S L Perrin1, S M Jay2, G E Vincent3, M Sprajcer3, L Lack4, S A Ferguson3, A Vakulin5. 1. University of South Australia, Centre for Cancer Biology, School of Pharmacy and Medical Sciences, Adelaide, SA, Australia; Central Queensland University, Appleton Institute, School of Health, Medical and Applied Sciences, Wayville, SA, Australia. Electronic address: sally.perrin@mymail.unisa.edu.au. 2. Central Queensland University, Appleton Institute, School of Health, Medical and Applied Sciences, Wayville, SA, Australia. Electronic address: s.jay@cqu.edu.au. 3. Central Queensland University, Appleton Institute, School of Health, Medical and Applied Sciences, Wayville, SA, Australia. 4. Adelaide Institute for Sleep Health: Flinders Centre of Research Excellence, College of Medicine and Public Health, Flinders University, SA, Australia. 5. Adelaide Institute for Sleep Health: Flinders Centre of Research Excellence, College of Medicine and Public Health, Flinders University, SA, Australia; NeuroSleep, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Australia.
Abstract
INTRODUCTION: On-call schedules are associated with stress and disrupted sleep. In a recent study, under non-sleep deprived conditions, low and high-stress on-call conditions did not significantly impact sleep quality but did impact next day performance. Our aim was to determine whether quantitative electroencephalography (qEEG) would reflect changes in cortical activity in on-call conditions, predicting that the high-stress condition would display faster qEEG frequencies compared with the control and low-stress condition. METHODS: Twenty-four healthy male participants (age: 26.5 ± 4.0 yrs) spent four nights in a time-isolated sleep laboratory. The within-subjects, repeated measures experimental design assessed waking EEG, via the Karolinska Drowsiness Test (KDT) during four time-points across a control day and two experimental (on-call) days. Experimental days comprised a low-stress (LS - reading task) and high-stress (HS - speech task) condition and were counterbalanced. Mixed-models analysis was used to assess condition and time by EEG biomarkers: Alpha Attenuation Coefficient (AAC), Slowing Ratio (SR) and Scaling Exponent (SE). RESULTS: Main effects were found for all three biomarkers by condition, with pairwise analysis reported. There was a significant difference in AAC between the LS condition (M = 1.26 ± =1.24) and HS condition (M = 1.01 ± 0.76 p = .02) indicating decreased alertness between LS and HS. A significant increase in SR between control (M = 7.1 ± 4.3) and LS (M = 10.1 ± 8.5 p = .0001), and a significant increase between the LS and HS (M = 7.8 ± 6.8 p = .018) showing greatest EEG slowing in the LS condition, reflecting of a passive, sleepier brain state. The SE was significantly higher in the LS (M = 1.09, ±0.17) condition compared with control (M = 1.0, ±0.11 p = .001) indicating decreased alertness in the LS task. DISCUSSION: Using qEEG biomarkers, in contrast with our initial hypothesis, the current study found that compared with control, the LS condition resulted in greater EEG slowing. These findings have implications for on-call workers who engage in periods of passive attention and highlight a protective role task stress may play in maintaining alertness levels during on-call conditions.
INTRODUCTION: On-call schedules are associated with stress and disrupted sleep. In a recent study, under non-sleep deprived conditions, low and high-stress on-call conditions did not significantly impact sleep quality but did impact next day performance. Our aim was to determine whether quantitative electroencephalography (qEEG) would reflect changes in cortical activity in on-call conditions, predicting that the high-stress condition would display faster qEEG frequencies compared with the control and low-stress condition. METHODS: Twenty-four healthy male participants (age: 26.5 ± 4.0 yrs) spent four nights in a time-isolated sleep laboratory. The within-subjects, repeated measures experimental design assessed waking EEG, via the Karolinska Drowsiness Test (KDT) during four time-points across a control day and two experimental (on-call) days. Experimental days comprised a low-stress (LS - reading task) and high-stress (HS - speech task) condition and were counterbalanced. Mixed-models analysis was used to assess condition and time by EEG biomarkers: Alpha Attenuation Coefficient (AAC), Slowing Ratio (SR) and Scaling Exponent (SE). RESULTS: Main effects were found for all three biomarkers by condition, with pairwise analysis reported. There was a significant difference in AAC between the LS condition (M = 1.26 ± =1.24) and HS condition (M = 1.01 ± 0.76 p = .02) indicating decreased alertness between LS and HS. A significant increase in SR between control (M = 7.1 ± 4.3) and LS (M = 10.1 ± 8.5 p = .0001), and a significant increase between the LS and HS (M = 7.8 ± 6.8 p = .018) showing greatest EEG slowing in the LS condition, reflecting of a passive, sleepier brain state. The SE was significantly higher in the LS (M = 1.09, ±0.17) condition compared with control (M = 1.0, ±0.11 p = .001) indicating decreased alertness in the LS task. DISCUSSION: Using qEEG biomarkers, in contrast with our initial hypothesis, the current study found that compared with control, the LS condition resulted in greater EEG slowing. These findings have implications for on-call workers who engage in periods of passive attention and highlight a protective role task stress may play in maintaining alertness levels during on-call conditions.
Authors: Gert Vanhollebeke; Stefanie De Smet; Rudi De Raedt; Chris Baeken; Pieter van Mierlo; Marie-Anne Vanderhasselt Journal: Neurobiol Stress Date: 2022-04-26
Authors: Grace E Vincent; Charlotte C Gupta; Madeline Sprajcer; Corneel Vandelanotte; Mitch J Duncan; Phil Tucker; Michele Lastella; Georgia A Tuckwell; Sally A Ferguson Journal: BMJ Open Date: 2020-07-27 Impact factor: 2.692
Authors: Madeline Sprajcer; Sarah L Appleton; Robert J Adams; Tiffany K Gill; Sally A Ferguson; Grace E Vincent; Jessica L Paterson; Amy C Reynolds Journal: PLoS One Date: 2021-11-04 Impact factor: 3.240