A Gamified Smartphone-Based Intervention for Depression: Randomized Controlled Pilot Trial
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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: Data from this pilot trial shows that participants in the intervention group used the smartphone-based intervention for 46% of all days (6.4/14) and reported a significantly greater reduction of depressive symptoms than did participants in the control condition (F1,74=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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Gamification can increase motivation to engage with mental health apps.
“Previous research shows that gamification can increase motivation to engage with mental health apps [42-44], improve mood [45], and activate the ventral striatum, which can enhance individuals’ general reward responsiveness to positive stimuli [21].”
From Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis (2021) · cites this paper as [42]
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- Authors
- Lukas, C. A., Eskofier, B., & Berking, M.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Corresponding author
- Lukas, Christian Aljoscha
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2021-07-20
- Volume
- 8
- Issue
- 7
- Pages
- e16643
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC8335612
- OpenAlex
- W3185533807
- Cited by
- 49
- References
- 42
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 13 · 2023: 12 · 2022: 4 · 2021: 6
- FWCI
- 7.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- depression, gamification, smartphone-based intervention, cognitive behavioral therapy, patient engagement, depressive symptoms, digital mental health
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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