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Objective: Children and adolescents with intellectual and developmental disabilities (IDD), particularly those with autism spectrum disorder, are at increased risk of challenging behaviors such as self-injury, aggression, elopement, and property destruction. To mitigate these challenges, it is crucial to focus on early signs of distress that may lead to these behaviors. These early signs might not be visible to the human eye but could be detected by predictive machine learning (ML) models that utilizes real-time sensing. Current behavioral assessment practices lack such proactive predictive models. This study developed and pilot-tested real-time early agitation capture technology (REACT), a real-time multimodal ML model to detect early signs of distress, termed “agitations.” Integrating multimodal sensing, ML, and human expertise could make behavioral assessments for people with IDD safer and more efficient. Methods: We leveraged wearable technology to collect behavioral and physiological data from three children with IDD aged 6 to 9 years. The effectiveness of the REACT system was measured using F1 score, assessing its usefulness at the time of agitation to 20s prior. Results: The REACT system was able to detect agitations with an average F1 score of 78.69% at the time of agitation and 68.20% 20s prior. Conclusion: The findings support the use of the REACT model for real-time, proactive detection of agitations in children with IDD. This approach not only improves the accuracy of detecting distress signals that are imperceptible to the human eye but also increases the window for timely intervention before behavioral escalation, thereby enhancing safety, well-being, and inclusion for this vulnerable population. We believe that such technological support system will enhance user autonomy, self-advocacy, and self-determination.