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Paper

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes

Machiko Katori, Shoi Shi, Koji L. Ode, Yasuhiro Tomita, Hiroki R. Ueda
Proceedings of the National Academy of Sci2022CC BY 4.0
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Published articlePublication date: Mar 18, 2022Added to atlas: Oct 15, 2026Source
Citations52Crossref referencesChecked Oct 16, 2026
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Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

Katori and colleagues optimised ACCEL, their sleep/wake classification algorithm for wristband accelerometers, and applied it to roughly 100,000 arm-acceleration recordings in the UK Biobank. UMAP dimension reduction followed by DBSCAN clustering identified 16 sleep phenotypes. These included phenotypes related to social jet lag and chronotype, as well as seven different insomnia-like phenotypes.

  • Signals: Accelerometer
At a glance
Venue
Proceedings of the National Academy of Sciences
Published
2022
DOI
10.1073/pnas.2116729119 (opens in a new tab)
arXiv
Not reported
PMID
Not reported
Licence
CC BY 4.0 (opens in a new tab)
Signals
Not reported
Form factor
Not reported
Devices
Datasets
Subjects
Not reported
Code
Not reported
Added
Oct 15, 2026

Abstract

Human sleep phenotypes can be defined and diversified by both genetic and environmental factors. However, some sleep phenotypes are difficult to evaluate without long-term, precise sleep monitoring, for which simple yet accurate sleep measurement is required. To solve this problem, we recently developed a state-of-the-art sleep/wake classification algorithm based on wristband-type accelerometers, termed ACCEL (acceleration-based classification and estimation of long-term sleep-wake cycles). In this study, we optimized and applied ACCEL to large-scale analysis of human sleep phenotypes. The clustering of an about 100,000-arm acceleration dataset in the UK Biobank using uniform manifold approximation and projection (UMAP) dimension reduction and density-based spatial clustering of applications with noise (DBSCAN) clustering methods identified 16 sleep phenotypes, including those related to social jet lag, chronotypes (“morning/night person”), and seven different insomnia-like phenotypes. Considering the complex relationship between sleep disorders and other psychiatric disorders, these unbiased and comprehensive analyses of sleep phenotypes in humans will not only contribute to the advancement of biomedical research on genetic and environmental factors underlying human sleep patterns but also, allow for the development of better digital biomarkers for psychiatric disorders.

Verbatim fromthe publisher's DOI record· Publisher licence: https://creativecommons.org/licenses/by/4.0/

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes: ChatPPG research summary card

How it works

How Method works: Setup → Method → TargetSetup: Signals (Accelerometer). Method: Method.SetupSignals: AccelerometerxyzSignalsAccelerometerMethodMethod

Devices· 1

Axivity AX3
Axivity · Wrist
IMU

Illustrations by form factor; product photos are added as licences clear.

Datasets used· 1

Dataset
UK Biobank wrist accelerometer (Axivity AX3 / field 90001)
103,568 subjects · uk-biobank
IMU

Cite

@article{katoriukb16sleepphenotyp2022,
  title = {{The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes}},
  author = {Machiko Katori and Shoi Shi and Koji L. Ode and Yasuhiro Tomita and Hiroki R. Ueda},
  year = {2022},
  journal = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.2116729119}
}

History

  1. Added to the ChatPPG research hub

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