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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
Published on: June 20, 2020
Evaluating the effectiveness of intelligent interaction technology in autism interventions: A meta-analysis based on
Quan Zhou1, Dandan Li2, Yanan Zhang3
1First Clinical Medical College, Anhui Medical University, Hefei, China.
Purpose:
This research aims to conduct a systematic review of the literature on the utilization of intelligent interaction technologies, including Extended Reality (XR) and robotic systems, within the context of autism interventions.
Method:
This study commenced on June 16, 2022, and conducted a systematic search of publications from 2017 to 2024 using Boolean terms such as "Virtual Reality" AND "autism" AND "RCT" in PubMed, Web of Science, Cochrane Library, SpringerLink, and Embase. Two reviewers independently conducted research selection, data extraction, and quality assessment (using Cochrane Risk of Bias tool, RoB 2), and resolved differences through discussion. Statistical analysis was conducted in STATA using a random-effects model to calculate Standardized Mean Difference (SMD), supplemented by sensitivity analysis, funnel plot, and Egger's test to ensure robustness.
Result:
The meta-analysis included 13 studies involving 459 individuals with ASD from different regions (age range: 2-15 years, diagnosed using standardized instruments), including Europe, the Americas, Asia, and the Middle East, but without representatives from Africa. The results showed that intelligent interactive intervention demonstrated significant efficacy (SMD=0.66, 95 % CI: 0.27-1.05, p < 0.001). Subgroup analyses indicated that age and intervention targets influenced the effect size, with particularly pronounced positive impacts observed in preschool-aged children and cognitive interventions. Subgroup analysis of intervention measures showed that the XR group exhibited a more positive effect. In contrast, the robotic group, due to high heterogeneity and wide confidence intervals, did not lead to a conclusion that this intervention was effective overall. Additionally, sensitivity analysis, funnel plots, and Egger's test were conducted, demonstrating that the results are stable and no significant publication bias.
Conclusion:
In conclusion, the overall efficacy of intelligent interaction as an intervention is positive, showing benefits in social, cognitive, and behavioral aspects of ASD, particularly with XR (SMD=0.80, 95 % CI: 0.47-1.13). However, further research is required to ascertain the effects of robots. Furthermore, this study suggests intelligent interaction demonstrated larger effects for preschool-aged children(2-6 years; SMD=1.00, p = 0.007).
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