A Gamified Mobile App That Helps People Develop the Metacognitive Skills to Cope With Stressful Situations and Difficult Emotions: Formative Assessment of the InsightApp
Bibliographic record
Abstract
BACKGROUND: Ecological momentary interventions open up new and exciting possibilities for delivering mental health interventions and conducting research in real-life environments via smartphones. This makes designing psychotherapeutic ecological momentary interventions a promising step toward cost-effective and scalable digital solutions for improving mental health and understanding the effects and mechanisms of psychotherapy. OBJECTIVE: The first objective of this study was to formatively assess and improve the usability and efficacy of a gamified mobile app, the InsightApp, for helping people learn some of the metacognitive skills taught in cognitive behavioral therapy, acceptance and commitment therapy, and mindfulness-based interventions. The app aims to help people constructively cope with stressful situations and difficult emotions in everyday life. The second objective of this study was to test the feasibility of using the InsightApp as a research tool for investigating the efficacy of psychological interventions and their underlying mechanisms. METHODS: We conducted 2 experiments. In experiment 1 (n=65; completion rate: 63/65, 97%), participants (mean age 27, SD 14.9; range 19-55 years; 41/60, 68% female) completed a single session with the InsightApp. The intervention effects on affect, belief endorsement, and propensity for action were measured immediately before and after the intervention. Experiment 2 (n=200; completion rate: 142/200, 71%) assessed the feasibility of conducting a randomized controlled trial using the InsightApp. We randomly assigned participants to an experimental or a control condition, and they interacted with the InsightApp for 2 weeks (mean age 37, SD 12.16; range 20-78 years; 78/142, 55% female). Experiment 2 included all the outcome measures of experiment 1 except for the self-reported propensity to engage in predefined adaptive and maladaptive behaviors. Both experiments included user experience surveys. RESULTS: In experiment 1, a single session with the app seemed to decrease participants' emotional struggle, the intensity of their negative emotions, their endorsement of negative beliefs, and their self-reported propensity to engage in maladaptive coping behaviors (P<.001 in all cases; average effect size=-0.82). Conversely, participants' endorsement of adaptive beliefs and their self-reported propensity to act in accordance with their values significantly increased (P<.001 in all cases; average effect size=0.48). Experiment 2 replicated the findings of experiment 1 (P<.001 in all cases; average effect size=0.55). Moreover, experiment 2 identified a critical obstacle to conducting a randomized controlled trial (ie, asymmetric attrition) and how it might be overcome. User experience surveys suggested that the app's design is suitable for helping people apply psychotherapeutic techniques to cope with everyday stress and anxiety. User feedback provided valuable information on how to further improve app usability. CONCLUSIONS: In this study, we tested the first prototype of the InsightApp. Our encouraging preliminary results show that it is worthwhile to continue developing the InsightApp and further evaluate it in a randomized controlled trial.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".