%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e55635
%T Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study
%A Lee,Ting-Yi
%A Chen,Ching-Hsuan
%A Chen,I-Ming
%A Chen,Hsi-Chung
%A Liu,Chih-Min
%A Wu,Shu-I
%A Hsiao,Chuhsing Kate
%A Kuo,Po-Hsiu
%+ Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 521, No 17, Xuzhou Road, Taipei, 10055, Taiwan, 886 2 33668015, phkuo@ntu.edu.tw
%K ecological momentary assessment
%K digital phenotyping
%K GPS mobility
%K bipolar disorder
%K major depressive disorder
%K GPS
%K global positioning system
%K mood disorders
%K assessment
%K depression
%K anxiety
%K digital phenotype
%K smartphone app
%K technology
%K behavioral changes
%K patient
%K monitoring
%D 2024
%7 6.12.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring. Objective: This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology. Methods: Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints. Results: Our findings revealed a significant negative correlation between normalized entropy (r=–0.353; P=.04) and weekly depressed mood as well as between location variance (r=–0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=–0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=–0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns. Conclusions: This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients’ conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home. 
%R 10.2196/55635
%U https://www.jmir.org/2024/1/e55635
%U https://doi.org/10.2196/55635