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The importance of sleep: Relationships between sleep quality and work demands, the prioritization of sleep and pre-sleep arousal in day-time employees


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Sleep deprivation is an often hidden problem in working adults. In this study, we evaluated self- 10 regulation processes contributing to poor sleep patterns of full-time office employees. We investigated whether work-related demands and prioritizing sleep (in relation to other activities) predicted sleep behaviours over an 11-day period. Seventy-three adults in New Zealand completed online measures, including the Copenhagen Psychosocial Questionnaire, a measure of sleep prioritization, and the Pittsburgh Sleep Quality Index. Mixed-model analyses of daily data revealed 15 that higher sleep prioritization and positive work-related emotions during a workday predicted better sleep quality that night. Cognitive demands on a workday predicted a later bedtime that night, whereas emotional demands predicted an earlier bedtime (but also an earlier waking time). Regression analyses revealed that when controlling for baseline levels of each dependent measure, pre-sleep arousal predicted fewer hours of sleep and greater sleep difficulty whereas sleep 20 prioritization predicted a faster time getting to sleep, longer sleep and less sleep difficulty. High priority for sleep and positive emotions at work may promote sleep quality, whereas cognitive and emotional demands, or pre-sleep arousal may disrupt sleep patterns. These findings point to sleep prioritization and cognitive-emotional self-regulation skills as potential targets for work-based interventions aimed at promoting sleep.
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The importance of sleep: Relationships
between sleep quality and work
demands, the prioritization of sleep
and pre-sleep arousal in day-time
Marisa Lofta & Linda Cameronb
a Jeffrey Cheah School of Medicine and Health Sciences, Monash
University, Sunway Selangor, Malaysia
b Psychological Sciences, School of Social Sciences, Humanities
and the Arts, University of California, Merced, CA, USA
Published online: 15 Jul 2014.
To cite this article: Marisa Loft & Linda Cameron (2014) The importance of sleep: Relationships
between sleep quality and work demands, the prioritization of sleep and pre-sleep arousal in day-
time employees, Work & Stress: An International Journal of Work, Health & Organisations, 28:3,
289-304, DOI: 10.1080/02678373.2014.935523
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Downloaded by [] at 22:45 26 August 2015
The importance of sleep: Relationships between sleep quality and work
demands, the prioritization of sleep and pre-sleep arousal in day-time
Marisa Loft
* and Linda Cameron
Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Sunway Selangor,
Psychological Sciences, School of Social Sciences, Humanities and the Arts, University
of California, Merced, CA, USA
(Received 25 May 2012; final version accepted 1 January 2014)
Sleep deprivation is an often hidden problem in working adults. In this study, we evaluated self-
regulation processes contributing to poor sleep patterns of full-time office employees. We
investigated whether work-related demands and prioritizing sleep (in relation to other activities)
predicted sleep behaviours over an 11-day period. Seventy-three adults in New Zealand completed
online measures, including the Copenhagen Psychosocial Questionnaire, a measure of sleep
prioritization, and the Pittsburgh Sleep Quality Index. Mixed-model analyses of daily data revealed
that higher sleep prioritization and positive work-related emotions during a workday predicted better
sleep quality that night. Cognitive demands on a workday predicted a later bedtime that night,
whereas emotional demands predicted an earlier bedtime (but also an earlier waking time).
Regression analyses revealed that when controlling for baseline levels of each dependent measure,
pre-sleep arousal predicted fewer hours of sleep and greater sleep difficulty whereas sleep
prioritization predicted a faster time getting to sleep, longer sleep and less sleep difficulty. High
priority for sleep and positive emotions at work may promote sleep quality, whereas cognitive and
emotional demands, or pre-sleep arousal may disrupt sleep patterns. These findings point to sleep
prioritization and cognitive-emotional self-regulation skills as potential targets for work-based
interventions aimed at promoting sleep.
Keywords: sleep prioritization; work demands; pre-sleep arousal; sleep; day-time employees; self-
Sleep deprivation is a significant problem for many working adults, especially since it can
have detrimental effects on health and well-being (Barber, Munz, Bagsby, & Powell,
2010; Norra et al., 2011). Sleep deprivation can impair immune function (Taylor,
Lichstein, & Durrence, 2003), appetite regulation (Copinschi, 2005) and other physio-
logical processes influencing health (Brindle & Conklin, 2012). It can also increase
disease risk by undermining physical activity, healthy diet choices and other health
*Corresponding author. Email:
Work & Stress, 2014
Vol. 28, No. 3, 289304,
© 2014 Taylor & Francis
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behaviours requiring motivation and self-control (Hagger, 2010a). Although some
research has evaluated a range of psychosocial factors influencing sleep behaviour
(LeBlanc et al., 2009), more research is needed to better understand the sleep decisions of
working adults and how their workplace experiences and sleep priorities may contribute
to sleep deprivation.
The workplace can be a primary source of experiences responsible for poor sleep
(Basner et al., 2007). Working adults typically experience job-related demands and
emotions that can undermine the relaxation and sleep initiation processes required for a
good nights sleep (Jones, Burke, & Westman, 2006). Moreover, work-related demands
and conflicts can motivate decisions to prioritize work goals over sleep-related goals.
Sleep self-regulation, which typically requires effortful goal-setting, planning and
behavioural control, can be difficult to achieve when confronted with the cognitive and
emotional demands of work (Hagger, 2010b). Prioritizing the need for sleep could be one
strategy for achieving sleep self-regulation in the face of work demands. Individuals who
give sleep high priority may be better able to maintain healthy sleep patterns despite
work-related demands. Giving priority to sleep involves highlighting its general
importance in terms of need, as well as its relative importance with regard to other life
domain activities. In this study, we examined whether work-related demands and sleep
prioritization predict sleep behaviour and quality among office employees.
