Effectiveness of Serious Games as Digital Therapeutics for Enhancing the Abilities of Children With Attention-Deficit/Hyperactivity Disorder (ADHD): Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often begins in childhood and requires long-term treatment and management. Given the potential adverse effects of pharmacological interventions in children, interest in alternative treatments has increased. Among alternative therapies, serious games have emerged as a promising digital therapeutic approach and are increasingly recognized as an important intervention for children with ADHD. OBJECTIVE: This systematic review aims to evaluate the effectiveness of serious games as digital therapeutics for children with ADHD. It focuses on assessing therapeutic outcomes, including improvements in attention, hyperactivity-impulsivity, social skills, motor skills, executive functions, and enjoyment. METHODS: The review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive literature search was performed across 5 databases: PubMed, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, covering English studies published from January 2010 to January 2024. Eligibility criteria were established based on the PICOS (Participants, Intervention, Comparison, Outcomes, Study design) framework, with digital therapeutics guidelines pragmatically applied to inform inclusion criteria, exclusion criteria, and quality assessment. Standardized tools including the Cochrane Risk of Bias Tool for randomized controlled trials, the Cochrane Risk of Bias Tool for Non-Randomized Studies of Interventions (ROBINS-I) for nonrandomized controlled trial studies, and the Critical Appraisal Skills Program checklists were used to evaluate risk of bias. Data on study design, targeted abilities, game software and hardware, and intervention parameters (duration, frequency, and length) were extracted and synthesized descriptively. RESULTS: Of the 35 studies identified (1408 participants), gender data were available for 22 studies (888 participants), comprising 660 male and 228 female participants. Analysis revealed multiple abilities focused across many studies: 80% (28/35) assessed attention, 29% (10/35) addressed hyperactivity-impulsivity, 17% (6/35) explored improvements in social skills, 20% (7/35) evaluated motor skills, and 43% (15/35) investigated executive functions. Furthermore, in 89% (31/35) of the trials, children exhibited a positive attitude toward game interventions. Evidence suggests that serious games may contribute to improvements in attention, hyperactivity-impulsivity, social skills, and executive functions in children with ADHD. Although findings on motor skills were inconclusive, interventions incorporating somatosensory inputs demonstrated benefits for hand-eye coordination. CONCLUSIONS: The findings support the potential of serious games as a digital therapeutic modality for children with ADHD, offering benefits in core symptoms and associated impairments while promoting engagement. TRIAL REGISTRATION: PROSPERO CRD420250509693; https://www.crd.york.ac.uk/PROSPERO/view/CRD420250509693.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".