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

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

Journal

JMIR mental health
ISSN: 2368-7959
Titre abrégé: JMIR Ment Health
Pays: Canada
ID NLM: 101658926

Informations de publication

Date de publication:
11 Mar 2022
Historique:
received: 11 11 2021
accepted: 12 01 2022
revised: 09 12 2021
entrez: 11 3 2022
pubmed: 12 3 2022
medline: 12 3 2022
Statut: epublish

Résumé

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.

Sections du résumé

BACKGROUND 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 OBJECTIVE
We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.
METHODS 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 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 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.

Identifiants

pubmed: 35275087
pii: v9i3e34898
doi: 10.2196/34898
pmc: PMC8957008
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e34898

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : 171
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17214
Pays : United Kingdom

Informations de copyright

©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 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, RADAR-CNS consortium. Originally published in JMIR Mental Health (https://mental.jmir.org), 11.03.2022.

Références

Psychol Methods. 2020 Oct;25(5):610-635
pubmed: 31855015
BMC Psychiatry. 2019 Feb 18;19(1):72
pubmed: 30777041
J Psychosom Res. 2002 Oct;53(4):859-63
pubmed: 12377294
Sports Med. 1994 Feb;17(2):108-16
pubmed: 8171221
JMIR Mhealth Uhealth. 2019 Aug 01;7(8):e11734
pubmed: 31373275
Psychol Health Med. 2003 Feb 1;8(1):57-69
pubmed: 21888489
J Affect Disord. 2002 Dec;72(3):227-36
pubmed: 12450639
Int J Geriatr Psychiatry. 2017 Oct;32(10):1150-1157
pubmed: 27633329
Ann Fam Med. 2011 Jul-Aug;9(4):305-11
pubmed: 21747101
Can J Psychiatry. 2015 Jan;60(1):14-22
pubmed: 25886545
J Med Internet Res. 2021 Sep 3;23(9):e22844
pubmed: 34477562
Arch Gen Psychiatry. 2005 Jun;62(6):617-27
pubmed: 15939839
PLoS Med. 2013 Nov;10(11):e1001547
pubmed: 24223526
Transl Psychiatry. 2020 Jan 23;10(1):28
pubmed: 32066704
Psychol Med. 2014 May;44(7):1349-60
pubmed: 23942140
J Med Internet Res. 2015 Jul 15;17(7):e175
pubmed: 26180009
Struct Equ Modeling. 2019;26(6):948-966
pubmed: 32863699
JMIR Mhealth Uhealth. 2022 Jan 28;10(1):e28095
pubmed: 35089148
Gen Hosp Psychiatry. 2009 Jul-Aug;31(4):306-15
pubmed: 19555789
J Med Internet Res. 2013 Nov 15;15(11):e247
pubmed: 24240579
Depress Anxiety. 2019 Jan;36(1):72-81
pubmed: 30129691
BMC Psychiatry. 2022 Feb 21;22(1):136
pubmed: 35189842
Med Hypotheses. 2001 Jan;56(1):20-3
pubmed: 11133250
NPJ Digit Med. 2019 Nov 8;2:108
pubmed: 31728415
J Affect Disord. 2009 Apr;114(1-3):163-73
pubmed: 18752852
Int J Ment Health Syst. 2007 Sep 04;1(1):4
pubmed: 18271976
PeerJ. 2016 Sep 29;4:e2537
pubmed: 28344895
Psychiatr Rehabil J. 2015 Sep;38(3):218-226
pubmed: 25844912
Biol Psychiatry. 2003 Aug 1;54(3):208-15
pubmed: 12893097
JMIR Mhealth Uhealth. 2021 Jul 30;9(7):e29840
pubmed: 34328441
Aging Ment Health. 2010 Mar;14(2):211-9
pubmed: 20336553
J Affect Disord. 2004 Jan;78(1):27-35
pubmed: 14672794
JMIR Mhealth Uhealth. 2021 Apr 12;9(4):e24604
pubmed: 33843591
J Med Internet Res. 2020 Sep 25;22(9):e19992
pubmed: 32877352
J Med Internet Res. 2017 Mar 03;19(3):e62
pubmed: 28258049

Auteurs

Yuezhou Zhang (Y)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Amos A Folarin (AA)

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.
NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
Health Data Research UK London, University College London, London, United Kingdom.
NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom.

Shaoxiong Sun (S)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Nicholas Cummins (N)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Srinivasan Vairavan (S)

Janssen Research and Development LLC, Titusville, NJ, United States.

Rebecca Bendayan (R)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.

Yatharth Ranjan (Y)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Zulqarnain Rashid (Z)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Pauline Conde (P)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Callum Stewart (C)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Petroula Laiou (P)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Heet Sankesara (H)

Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Faith Matcham (F)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Katie M White (KM)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Carolin Oetzmann (C)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Alina Ivan (A)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Femke Lamers (F)

Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands.

Sara Siddi (S)

Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.
Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.
Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain.

Elisabet Vilella (E)

Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.
Hospital Universitari Institut Pere Mata, Institute of Health Research Pere Virgili, Universitat Rovira i Virgili, Reus, Spain.

Sara Simblett (S)

Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Aki Rintala (A)

Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.
Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland.

Stuart Bruce (S)

RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom.

David C Mohr (DC)

Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Evanston, IL, United States.

Inez Myin-Germeys (I)

Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.

Til Wykes (T)

Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
South London and Maudsley NHS Foundation Trust, London, United Kingdom.

Josep Maria Haro (JM)

Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.
Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.
Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain.

Brenda Wjh Penninx (BW)

Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands.

Vaibhav A Narayan (VA)

Janssen Research and Development LLC, Titusville, NJ, United States.

Peter Annas (P)

H. Lundbeck A/S, Copenhagen, Denmark.

Matthew Hotopf (M)

NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
South London and Maudsley NHS Foundation Trust, London, United Kingdom.

Richard Jb Dobson (RJ)

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.
NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
Health Data Research UK London, University College London, London, United Kingdom.
NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom.
See Acknowledgments, .

Classifications MeSH