Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms
Authors Quante M, Kaplan ER, Cailler M, Rueschman M, Wang R, Weng J, Taveras EM, Redline S
Received 7 September 2017
Accepted for publication 6 November 2017
Published 18 January 2018 Volume 2018:10 Pages 13—20
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Steven A Shea
Mirja Quante,1–3 Emily R Kaplan,2 Michael Cailler,2 Michael Rueschman,2 Rui Wang,2–5 Jia Weng,2 Elsie M Taveras,3,5,6 Susan Redline2,3,7
1Department of Neonatology, University of Tuebingen, Tuebingen, Germany; 2Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA; 3Harvard Medical School, Boston, MA, USA; 4Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; 5Department of Population Medicine, Harvard Medical School and The Harvard Pilgrim Health Care Institute, Boston, MA, USA; 6Division of General Academic Pediatrics, Department of Pediatrics, MassGeneral Hospital for Children, Boston, MA, USA; 7Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
Objectives: Actigraphy is widely used to estimate sleep–wake time, despite limited information regarding the comparability of different devices and algorithms. We compared estimates of sleep–wake times determined by two wrist actigraphs (GT3X+ versus Actiwatch Spectrum [AWS]) to in-home polysomnography (PSG), using two algorithms (Sadeh and Cole–Kripke) for the GT3X+ recordings.
Subjects and methods: Participants included a sample of 35 healthy volunteers (13 school children and 22 adults, 46% male) from Boston, MA, USA. Twenty-two adults wore the GT3X+ and AWS simultaneously for at least five consecutive days and nights. In addition, actigraphy and PSG were concurrently measured in 12 of these adults and another 13 children over a single night. We used intraclass correlation coefficients (ICCs), epoch-by-epoch comparisons, paired t-tests, and Bland–Altman plots to determine the level of agreement between actigraphy and PSG, and differences between devices and algorithms.
Results: Each actigraph showed comparable accuracy (0.81–0.86) for sleep–wake estimation compared to PSG. When analyzing data from the GT3X+, the Cole–Kripke algorithm was more sensitive (0.88–0.96) to detect sleep, but less specific (0.35–0.64) to detect wake than the Sadeh algorithm (sensitivity: 0.82–0.91, specificity: 0.47–0.68). Total sleep time measured using the GT3X+ with both algorithms was similar to that obtained by PSG (ICC=0.64–0.88). In contrast, agreement between the GT3X+ and PSG wake after sleep onset was poor (ICC=0.00–0.10). In adults, the GT3X+ using the Cole–Kripke algorithm provided data comparable to the AWS (mean bias=3.7±19.7 minutes for total sleep time and 8.0±14.2 minutes for wake after sleep onset).
Conclusion: The two actigraphs provided comparable and accurate data compared to PSG, although both poorly identified wake episodes (i.e., had low specificity). Use of actigraphy scoring algorithm influenced the mean bias and level of agreement in sleep–wake times estimates. The GT3X+, when analyzed by the Cole–Kripke, but not the Sadeh algorithm, provided comparable data to the AWS.
Keywords: validation, actigraphy, polysomnography, scoring algorithm
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