To date, only a few studies have evaluated associations between daily work demands
and sleep quality, and nearly all have utilized a cross-sectional design (Åkerstedt, 2006).
A more recent longitudinal study (de Lange et al., 2009) provides initial evidence that
work-related demands predict poor sleep quality over a one-year time lag, but the study
did not explore the daily dynamics of work demands on sleep. The relationships between
work demands and sleep deprivation in the non-clinical population thus remain unclear.
Moreover, research demonstrating that prioritizing sleep predicts beneficial adaptive sleep
behaviours and better sleep quality is lacking. Further prospective theory-guided research
is needed to examine how work demands, work goal conflicts and sleep prioritization
predict sleep behaviours and quality on a daily basis.
Psychological theory on the self-regulation of behaviour can offer new insights into
the cognitive, emotional and decision-making factors influencing sleep behaviour. Self-
regulation theory (Cameron & Leventhal, 2003; Scheier & Carver, 2003) provides such a
framework for advancing our understanding of work and sleep dynamics, goals and
behaviours. According to self-regulation theory, individuals set priorities and goals in
multiple life domains, including work and sleep. Individuals strive to achieve these goals,
and their appraisals of progress influence both cognitive and emotional experiences and
subsequent decisions. Appraisals that ones progress towards achieving a goal is
hampered can elicit experiences of cognitive demands and increased efforts to meet that
goal, as well as emotional reactions of anxiety, worry, frustration, depression or anger.
Appraisals that ones progress is appropriate or faster than expected can elicit positive
experiences of challenge and emotions such as contentment, happiness and pleasure.
Within the context of work, appraisals of poor progress on tasks can stimulate
cognitive demands requiring significant regulation of attention and actions, as well as
emotional demands requiring the regulation of anxiety, frustration and other affective
experiences. These cognitive and emotional demands are likely to influence sleep
behaviour and quality through several pathways. For example, adults facing work tasks
demanding high cognitive effort may have difficulty disengaging from them to attend to
sleep preparations that evening (Sonnentag & Bayer, 2005). Work-related emotional
M. Loft and L. Cameron290
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demands may impair sleep through their impact on physiological processes influencing
sleep and, in particular, pre-sleep arousal (Winwood & Lushington, 2006). For example,
negative emotions arising from work demands can evoke sympathetic arousal (Waters,
Adams, Binks, & Varnado, 1993), which could foster pre-sleep arousal and earlier waking
times. In contrast, positive emotions arising from work activities can enhance parasym-
pathetic regulation (Lopez & Snyder, 2009) that, in turn, reduces pre-sleep arousal and
promotes sleep quality (Kivisto, Harma, Sallinen, & Kalimo, 2008).
Some individuals may respond to the continuing challenge of juggling work and sleep
demands by setting a high priority for sleep. Individuals who are concerned about sleep
deprivation and place sleep as an important goal may be more likely to disengage with the
pursuit of work goals and initiate the sleep preparation process each night.
Guided by these theoretical principles, we investigated how work-related demands,
work-related emotions and sleep prioritization predict sleep indicators on a daily basis,
and whether they do so independently of more general states of mental distress (e.g.
anxiety, depression, perceived stress and fatigue) that are known to interfere with sleep
(Stein, Belik, Jacobi, & Sareen, 2008). Adults with full-time office jobs first completed
baseline measures of sleep prioritization, general mental distress, pre-sleep arousal and
sleep quality. They then completed daily measures of work goal conflict, cognitive
and emotional demands at work, positive and negative emotions experienced at work and
sleep behaviour over the course of 10 workdays. They completed measures of pre-sleep
arousal on Day 9 and sleep quality on Day 10. Using analyses that controlled for
demographic and general distress variables, we tested the following hypotheses:
Hypothesis 1: Higher sleep prioritization will predict better sleep behaviours and quality over
the 10-day period.
Hypothesis 2: Higher levels of work goal conflicts, cognitive demands, emotional demands,
negative emotions, and lower levels of positive emotions during a workday will
independently predict poorer sleep behaviours and quality that night.
Hypothesis 3: Work-related demands and emotions will influence pre-sleep arousal reported
on Day 9.
Hypothesis 4: Pre-sleep arousal will in turn reduce sleep duration and quality on Day 10.
Survey design and procedure
A Human Participants Ethics Committee approved this internet-based study. We utilized a
prospective design with a baseline survey followed by repeated daily measures over the
next nine workdays (that is, not including non-work days) and a follow-up survey on the
tenth day. All surveys were emailed to participants at 3p.m. each day and completed by
the end of the workday.
Adults were recruited through advertisements circulated by five companies and non-profit
organizations in New Zealand. These organizations included a law firm, a corporate travel
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agency, a university and two sports organizations; the five businesses included a total of
5407 employees. The advertisements encouraged employees who were interested in
answering questions about how their work may be affecting their sleep to go to the study
website, which provided additional study information and access to the consent form and
baseline measures. Eligibility criteria were: (i) ability to read and write in English; (ii)
full-time employment; (iii) daytime work shifts (participants were excluded if they
worked night shifts through their organizations or through secondary jobs); (iv) a
primarily sedentary job (to maintain consistency in work-related physical activity, given
the impact of physical exertion on sleep) that provided daily access to email; (v) no
identified biological cause of current sleep deprivation (e.g. sleep apnoea, narcolepsy,
restless leg syndrome, periodic limb movement disorder or pregnancy); (vi) no identified
psychological disorder; and (vii) no participation in any sleep-related study within the last
two months.
Of the 75 employees initially recruited, two further participants were excluded
because they did not meet inclusion criteria (due to pregnancy and depression). The 73
participants (48 women and 25 men) ranged in age from 21 to 65; 43% (n= 31) were 26
to 30. Most were in de facto relationships (38%, n= 28) or married (27%, n= 20); the
rest were single, divorced/widowed or did not specify (34%, n= 25). Ethnicities included
New Zealand European (80%, n= 58), Asian (15%, n= 11) and other (Maori/Samoan/
Australian; 6%, n= 4). Occupations included professionals such as accountants, lawyers,
teachers, consultants or technical staff (37%, n= 27), office workers (25%, n= 18), sales
or technical staff (19%, n= 14) and market or science researchers (19%, n= 14). Average
time in the position was 2.8 years (SD = 3.1) with 24 (33%) being in the first year. Most
people indicated that their combined income was NZD$51,000$100,000 (44%; n= 32),
31% (n= 22) reported a combined income of NZD$100,000 or higher, and that for 23%
(n= 17) their combined income was under $51,000 per annum; two participants (3%) did
not specify. No participants reported having insomnia or another sleep-related disorder in
the previous two weeks.
The baseline survey included measures of demographic characteristics; general mental
distress (depression, anxiety, perceived stress and fatigue); sleep prioritization; pre-sleep
arousal; and the Pittsburgh Sleep Quality Index (PSQI; sleep quality, time of lights out,
time to sleep, hours of sleep, time of waking and global PSQI scores). Daily measures
included work-related demands (cognitive and emotional), work goal conflict and work-
related emotions and sleep-related measures for the previous night: sleep quality, time of
lights out, time taken to get to sleep, hours of sleep and time of waking that morning. Pre-
sleep arousal was measured again on Day 9. The follow-up survey on Day 10 included
the daily measures and the PSQI.
Demographic and personal characteristics. The baseline survey included items assessing
age, gender, marital status, occupation, employment length, household income (e.g.
combined income of participant and partner if applicable), presence of children at home,
existing medical conditions, insomnia, restless legs syndrome, sleep apnoea and
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General mental distress. Participants completed measures of anxiety (State-Trait Anxiety
Index Short Form for State Anxiety, e.g. I am worried;α= .84; Marteau & Bekker,
1992), depression (Centre for Epidemiological Studies in Depression Short Form, e.g. I
could not get going;α= .82; Kohout, Berkman, Evans, & Cornoni-Huntley, 1993),
perceived stress (Perceived Stress Scale, e.g. how often have you felt that things were
going your way?;α= .81; Cohen, Kamarck, & Mermelstein, 1983) and fatigue (subscale
of the Profile of Mood States, e.g. worn out;α= .86; McNair, Lorr, & Droppleman,
1992). Item ratings were averaged for all scales.
Sleep prioritization. A purpose-built measure of sleep prioritization consisted of 10 items
(α= .79), rated from 1 (strongly disagree)to5(strongly agree), focusing on daily sleep
quality as a goal. Items were selected from a larger pool of preliminary items based on
content validity evaluations provided by 29 employees and by nine researchers trained in
self-regulation, sleep and health. The measure was designed to assess two facets of sleep
prioritization: sleep importance and sleep in relation to other activities. Together, these
two components represent both the general and relative importance of sleep. Principal
components analyses with direct oblimin rotation revealed two factors. These were a
sleep general importance factor (with six items, e.g. getting a good nights sleep is one of
my top priorities; factor loadings ranging from .50 to .83) and a sleep relative
importance factor (with four items, e.g. I would rather use my time to do other things
rather than spend the time sleeping; factor loadings ranging from .54 to .90). The two
subscales were correlated (r= .31, p< .05), and they were combined with an average of
all ratings representing sleep prioritization.
Pre-sleep arousal. The Glasgow Content of Thoughts Inventory (Harvey & Espie, 2004)
was used at baseline and follow-up to measure pre-sleep arousal. The 25 items are rated
from 0 (never)to3(always) to indicate how often the listed thoughts kept one awake over
the past two weeks (baseline) or past week (follow-up); e.g. things that happened during
the dayand how you cant stop your mind from racing; at baseline, α= .91. Item
ratings were averaged.
Sleep behaviour and quality. The PSQI (Buysse, Reynolds, Monk, Berman, & Kupfer,
1989) was used to assess sleep quality, sleep duration, sleep latency and the frequency
and severity of specific sleep-related problems. The baseline version assessed sleep
behaviours and quality over the past two weeks (including non-work days). Items are
assessed individually as well as combined to create overall scores of sleep quality. The 19
items are combined to form seven component scores, each of which ranges from 0 (no
severe difficulty). The components are sleep quality (e.g. how would you
rate your sleep quality overall?), sleep latency (e.g. how long in minutes did it take you
to get to sleep each night on average?), sleep duration (e.g. how many hours sleep did
you get per night on average?), habitual sleep efficiency (compares the time in bed
against sleep duration), sleep disturbances (e.g. how often have you had trouble sleeping
because you feel too cold), use of sleeping medication (e.g. have you taken medicine
to help you sleep?) and daytime dysfunction (e.g. have you had trouble staying awake
while driving, eating meals or engaging in social activity). For Sleep Disturbances and
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Daytime Dysfunction, the item ratings in these subscales were averaged. Component
scores were then averaged to provide a global score ranging from 0 (no difficulty)to21
(severe difficulties in all areas). It has shown to have excellent reliability and validity
with samples of good and poor sleepers, including those who are employees (Doi,
Minowa, & Tango, 2003). For the global PSQI scores at baseline, α= .74. For daily
assessments, the following PSQI items were adapted to assess the previous nights sleep:
sleep quality (ratings ranged from 1 to 10), time of lights out (coded using a 24-hour
clock), time taken to get to sleep (minutes from lights out until falling asleep), total hours
of sleep and time of waking (coded using a 24-hour clock).
Cognitive and emotional demands at work. Subscales from the Copenhagen Psychosocial
Questionnaire (Kristensen, Hannerz, Høgh, & Borg, 2005) were used to assess cognitive
and emotional demands experienced at work that day. Participants rated items from 1 (to
a very small extent)to5(to a very large extent). The cognitive demand subscale included
eight items assessing task difficulty (e.g. does your work require you to make difficult
decisions?); α= .86 to .91 over the nine days. A ninth item from the original subscale,
do you have a responsible job?was not used because it was unlikely to change on a
daily basis. The emotional demands subscale included four items (e.g. is your work
emotionally demanding?); α= .76 to .88 over the nine days. Variables were computed by
taking the average of the item ratings for each subscale.
Work goal conflict. Experience of work goal conflict was assessed with the item How
much did this event conflict with your 12 month workplace goal?This question came
after participants had listed a 12-month work goal (e.g. Making my business successful)
and also a work-related event occurring that day (e.g. trouble with a large delivery).
Responses ranged from 1 (not at all)to5(completely).
Work-related positive and negative emotions. Each day, participants completed a modified
version of the Job Emotions Scale (Fisher, 2000) to rate their experience of 16 emotions
relating to a work event that the participant selected as the most significant event of the
day. Eight terms related to positive emotions (happy, content, enthusiastic, pleased, proud,
optimistic, liking for someone or something and enjoying something) and eight terms
related to negative emotions (depressed, frustrated, angry, disgusted, unhappy, disap-
pointed, embarrassed and worried). Responses ranged from 0 (not at all)to10
(extremely). For each subscale, ratings were averaged to generate positive and negative
emotion scores. Principal components analyses revealed that the items loaded as intended
onto two factors, one with positive emotion items and one with negative emotion items.
Over the nine days, α= .87.94 for the positive emotion subscale and α= .80.89 for the
negative emotion subscale.
Statistical analyses
SPSS statistical software version 15.0 and SAS statistical software version 9.1.3 were
used to analyze the data. All statistical tests utilized a 95% confidence interval. When
generating scores using item ratings, missing values were replaced with the mean of the
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participants ratings on the other scale items. Overall, 66% of participants completed 70%
or more of the daily surveys. A priori analysis indicated that a sample size of N=73
provided sufficient power to detect medium-sized associations (Cohensd= .50).
Correlation analyses were used to assess bivariate relationships among work and sleep
variables at baseline and Day 1. To identify potential covariates for the final analyses, we
conducted multiple regressions of the dependent variables with age, gender, ethnicity,
marital status, occupation, income level, employment length, anxiety, depression,
perceived stress and fatigue as predictor variables. Marital status (being in a de facto
relationship versus other status) was associated with global PSQI scores (those in de facto
relationships reported poorer sleep quality), and perceived stress positively predicted pre-
sleep arousal. These variables were therefore included in the corresponding analyses.
Marital status was not a significant predictor of global PSQI scores in these analyses and
so it was not included in the final models.
Multi-level, mixed-model analyses were used to test whether, over the course of the
10 days, sleep prioritization and the daily reports of work-related cognitive demands,
emotional demands, positive emotion, negative emotion and work goal conflict predicted
sleep behaviour and quality for the corresponding night. These analyses take into account
the non-independence of the individual-level data. The modelling of time-specific
associations assumed a first-order autoregressive process, which was deemed most
appropriate from a theoretical perspective given the likelihood that the daily sleep
variables would be correlated (Gueorguieva & Krystal, 2004).
Multiple linear regression analysis was used to test whether work goal conflict,
cognitive demands, emotional demands and positive and negative work-related emotions
experienced over the past week predicted pre-sleep arousal over the past week (assessed
on Day 9). For this analysis, the daily reports of a variable (e.g. cognitive demand) for
Days 28 were averaged to generate a weekly score. Pre-sleep arousal at baseline and
perceived stress at baseline were entered as covariates. A final set of regression analyses
tested whether pre-sleep arousal and sleep prioritization independently predicted sleep
quality at follow-up (Day 10).
Descriptive statistics and correlations for the work and sleep variables
Participants varied in their sleep quality and prioritization of sleep, as indicated by the
moderate variance in global PSQI scores, pre-sleep arousal and sleep prioritization at
baseline (see Table 1). Depression, anxiety, perceived stress and fatigue were positively
associated with global PSQI scores and levels of pre-sleep arousal. Higher levels of
depression and perceived stress were associated with higher sleep prioritization.
Day 1 reports of work-related experiences (see Table 2) revealed that work goal
conflict was generally low and its occurrence was associated with greater emotional
demands and negative emotions. Cognitive demands were relatively high, whereas
emotional demands, on average, were moderate. Positive and negative emotions were
only moderately correlated, supporting their conceptual distinctiveness. Analyses of sleep
experiences that night (i.e. following the reports) revealed that sleep quality levels were
moderate, although the average time taken to get to sleep was almost 30 minutes, which
suggests some difficulty in sleep initiation on average. Although participants reported just
under seven hours of sleep on average for this initial night of the study, analyses of sleep
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Table 1. Correlations between sleep difficulty (Global PSQI), pre-sleep arousal, sleep prioritization and general mental distress at baseline.
Measure 1234567
1. Global PSQI -
2. Pre-sleep arousal .49** -
3. Sleep prioritization .02 .36** -
4. Depression .39** .49** .27* -
5. Anxiety .33** .32** .09 .54** -
6. Perceived stress .29** .52** .28* .66** .59** -
7. Fatigue .52** .34** .20 .47** .36** .41** -
M(SD) 1.50 (0.30) 0.17 (0.51) 3.32 (0.74) 0.58 (0.43) 1.97 (0.59) 1.75 (0.53) 1.90 (0.85)
Possible score range 03031503140404
Notes: PSQI = Pittsburgh Sleep Quality Index. For Global PSQI scores, higher scores reflect poorer sleep quality. For all other measures, higher scores reflect higher levels of that
construct. Mand SD represent averaged scores.
*p< .05; **p< .01.
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duration over the 10-day period suggest that short sleep duration was a problem for a
proportion of the sample, with 24% (n= 17) reporting that they had less than six hours of
sleep on the majority of the 10 days. Higher cognitive demands were associated with
fewer total hours of sleep and higher emotional demands were related to both an earlier
time of waking the next morning and higher levels of pre-sleep arousal on Day 9.
Negative work-related emotions were also associated with higher levels of pre-sleep
Work-related experiences as predictors of sleep-related indicators
Mixed-model analyses (see Table 3) tested Hypothesis 1 that higher sleep prioritization
would predict better sleep behaviours and quality over the 10-day period and Hypothesis
2, that higher levels of work goal conflicts, cognitive demands, emotional demands and
negative emotions and lower levels of positive emotions during a workday would
independently predict poorer sleep behaviours and quality that night. Consistent with
these hypotheses, higher sleep prioritization and positive work-related emotions were
both associated with higher sleep quality that night. In further support of Hypothesis 2,
Table 2. Correlations between work-related measures on Day 1, sleep measures for the night of
Day 1, pre-sleep arousal on Day 9 and global PSQI scores on Day 10.
Measure 12345M(SD)
1. Work goal
- 1.20 (1.67) 15
2. Cognitive
.12 - 2.18 (0.90) 15
3. Emotional
.27* .47** - 1.88 (0.97) 15
4. Positive work
.19 .24* .07 - 4.88 (2.04) 010
5. Negative work
.46** .04 .19 .42** - 2.16 (1.74) 010
6. Sleep quality TN .08 .03 .05 .09 .09 6.28 (1.96) 110
7. Time of lights
out TN
.11 .15 .03 .14 .15 22.87 (0.94) 2025
8. Time to
sleep TN
.02 .17 .13 .22 .19 26.19 (32.88) 1180
9. Hours of
sleep TN
.20 .26* .15 .04 .13 6.93 (1.32) 2.59.0
10. Time of
waking TN
.21 .03 .28* .24 .22 6.59 (0.81) 59
11. Pre-sleep arousal
Day 9
.26 .25 .46** .16 .39** 0.78 (0.60) 03
12. Global PSQI
at FU
.10 .08 .09 .04 .16 0.71 (0.38) 03
Notes: PSQI = Pittsburgh Sleep Quality Index. TN = That night; FU = Follow-up. Inter-correlations between the
sleep-related variables (measures 612) are not reported here. Scores reflect the mean ratings on the measure.
reflects actual range of scores, as the possible range is infinite; the remaining score ranges are theoretical.
*p< .05; **p< .01.
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Table 3. Mixed-model analyses of sleep prioritization and work-related experiences as predictors of sleep-related outcomes.
Sleep prioritization Work-goal conflict Cognitive demands Emotional demands
Positive work-related
Negative work-related
Sleep outcomes
Est. (SE)[CI]
Est. (SE) [CI]
Est. (SE) [CI]
Est. (SE) [CI]
Est. (SE) [CI]
Est. (SE)[CI]
Sleep quality
.10 (0.03)
[.04, .15]
.05 (0.08)
[.10, .20]
.03 (0.02)
[.07, .02]
.03 (0.04)
[.04, .11]
.19 (0.07)
[.05, .32]
.01 (0.09)
[.17, .19]
Time of lights out
.02 (0.02)
[.05, .01]
.03 (0.04)
[.05, .11]
.04 (0.01)
[.01, .06]
.04 (0.02)
[.08, .01]
.02 (0.04)
[.05, .10]
.06 (0.05)
[.16, .04]
Time to get to
.35 (0.39)
[1.12, .42]
.06 (0.78)
[1.60, 1.48]
.12 (0.28)
[.66, .43]
.20 (0.41)
[.60, 1.01]
.66 (0.82)
[.96, 2.29]
.73 (1.01)
[1.26, 2.73]
Hours of sleep
.03 (0.03)
[.09, .04]
.11 (0.11)
[.33, .11]
.02 (0.03)
[.08, .04]
.08 (0.05)
[.18, .02]
.09 (0.09)
[.09, .27]
.27 (0.13)
[.02, .53]
Time of waking
.00 (0.01)
[.03, .03]
.06 (0.02)
[.10, .02]
.01 (0.01)
[.01, .02]
.03 (0.01)
[.05, .01]
.00 (0.03)
[.05, .05]
.03 (0.03)
[.03, .08]
Notes: Analyses include 315
, 317
or 318
observations from 73 employees over 10 days. Independent measures are entered into the model together. Est. = Estimate, (SE)=
Standard Error, [CI] = Upper and Lower Estimate.
*p< .05; **p< .01.
M. Loft and L. Cameron298
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higher cognitive demands were associated with a later time of lights out that night, and
work goal conflict predicted an earlier time of waking. Contrary to Hypothesis 2,
however, higher emotional demands were associated with an earlier time of lights out and
an earlier time of waking the next day; and higher levels of negative work-related
emotions were associated with greater total hours of sleep.
Regression analysis of pre-sleep arousal over the past week (reported on Day 9) with
baseline pre-sleep arousal and perceived stress as covariates (Table 4) revealed that,
consistent with Hypothesis 1, higher sleep prioritization predicted lower pre-sleep arousal.
Further, and consistent with Hypothesis 3, higher levels of both emotional demands at
work and negative work emotions over the past week predicted higher pre-sleep arousal.
The other work-related variables did not significantly predict pre-sleep arousal over the
previous week.
Regression analyses of sleep outcomes controlling for baseline levels (Table 5)
revealed that, consistent with Hypothesis 4, pre-sleep arousal predicted fewer hours of
sleep and higher global PSQI scores at the Day 10 follow-up. In further support of
Hypothesis 1, higher sleep prioritization at baseline was associated with faster times to get
to sleep, more hours of sleep and lower global PSQI scores at the Day 10 follow-up.
The present findings provide new insights into psychological factors involved in the self-
regulation of sleep and, in particular, how sleep prioritization and daily work experiences
may contribute to sleep behaviours and experiences. Consistent with Hypothesis 1, sleep
prioritization emerged as an important predictor of sleep, with higher sleep prioritization
predicting better sleep quality on a daily basis, as well as more hours of sleep, faster time
to get to sleep and less sleep difficulty (indicated by global PSQI scores as measured at
follow-up on Day 10). Sleep prioritization was also associated with lower pre-sleep
arousal on Day 9. Although some experts recommend against attempts to increase
motivations to sleep well due to concerns that they will increase pre-sleep arousal in the
short term (Espie, 2002), these findings suggest that enhancing sleep prioritization could
prove beneficial for sleep and can decrease pre-sleep arousal over time. The correlational
findings from the current study demonstrate only that individuals who naturally prioritize
Table 4. Regression analyses of key predictors of pre-sleep arousal over the past week, as
measured on Day 9.
Measure BSEBB t Model FR
Pre-sleep arousal Day 9 10.26** .56
Pre-sleep arousal (B) 0.46 0.11 0.45 4.30**
Perceived stress (B) 0.09 0.10 0.09 0.93
Sleep prioritization (B) 0.17 0.07 0.23 2.46*
Work goal conflict (Days 28) 0.07 0.04 0.16 1.63
Cognitive demands (Days 28) 0.05 0.08 0.07 0.63
Emotional demands (Days 28) 0.27 0.08 0.44 3.53**
Positive work emotions (Days 28) 0.02 0.02 0.11 1.18
Negative work emotions (Days 28) 0.07 0.03 0.21 2.12*
Notes: B = Baseline; Days 28 = mean scores over days 2 to 8.
*p < .05; **p< .01.
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sleep have better sleep quality and sleep longer, however; and so it remains an empirical
question as to whether attempts to increase the priority of sleep will improve sleep for
individuals who naturally give it a low priority. Future research should investigate the
efficacy of interventions aimed at enhancing sleep priorities, either as an individual
strategy or as part of a broader self-regulation programme for improving sleep behaviour
through goal-setting, planning and monitoring of adaptive behaviours.
The findings further indicate how psychological experiences in the workplace could
impact on health and well-being via their effects on sleep. They provide partial support
for Hypothesis 2 that work goal conflict, cognitive and emotional demands and work-
related emotions predict poorer sleep behaviours and sleep quality that night. The patterns
suggest that cognitive and emotional work demands have distinctive relationships with
sleep behaviour, further strengthening the argument that work demands can affect sleep in
addition to the reciprocal relationship of sleep predicting work difficulties demonstrated
in previous research (Hanson et al., 2011). These patterns of findings are consistent with
those of a recent study of young adults demonstrating that higher work demands are
negatively associated with recovery from work (as indicated by fatigue and low vigour),
whereas higher levels of pleasure at work were positively related to recovery (van Hooff,
Geurts, Beckers, & Kompier, 2011). Cognitive demands appear to be more clearly
Table 5. Regression analyses of pre-sleep arousal and sleep prioritization as predictors of sleep-
related measures at follow-up (Day 10), controlling for baseline levels.
Measure βSE ββ t Model F R
Time of lights out, FU 12.74*** .31
Time of lights out, B .57 .10 .56 6.03***
Sleep prioritization, B .01 .11 .01 0.13
Pre-sleep arousal, Day 9 .00 .01 .01 0.07
Time to get to sleep, FU 13.97*** .33
Time to get to sleep B .33 .06 .49 5.27***
Sleep prioritization, B 4.66 2.10 .20 2.21*
Pre-sleep arousal, Day 9 .13 .10 .12 1.28
Hours of sleep, FU 12.58*** .31
Hours of sleep, B .34 .10 .34 3.49**
Sleep prioritization, B .38 .15 .23 2.45*
Pre-sleep arousal, Day 9 .02 .01 .25 2.54*
Sleep quality, FU 3.42* .11
Sleep quality, B .18 .09 .22 2.08*
Sleep prioritization, B .20 .31 .07 0.63
Pre-sleep arousal, Day 9 .03 .02 .19 1.83
Time of waking, FU 9.74*** .26
Time of waking, B .51 .10 .50 5.35***
Sleep prioritization, B .04 .13 .03 0.26
Pre-sleep arousal, Day 9 .01 .01 .07 0.73
Global PSQI, FU
Global PSQI, B .32 .07 .44 4.72*** 14.74*** .34
Sleep prioritization, B .78 .38 .18 2.07*
Pre-sleep arousal, Day 9 .05 .02 .25 2.66**
Notes: B = Baseline, FU = follow-up as measured on Day 10.
*p < .05, **p< .01, ***p< .001.
M. Loft and L. Cameron300
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implicated in prolonging decisions to go to bed, whereas emotional demands may
precipitate earlier bedtimes and earlier waking. Although the idea that emotional and
cognitive demands affect people differently is not new (Vohs, Baumeister, & Ciarocco,
2005), the current research adds evidence regarding how these types of demand may
differentially influence behaviour within the domain of sleep.
Work goal conflict was linked to an earlier time of waking, suggesting that events
occurring in the workplace that disrupt progress towards work goals could alter sleep
patterns. It is likely that emotional reactions (e.g. frustration) generated by work goal
conflicts triggered the early waking. Alternatively, they may also have triggered
motivations and behaviours (e.g. setting an alarm for an earlier time) to get up earlier
in order to deal with a more demanding work situation. In contrast to the association
between work goal conflict and earlier waking, negative work-related emotions were
associated with more hours of sleep (although it was unrelated to sleep quality).
Individuals experiencing these negative emotions may require longer sleep in order to
meet their sleep needs. In contrast, positive work-related emotions predicted higher sleep
quality (but not more sleep hours), suggesting the opposite influences on sleep quality
and quantity. These differential relationships of negative and positive work emotions with
sleep quality and behaviour support further research on the distinctive influences of
negative and positive emotions on sleep.
The findings are consistent with Hypothesis 3 that work-related demands and
emotions would be associated with pre-sleep arousal, in that higher levels of work-
related emotional demands and negative emotions independently predicted higher pre-
sleep arousal. Pre-sleep arousal is an important outcome in its own right because it can be
considered aversive even when sleep is not impaired. Consistent with Hypothesis 4, the
findings indicate that pre-sleep arousal could also undermine both the quantity and quality
of sleep, as it predicted longer time to get to sleep, fewer hours of sleep and higher global
PSQI scores at follow-up. The long length of the pre-sleep arousal measure precluded its
use in the daily assessments; however, the fact that it emerged as an important predictor
of sleep behaviour suggests the utility of developing a shorter measure for use in daily
assessments. It may be that interventions aimed at stress reduction and managing
emotional demands of work will be most likely to reduce pre-sleep arousal, whereas
strategies for simply reducing cognitive demands (e.g. simplifying work tasks) may be
less effective.
Strengths, limitations and practical implications
Several strengths and limitations of the present study warrant comment. The completion
rate was reasonably high for surveys involving daily assessments, with two-thirds of
participants completing most of the daily surveys. The use of mixed-model analyses to
evaluate relationships between daily assessments of work-related factors and sleep
experiences that night (as opposed to other types of repeated measures analyses or
reliance on measures of behaviours over a longer time frame) provided more powerful
tests of the hypotheses. The primary value of this design is that it takes into account the
daily fluctuations inherent in work experiences and sleep patterns, that can be lost in a
design in which analyses test predictors of daily reports of work experiences and sleep
that are averaged over a period of time (Gueorguieva & Krystal, 2004). Given that the
sample was limited to New Zealanders working in a select number of organizations,
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however, caution is needed before generalizing the observed patterns to adults in other
work cultures. For example, the average income level was reflective of a middle- to upper
middle-class population. Although the sample was relatively small, it is comparable to
those of other studies using mixed-model analysis to evaluate daily observations over
extended periods of time (Bower, Bylsma, Morris, & Rottenberg, 2010; Schlatter &
Cameron, 2010) and it yielded a large sample of daily observations. The findings support
the construct validity of the sleep prioritization measure by demonstrating that it predicts
multiple sleep outcomes. Its use in further research could expand our understanding of the
role of sleep prioritization in guiding sleep behaviour.
The findings further our understanding of the self-regulation of the (often competing)
demands of work and sleep. The absence of uniformly consistent associations between
the work demand and sleep variables suggests that work demand effects on sleep are
subtle and nuanced, with specific types of work demand influencing specific steps of the
sleep process. Guided by these results, future research could further hone in on how
cognitive and emotional demands and emotions at work differentially influence bedtime,
hours of sleep, time of waking and general sleep quality.
This study highlights the need to develop and test self-regulation techniques that
minimize the detrimental effects of work-related demands and conflicts on sleep-related
behaviours, in ways that reduce sleep deprivation and its increasingly recognized
consequences for health and well-being. Developing goal-setting strategies that help
individuals to prioritize sleep may be one way to improve their sleep experiences.
Interventions aimed at improving coping and emotional regulation skills for managing
cognitive and emotional demands and conflicts at work could accelerate recovery after
work and improve sleep (Cameron & Jago, 2008; van Hooff et al., 2011). Recent
evidence supports the efficacy of a mental imagery intervention for implementing
adaptive sleep hygiene patterns in improving sleep for working adults (Loft & Cameron,
2013). These self-regulation strategies could be tested alone and in combination to
determine an optimal intervention package for improving the sleep habits of working
Neither author has any conflicts of interest in that no organization has financial interest in the
subject matter of this paper. This research was performed through the University of Auckland, New
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... Sleep impairment and disorders have various repercussions on the quality of life of an individual, affecting his professional and academic performance with degraded attention, increased error rate, changes in psychomotor capacity and reduced ability to perform a task [5,6]. There are also psychosocial and health repercussions in view of the cognitive, immunological and metabolic impairment, with an increased risk of diabetes, overweight and hormonal changes such as those affecting cortisol due to neuroendocrine deregulation [11][12][13][14][15][16]. ...
... Seventy-nine of the students evaluated (58.5%) think that their sleeping pattern impairs their work and 55 (40.7%) think that it impairs their personal relations. These data agree with studies that have shown professional impairment and an impaired ability to perform tasks [5,6,11,14,15]. In addition, some studies have shown the presence of impaired metabolism in patients with sleep disorders, which results in higher risks of developing certain diseases such as diabetes, hormonal changes and overweight. This type of finding was not detected in our sample since there was no relationship between BMI and poor sleep quality, in contrast to these previously reported aspects [12,13,16]. ...
... They sleep late at night, wake up too early, or unable to sleep at all. Sleep plays an important role in the life of human beings; it refreshes the individual and gives the required rest [13,14]. It creates stability in physical and cardiovascular health, and cognition. ...
Full-text available
Grief does not only affect human emotions but also impacts their physical health. Understanding physical grief of people can bring to bear the grip of its daunting nature, a situation where routines become challenging. A qualitative explorative descriptive research method was used. A purposive sample of 18 ward supervisors and 39 ward midwives was used to ascertain the physical effects of maternal deaths on these caregivers in the Ashanti Region of Ghana. Data were collected through semistructured and focus group discussions. Data analysis was done parallel with data collection till saturation was reached. Ethics was obtained from the University of the Western Cape, South Africa, and Ghana Health Service. The findings indicated that generally, as a result of grieving over the deaths of their patients, midwives experienced physical health sufferings. Therefore, reported depression is expressed as insomnia, appetite loss, exhaustion, and social isolation. There is the need to reduce the physical effects of patients’ death on caregivers in Ghana and therefore, the study recommends that all hospitals in Ghana utilize employee assistance programmes, a workplace intervention programme designed for such purposes.
