Rosalind Elliott1, Tapan Rai2, Sharon McKinley3. 1. Faculty of Health, University of Technology Sydney, Broadway 2007, New South Wales, Australia. Electronic address: Rosalind.Elliott@uts.edu.au. 2. School of Mathematical Sciences, Faculty of Science, University of Technology Sydney, Broadway 2007, New South Wales, Australia. Electronic address: Tapan.Rai@uts.edu.au. 3. Faculty of Health, University of Technology Sydney, Broadway 2007, New South Wales, Australia; Intensive Care Unit, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards 2065, New South Wales, Australia. Electronic address: Sharon.McKinley@uts.edu.au.
Abstract
PURPOSE: The aims of the current study were to describe the extrinsic and intrinsic factors affecting sleep in critically ill patients and to examine potential relationships with sleep quality. MATERIALS AND METHODS: Sleep was recorded using polysomnography (PSG) and self-reports collected in adult patients in intensive care. Sound and illuminance levels were recorded during sleep recording. Objective sleep quality was quantified using total sleep time divided by the number of sleep periods (PSG sleep period time ratio). A regression model was specified using the "PSG sleep period time ratio" as a dependent variable. RESULTS: Sleep was highly fragmented. Patients rated noise and light as the most sleep disruptive. Continuous equivalent sound levels were 56 dB (A). Median daytime illuminance level was 74 lux, and nighttime levels were 1 lux. The regression model explained 25% of the variance in sleep quality (P = .027); the presence of an artificial airway was the only statistically significant predictor in the model (P = .007). CONCLUSIONS: The presence of an artificial airway during sleep monitoring was the only significant predictor in the regression model and may suggest that although potentially uncomfortable, an artificial airway may actually promote sleep. This requires further investigation.
PURPOSE: The aims of the current study were to describe the extrinsic and intrinsic factors affecting sleep in critically illpatients and to examine potential relationships with sleep quality. MATERIALS AND METHODS: Sleep was recorded using polysomnography (PSG) and self-reports collected in adult patients in intensive care. Sound and illuminance levels were recorded during sleep recording. Objective sleep quality was quantified using total sleep time divided by the number of sleep periods (PSG sleep period time ratio). A regression model was specified using the "PSG sleep period time ratio" as a dependent variable. RESULTS: Sleep was highly fragmented. Patients rated noise and light as the most sleep disruptive. Continuous equivalent sound levels were 56 dB (A). Median daytime illuminance level was 74 lux, and nighttime levels were 1 lux. The regression model explained 25% of the variance in sleep quality (P = .027); the presence of an artificial airway was the only statistically significant predictor in the model (P = .007). CONCLUSIONS: The presence of an artificial airway during sleep monitoring was the only significant predictor in the regression model and may suggest that although potentially uncomfortable, an artificial airway may actually promote sleep. This requires further investigation.
Authors: Marcus T Altman; Catherine Pulaski; Francis Mburu; Margaret A Pisani; Melissa P Knauert Journal: Heart Lung Date: 2018-08-22 Impact factor: 2.210
Authors: Melissa P Knauert; Margaret Pisani; Nancy Redeker; Terry Murphy; Katy Araujo; Sangchoon Jeon; Henry Yaggi Journal: BMJ Open Respir Res Date: 2019-06-07