Developing Serious Video Games to Treat Attention Deficit Hyperactivity Disorder: Tutorial Guide
Bibliographic record
Abstract
Video game-based therapeutic interventions have demonstrated some effectiveness in decreasing the symptoms of attention deficit hyperactivity disorder (ADHD). Compared with more traditional strategies within the multimodal treatment of ADHD, video games have certain advantages such as being comfortable, flexible, and cost-efficient. However, establishing the most appropriate type(s) of video games that should be used for this treatment remains a matter of debate, including the commercial existing video games or serious video games that are specifically constructed to target specific disorders. This guide represents a starting point for developing serious video games aimed at treating ADHD. We summarize the key points that need to be addressed to generate an effective and motivating game-based treatment. Following recommendations from the literature to create game-based treatments, we describe the development stages of a serious video game for treating ADHD. Game design should consider the interests of future users; game mechanics should be based on cognitive exercises; and therapeutic mechanisms must include the control of difficulty, engagement, motivation, time constraints, and reinforcement. To elaborate upon this guide, we performed a narrative review focused on the use of video games for the treatment of ADHD, and were inspired by our own experience during the development of the game "The Secret Trail of Moon."
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".