Key result
HRV-based model predicts depressive symptoms with ~93% accuracy in university students.
Why the study?
Current depression assessment tools are limited by subjectivity and potential bias, motivating the development of an objective depression screening model using heart rate variability.
Does an HRV-based predictive model accurately screen for depressive symptoms in Chinese university students?
Observational (n=2,094)
Does an HRV-based predictive model accurately screen for depressive symptoms in Chinese university students?
An HRV-based predictive model demonstrated 92.61% accuracy in screening for depressive symptoms among Chinese university students, offering a potential objective mental health assessment tool.
May enable objective student depression screening; leaves open external validation and outcome trials before clinical adoption.
The current depression assessment tools are limited by subjectivity and potential bias. This study investigated the relationship between heart rate variability (HRV), body composition, and self-reported depressive symptoms to develop an objective depression screening model for Chinese university students. Data from 2,094 students, including demographics, body composition, Self-Rating Depression Scale (SDS) scores, and HRV indicators, were analyzed using SPSS 26.0 to construct a predictive regression model, with accuracy validated in GraphPad Prism 9.4.1. Subsequently, a subgroup of 359 students with depressive symptoms was screened using the model. The results showed no significant differences between the predicted and actual SDS scores (p > .05), with over 91% of the predicted scores falling within the 95% confidence interval of the actual scores. The strong correlation between HRV and SDS scores supports the use of HRV as a reliable indicator for depression screening. Overall, the model demonstrated a prediction accuracy of 92.61%, highlighting its potential for objective mental health assessment among university populations.
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Huo et al. (2026) conducted an observational in Depressive symptoms (n=2,094). Heart rate variability-based predictive model vs. Self-Rating Depression Scale (SDS) scores was evaluated on Prediction accuracy of the model (p=>.05). A heart rate variability-based predictive model demonstrated a prediction accuracy of 92.61% for depressive symptoms among Chinese university students.
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