Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

Yuezhou Zhang, Amos A. Folarin, Shaoxiong Sun, Nicholas Cummins, Srinivasan Vairavan, Rebecca Bendayan, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Heet Sankesara, Faith Matcham, Katie M. White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Elisabet Vilella, Sara SimblettAki Rintala, Stuart Bruce, David C. Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W.J.H. Penninx, Vaibhav A. Narayan, Peter Annas, Matthew Hotopf, Richard J.B. Dobson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere34898
JournalJMIR Mental Health
Volume9
Issue number3
DOIs
StatePublished - Mar 1 2022

Keywords

  • depression
  • dynamic structural equation modeling
  • location data
  • mHealth
  • medical informatics
  • mental health
  • mobile health
  • mobility
  • modeling

ASJC Scopus subject areas

  • Psychiatry and Mental health

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