, the intervention should be compared to active control conditions. TRIAL REGISTRATION: German Clinical Trial Registry DRKS00012769; https://tinyurl.com/47mw8du7.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".