Download PDF
CC BY-NC-ND 4.0 · Sleep Sci 2023; 16(02): 159-164
DOI: 10.1055/s-0043-1770809
Original Article

Automatic-Scoring Actigraph Compares Favourably to a Manually-Scored Actigraph for Sleep Measurement in Healthy Adults

David T. Edgar 
1   Te Huataki Waiora, School of Health, University of Waikato, Hamilton, Waikato, New Zealand
2   Joint Support Group, Human Performance Cell, New Zealand Army, Upper Hutt, Wellington, New Zealand
,
C. Martyn Beaven 
1   Te Huataki Waiora, School of Health, University of Waikato, Hamilton, Waikato, New Zealand
,
Nicholas D. Gill
1   Te Huataki Waiora, School of Health, University of Waikato, Hamilton, Waikato, New Zealand
,
Jennifer L. Zaslona
3   Sleep/Wake Research Centure, Massey University, Wellington, Wellington, New Zealand
,
Matthew W. Driller 
4   Sport, Performance, and Nutrition Research Group, School of Allied Health, Human Services, and Sport, La Trobe University, Melbourne, Australia
› Author Affiliations
› Further Information
   Permissions and Reprints

Abstract

Introduction Actigraphy has been used widely in sleep research due to its non-invasive, cost-effective ability to monitor sleep. Traditionally, manually-scored actigraphy has been deemed the most appropriate in the research setting; however, technological advances have seen the emergence of automatic-scoring wearable devices and software.

Methods A total of 60-nights of sleep data from 20-healthy adult participants (10 male, 10 female, age: 26 ± 10 years) were collected while wearing two devices concomitantly. The objective was to compare an automatic-scoring device (Fatigue Science Readiband™ [AUTO]) and a manually-scored device (Micro Motionlogger® [MAN]) based on the Cole-Kripke method. Manual-scoring involved trained technicians scoring all 60-nights of sleep data. Sleep indices including total sleep time (TST), total time in bed (TIB), sleep onset latency (SOL), sleep efficiency (SE), wake after sleep onset (WASO), wake episodes per night (WE), sleep onset time (SOT) and wake time (WT) were assessed between the two devices using mean differences, 95% levels of agreement, Pearson-correlation coefficients (r), and typical error of measurement (TEM) analysis.

Results There were no significant differences between devices for any of the measured sleep variables (p ≥0.05). All sleep indices resulted in very-strong correlations (all r ≥0.84) between devices. A mean difference between devices of <1 minutes for TST was associated with a TEM of 15.5 minute (95% CI =12.3 to 17.7 minutes).

Conclusion Given there were no significant differences between devices in the current study, automatic-scoring actigraphy devices may provide a more practical and cost-effective alternative to manually-scored actigraphy in healthy populations.

Keywords

Polysomnography - Technology - Reproducibility of results - Sleep deprivation - Monitoring


Publication History

Article published online:
06 July 2023

© 2023. Brazilian Sleep Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Thieme Revinter Publicações Ltda.
Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil

 

