The Relationship Between GPS-Based Physical Activity Patterns and Depression
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This study analyzed the relationship between GPS-based physical activity patterns and mental health using Kaggle Student Life data. Data were collected over a 10-week period from 48 students at Dartmouth College through Android smartphones and included GPS, dark, and phone lock data, and measures such as the Patient Health Questionnaire-9 (PHQ-9), Loneliness Scale, the Positive and Negative Affect Schedule (PANAS), and Perceived Stress Scale. Using latitude and longitude data obtained from GPS measurements, various physical activity indicators were calculated, including the total distance traveled, average distance traveled, average distance traveled in the morning, average distance traveled in the afternoon, average distance traveled in the evening, and average distance traveled in the middle of the night. Pearson’s correlation analysis was performed to explore the relationship between GPS-based physical activity patterns and mental health. The study results indicated a significant negative correlation between the average distance traveled in the afternoon and PHQ-9 scores. Resultsindicated that the higher the afternoon activity, the lower the depressive symptoms. There was a positive correlation be-tween the PANAS-Pos score and the average distance traveled in the evening, indicating that positive emotions tended to increase as evening activitiesincreased. This finding suggests a relationship between physical activity atspecific times and mental health.
Abstract
This study analyzed the relationship between GPS-based physical activity patterns and mental health using Kaggle Student Life data. Data were collected over a 10-week period from 48 students at Dartmouth College through Android smartphones and included GPS, dark, and phone lock data, and measures such as the Patient Health Questionnaire-9 (PHQ-9), Loneliness Scale, the Positive and Negative Affect Schedule (PANAS), and Perceived Stress Scale. Using latitude and longitude data obtained from GPS measurements, various physical activity indicators were calculated, including the total distance traveled, average distance traveled, average distance traveled in the morning, average distance traveled in the afternoon, average distance traveled in the evening, and average distance traveled in the middle of the night. Pearson’s correlation analysis was performed to explore the relationship between GPS-based physical activity patterns and mental health. The study results indicated a significant negative correlation between the average distance traveled in the afternoon and PHQ-9 scores. Resultsindicated that the higher the afternoon activity, the lower the depressive symptoms. There was a positive correlation be-tween the PANAS-Pos score and the average distance traveled in the evening, indicating that positive emotions tended to increase as evening activitiesincreased. This finding suggests a relationship between physical activity atspecific times and mental health.
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