The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder

F Matcham; E Carr; N Meyer; K M White; C Oetzmann; D Leightley; F Lamers; S Siddi; N Cummins; P Annas; G de Girolamo; J M Haro; G Lavelle; Q Li; F Lombardini; D C Mohr; V A Narayan; B W H J Penninx; M Coromina; G Riquelme Alacid; S K Simblett; R Nica; T Wykes; J C Brasen; I Myin-Germeys; R J B Dobson; A A Folarin; Y Ranjan; Z Rashid; J Dineley; S Vairavan; M Hotopf; RADAR-CNS consortium

doi:10.1016/j.jad.2024.07.136

The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder

J Affect Disord. 2024 Oct 15:363:90-98. doi: 10.1016/j.jad.2024.07.136. Epub 2024 Jul 20.

Authors

F Matcham¹, E Carr², N Meyer³, K M White⁴, C Oetzmann⁴, D Leightley⁴, F Lamers⁵, S Siddi⁶, N Cummins², P Annas⁷, G de Girolamo⁸, J M Haro⁶, G Lavelle⁴, Q Li⁷, F Lombardini⁶, D C Mohr⁹, V A Narayan¹⁰, B W H J Penninx³, M Coromina⁶, G Riquelme Alacid⁶, S K Simblett¹¹, R Nica¹², T Wykes¹³, J C Brasen⁷, I Myin-Germeys¹⁴, R J B Dobson², A A Folarin¹⁵, Y Ranjan², Z Rashid², J Dineley², S Vairavan¹⁶, M Hotopf¹⁷; RADAR-CNS consortium¹⁸

Affiliations

¹ School of Psychology, University of Sussex, Falmer, UK; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. Electronic address: [email protected].
² Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
³ Insomnia and Behavioural Sleep Medicine Clinic, University College London Hospitals NHS Foundation Trust, London, UK.
⁴ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
⁵ Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
⁶ Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.
⁷ H. Lundbeck A/S, Valby, Denmark.
⁸ IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
⁹ Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA.
¹⁰ Davos Alzheimer's Collaborative, Wayne, PA, USA.
¹¹ Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
¹² RADAR-CNS Patient Advisory Board.
¹³ Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
¹⁴ Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium.
¹⁵ Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
¹⁶ Janssen Research and Development, LLC, Titusville, NJ, USA.
¹⁷ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
¹⁸ Radar-cns.org.

PMID: 39038618
DOI: 10.1016/j.jad.2024.07.136

Abstract

Background: Changes in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity.

Methods: Data were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393).

Results: Increased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %.

Limitations: Limitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected.

Conclusion: Our study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions.

Keywords: Longitudinal; Major Depressive Disorder; Sleep; Wearable technology.

Publication types

Observational Study

MeSH terms

Adult
Depressive Disorder, Major* / physiopathology
Depressive Disorder, Major* / therapy
Female
Humans
Longitudinal Studies
Male
Middle Aged
Recurrence*
Severity of Illness Index
Sleep / physiology
Sleep Quality
Telemedicine / instrumentation
Wearable Electronic Devices*