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An exploratory study on predicting depressive symptoms in autistic individuals using wearable devices and machine learning

Year: 2025

Type: article

Abstract: BACKGROUND: Monitoring depressive symptoms in autistic individuals is challenging due to conditions in communication and emotional expression inherent to this population. For early detection of depressive symptoms in autistic adults, this study aims to leverage digital biomarkers from wearable devices and develop monitoring models using machine learning algorithms. METHODS: This prospective, observational study recruited 17 autistic adults (mean age 29.1 ± 8.2 years). Physiological biomarkers, including activity level, heart rate, and sleep duration, were continuously collected via smartwatches. Depressive symptoms were self-rated weekly using the Beck Depression Inventory (BDI). Machine learning models, including Extreme Gradient Boosting (XGBoost), were applied to analyze the longitudinal data. The models were assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, precision, and F1 score. Feature importance analysis was conducted to identify key digital biomarkers. RESULTS: The XGBoost model demonstrated superior predictive efficacy, identifying depressive states with an 84 % accuracy and an area under the receiver operating curve (AUROC) of 0.91. Lower activity levels, decreased sleep duration, and reduced average heart rate emerged as potential predictors for depressive states. CONCLUSION: Digital phenotypes derived from wearable devices may facilitate the detection of depressive symptoms in autistic adults, offering potential benefits for clinical assessments and emotion self-care.

Source: Journal of the Formosan Medical Association

DOI: 10.1016/j.jfma.2025.11.026