Considering Theory-Based Gamification in the Co-Design and Development of a Virtual Reality Cognitive Remediation Intervention for Depression (bWell-D): Mixed Methods Study
Bibliographic record
Abstract
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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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 | 0.062 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".