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

RADAR-CNS Consortium, 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 VilellaSara Simblett, Aki Rintala, Stuart Bruce, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda Wjh Penninx, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard Jb Dobson

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
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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
Article numbere34898
JournalJMIR Mental Health
Volume9
Issue number3
DOIs
Publication statusPublished - 11 Mar 2022

Keywords

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

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