Long-Term Vital Sign Tracking Study of Depression Patients Based on Wearable Devices

Abstract

Depression is a critical mental health issue that increasingly affects millions worldwide. Traditional monitoring methods, relying on self-reported symptoms and periodic clinical assessments, are often subjective and infrequent. Wearable devices, offering continuous and real-time data on various physiological parameters, present a promising alternative. These devices provide a comprehensive picture of a patient's condition by tracking vital signs such as heart rate, sleep patterns, and physical activity. Our study utilized wearable devices to monitor 302 hospitalized depression patients over six months. We collected data on heart rate, sleep conditions, and physical activity, which were then correlated with Hamilton Anxiety (HAMA) and Hamilton Depression (HAMD) scales. The results showed significant differences in these vital signs between mild and severe depression cases. The logistic regression model yielded promising results, with an Area Under the Curve (AUC) value of 0.84 on the Receiver Operating Characteristic (ROC) curve, indicating a high level of classification accuracy. The model's performance suggests that the selected features are significantly correlated with depression severity and can effectively aid in clinical classification. In conclusion, wearable devices offer significant advancements in monitoring and managing depression. By integrating continuous physiological data with clinical assessments, these devices can improve the understanding and treatment of depression, potentially transforming mental health care into a more precise, personalized, and proactive field.