Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

Autor:	Zhang Y; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., 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, London, United Kingdom.; NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom.; Health Data Research UK London, University College London, London, United Kingdom., Sun S; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; Department of Computer Science, University of Sheffield, Sheffield, United Kingdom., Cummins N; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Ranjan Y; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Rashid Z; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Stewart C; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Conde P; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Sankesara H; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Laiou P; Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Matcham F; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; School of Psychology, University of Sussex, Falmer, United Kingdom., White KM; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Oetzmann C; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Lamers F; Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands.; Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands., Siddi S; Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.; Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.; Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain., Simblett S; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Vairavan S; Janssen Research and Development LLC, Titusville, NJ, United States., Myin-Germeys I; Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium., Mohr DC; Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States., 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., Haro JM; Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.; Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.; Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain., Annas P; H Lundbeck A/S, Copenhagen, Denmark., Penninx BW; Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands.; Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands., Narayan VA; Janssen Research and Development LLC, Titusville, NJ, United States.; Davos Alzheimer's Collaborative, Geneva, Switzerland., Hotopf M; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, 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., 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, London, United Kingdom.; NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom.; Health Data Research UK London, University College London, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	Journal of medical Internet research [J Med Internet Res] 2024 Jun 28; Vol. 26, pp. e55302. Date of Electronic Publication: 2024 Jun 28.
DOI:	10.2196/55302
Abstrakt:	Background: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. Methods: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. Results: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. Conclusions: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression. (©Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Pauline Conde, Heet Sankesara, Petroula Laiou, Faith Matcham, Katie M White, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Srinivasan Vairavan, Inez Myin-Germeys, David C Mohr, Til Wykes, Josep Maria Haro, Peter Annas, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, RADAR-CNS consortium. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.06.2024.)
Databáze:	MEDLINE
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