A Gamified Mobile App That Helps People Develop the Metacognitive Skills to Cope With Stressful Situations and Difficult Emotions: Formative Assessment of the InsightApp
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».