Estimating sleep parameters using an accelerometer without sleep diary - PubMed (original) (raw)

Clinical Trial

. 2018 Aug 28;8(1):12975.

doi: 10.1038/s41598-018-31266-z.

S Sabia 2 3, S E Jones 4, A R Wood 4, K N Anderson 5, M Kivimäki 3, T M Frayling 4, A I Pack 6, M Bucan 7 8, M I Trenell 9, Diego R Mazzotti 6, P R Gehrman 6 8, B A Singh-Manoux 2 3, M N Weedon 4

Affiliations

Clinical Trial

Estimating sleep parameters using an accelerometer without sleep diary

Vincent Theodoor van Hees et al. Sci Rep. 2018.

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.

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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.

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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. - DOI - PMC - PubMed
    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. - DOI - PMC - PubMed
    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. - DOI - PMC - PubMed
    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. - DOI - PubMed
    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. - DOI - PubMed

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