Effectiveness of a Personalized, Chess-Based Training Serious Video Game in the Treatment of Adolescents and Young Adults With Attention-Deficit/Hyperactivity Disorder: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Compared with traditional approaches, gaming strategies are promising interventions for the treatment of attention-deficit/hyperactivity disorder (ADHD). We developed a serious game, The Secret Trail of Moon (TSTM), for ADHD treatment. OBJECTIVE: The main objective of this clinical trial was to demonstrate the effectiveness of an add-on, either TSTM or Therapeutic Chess (TC), in previously optimally drug-titrated, clinically stable patients with ADHD. METHODS: This study is a prospective, unicentric, randomized clinical trial in clinically stable patients with ADHD, aged 12 to 22 years. The TSTM (n=35) and TC groups (n=34) performed 12 weekly sessions of their respective treatments. The control group (CG) patients (n=35) were called by phone every week, but they received no cognitive intervention. The primary end point was the change from baseline to end point in the parent "Behavior Rating Inventory of Executive Function-2" (BRIEF-2; patients' parents) in the per-protocol population (31 serious videogame: 24 TC and 34 CG). RESULTS: Our study failed to probe clear-cut improvements in the global score of the BRIEF-2. However, the TC group showed improvements in measures of emotional control, emotional regulation, and inattention. The TSTM group showed improvements in measures of emotional regulation, inattention, and school context. CONCLUSIONS: TSTM and TC did not improve executive function symptoms, but they improved ADHD symptomatology related to emotional regulation. Further studies with bigger samples are required to confirm these preliminary findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT04355065; https://clinicaltrials.gov/ct2/show/NCT04355065.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".