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An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data

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Abstract

Sleep duration is a recognized determinant of mental health, obesity and cardiovascular disease, cognition, and memory across the lifespan. Due to convenience and cost, sleep duration is often measured through self-report; yet, self-reported sleep duration can be highly biased. Actigraphy is a viable alternative as an objective measure of sleep. To analyze this actigraphy data, various sleep evaluation algorithms have been developed using regression methods, with coefficients constructed on minute-by-minute data measured at a specific device placement (wrist or hip). Because activity counts per minute may be affected by various factors in the study (e.g., type of device, sampling frequencies), regression-based algorithms developed within specific populations may not be generalizable to wider use. To address these concerns, we propose a new learning method to obtain robust and consistent sleep duration estimates. First, we identify temporal segments via pruned dynamic programming; then, we develop a calling algorithm with individual-specific thresholds and capture sleep periods. Our proposed method is motivated by and demonstrated in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study and the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study.

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Acknowledgements

This research is supported by National Institute of Environmental Health Sciences Grant R01ES024732. Dr. Jansen is funded through a T32 grant from the National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. 5T32DK071212-12). MESA is supported by NHLBI funded contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by cooperative agreements UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 funded by NCATS. MESA Sleep was supported by NHLBI R01 L098433.

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Authors and Affiliations

  1. Division of Biostatistics, Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA

    Jonggyu Baek

  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Jonggyu Baek, Magaret Banker, Xichen She & Peter X. K. Song

  3. Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Erica C. Jansen & Karen E. Peterson

  4. Department of Kinesiology, Iowa State University, Ames, IA, USA

    E. Andrew Pitchford

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  1. Jonggyu Baek
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  2. Magaret Banker
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  3. Erica C. Jansen
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  4. Xichen She
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  7. Peter X. K. Song
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Correspondence to Peter X. K. Song.

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Baek, J., Banker, M., Jansen, E.C. et al. An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data . Stat Biosci 13, 563–583 (2021). https://doi.org/10.1007/s12561-021-09309-3

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