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Development of a Heart Rate Variability-Based Predictive Model for Depressive Symptoms in Chinese University Students
GuiQuan Huo1, Ruixue Zhao2, Jiayi Li2
1Kyonggi University, Suwon-si, Korea.
Assessment
|April 22, 2026
Summary
This study developed an objective depression screening model for university students using heart rate variability (HRV) and body composition. The model achieved 92.61% accuracy, offering a reliable tool for mental health assessment.
Area of Science:
- Psychiatry
- Biomedical Engineering
- Public Health
Background:
- Current depression assessment tools suffer from subjectivity and bias.
- Objective screening methods are needed, especially for university students.
- Heart rate variability (HRV) shows potential as a physiological indicator.
Purpose of the Study:
- To investigate the relationship between HRV, body composition, and depressive symptoms.
- To develop an objective depression screening model for Chinese university students.
- To validate the accuracy and reliability of the developed screening model.
Main Methods:
- Collected data from 2,094 Chinese university students, including demographics, body composition, Self-Rating Depression Scale (SDS) scores, and HRV indicators.
- Utilized SPSS 26.0 for predictive regression model construction.
- Validated model accuracy using GraphPad Prism 9.4.1, including screening a subgroup of 359 students.
Main Results:
- No significant differences were found between predicted and actual SDS scores (p > .05).
- Over 91% of predicted scores fell within the 95% confidence interval of actual scores.
- The developed model demonstrated a prediction accuracy of 92.61%.
Conclusions:
- HRV is a reliable indicator for objective depression screening.
- The developed model shows significant potential for objective mental health assessment in university populations.
- This approach can help overcome limitations of subjective depression assessment tools.
