TY - JOUR
T1 - Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility
T2 - Dynamic Structural Equation Modeling Study
AU - Zhang, Yuezhou
AU - Folarin, Amos A.
AU - Sun, Shaoxiong
AU - Cummins, Nicholas
AU - Vairavan, Srinivasan
AU - Bendayan, Rebecca
AU - Ranjan, Yatharth
AU - Rashid, Zulqarnain
AU - Conde, Pauline
AU - Stewart, Callum
AU - Laiou, Petroula
AU - Sankesara, Heet
AU - Matcham, Faith
AU - White, Katie M.
AU - Oetzmann, Carolin
AU - Ivan, Alina
AU - Lamers, Femke
AU - Siddi, Sara
AU - Vilella, Elisabet
AU - Simblett, Sara
AU - Rintala, Aki
AU - Bruce, Stuart
AU - Mohr, David C.
AU - Myin-Germeys, Inez
AU - Wykes, Til
AU - Maria Haro, Josep
AU - Penninx, Brenda W. J. H.
AU - Narayan, Vaibhav A.
AU - Annas, Peter
AU - Hotopf, Matthew
AU - Dobson, Richard J. B.
N1 - Funding Information: RJBD is supported by (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust; (3) the BigData@Heart Consortium, which funded by the Innovative Medicines Initiative-2 Joint Undertaking (grant 116074), which receives support from the European Union Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations; (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Health care; and (6) the NIHR Applied Research Collaboration South London at King’s College Hospital NHS Foundation Trust. Funding Information: This study was funded in part by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS (National Health Service) Foundation Trust and King’s College London. Funding Information: We thank all the members of the RADAR-CNS patient advisory board for their contribution to the device selection procedures, and their invaluable advice throughout the study protocol design. We acknowledge that this research was reviewed by the Feasibility and Acceptability Support Team for Researchers (National Institute for Health Research Maudsley Biomedical Research Centre, King’s College London and South London and Maudsley NHS Foundation Trust). Funding Information: RB is funded in part by the King’s College London Medical Research Council Skills Development Fellowship (grant MR/R016372/1), which is funded by the UK Medical Research Council (grant IS-BRC-1215-20018) for the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Funding Information: The Remote Assessment of Disease and Relapse–Major Depressive Disorder project is a component of the Remote Assessment of Disease and Relapse–Central Nervous System (RADAR-CNS) project, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (grant 115902), which receives support from European Union Horizon 2020 and the European Federation of Pharmaceutical Industries and Associations. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice, adhering to principles outlined in the National Health Service Research Governance Framework for Health and Social Care (2nd edition). Publisher Copyright: © 2022 JMIR Publications Inc.. All right reserved.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Background: 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. Objective: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods: 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. Results: 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. Conclusions: 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.
AB - Background: 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. Objective: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. Methods: 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. Results: 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. Conclusions: 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.
KW - depression
KW - dynamic structural equation modeling
KW - location data
KW - mHealth
KW - medical informatics
KW - mental health
KW - mobile health
KW - mobility
KW - modeling
UR - http://www.scopus.com/inward/record.url?scp=85126456293&partnerID=8YFLogxK
U2 - https://doi.org/10.2196/34898
DO - https://doi.org/10.2196/34898
M3 - Article
C2 - 35275087
SN - 2368-7959
VL - 9
JO - JMIR Mental Health
JF - JMIR Mental Health
IS - 3
M1 - e34898
ER -