Zhang Yuezhou, Folarin Amos A, Sun Shaoxiong, Cummins Nicholas, Vairavan Srinivasan, Bendayan Rebecca, Ranjan Yatharth, Rashid Zulqarnain, Conde Pauline, Stewart Callum, Laiou Petroula, Sankesara Heet, Matcham Faith, White Katie M, Oetzmann Carolin, Ivan Alina, Lamers Femke, Siddi Sara, Vilella Elisabet, Simblett Sara, Rintala Aki, Bruce Stuart, Mohr David C, Myin-Germeys Inez, Wykes Til, Haro Josep Maria, Penninx Brenda Wjh, Narayan Vaibhav A, Annas Peter, Hotopf Matthew, Dobson Richard Jb
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Institute of Health Informatics, University College London, London, United Kingdom.
JMIR Ment Health. 2022 Mar 11;9(3):e34898. doi: 10.2196/34898.
The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored.
We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.
Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.
This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility.
Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.
通过手机收集的位置数据测量的个体移动性已被发现与抑郁症有关；然而，抑郁症状严重程度与手机测量的移动性之间的纵向关系（关系的时间方向）尚未得到充分探索。
我们旨在探讨抑郁症状严重程度与手机测量的移动性随时间的关系及关系方向。
本文使用的数据来自一个名为“疾病与复发远程评估-重度抑郁症”的欧盟主要项目，该项目在3个欧洲国家进行。每2周通过手机使用8项患者健康问卷（PHQ-8）测量抑郁症状严重程度。参与者的位置数据每10分钟由手机中的GPS和网络传感器记录一次，并在PHQ-8评估前2周从位置数据中提取11个移动性特征。使用动态结构方程模型来探索抑郁症状严重程度与手机测量的移动性之间的纵向关系。
本研究包括来自290名参与者（年龄：中位数50.0，四分位距34.0，59.0岁）的2341条PHQ-8记录及相应的手机收集的位置数据；其中215名（74.1%）为女性，149名（51.4%）有工作。在抑郁症状严重程度与手机测量的移动性之间发现了显著的负相关，且这些相关性在个体内部水平比个体间水平更显著。对于随时间的关系方向，居家时间（在家时间）（φ=0.09，P=0.01）、位置熵（在不同位置的时间分布）（φ=-0.04，P=0.02）和居住位置计数（反映出行）（φ=0.05，P=0.02）与随后的PHQ-8评分变化显著相关，而PHQ-8评分的变化显著影响（φ=-0.07，P<0.001）随后的移动性周期性。
几种源自手机的移动性特征有可能预测未来的抑郁症，这可能为未来现实环境中的临床应用、复发预防和远程心理健康监测实践提供支持。
