Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/26723
Title: Prediction Of Shiftworker Alertness, Sleep, And Circadian Phase Using A Model Of Arousal Dynamics Constrained By Shift Schedules And Light Exposure.
Austin Authors: Knock, Stuart A;Magee, Michelle;Stone, Julia E;Ganesan, Saranea;Mulhall, Megan D;Lockley, Steven W;Howard, Mark E ;Rajaratnam, Shantha M W;Sletten, Tracey L;Postnova, Svetlana
Affiliation: Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
Institute for Breathing and Sleep
Sydney Nano, the University of Sydney, Camperdown, Australia
Woolcock Institute of Medical Research, Glebe, Australia
Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
The University of Sydney, School of Physics, Camperdown, Australia
Issue Date: 2021
Date: 2021-06-10
Publication information: Sleep 2021; 44(11)
Abstract: The study aimed to, for the first time, (i) compare sleep, circadian phase, and alertness of Intensive Care Unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (ii) investigate how different environmental constraints affect predictions and agreement with data. The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison. All input constraints produced similar prediction for KSS, with 56-60% of KSS scores predicted within ±1 on a day and 48-52% on a night shift. Accurate prediction of an individual's circadian phase required individualised light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35-47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81±6% and 87±5% depending on choice of input constraint. The use of individualised environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.
URI: https://ahro.austin.org.au/austinjspui/handle/1/26723
DOI: 10.1093/sleep/zsab146
ORCID: 0000-0001-6642-0882
Journal: Sleep
PubMed URL: 34111278
Type: Journal Article
Subjects: alertness
circadian rhythms
healthcare
nurses
quantitative modelling
rotating shiftwork
sleep
sleepiness
Appears in Collections:Journal articles

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