JMIR Publications

ABSTRACT

Background:

Mood disorders, including bipolar disorder (BP) and major depressive disorder (MDD), are characterized by significant psychological and behavioral fluctuations, with mobility patterns serving as potential markers of emotional states.

Objective:

Leveraging GPS data as an objective measure, this study explores the diagnostic and monitoring capabilities of Fourier transform, a frequency-domain analysis method, in mood disorders.

Methods:

A total of 62 participants (BP: 20; MDD: 27; healthy controls: 15) contributed 5,177 person-days of data over observation periods ranging from 5 days to 6 months. Key GPS indicators—location variance (LV), transition time (TT), and entropy (EN)—were identified as reflective of mood fluctuations and diagnostic differences between BP and MDD.

Results:

Fourier transform analysis revealed that the maximum power spectra of LV and EN differed significantly between BP and MDD groups, with BP patients exhibiting greater periodicity and intensity in mobility patterns. Notably, BP participants demonstrated consistent periodic waves (e.g., 1-day, 4-day, and 9-day cycles), while such patterns were absent in MDD. Daily GPS data showed stronger correlations with ecological momentary assessment (EMA)-reported mood states compared to weekly or monthly aggregations, emphasizing the importance of day-to-day monitoring. Depressive states were associated with reduced LV and TT on weekdays, and lower EN on weekends, indicating that mobility features vary with social and temporal contexts.

Conclusions:

This study underscores the potential of GPS-derived mobility data, analyzed through Fourier transform, as a non-invasive and real-time diagnostic and monitoring tool for mood disorders. The findings suggest that the intensity of mobility patterns, rather than their frequency, may better differentiate BP from MDD. Integrating GPS data with EMA could enhance the precision of clinical assessments, provide early warnings for mood episodes, and support personalized interventions, ultimately improving mental health outcomes. This approach represents a promising step toward digital phenotyping and advanced mental health monitoring strategies.