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Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

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RADAR-CNS Consortium, Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Rebecca Bendayan, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith Matcham, Katie M White, Femke Lamers, Sara Siddi, Sara Simblett, Inez Myin-Germeys, Aki Rintala, Til Wykes, Josep Maria Haro & 4 more Brenda Wjh Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard Jb Dobson

Original languageEnglish
Article numbere24604
Pages (from-to)e24604
JournalJMIR mHealth and uHealth
Volume9
Issue number4
Early online date12 Apr 2021
DOIs
Accepted/In press3 Feb 2021
E-pub ahead of print12 Apr 2021
PublishedApr 2021

Bibliographical note

Funding Information: The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant 115902. This joint undertaking receives support from the European Union's Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA). This communication reflects the views of the RADAR-CNS consortium and neither the Innovative Medicines Initiative nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Participants in the CIBER site came from the following 4 clinical communities in Spain: Parc Sanitari Sant Joan de D?u Network services, Institut Catal? de la Salut, Institut Pere Mata, and Hospital Cl?nico San Carlos. Participant recruitment in Amsterdam was partially accomplished through Hersenonderzoek.nl, a Dutch online registry that facilitates participant recruitment for neuroscience studies [24]. Hersenonderzoek.nl is funded by grant 73305095003 from ZonMw-Memorabel, a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer's Society in the Netherlands, and Brain Foundation Netherlands. This paper represents independent research partially funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or the Department of Health and Social Care. RB is funded in part by grant MR/R016372/1 from the King's College London Medical Research Council Skills Development Fellowship program funded by the UK Medical Research Council and by grant IS-BRC-1215-20018 from the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. Funding Information: The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant 115902. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA). This communication reflects the views of the RADAR-CNS consortium and neither the Innovative Medicines Initiative nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Participants in the CIBER site came from the following 4 clinical communities in Spain: Parc Sanitari Sant Joan de Déu Network services, Institut Català de la Salut, Institut Pere Mata, and Hospital Clínico San Carlos. Participant recruitment in Amsterdam was partially accomplished through Hersenonderzoek.nl, a Dutch online registry that facilitates participant recruitment for neuroscience studies [24]. Hersenonderzoek.nl is funded by grant 73305095003 from ZonMw-Memorabel, a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands, and Brain Foundation Netherlands. This paper represents independent research partially funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or the Department of Health and Social Care. RB is funded in part by grant MR/R016372/1 from the King’s College London Medical Research Council Skills Development Fellowship program funded by the UK Medical Research Council and by grant IS-BRC-1215-20018 from the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Publisher Copyright: © 2021 Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Rebecca Bendayan, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith Matcham, Katie M White, Femke Lamers, Sara Siddi, Sara Simblett, Inez Myin-Germeys, Aki Rintala, Til Wykes, Josep Maria Haro, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, RADAR-CNS Consortium. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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King's Authors

  • RADAR-CNS Consortium

Abstract

Background: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

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