Multimodal Digital Therapeutics Enhanced by Task Design and AI for Attention-Deficit/Hyperactivity Disorder Core Symptoms and Executive Functions in Children and Adolescents: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children and adolescents. Digital therapeutics (DTx) show promise as nonpharmacological interventions, but the comparative efficacy of different DTx modalities remains unclear. Objective: This network meta-analysis (NMA) compared 4 DTx modalities (single-task, cognitive-motor dual-task, AI-integrated single-task, and AI-integrated cognitive-motor dual-task DTx) on core ADHD symptoms and executive functions, identified the optimal modality, and explored treatment moderators. Methods: We included randomized controlled trials (RCTs) in children and adolescents aged 4 to 17 years with ADHD diagnosed per the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) or ICD-10 (International Classification of Diseases, Tenth Revision). We searched PubMed/MEDLINE, PsycINFO, Web of Science, EMBASE, Scopus, ProQuest Dissertations and Theses, Cochrane Library, and ClinicalTrials.gov (gray literature) to identify trials published between January 2000 to May 2026 (last search May 22, 2026) without language restrictions, supplemented by snowballing. Risk of bias was assessed with the Cochrane Risk of Bias (RoB) 2 tool. Data were synthesized using Bayesian NMA with random-effects models. The surface under the cumulative ranking curve (SUCRA) was used to rank interventions. Heterogeneity was evaluated via 95% prediction intervals (95% PI) and explored through subgroup analyses and meta-regression. Small-study effects were assessed using Egger test, and sensitivity analyses were also performed. Results: Thirty-two RCTs (2819 patients) were included. The risk of bias assessment identified a low risk in 37.5% of the studies, some concerns in 21.9% of the studies, and a high risk in 40.6% of the studies, mainly due to inadequate reporting of randomization or blinding. AI-integrated cognitive-motor dual-task DTx ranked first for all outcomes in Bayesian network meta-analysis. For the Attention-Deficit/Hyperactivity Disorder-Rating Scale (ADHD-RS; 7 studies, n=1642), SUCRA was 57.5% (mean difference [MD] -3.03, 95% credible intervals [95% CrI] -5.59 to -0.47). For the Swanson, Nolan, and Pelham Rating Scale (Version IV; SNAP-IV) inattention subscale (SNAP-IV-PI; 8 studies, n=468), SUCRA was 82.5% (MD -5.58, 95% CrI -8.76 to -2.39); for the SNAP-IV hyperactivity-impulsivity subscale (SNAP-IV-PHI; 8 studies, n=468), SUCRA was 92.6% (MD -6.84, 95% CrI -10.37 to -3.31). For the Behavior Rating Inventory of Executive Function (BRIEF; 23 studies, n=1927), SUCRA was 84.4% (MD -7.75, 95% CrI -10.06 to -5.43). In pairwise meta-analyses, the 95% PI for ADHD-RS did not cross zero (-7.19 to -0.11), whereas those for the SNAP-IV (PI subscale: -5.62 to 1.87; PHI subscale: -6.66 to 2.82) and BRIEF (-6.91 to 1.94) did, indicating limited generalizability and substantial between-study heterogeneity. Subgroup analyses suggested intervention duration as a heterogeneity source for the SNAP-IV (both subscales) and BRIEF and mean age as a heterogeneity source for the SNAP-IV-PI and BRIEF. Conclusions: This NMA provides the first dual-dimension classification framework for ADHD DTx, combining SUCRA ranking, PI, and GRADE (Grading of Recommendations Assessment, Development and Evaluation). AI-integrated cognitive-motor dual-task DTx had the highest probability of improving core symptoms and executive functions, with duration and age as potential heterogeneity sources. These findings inform clinical decision-making and DTx development, although interpretation should account for evidence limitations.