An Interactive Mobile App Game to Address Aggression (RegnaTales): Pilot Quantitative Study
Bibliographic record
Abstract
BACKGROUND: The rapid advancement in media technology has radically changed the way we learn and interact with one another. Games, with their engaging and interactive approach, hold promise in the delivery of knowledge and building of skills. This has potential in child and adolescent mental health work, where the lack of insight and motivation for therapy are major barriers to treatment. However, research on the use of serious games in mental health interventions for children and adolescents is still in its infancy. OBJECTIVE: This study adds to the research on serious games in mental health interventions through the development and evaluation of RegnaTales, a series of 6 mobile apps designed to help children and adolescents manage anger. We examined the usability and playability of RegnaTales, as well as children's aggression levels before and after the game play. METHODS: A total of 72 children aged between 6 and 12 years were recruited for the study. Thirty-five participants had a clinical diagnosis of disruptive behavior disorders (DBD), whereas 37 were typically developing (TD) children. Each child played 1 of the 6 RegnaTales apps for approximately 50 min before completing the Playability and Usability Questionnaire. The Reactive-Proactive Aggression Questionnaire was completed before and after the game play. RESULTS: The overall results showed high levels of enjoyment and playability. TD children and children with DBD had similar experienced fun and perceived playability scores on all 6 mobile apps. All 6 mobile apps garnered comparable experienced fun and perceived playability scores. Furthermore, 42% (5/12) to 67% (8/12) of the children indicated that they would like to play the games again. Importantly, children felt that they acquired skills in anger management, were motivated to use them in their daily lives, and felt confident that the skills would help them better manage their anger. Children reported significantly lower reactive aggression after playing the mobile apps Rage Raver (P=.001), Abaddon (P=.008), and RegnaTools (P=.03). These apps focused on the psychoeducation of the link between thoughts and emotions, as well as equipping the participants with various emotion regulation strategies such as relaxation and cognitive restructuring. CONCLUSIONS: This study presents evidence to support RegnaTales as a feasible serious game. The preliminary findings associated with reduction in reactive aggression, coupled with future research to further establish its efficacy, could warrant RegnaTales as a potential intervention for anger issues among clinical and community populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".