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- MakaleAçık Erişim2026Game-based and individualized movement training improves physiological and motor outcomes in young adults with autism spectrum disorder: an experimental study from Türkiye
Aydın, Ahmet Serhat; Söyler, Mehmet; Zileli, Raif; Halmatov, Medera; Gürkan, Alper Cenk; Akcan, İbrahim Orkun; Yılmaz, Coşkun
Background: This study aims to examine the effects of game-based and individualized movement training (GBIMT) program on physiological (body weight, body fat percentage, BMI) and motor outcomes (balance, reaction time, motor speed) in young adults diagnosed with autism spectrum disorder (ASD). Methods: An experimental design with pre-test–post-test control groups was used in the study. A total of 20 participants (10 in the experimental group, 10 in the control group) with a mean age of 22.2 ± 4.8 years were monitored for eight weeks. The experimental group participated in GBIMT sessions for 60 min per day, two days per week, while the control group continued their usual educational routines. Body composition (InBody 270 Body Composition Analyzer, model Plus 270, South Korea), balance (TechnoGym, Cesena, Italy), reaction time (BlazePod Ltd., Israel), and motor speed (Computerized Tapping Test, Neurosoft Inc., USA) parameters were used as measurements. Results: Repeated measures ANOVA results indicated a significant reduction in body weight in terms of the experimental group (p = 0.014, η² = 0.289). Large effect sizes were observed for body fat percentage and BMI; however, the group × time interactions were not found to be statistically significant. A significant improvement was observed in the motor speed parameter within the experimental group (p = 0.032, η² = 0.231). Reaction time decreased by 9.8%, although the group × time interaction stayed within the significance limit (p = 0.063). A trend toward improvement was observed in balance parameters; however, no statistically significant difference was found. Conclusion: The findings indicate that GBIMT produced significant improvements in body composition and motor speed after implementing for eight weeks, and also showed a strong tendency to affect reaction time. As a controlled experimental study conducted in Türkiye, this research offers a unique contribution to the literature by demonstrating the effects of game-based and individualized movement programs on both physiological and motor outcomes in individuals with ASD. Trial registration: GameBased and Individualized Movement Training Improves Physiological and Motor Outcomes in Young Adults with Autism Spectrum Disorder An Experimental Study From Türkiye, ClinicalTrials.gov NCT07170891, Date 20,062,025.
- Derleme MakaleAçık Erişim2026Artificial-intelligence–based developmental screening for pre-school children (ages 3–6): a scoping review of deployed tools, clinical validity, equity, and implementation in guidance-center settings
Arslan, Adem; Gül, Fatih
Background – Early identification of neurodevelopmental conditions during the pre-school years (ages 3–6) is critical for timely intervention, yet traditional questionnaire-based screeners suffer from low positive predictive value, late detection, and inequitable access. Artificial intelligence (AI) and machine-learning (ML) methods promise more objective, scalable identification, and two devices (Cognoa Canvas Dx, 2021; EarliPoint Evaluation, 2023) are already US Food and Drug Administration (FDA)-authorized. Recent reviews summarize the technical literature, but few address how deployed tools perform on clinical validity, equity, and real-world implementation, particularly outside high-income, English-speaking settings. Methods – Following Joanna Briggs Institute methodology and the PRISMA extension for scoping reviews (PRISMA-ScR), we searched seven databases (1 January 2020–15 June 2026) and charted primary validation studies of AI/ML developmental-screening tools for children aged 72 months or younger. We mapped tools by modality, diagnostic performance, regulatory status, geographic origin, and, as a structured analytic axis, demographic subgroup (equity) reporting and deployment readiness. Results – Sixteen primary AI-tool studies met inclusion, spanning eight modality classes (eye-tracking, multimodal questionnaire-plus-video, computer-vision home/clinical video, tablet/smartphone digital phenotyping, voice/acoustic ML, electronic-health-record risk prediction, questionnaire-based ML, and wearable sensors) and eight countries (USA, South Korea, Australia, Switzerland, France, Israel, China, Vietnam). Reported sensitivities ranged from approximately 75 to 99.2% and specificities from 78.9 to 95.5%. Only one tool demonstrated stable performance across sex, race, and ethnicity; subgroup-stratified validation was otherwise near-absent. No CE-marked autism-specific screening device was identified. Conclusions – The evidence base has broadened in modality and geography but remains autism-concentrated and equity-blind, with a wide gap between analytic accuracy and primary-care deployment. We propose equity validation, deployment-readiness reporting, and an implementation pathway for low-resource guidance-center settings as priorities.