  • References

  • 1 Chinoy ED, Cuellar JA, Huwa KE. et al. Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep 2021; 44 (05) 1-16
    CrossrefPubMedGoogle Scholar
  • 2 Van de Water AT, Holmes A, Hurley DA. Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography--a systematic review. J Sleep Res 2011; 20 (1 Pt 2, 1pt2) 183-200
    CrossrefPubMedGoogle Scholar
  • 3 Dunican IC, Murray K, Slater JA. et al. Laboratory and home comparison of wrist-activity monitors and polysomnography in middle-aged adults. Sleep Biol Rhythms 2018; 16: 85-97 DOI: 10.1007/s41105-017-0130-x.
    CrossrefGoogle Scholar
  • 4 O'Donnell S, Beaven CM, Driller MW. From pillow to podium: a review on understanding sleep for elite athletes. Nat Sci Sleep 2018; 10: 243-253
    CrossrefPubMedGoogle Scholar
  • 5 Driller M, McQuillan J, O'Donnell S. Inter-device reliability of an automatic-scoring actigraph for measuring sleep in healthy adults. Sleep Sci 2016; 9 (03) 198-201
    CrossrefPubMedGoogle Scholar
  • 6 Palotti J, Mall R, Aupetit M. et al. Benchmark on a large cohort for sleep-wake classification with machine learning techniques. NPJ Digit Med 2019; 2: 50 DOI: 10.1038/s41746-019-0126-9.
    CrossrefPubMedGoogle Scholar
  • 7 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 (03) 342-392
    CrossrefPubMedGoogle Scholar
  • 8 Noor Z, Smith A, Smith S, Nissen L. Feasibility and acceptability of wrist actigraph in assessing sleep quality and sleep quantity: A home-based pilot study in healthy volunteers. Health 2013; 5: 63-72 DOI: 10.4236/health.2013.58A2010.
    CrossrefGoogle Scholar
  • 9 Wulff K, Dijk DJ, Middleton B, Foster RG, Joyce EM. Sleep and circadian rhythm disruption in schizophrenia. Br J Psychiatry 2012; 200 (04) 308-316
    CrossrefPubMedGoogle Scholar
  • 10 Fullagar HH, Duffield R, Skorski S. et al. Sleep, travel, and recovery responses of national footballers during and after long-haul international air travel. Int J Sports Physiol Perform 2016; 11 (01) 86-95
    CrossrefPubMedGoogle Scholar
  • 11 Arendt J. Shift work: coping with the biological clock. Occup Med (Lond) 2010; 60 (01) 10-20
    CrossrefPubMedGoogle Scholar
  • 12 Tremaine R, Dorrian J, Paterson J. et al. Actigraph estimates of the sleep of Australian midwives: the impact of shift work. Biol Res Nurs 2013; 15 (02) 191-199
    CrossrefPubMedGoogle Scholar
  • 13 McGillis Z, Dorman SC, Robertson A. et al. Sleep quantity and quality of Ontario wildland firefighters across a low-hazard fire season. J Occup Environ Med 2017; 59 (12) 1188-1196
    CrossrefPubMedGoogle Scholar
  • 14 Adler AB, Gunia BC, Bliese PD, Kim PY, LoPresti ML. Using actigraphy feedback to improve sleep in soldiers: an exploratory trial. Sleep Health 2017; 3 (02) 126-131
    CrossrefPubMedGoogle Scholar
  • 15 Sadeh A. The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev 2011; 15 (04) 259-267
    CrossrefPubMedGoogle Scholar
  • 16 Chow CM, Wong SN, Shin M. et al. Defining the rest interval associated with the main sleep period in actigraph scoring. Nat Sci Sleep 2016; 8: 321-328
    CrossrefPubMedGoogle Scholar
  • 17 Follesø HS, Austad SB, Olsen A, Saksvik-Lehouillier I. The development, inter-rater agreement and performance of a hierarchical procedure for setting the rest-interval in actigraphy data. Sleep Med 2021; 85: 221-229
    CrossrefPubMedGoogle Scholar
  • 18 Dennis J, Dawson B, Heasman J, Rogalski B, Robey E. Sleep patterns and injury occurrence in elite Australian footballers. J Sci Med Sport 2016; 19 (02) 113-116
    CrossrefPubMedGoogle Scholar
  • 19 Athavale Y, Krishnan S, Raissi A. et al. Actigraphic detection of periodic limb movements: development and validation of a potential device-independent algorithm. A proof of concept study. Sleep 2019; 42 (09) zsz117
    CrossrefPubMedGoogle Scholar
  • 20 Arifin W. . A Web-based Sample Size Calculator for Reliability Studies. Educ Med J 2018
  • 21 Driller MW, O'Donnell S, Tavares F. What wrist should you wear your actigraphy device on? Analysis of dominant vs. non-dominant wrist actigraphy for measuring sleep in healthy adults. Sleep Sci 2017; 10 (03) 132-135
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Fowler P, Duffield R, Vaile J. Effects of simulated domestic and international air travel on sleep, performance, and recovery for team sports. Scand J Med Sci Sports 2015; 25 (03) 441-451
    CrossrefPubMedGoogle Scholar
  • 23 Lee S, Bonnar D, Roane B, Gradisar M, Dunican IC, Lastella M, Maisey G, Suh S. Sleep characteristics and mood of professional esports athletes: A multi-national study. Int J Environ Res Public Health 2021; Jan 14; 18 (02) 664 DOI: 10.3390/ijerph18020664. . PMID: 33466788; PMCID: PMC7830734
    CrossrefPubMedGoogle Scholar
  • 24 Russell C, Caldwell J, Arand D, Myers L, Wubbels P, Downs HJAL. Validation of the fatigue science readiband actigraph and associated sleep/wake classification algorithms. 2000. Archinoetics, LLC;
    Google Scholar
  • 25 Meltzer LJ, Walsh CM, Traylor J, Westin AM. Direct comparison of two new actigraphs and polysomnography in children and adolescents. Sleep 2012; 35 (01) 159-166
    PubMedGoogle Scholar
  • 26 Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep 1992; 15 (05) 461-469
    CrossrefPubMedGoogle Scholar
  • 27 McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012; 22 (03) 276-282
    CrossrefPubMedGoogle Scholar
  • 28 Chan YH. Biostatistics 104: correlational analysis. Singapore Med J 2003; 44 (12) 614-619
    PubMedGoogle Scholar
  • 29 Hopkins W. Spreadsheets for analysis of validity and reliability. Sports Science. 2017;21.
    Google Scholar
  • 30 Werner H, Molinari L, Guyer C, Jenni OG. Agreement rates between actigraphy, diary, and questionnaire for children's sleep patterns. Arch Pediatr Adolesc Med 2008; 162 (04) 350-358
    CrossrefPubMedGoogle Scholar