Considering theory-based gamification in the co-design and development of virtual reality cognitive remediation for depression (bWell-D): mixed methods study
Notice bibliographique
Résumé
Background: In collaboration with clinical domain experts, we have developed a prototype of immersive 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 non-compliance rates. Gamification may play a role in addressing this since it can boost adherence. However, careful consideration is required in its application in order to promote user motivation intrinsically. Objective: We aimed to address these challenges with 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 behavioural change theories. This effort followed the methodological framework guidelines outlined by an international working group for development of VR therapies. Methods: Following best practice guidelines, 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 behavioural change, and obtain suggestions for changes specific to the VR program. These findings were translated into concrete representative software functionalities/features and evaluated against behavioural theories to characterize gamification elements in terms of factors that drive behavioural change and intrinsic engagement. Results: The results indicated that feedback from end-users centred around using gamification to add artificial challenges, personalization/customization options and artificial assistance while focusing on Capability as the behavioural change driver. It was also found that in terms of promoting intrinsic engagement, the need to meet Competence was most frequently raised. Feedback was obtained from users to evaluate the impact of the implemented changes. It was found that bWell-D was well tolerated, and that the improvements led to an increase in user experience ratings with high engagement reported throughout the duration of an 8-week training program. Conclusions: Here, 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/scoring and rewards/prizes, target Motivation in an extrinsic fashion. In this work, it was found that modifications suggested from end users resulted in the inclusion of gamification elements less commonly observed and tended 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 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 | 0,056 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».