An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Bai J, Di C, Xiao L, Evenson KR, Lacroix AZ, Crainiceanu M, Buchner DM (2016) An activity index for raw accelerometry data and its comparison with other activity metrics. PLoS ONE 11(8):1–14.

    Article  Google Scholar 

  2. 2.

    Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, Greenland P, Jacobs DR, Kronmal R, Liu K, Nelson JC, O’Leary D, Saad MF, Shea S, Szklo M, Tracy RP (2002) Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 156(9):871–881.

    Article  Google Scholar 

  3. 3.

    Brønd JC, Arvidsson D (2016) Sampling frequency affects the processing of ActiGraph raw acceleration data to activity counts to activity counts. J Appl Physiol 120(3):362–369.

    Article  Google Scholar 

  4. 4.

    Campanini MZ, Lopez-Garcia E, Rodríguez-Artalejo F, González AD, Andrade SM, Mesas AE (2017) Agreement between sleep diary and actigraphy in a highly educated Brazilian population. Sleep Med 35:27–34.

    Article  Google Scholar 

  5. 5.

    Cleynen A, Koskas M, Lebarbier E, Rigaill G, Robin S (2014) Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data. Algorith Mol Biol 9(1):6, arXiv:1204.5564v3

  6. 6.

    Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC (1992) Technical note: automatic sleep/wake identification from wrist activity. Sleep 15(5):461–469

    Article  Google Scholar 

  7. 7.

    Dean DAn, Goldberger AL, Mueller R, Kim M, Rueschman M, Mobley D, Sahoo SS, Jayapandian CP, Cui L, Morrical MG, Surovec S, Zhang GQ, Redline S, (2016) Scaling up scientific discovery in sleep medicine: the national sleep research resource. Sleep 39(5):1151–1164.

  8. 8.

    Domelen DRV, Pittard WS (2014) Flexible R Functions for Processing Accelerometer Data, with Emphasis on NHANES 2003–2006 Flexible R Functions for Processing Accelerometer Data, with Emphasis on NHANES 2003–2006. R J 6(2)

  9. 9.

    Gangwisch JE, Heymsfield SB, Boden-albala B, Buijs RM, Kreier F, Pickering TG, Rundle AG, Zammit GK, Gangwisch JE, Malaspina D (2006) Short sleep duration as a risk factor for hypertension analyses of the First National Health and Nutrition Examination Survey. Hypertension 47(5):833–839.

    Article  Google Scholar 

  10. 10.

    Gottlieb DJ, Punjabi NM, Newman AB, Resnick HE, Redline S, Baldwin CM, Nieto FJ (2017) Association of sleep time with diabetes mellitus and impaired glucose tolerance. Arch Intern Med 165(8):863–867

    Article  Google Scholar 

  11. 11.

    Hedner J, Pillar G, Pittman SD, Zou D, Grote L, White DP (2004) A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep 27(8):1560–1566

    Article  Google Scholar 

  12. 12.

    Knutson KL (2010) Sleep duration and cardiometabolic risk: a review of the epidemiologic evidence. Best Pract Res Clin Endocrinol Metab 24(5):731–743.

    Article  Google Scholar 

  13. 13.

    Kölling S, Endler S, Ferrauti A, Meyer T, Kölling S, Endler S, Ferrauti A, Meyer T (2016) Comparing subjective with objective sleep parameters via multisensory actigraphy in German physical education students comparing subjective with objective sleep parameters via multisensory actigraphy in German physical education students. Behav Sleep Med 14(4):389–405.

    Article  Google Scholar 

  14. 14.

    Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ (2008) Sleep duration: how well do self-reports reflect objective measures? The CARDIA Sleep Study. Epidemiology 19(6):838–845.

    Article  Google Scholar 

  15. 15.

    Maidstone R, Hocking T, Rigaill G, Fearnhead P (2017) On optimal multiple changepoint algorithms for large data. Stat Comput 27(2):519–533. arXiv:1409.1842

    MathSciNet  Article  MATH  Google Scholar 

  16. 16.

    Patel SR, Zhu X, Storfer-Isser A, Mehra R, Jenny NS, Tracy R, Redline S (2009) Sleep duration and biomarkers of inflammation. Sleep 32(2):200–204.

    Article  Google Scholar 

  17. 17.

    Perng W, Tamayo-Ortiz M, Tang L, Sánchez BN, Cantoral A, Meeker JD, Dolinoy DC, Roberts EF, Martinez-Mier EA, Lamadrid-Figueroa H, Song PX, Ettinger AS, Wright R, Arora M, Schnaas L, Watkins DJ, Goodrich JM, Garcia RC, Solano-Gonzalez M, Bautista-Arredondo LF, Mercado-Garcia A, Hu H, Hernandez-Avila M, Tellez-Rojo MM, Peterson KE (2019) Early life exposure in Mexico to ENvironmental Toxicants (ELEMENT) Project. BMJ Open 9(8):1–14.

    Article  Google Scholar 

  18. 18.

    Reynolds CF 3rd, Grochocinski VJ, Monk TH, Buysse DJ, Giles DE, Coble PA, Matzzie JV, Doman J, Monahan J, Kupfer DJ (1992) Concordance between habitual sleep times and laboratory recording schedules. Sleep 15(6):571–575

    Article  Google Scholar 

  19. 19.

    Rigaill G (2010) Pruned dynamic programming for optimal multiple change-point detection. arXiv:1004.0887

  20. 20.

    Sadeh A, Sharkey KM, Carskadon MA (1994) Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep 17(3):201–207.

    Article  Google Scholar 

  21. 21.

    St-Onge MP, Roberts AL, Chen J, Kelleman M, O’Keeffe M, RoyChoudhury A, Jones PJH (2011) Short sleep duration increases energy intakes but does not change energy expenditure in normal-weight individuals. Am J Clin Nutr 94(2):410–416.

    Article  Google Scholar 

  22. 22.

    Taheri S, Lin L, Austin D, Young T, Mignot E (2004) Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med 1(3):e62.

    Article  Google Scholar 

  23. 23.

    Tudor-Locke C, Barreira TV, Schuna JM, Mire EF, Katzmarzyk PT (2014) Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab 39(1):53–57

    Article  Google Scholar 

  24. 24.

    Van Hees VT, Sabia S, Anderson KN, Denton SJ, Oliver J, Catt M, Abell JG, Kivimäki M, Trenell MI, Singh-Manoux A (2015) A novel, open access method to assess sleep duration using a wrist-worn accelerometer. PLoS ONE 10(11):1–13.

    Article  Google Scholar 

  25. 25.

    Wolfson AR, Carskadon MA (1998) Sleep schedules and daytime functioning in adolescents. Child Dev 69(4):875–887.

    Article  Google Scholar 

  26. 26.

    Wong WW, Ortiz CL, Lathan D, Moore LA, Konzelmann KL, Adolph AL, Smith EO, Butte NF (2013) Sleep duration of underserved minority children in a cross-sectional study. BMC Public Health 13(1):648.

    Article  Google Scholar 

  27. 27.

    Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S (2018) The National Sleep Research Resource: Towards a sleep data commons. J Am Med Inform Assoc 25(10):1351–1358.

    Article  Google Scholar 

Download references


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.

Author information



Corresponding author

Correspondence to Peter X. K. Song.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 253 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baek, J., Banker, M., Jansen, E.C. et al. An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data . Stat Biosci (2021).

Download citation


  • Actigraphy
  • Change-point
  • Pruned dynamic programming
  • Sleep duration