Considering Theory-Based Gamification in the Co-Design and Development of a Virtual Reality Cognitive Remediation Intervention for Depression (bWell-D): Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: In collaboration with clinical domain experts, we developed a prototype of immersive virtual reality (VR) cognitive remediation for major depressive disorder (bWell-D). In the development of a new digital intervention, there is a need to determine the effective components and clinical relevance using systematic methodologies. From an implementation perspective, the effectiveness of digital intervention delivery is challenged by low uptake and high noncompliance rates. Gamification may play a role in addressing this as it can boost adherence. However, careful consideration is required in its application to promote user motivation intrinsically. OBJECTIVE: We aimed to address these challenges through an iterative process for development that involves co-design for developing content as well as in the application of gamification while also taking into consideration behavior change theories. This effort followed the methodological framework guidelines outlined by an international working group for development of VR therapies. METHODS: In previously reported work, we collected qualitative data from patients and care providers to understand end-user perceptions on the use of VR technologies for cognitive remediation, reveal insights on the drivers for behavior change, and obtain suggestions for changes specific to the VR program. In this study, we translated these findings into concrete representative software functionalities or features and evaluated them against behavioral theories to characterize gamification elements in terms of factors that drive behavior change and intrinsic engagement, which is of particular importance in the context of cognitive remediation. The implemented changes were formally evaluated through user trials. RESULTS: The results indicated that feedback from end users centered on using gamification to add artificial challenges, personalization and customization options, and artificial assistance while focusing on capability as the behavior change driver. It was also found that, in terms of promoting intrinsic engagement, the need to meet competence was most frequently raised. In user trials, bWell-D was well tolerated, and preliminary results suggested an increase in user experience ratings with high engagement reported throughout a 4-week training program. CONCLUSIONS: In this paper, we present a process for the application of gamification that includes characterizing what was applied in a standardized way and identifying the underlying mechanisms that are targeted. Typical gamification elements such as points and scoring and rewards and prizes target motivation in an extrinsic fashion. In this work, it was found that modifications suggested by end users resulted in the inclusion of gamification elements less commonly observed and that tend to focus more on individual ability. It was found that the incorporation of end-user feedback can lead to the application of gamification in broader ways, with the identification of elements that are potentially better suited for mental health domains.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».