Lee Ting-Yi, Chen Ching-Hsuan, Liu Chih-Min, Chen I-Ming, Chen Hsi-Chung, Wu Shu-I, Hsiao Chuhsing Kate, Kuo Po-Hsiu
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.
Department of Obstetrics and Gynecology, Taipei City Hospital Heping Fuyou Branch, Taipei, Taiwan.
J Med Internet Res. 2025 Jul 15;27:e71658. doi: 10.2196/71658.
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.
This study explores the diagnostic and monitoring capabilities of Fourier transform, a frequency-domain analysis method, in mood disorders by leveraging GPS data as an objective measure.
A total of 62 participants (BP: n=20, MDD: n=27, and healthy controls: n=15) contributed 5177 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-were identified as reflective of mood fluctuations and diagnostic differences between BP and MDD.
Fourier transform analysis revealed that the maximum power spectra of LV and entropy differed significantly between BP and MDD groups, with patients with BP exhibiting greater periodicity and intensity in mobility patterns. Notably, participants with BP demonstrated consistent periodic waves (eg, 1-d, 4-d, and 9-d cycles), while such patterns were absent in those with MDD. In addition, after adjusting for age, gender, and employment status, only the power spectrum of LV remained a significant predictor of depressed mood (odds ratio [OR] 0.9976, 95% CI 0.9956-0.9996; P=.02). 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 (OR 0.975, 95% CI 0.957-0.993; P=.008) and TT (OR 0.048, 95% CI 0.012-0.200; P<.001) on weekdays, and lower entropy (OR 0.662, 95% CI 0.520-0.842; P=.001) on weekends, indicating that mobility features vary with social and temporal contexts.
This study underscores the potential of GPS-derived mobility data, analyzed through Fourier transform, as a noninvasive 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 EMAs 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.
情绪障碍，包括双相情感障碍（BP）和重度抑郁症（MDD），其特征是显著的心理和行为波动，移动模式可作为情绪状态的潜在标志物。
本研究通过利用全球定位系统（GPS）数据作为客观测量手段，探索频域分析方法傅里叶变换在情绪障碍中的诊断和监测能力。
共有62名参与者（BP组：n = 20，MDD组：n = 27，健康对照组：n = 15）在5天至6个月的观察期内贡献了5177人日的数据。关键的GPS指标——位置方差（LV）、转换时间（TT）和熵——被确定为反映情绪波动以及BP和MDD之间的诊断差异。
傅里叶变换分析显示，BP组和MDD组之间LV和熵的最大功率谱存在显著差异，BP患者在移动模式中表现出更大的周期性和强度。值得注意的是，BP参与者表现出一致的周期性波动（例如，1天、4天和9天周期），而MDD参与者则没有这种模式。此外，在调整年龄、性别和就业状况后，只有LV的功率谱仍然是抑郁情绪的显著预测指标（优势比[OR]0.9976，95%置信区间0.9956 - 0.9996；P = 0.02）。与每周或每月汇总数据相比，每日GPS数据与生态瞬时评估（EMA）报告的情绪状态相关性更强，强调了日常监测的重要性。抑郁状态与工作日LV降低（OR 0.975，95%置信区间0.957 - 0.993；P = 0.008）和TT降低（OR 0.048，95%置信区间0.012 - 0.200；P < 0.001）以及周末熵降低（OR 0.662，95%置信区间0.520 - 0.842；P = 0.001）相关，表明移动特征随社会和时间背景而变化。
本研究强调了通过傅里叶变换分析的GPS衍生移动数据作为情绪障碍的非侵入性实时诊断和监测工具的潜力。研究结果表明，移动模式的强度而非频率可能更有助于区分BP和MDD。将GPS数据与EMA相结合可以提高临床评估的精度，为情绪发作提供早期预警，并支持个性化干预，最终改善心理健康结果。这种方法代表了朝着数字表型分析和先进心理健康监测策略迈出的有希望的一步。
