A heart rate variability-based predictive model demonstrated a prediction accuracy of 92.61% for depressive symptoms among 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.
p-value: p=>.05
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.
Huo et al. (Tue,) conducted a 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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