Estimating sleep parameters using an accelerometer without sleep diary

Vincent Theodoor van Hees, S Sabia, S E Jones, A R Wood, K N Anderson, M Kivimäki, T M Frayling, A I Pack, M Bucan, M I Trenell, Diego R Mazzotti, P R Gehrman, B A Singh-Manoux, M N Weedon, Vincent Theodoor van Hees, S Sabia, S E Jones, A R Wood, K N Anderson, M Kivimäki, T M Frayling, A I Pack, M Bucan, M I Trenell, Diego R Mazzotti, P R Gehrman, B A Singh-Manoux, M N Weedon

Abstract

Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60-82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Steps of the heuristic algorithm HDCZA for SPT-window detection.
Figure 2
Figure 2
Probability density distributions for accelerometer-based estimates of sleep duration, sleep onset, and waking up time using dots to indicate the 5th, 25th, 75th and 95th percentile.
Figure 3
Figure 3
Modified Bland-Altman plots with 95% limits of agreement (LoA) for SPT-window duration and sleep duration relative to polysomnography (PSG) in sleep clinic patients, with dashed lines indicating LoA and straight line indicating the mean. Open bullets reflect individuals with a sleep disorder, while closed bullets reflect normal sleepers.
Figure 4
Figure 4
Modified Bland-Altman plots with 95% limits of agreement (LoA) for SPT-window duration and sleep duration relative to polysomnography (PSG) in healthy good sleepers, with dashed lines indicating LoA and straight line indicating the mean.

References

    1. Doherty A, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS One. 2017;12:e0169649. doi: 10.1371/journal.pone.0169649.
    1. Sabia S, et al. Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am. J. Epidemiol. 2014;179:781–90. doi: 10.1093/aje/kwt330.
    1. da Silva IC, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int. J. Epidemiol. 2014;43:1959–68. doi: 10.1093/ije/dyu203.
    1. Rowlands AV, Yates T, Davies M, Khunti K, Edwardson CL. Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter? Med. Sci. Sports Exerc. 2016;48:1935–41. doi: 10.1249/MSS.0000000000000978.
    1. Rowlands AV, et al. Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent? Med. Sci. Sports Exerc. 2018;50:257–265. doi: 10.1249/MSS.0000000000001435.
    1. van Hees VT, et al. Challenges and Opportunities for Harmonizing Research Methodology: Raw Accelerometry. Methods Inf. Med. 2016;55:525–532. doi: 10.3414/ME15-05-0013.
    1. Anderson KN, et al. Assessment of sleep and circadian rhythm disorders in the very old: the Newcastle 85+ Cohort Study. Age Ageing. 2014;43:57–63. doi: 10.1093/ageing/aft153.
    1. Girschik J, Fritschi L, Heyworth J, Waters F. Validation of self-reported sleep against actigraphy. J. Epidemiol. 2012;22:462–468. doi: 10.2188/jea.JE20120012.
    1. Lockley SW, Skene DJ, Arendt J. Comparison between subjective and actigraphic measurement of sleep and sleep rhythms. J. Sleep Res. 1999;8:175–183. doi: 10.1046/j.1365-2869.1999.00155.x.
    1. Littner M, et al. Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep. 2003;26:337–341. doi: 10.1093/sleep/26.3.337.
    1. van Hees VT, et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS One. 2015;10:e0142533. doi: 10.1371/journal.pone.0142533.
    1. Marmot MG, et al. Health inequalities among British civil servants: the Whitehall II study. Lancet (London, England) 1991;337:1387–93. doi: 10.1016/0140-6736(91)93068-K.
    1. van Hees VT, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J. Appl. Physiol. 2014;117:738–44. doi: 10.1152/japplphysiol.00421.2014.
    1. van Hees VT, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013;8:e61691. doi: 10.1371/journal.pone.0061691.
    1. Benjamin DJ, et al. Redefine statistical significance. Nat. Hum. Behav. 2018;2:6–10. doi: 10.1038/s41562-017-0189-z.
    1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–310. doi: 10.1016/S0140-6736(86)90837-8.
    1. Cohen, J. In Statistical Power Analysis for the Behavioral Sciences 19–66 (Elsevier, 1977). doi:10.1016/B978-0-12-179060-8.50012-8
    1. van Hees, V. et al. GGIR. 10.5281/zenodo.1154149 (2018).
    1. Doherty AR, et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int. J. Behav. Nutr. Phys. Act. 2013;10:22. doi: 10.1186/1479-5868-10-22.
    1. Shinmoto Torres RL, Visvanathan R, Abbott D, Hill KD, Ranasinghe DC. A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people. PLoS One. 2017;12:e0185670. doi: 10.1371/journal.pone.0185670.
    1. Bussmann JBJ, Veltink PH, Koelma F, van Lummel RC, Stam HJ. Ambulatory monitoring of mobility-related activities: the initial phase of the development of an activity monitor. Eur. J Phys Med Rehabil. 1995;5:2–7.
    1. Gloeckl R, et al. Validation of an activity monitor during sleep in patients with chronic respiratory disorders. Respir. Med. 2015;109:286–8. doi: 10.1016/j.rmed.2014.12.017.
    1. O’Donnell, J. et al. Automated detection of sleep-boundary times using wrist-worn accelerometry. bioRxiv10.1101/225516 (2017).
    1. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992;15:461–9. doi: 10.1093/sleep/15.5.461.
    1. Jean-Louis G, Kripke DF, Mason WJ, Elliott JA, Youngstedt SD. Sleep estimation from wrist movement quantified by different actigraphic modalities. J. Neurosci. Methods. 2001;105:185–91. doi: 10.1016/S0165-0270(00)00364-2.
    1. Blackwell T, et al. Comparison of sleep parameters from actigraphy and polysomnography in older women: the SOF study. Sleep. 2008;31:283–91. doi: 10.1093/sleep/31.2.283.
    1. Lane JM, et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat. Genet. 2017;49:274–281. doi: 10.1038/ng.3749.
    1. Cade BE, et al. Common variants in DRD2 are associated with sleep duration: the CARe consortium. Hum. Mol. Genet. 2016;25:167–79. doi: 10.1093/hmg/ddv434.
    1. Byrne EM, Gehrman PR, Trzaskowski M, Tiemeier H, Pack AI. Genetic Correlation Analysis Suggests Association between Increased Self-Reported Sleep Duration in Adults and Schizophrenia and Type 2 Diabetes. Sleep. 2016;39:1853–1857. doi: 10.5665/sleep.6168.
    1. Jones SE, et al. Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci. PLoS Genet. 2016;12:e1006125. doi: 10.1371/journal.pgen.1006125.
    1. Jones, S. E. et al. Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease. bioRxiv 10.0.4.77/303941 (2018).
    1. Dashti, H. et al. GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. bioRxiv 10.0.4.77/274977 (2018).
    1. Jones, S. E. et al. Genetic studies of accelerometer-based sleep measures in 85,670 individuals yield new insights into human sleep behaviour. bioRxiv10.1101/303925 (2018).
    1. Lane, J. M. et al. Biological and clinical insights from genetics of insomnia symptoms. bioRxiv10.1101/257956 (2018).
    1. van Hees VT, Charman S, Anderson KN. Newcastle polysomnography and accelerometer data. . 2018

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren