Validation of a physical activity accelerometer device worn on the hip and wrist against polysomnography

Kelsie M Full, Jacqueline Kerr, Michael A Grandner, Atul Malhotra, Kevin Moran, Suneeta Godoble, Loki Natarajan, Xavier Soler, Kelsie M Full, Jacqueline Kerr, Michael A Grandner, Atul Malhotra, Kevin Moran, Suneeta Godoble, Loki Natarajan, Xavier Soler

Abstract

Study purpose: The integration of methods to assess daytime physical activity (PA) and sedentary behavior (SB) and nighttime sleep would allow the evaluation of 24-hour daily activity using a single device. Accelerometer devices used to assess daytime PA have not been substantially validated to evaluate sleep. The objective of this study was to use polysomnography (PSG) to validate a commonly used PA accelerometer worn on both wrists and the hip.

Methods: Seventeen participants (50-75years) completed a single-night in-home PSG recording while concurrently wearing 3 PA accelerometers. Accelerometer devices were worn on each wrist and the hip. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) were compared for each device against PSG. Correlation coefficients estimated measurement agreement. Paired t tests and Bland-Altman plots assessed measurement differences.

Results: Between PSG and devices, mean TST ranged from 361.6 to 403.2minutes. Mean SE estimates ranged from 86.9% to 96.9%. Mean WASO estimates ranged from 12 to 51.2minutes. For TST, SE, and WASO hip estimates differed significantly from PSG estimates (paired t tests, TST: P=.03, SE: P<.001, WASO: P< .001). No significant differences were found between wrist accelerometers and PSG estimates of TST, SE, or WASO.

Conclusions: PA accelerometer devices worn on either wrist provide valid estimates of TST, WASO, and SE when compared with PSG. Further studies are needed to investigate methods to improve assessment of sleep parameters by PA accelerometer devices to advance device integration and assessment 24-hour activity in populations.

Keywords: Actigraphy; Measurement; PSG; Sleep duration; Sleep efficiency; Validation.

Conflict of interest statement

Conflicts of Interest

The authors declare no conflicts of interest. The results of the present study do not constitute endorsement by ACSM. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1. Bland-Altman plots of Total Sleep…
Figure 1. Bland-Altman plots of Total Sleep Time of Wrist Actigraphy vs PSG
Foot note: Circle: healthy participants Triangle: participants with sleep-related respiratory conditions Solid line: mean difference Dash line: 95 confidence interval
Figure 2. Bland-Altman plots of Sleep Efficiency…
Figure 2. Bland-Altman plots of Sleep Efficiency of Wrist Actigraphy vs PSG
Foot note: Circle: healthy participants Triangle: participants with sleep-related respiratory conditions Solid line: mean difference Dash line: 95 confidence interval Dot line: 5% error tolerance (sleep efficiency only)
Figure 3. Bland-Altman plots of WASO of…
Figure 3. Bland-Altman plots of WASO of Wrist Actigraphy vs PSG
Foot note: Circle: healthy participants Triangle: participants with sleep-related respiratory conditions Solid line: mean difference Dash line: 95 confidence interval

