Perception versus Reality: The Relationship between Subjective and Objective Measures of Sleep When On-call under Simulated Laboratory Conditions.

doi:10.1080/15402002.2020.1807985

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Perception versus Reality: The Relationship between Subjective and Objective Measures of Sleep When On-call under Simulated Laboratory Conditions.

Journal Article•DOI•

Perception versus Reality: The Relationship between Subjective and Objective Measures of Sleep When On-call under Simulated Laboratory Conditions.

Madeline Sprajcer¹, Grace E. Vincent¹, Sarah M. Jay¹, Andrew Vakulin², Andrew Vakulin³, Leon Lack², Sally A. Ferguson¹ - Show less +3 more•Institutions (3)

Central Queensland University¹, Flinders University², Woolcock Institute of Medical Research³

04 Jul 2021-Behavioral Sleep Medicine (Informa UK Limited)-Vol. 19, Iss: 4, pp 533-546

TL;DR: Some objective measures of sleep were associated with subjective estimates of sleep duration and sleep quality, however, individuals may overestimate sleep onset latency and underestimate sleep duration during on-call periods.

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Abstract: On-call working arrangements have been shown to negatively impact sleep. However, workers may perceive their sleep to be worse than it actually is. The aim of this study was to compare participants...

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Effects of pre-sleep simulated on-call instructions on subsequent sleep

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Johan Wuyts, Elke De Valck, Marie Vandekerckhove, Nathalie Pattyn, Daniel Berckmans¹, Bart Haex¹, Jana Maes, Raymond Cluydts - Show less +4 more•Institutions (1)

Katholieke Universiteit Leuven¹

01 Jan 2011-Sleep

TL;DR: Increased vigilance during the night causes sleep to be less efficient and less qualitative as shown by an increase in wake-activity and a distorted sleep perception.

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Abstract: Nightly interventions, prevalent to on-call situations, can have negative consequences for those involved. We investigated if intervention-free-on-call-nights would also mean disturbance-free-sleep for people on-call. 16 healthy sleepers spent three nights in the laboratory: after a habituation night, reference and on-call night were counterbalanced. Subjects were instructed to react to a sound, presented at unpredictable moments during the night. Participants were unaware of the fact that the sound would never be presented. These vigilance instructions resulted in more subjective wake after sleep onset (WASO), lower subjective sleep efficiency and significantly lower experienced sleep quality. Objectively, a longer sleep onset, an increased amount of WASO and significantly lower sleep efficiency were observed. During deep sleep, significantly more beta activity was recorded. Apart from real nightly interventions increased vigilance during the night causes sleep to be less efficient and less qualitative as shown by an increase in wake-activity and a distorted sleep perception.

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8 citations

Journal Article•DOI•

Who is 'on-call' in Australia? A new classification approach for on-call employment in future population-level studies.

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Madeline Sprajcer¹, Sarah Appleton², Robert J. Adams², Tiffany K. Gill³, Sally A. Ferguson¹, Grace E. Vincent¹, Jessica L. Paterson¹, Amy C. Reynolds² - Show less +4 more•Institutions (3)

Central Queensland University¹, Flinders University², University of Adelaide³

04 Nov 2021-PLOS ONE

TL;DR: In this article, a survey of 2044 adults assessed sociodemographic and work arrangements of Australian on-call workers and found that 45.5% reported working at least one day oncall in the previous month.

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Abstract: Background On-call research and guidance materials typically focus on ‘traditional’ on-call work (e.g., emergency services, healthcare). However, given the increasing prevalence of non-standard employment arrangements (e.g., gig work and casualisation), it is likely that a proportion of individuals who describe themselves as being on-call are not included in current on-call literature. This study therefore aimed to describe the current sociodemographic and work characteristics of Australian on-call workers. Methods A survey of 2044 adults assessed sociodemographic and work arrangements. Of this population, 1057 individuals were workforce participants, who were asked to provide information regarding any on-call work they performed over the last three months, occupation type, weekly work hours, and the presence or absence of non-standard work conditions. Results Of respondents who were working, 45.5% reported working at least one day on-call in the previous month. There was a high prevalence of on-call work in younger respondents (63.1% of participants aged 18–24 years), and those who worked multiple jobs and more weekly work hours. Additionally, high prevalence rates of on-call work were reported by machinery operators, drivers, community and personal service workers, sales workers, and high-level managers. Conclusions These data suggest that on-call work is more prevalent than previously recorded and is likely to refer to a broad set of employment arrangements. Current classification systems may therefore be inadequate for population-level research. A taxonomy for the classification of on-call work is proposed, incorporating traditional on-call work, gig economy work, relief, or unscheduled work, and out of hours work.

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References

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Journal Article•

R: A language and environment for statistical computing.

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R Core Team

01 Jan 2014-MSOR connections

TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.

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Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

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272,030 citations

Journal Article•DOI•

The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research.

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Daniel J. Buysse, Charles F. Reynolds, Timothy H. Monk, Susan Berman, David J. Kupfer - Show less +1 more

01 May 1989-Psychiatry Research-neuroimaging

TL;DR: The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.

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Abstract: Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.

