Using Computer Games to Support Mental Health Interventions: Naturalistic Deployment Study
Bibliographic record
Abstract
BACKGROUND: Recent research has highlighted naturalistic uptake as a key barrier to maximizing the impact of mental health technologies. Although there is increasing evidence regarding the efficacy of digital interventions for mental health, as demonstrated through randomized controlled trials, there is also evidence that technologies do not succeed as expected when deployed in real-world settings. OBJECTIVE: This paper describes the naturalistic deployment of Pesky gNATs, a computer game designed to support cognitive behavioral therapy (CBT) for children experiencing anxiety or low mood. The objective of this deployment study was to identify how therapists use Pesky gNATs in real-world settings and to discover positive and negative factors. On the basis of this, we aimed to derive generalizable recommendations for the development of mental health technologies that can have greater impact in real-world settings. METHODS: Pesky gNATs has been made available through a not-for-profit organization. After 18 months of use, we collected usage and user experience data from therapists who used the game. Data were collected through an online survey and semistructured interviews addressing the expectations and experiences of both therapists and young people. Thematic analysis was used to identify key themes in the interview and survey data. RESULTS: A total of 21 therapists, who used Pesky gNATs with 95 young people, completed the online survey. Furthermore, 5 therapists participated in the follow-up interview. Confirming previous assessments, data suggest that the game can be helpful in delivering therapy and that young people generally liked the approach. Therapists shared diverse opinions regarding the young people for whom they deemed the game appropriate. The following 3 themes were identified: (1) stages of use, (2) impact on the delivery of therapy, and (3) customization. We discuss therapists' reflections on the game with regard to their work practices and consider the question of customization, including the delicate balance of adaptable interaction versus the need for fidelity to a therapeutic model. CONCLUSIONS: This study provides further evidence that therapeutic games can support the delivery of CBT for young people in real-world settings. It also shows that deployment studies can provide a valuable means of understanding how technologies integrate with the overall mental health ecosystem and become a part of therapists' toolbox. Variability in use should be expected in real-world settings. Effective training, support for therapist autonomy, careful consideration of different approaches to customization, the reporting of deployment data, and support for communities of practice can play an important role in supporting variable, but effective, use.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".