References

    1. Chen J-C, Brunner RL, Ren H, et al. Sleep duration and risk of ischemic stroke in postmenopausal women. Stroke. 2008;39(12):3185–3192.
    1. Sands-Lincoln M, Loucks EB, Lu B, et al. Sleep Duration, Insomnia, and Coronary Heart Disease Among Postmenopausal Women in the Women’s Health Initiative. J Womens Heal. 2013;22(6):477–486.
    1. Chaput J-P, Després J-P, Bouchard C, Tremblay A. Short sleep duration is associated with reduced leptin levels and increased adiposity: Results from the Quebec family study. Obesity (Silver Spring) 2007;15(1):253–261.
    1. Chaput J-P, McNeil J, Després J-P, Bouchard C, Tremblay A. Short sleep duration as a risk factor for the development of the metabolic syndrome in adults. Prev Med (Baltim) 2013;57(6):872–877.
    1. McNeil J, Doucet E, Chaput J-P. Inadequate sleep as a contributor to obesity and type 2 diabetes. Can J diabetes. 2013;37(2):103–108.
    1. Kripke DF, Langer RD, Elliot JA, Klauber M, Rex KM. Mortality Related to Actigraphic Long and Short Sleep. Sleep Med. 2011;12(1):28–33.
    1. Ancoli-Israel S, Martin JL, Blackwell T, et al. The SBSM Guide to Actigraphy Monitoring: Clinical and Research Applications. Behav Sleep Med. 2015;13(sup1):S4–S38.
    1. Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the study of sleep and circadian rhythms. Sleep. 2003;26(3):342–392.
    1. Lauderdale D. Sleep duration: how well do self-reports reflect objective measures? The CARDIA Sleep Study. Epidemiology. 2008;19(6):838–845.
    1. Van Den Berg JF, Van Rooij FJa, Vos H, et al. Disagreement between subjective and actigraphic measures of sleep duration in a population-based study of elderly persons. J Sleep Res. 2008;17(3):295–302.
    1. Kupfer DJ, Detre TP, Foster G, Tucker GJ, Delgado J. The Application of Delgado’s Telemetric Mobility Recorder for Human Studies. 1972
    1. Foster FG, Kupfer D, Weiss G, Lipponen V, McPartland R, Delgado J. Mobility recording and cycle research in neuropsychiatry. J Interdiscipl Cycle Res. 1972;3(1):61–72.
    1. Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167(7):875–881.
    1. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, Mcdowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–188.
    1. Tudor-Locke C, Barreira TV, Schuna JM, Mire EF, Katzmarzyk PT. 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. 2014;39(1):53–57.
    1. Huberty J, Ehlers DK, Kurka J, Ainsworth B, Buman M. Feasibility of three wearable sensors for 24 hour monitoring in middle-aged women. BMC Womens Health. 2015;15(1):55.
    1. Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille SS. Estimating Activity and Sedentary Behavior From an Accelerometer on the Hip or Wrist. Med Sci Sport Exerc. 2013;45(5):964–975.
    1. Staudenmayer J, He S, Hickey A, Sasaki J, Freedson P. Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements. J Appl Physiol. 2015;119(4):396–403.
    1. Zinkhan M, Berger K, Hense S, et al. Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography. Sleep Med. 2014;15(9):1107–1114.
    1. Ray Ma, Youngstedt SD, Zhang H, et al. Examination of wrist and hip actigraphy using a novel sleep estimation procedure. Sleep Sci. 2014;7(2):74–81.
    1. Johns M. A new method for measuring daytime sleepiness: The Epworth Sleepiness Scale. 1991;14(6):540–545.
    1. American Sleep Disorders Association. American Sleep Disorders Association: EEG arousals: scoring rules and examples: a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep. 1992;15(2):173–184.
    1. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992;15(5):461–469.
    1. Martin Bland J, Altman DG. Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement. Lancet. 1986;327(8476):307–310.
    1. R Core Team. R: A language and environment for statistical computing. 2013
    1. Sadeh A. The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev. 2011;15(4):259–267.
    1. Morgenthaler T, Alessi C, Friedman L, et al. Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: an update for 2007. Sleep. 2007
    1. Buman MP, Winkler EaH, Kurka JM, et al. Reallocating time to sleep, sedentary behaviors, or active behaviors: Associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol. 2014;179(3):323–334.
    1. Hamer M, Stamatakis E, Steptoe A. Effects of substituting sedentary time with physical activity on metabolic risk. Med Sci Sports Exerc. 2014;46(10):1946–1950.
    1. Healy GN, Matthews CE, Dunstan DW, Winkler EAH, Owen N. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J. 2011;32(5):590–597.
    1. Weiss AR, Johnson NL, Berger NA, Redline S. Validity of activity-based devices to estimate sleep. [Accessed September 28, 2016];J Clin Sleep Med. 2010 6(4):336–342. .
    1. National Health and Nutrition Examination Survey (NHANES) Physical Activity Monitor (PAM) Procedures Manual. 2011 .
    1. Foley D, Ancoli-Israel S, Britz P, Walsh J. Sleep disturbances and chronic disease in older adults: results of the 2003 National Sleep Foundation Sleep in America Survey. J Psychosom Res. 2004;56(5):497–502.
    1. Stranges S, Tigbe W, Gomez-Olive F, Thorogood M, Kandala N-B. Sleep Problems: An Emerging Global Epidemic? Findings from the Indepth Who-Sage Study among over 40,000 Older Adults from Eight Countries Across Africa and Asia. J Epidemiol Community Heal. 2012;66(Suppl 1):A42–A42.
    1. Keadle SK, McKinnon R, Graubard BI, Troiano RP. Prevalence and trends in physical activity among older adults in the United States: A comparison across three national surveys. Prev Med (Baltim) 2016;89:37–43.

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