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23,155 citations

"Perception versus Reality: The Rela..." refers background or methods in this paper

...…selected based on the following inclusion criteria; male, aged 18–35 years, BMI 18–30 kg/m2, nonsmoking, non-shift working, no travel across multiple time zones in previous month, a Pittsburgh Sleep Quality Index score ≤5 (Buysse et al., 1989), and an Epworth Sleepiness Score <10 (Johns, 1991)....
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...Participants completed a general health questionnaire to screen for preexisting medical conditions likely to impact sleep (including sleep disorders) and were selected based on the following inclusion criteria; male, aged 18–35 years, BMI 18–30 kg/m(2), nonsmoking, non-shift working, no travel across multiple time zones in previous month, a Pittsburgh Sleep Quality Index score ≤5 (Buysse et al., 1989), and an Epworth Sleepiness Score <10 (Johns, 1991)....
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...While extensive validation has been performed on many subjective measures of sleep (e.g., the Pittsburgh Sleep Quality Index, Insomnia Severity Index, etc.) (Bastien et al., 2001; Buysse et al., 1989), these validation studies have generally occurred in non-shift working/not on-call populations....
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...) (Bastien et al., 2001; Buysse et al., 1989), these validation studies have generally occurred in non-shift working/not on-call populations....
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Journal Article•DOI•

A new method for measuring daytime sleepiness: the Epworth sleepiness scale.

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Murray W. Johns¹•Institutions (1)

Epworth Hospital¹

01 Nov 1991-Sleep

TL;DR: The development and use of a new scale, the Epworth sleepiness scale (ESS), is described, which is a simple, self-administered questionnaire which is shown to provide a measurement of the subject's general level of daytime sleepiness.

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Abstract: The development and use of a new scale, the Epworth sleepiness scale (ESS), is described. This is a simple, self-administered questionnaire which is shown to provide a measurement of the subject's general level of daytime sleepiness. One hundred and eighty adults answered the ESS, including 30 normal men and women as controls and 150 patients with a range of sleep disorders. They rated the chances that they would doze off or fall asleep when in eight different situations commonly encountered in daily life. Total ESS scores significantly distinguished normal subjects from patients in various diagnostic groups including obstructive sleep apnea syndrome, narcolepsy and idiopathic hypersomnia. ESS scores were significantly correlated with sleep latency measured during the multiple sleep latency test and during overnight polysomnography. In patients with obstructive sleep apnea syndrome ESS scores were significantly correlated with the respiratory disturbance index and the minimum SaO2 recorded overnight. ESS scores of patients who simply snored did not differ from controls.

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13,788 citations

"Perception versus Reality: The Rela..." refers background in this paper

...…selected based on the following inclusion criteria; male, aged 18–35 years, BMI 18–30 kg/m2, nonsmoking, non-shift working, no travel across multiple time zones in previous month, a Pittsburgh Sleep Quality Index score ≤5 (Buysse et al., 1989), and an Epworth Sleepiness Score <10 (Johns, 1991)....
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..., 1989), and an Epworth Sleepiness Score <10 (Johns, 1991)....
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Journal Article•DOI•

A general and simple method for obtaining R2 from generalized linear mixed-effects models

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Shinichi Nakagawa¹, Shinichi Nakagawa², Holger Schielzeth³•Institutions (3)

University of Otago¹, Max Planck Society², Bielefeld University³

01 Feb 2013-Methods in Ecology and Evolution

TL;DR: In this article, the authors make a case for the importance of reporting variance explained (R2) as a relevant summarizing statistic of mixed-effects models, which is rare, even though R2 is routinely reported for linear models and also generalized linear models (GLM).

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Abstract: Summary The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

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7,749 citations

"Perception versus Reality: The Rela..." refers background or methods in this paper

...This figure shows likelihood-based R2Δ for linear mixed effects models (Nakagawa & Schielzeth, 2013)....
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...The adjustment procedure reduces between-participant differences in a given measure while preserving within-participant differences, such that the relationship between subjective and objective measures to a greater extent reflects one within individuals (Nakagawa & Schielzeth, 2013)....
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...For each selected model, we computed a likelihood-based R squared using the equation below (Nakagawa & Schielzeth, 2013): Figure 1....
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Journal Article•DOI•

Validation of the Insomnia Severity Index as an outcome measure for insomnia research

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Célyne H. Bastien¹, Annie Vallières¹, Charles M. Morin¹•Institutions (1)

Laval University¹

01 Jul 2001-Sleep Medicine

TL;DR: The clinical validation of the Insomnia Severity Index (ISI) indicates that the ISI is a reliable and valid instrument to quantify perceived insomnia severity and is likely to be a clinically useful tool as a screening device or as an outcome measure in insomnia treatment research.

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5,143 citations

"Perception versus Reality: The Rela..." refers background in this paper

...While extensive validation has been performed on many subjective measures of sleep (e.g., the Pittsburgh Sleep Quality Index, Insomnia Severity Index, etc.) (Bastien et al., 2001; Buysse et al., 1989), these validation studies have generally occurred in non-shift working/not on-call populations....
[...]
...) (Bastien et al., 2001; Buysse et al., 1989), these validation studies have generally occurred in non-shift working/not on-call populations....
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