Effectiveness, User Engagement and Experience, and Safety of a Mobile App (Lumi Nova) Delivering Exposure-Based Cognitive Behavioral Therapy Strategies to Manage Anxiety in Children via Immersive Gaming Technology: Preliminary Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Childhood anxiety disorders are a prevalent mental health problem that can be treated effectively with cognitive behavioral therapy, in which exposure is a key component; however, access to treatment is poor. Mobile-based apps on smartphones or tablets may facilitate the delivery of evidence-based therapy for child anxiety, thereby overcoming the access and engagement barriers of traditional treatment. Apps that deliver therapeutic content via immersive gaming technology could offer an effective, highly engaging, and flexible treatment proposition. OBJECTIVE: In this paper, we aim to describe a preliminary multi-method evaluation of Lumi Nova, a mobile app intervention targeting mild to moderate anxiety problems in children aged 7-12 years using exposure therapy delivered via an immersive game. The primary objective is to evaluate the effectiveness, user engagement and experience, and safety of the beta version of Lumi Nova. METHODS: Lumi Nova was co-designed with children, parents, teachers, clinicians, game industry experts, and academic partnerships. In total, 120 community-based children with mild to moderate anxiety and their guardians were enrolled to participate in an 8-week pilot study. The outcome measures captured the app's effectiveness (anxiety symptoms, child-identified goal-based outcomes, and functional impairment), user engagement (game play data and ease-of-use ratings), and safety (mood ratings and adverse events). The outcome measures before and after the intervention were available for 30 children (age: mean 9.8, SD 1.7 years; girls: 18/30, 60%; White: 24/30, 80%). Additional game play data were automatically generated for 67 children (age: mean 9.6, SD 1.53 years; girls: 35/67, 52%; White: 42/67, 63%). Postintervention open-response data from 53% (16/30) of guardians relating to the primary objectives were also examined. RESULTS: =2.79; P=.009; Cohen d=0.35) and making progress toward treatment goals (z=2.43; P=.02), but there were no improvements in relation to functional impairment. Children found it easy to play the game and engaged safely with therapeutic content. However, the positive effects were small, and there were limitations to the game play data. CONCLUSIONS: This preliminary study provides initial evidence that an immersive mobile game app may safely benefit children experiencing mild to moderate anxiety. It also demonstrates the value of the rigorous evaluation of digital interventions during the development process to rapidly improve readiness for full market launch.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".