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The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones.
Laiou, Petroula; Kaliukhovich, Dzmitry A; Folarin, Amos A; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; Sun, Shaoxiong; Zhang, Yuezhou; Matcham, Faith; Ivan, Alina; Lavelle, Grace; Siddi, Sara; Lamers, Femke; Penninx, Brenda Wjh; Haro, Josep Maria; Annas, Peter; Cummins, Nicholas; Vairavan, Srinivasan; Manyakov, Nikolay V; Narayan, Vaibhav A; Dobson, Richard Jb; Hotopf, Matthew.
Affiliation
  • Laiou P; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Kaliukhovich DA; Data Science Analytics & Insights, Janssen Research & Development, Beerse, Belgium.
  • Folarin AA; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Ranjan Y; Institute of Health Informatics, University College London, London, United Kingdom.
  • Rashid Z; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
  • Conde P; Health Data Research UK London, University College London, London, United Kingdom.
  • Stewart C; NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom.
  • Sun S; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Zhang Y; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Matcham F; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Ivan A; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Lavelle G; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Siddi S; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Lamers F; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Penninx BW; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Haro JM; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Annas P; Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.
  • Cummins N; Centro de Investigación Biomédica, Red de Salud Mental, Madrid, Spain.
  • Vairavan S; Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain.
  • Manyakov NV; Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, Netherlands.
  • Narayan VA; Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, Netherlands.
  • Dobson RJ; Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.
  • Hotopf M; Centro de Investigación Biomédica, Red de Salud Mental, Madrid, Spain.
JMIR Mhealth Uhealth ; 10(1): e28095, 2022 01 28.
Article de En | MEDLINE | ID: mdl-35089148
ABSTRACT

BACKGROUND:

Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.

OBJECTIVE:

The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.

METHODS:

We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.

RESULTS:

Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed

participants:

25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed

participants:

25th-75th percentiles 20.4-23.5, median 22.6 hours a day).

CONCLUSIONS:

Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Trouble dépressif majeur Type d'étude: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limites: Humans Pays/Région comme sujet: Europa Langue: En Journal: JMIR Mhealth Uhealth Année: 2022 Type de document: Article Pays d'affiliation: Royaume-Uni

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Trouble dépressif majeur Type d'étude: Clinical_trials / Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limites: Humans Pays/Région comme sujet: Europa Langue: En Journal: JMIR Mhealth Uhealth Année: 2022 Type de document: Article Pays d'affiliation: Royaume-Uni
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