Meta-analysis of the efficacy of digital therapies in children with attention-deficit hyperactivity disorder

A mobile device-based game prototype for ADHD: development and preliminary feasibility testing

This research aimed to devise and assess a mobile game therapy software for children with Attention-Deficit/Hyperactivity Disorder (ADHD), as well as evaluating its suitability and effectiveness in improving the cognitive ability of typically developing children. The study encompassed 55 children diagnosed with ADHD and 55 neurotypical children. Initial assessments involved ADHD-related scales, computerized tests for information processing, and physiological-psychological evaluations. After a 4-week home-based game intervention, participants underwent re-evaluation using baseline measures and provided feedback on treatment satisfaction. Considering the small proportion of study participants who dropped out, data was analyzed using both the Intention-to-Treat (ITT) analysis and the Per-protocol (PP) analysis. The trial was registered at ClinicalTrials.gov (NCT06181747). In ITT analysis, post-intervention analysis using linear mixed models indicated that the ADHD group improved significantly more than the neurotypical group particularly in Continuous Performance Test (CPT) accuracy (B = -23.92, p < 0.001) and reaction time (B = 86.08, p < 0.01), along with enhancements in anti-saccade (B = -10.65, p < 0.05) and delayed-saccade tasks (B = 0.34, p < 0.05). A reduction in parent-rated SNAP-IV scores was also observed (B = 0.43, p < 0.01). In PP analysis, paired-sample t-tests suggested that the ADHD group had significant changes pre- and post-intervention, in terms of CPT Accuracy (t = -7.62, p < 0.01), Anti-saccade task Correct Rate (t = -3.90, p < 0.01) and SNAP-IV scores (t = -4,64, p < 0.01). However, no significant changes post-intervention were observed in the neurotypical group. Survey feedback highlighted a strong interest in the games across both groups, though ADHD participants found the game more challenging. Parents of ADHD children reported perceived benefits and a willingness to continue the game therapy, unlike the neurotypical group's parents. The findings advocated for the integration of serious video games as a complementary tool in ADHD treatment strategies, demonstrating the potential to augment attentional abilities and alleviate clinical symptoms. However, a randomized controlled trial (RCT) is needed to further verify its efficacy.

From Consensus Statement to Pills to Pixels: New Innovations in Attention-Deficit/Hyperactivity Disorder Care

Objectives: This review aims to present recent innovations and advancements in attention-deficit/hyperactivity disorder (ADHD) care, encompassing international consensus statement, new medication formulations, digital therapeutics, and neurostimulation devices. Methods: A comprehensive literature search of relevant articles published in the past five years was conducted, emphasizing the evidence base, efficacy, safety, and practical implications of these advancements. Results: The World Federation of ADHD Consensus Statement offers an updated diagnostic and treatment framework rooted in global scientific evidence. There are several newer ADHD medication formulations, including a nonstimulant (Viloxazine extended release) and the first transdermal amphetamine patch approved to treat ADHD. These options offer some unique benefits to personalize treatment based on symptom profile, lifestyle, preferences, and response. Digital tools offer additional means to restructure environments for individuals with ADHD, reducing impairment and reliance on others. In addition, digital therapeutics enhance access, affordability, personalization, and feasibility of ADHD care, complementing or augmenting existing interventions. Trigeminal nerve stimulation emerges as a well-tolerated nonpharmacological, device-based treatment for pediatric ADHD, with initial trials indicating effect sizes comparable to nonstimulant medications. Conclusions: These innovations in ADHD care represent clinically significant new treatment options and opportunities for personalized care. Health care professionals should integrate these developments into clinical practice, mindful of individual patient and family needs and preferences. Future research should assess long-term outcomes, cost-effectiveness, and acceptability of these innovations.

Problematic Use of Internet Associates with Poor Quality of Life via Psychological Distress in Invididuals with ADHD

Background

Problematic use of internet (PUI) may have negative impacts on psychological distress and quality of life (QoL). This situation might be more profound in people with attention-deficit/hyperactivity disorder (ADHD) due to poorer behavioral control and regulatory capacity. However, there is little evidence regarding mediated effects in the associations between PUI, psychological distress, and QoL in people with ADHD.

Aims

To investigate mediating effects of psychological distress in the associations of problematic smartphone use (PSPU), problematic use of social media (PUSM), and problematic gaming (PG) with QoL in individuals with ADHD.

Methods and Procedures

PUI behaviors of participants with ADHD (n = 99) were assessed using the Smartphone Application-Based Addiction Scale, Bergen Social Media Addiction Scale, and Internet Gaming Disorder-Short Form. Psychological distress was assessed using the Depression, Anxiety, Stress Scale and QoL using the Kid-KINDL.

Outcomes and Results

Psychological distress mediated the associations between PUI and different domains of QoL, except for self-esteem QoL. There were also positively direct effects between PG and physical QoL, PUSM and friends' QoL, and PSPU and physical QoL.

Conclusions and Implications

PUI may associate with poor QoL in people with ADHD via psychological distress. Programs on reducing PUI for people with ADHD are needed.

Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy

Introduction

The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest.

Materials and methods

We analyzed 5,777 multimodal candidate predictors from children aged 9-10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11-12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics.

Results

Multimodal models achieved ~86-97% accuracy, 0.919-0.996 AUROC, and ~82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance.

Conclusion

Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.