... Moreover, higher education students with a self-complaint of insomnia scored higher on each individual item from the GCTI compared with good sleepers [29]. In contrast, one study [63] with day workers found no association between the GCTI and subjective SOL; however, this result is difficult to interpret since the analysis controlled for SOL measured ten workdays earlier. Finally, Suh, Ong et al. [64] arbitrarily divided the GCTI into four subscales. ...
... One obvious intermediary mechanism linking stress exposure and sleep quality is a person's stress responses. Stress responses include a broad set of psychological and/or biological reactions to stress exposure (Harkness & Monroe, 2016), such as feelings of stress (Loft & Cameron, 2014) or appraisals of events as being stressful (Cohen, Kamarck, & Mermelstein, 1983). Research has shown that stress responses are associated with worse sleep outcomes (e.g., Benham, 2010;Vgontzas et al., 2008). ...
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Research has shown that greater stress responses predict worse sleep and that the quality of one's current romantic relationship predicts one's sleep. Despite these established links, research has not examined connections between ongoing patterns of interpersonal experiences and competencies (relationship effectiveness) and stress exposure on sleep. Participants in the Minnesota Longitudinal Study of Risk and Adaptation (MLSRA) completed measures assessing relationship effectiveness and stress exposure at ages 23 and 32 years, as well as sleep quality/duration at age 37 years. Analyses demonstrate that relationship effectiveness at age 23 years positively predicts sleep quality—but not sleep duration—at age 37 years via reduced stress exposure at age 32 years. These findings highlight the effects of relationship effectiveness and stress exposure across early to middle adulthood on sleep.
... Daylevel studies suggest that worry and rumination about work have a rather immediate impact on sleep: On days when people worry or ruminate in a negative way they have lower sleep quality (Flaxman et al., 2018;Slavish et al., 2018). It seems to be the negative valence that impairs sleep (Loft & Cameron, 2014), positive thinking about work appears unrelated to sleep (Flaxman et al., 2018). ...
Job stressors such as time pressure, organizational constraints, and interpersonal conflicts matter for individual well-being within organizations, both at the day level and over longer periods of time. Recovery-enhancing processes such as psychological detachment from work during nonwork time, physical exercise, and sleep have the potential to protect well-being. Although the experience of job stressors calls for effective recovery processes, empirical research shows that recovery processes actually are impaired when job stressors are high (recovery paradox). This article presents explanations for the recovery paradox, discusses moderating factors, and suggests avenues for future research.
... Moreover, higher education students with a self-complaint of insomnia scored higher on each individual item from the GCTI compared with good sleepers [29]. In contrast, one study [63] with day workers found no association between the GCTI and subjective SOL; however, this result is difficult to interpret since the analysis controlled for SOL measured ten workdays earlier. Finally, Suh, Ong et al. [64] arbitrarily divided the GCTI into four subscales. ...
The purpose of the research programme detailed in this paper is to update the attachment control system framework that John Bowlby set out in his formulation of Attachment Theory. It does this by reconceptualising it as a cognitive architecture that can operate within multi-agent simulations. This is relevant to computational psychiatry because attachment phenomena are broad in scope and range from healthy everyday interactions to psychopathology. The process of attachment modelling involves three stages and this paper makes contributions in each of these stages. Firstly, a survey of attachment research is presented which focuses on two important attachment behavioural measures: the Strange Situation Procedure and the Adult Attachment Interview (AAI). These studies are reviewed to draw out key behavioural patterns and dependencies. Secondly, the empirical observations that are to be explained in this research programme are abstracted into scenarios which capture key behavioural elements. The value of behavioural scenarios is that they can guide the simulation design process and help evaluate simulations which are produced. Thirdly, whilst the implementation of these scenarios is still a work in progress, several designs are described that have been created and implemented as simulations. These include normative and non-pathological infant behaviour patterns observed across the first year of life in naturalistic observations and ‘Strange Situation’ studies. Future work is described which includes simulating dysfunctional infant behaviour patterns and a range of adult attachment behaviour patterns observed in the Adult Attachment Interview. In conclusion, this modelling approach is distinguished from other approaches in computational psychiatry because of the psychologically high level at which it models phenomena of interest.
Background Understanding multiple components of risk perceptions is important because perceived risk predicts engagement in prevention behaviors. Purpose To examine how multiple components of risk perceptions (perceived magnitude of and worry about risk, prioritization of the management of one’s risk) changed following genetic counseling with or without test reporting, and to examine which of these components prospectively predicted improvements in sun-protection behavior 1 year later. Methods A prospective, nonrandomized study design was used. Participants were 114 unaffected members of melanoma-prone families who (i) underwent genetic testing for a CDKN2A/p16 mutation (n = 69) or (ii) were at comparably elevated risk based on family history and underwent genetic counseling but not testing (no-test controls, n = 45). Participants reported risk perception components and sun-protection behavior at baseline, immediately following counseling, and 1 month and 1 year after counseling. Results Factor analysis indicated three risk components. Carriers reported increased perceived magnitude and priority of risk, but not cancer worry. No-test controls showed no changes in any risk perception. Among noncarriers, priority of risk remained high at all assessments, whereas magnitude of risk and cancer worry decreased. Of the three risk components, greater priority of risk uniquely predicted improved self-reported sun protection 1 year post-counseling. Conclusions Priority of risk (i) seems to be a component of risk perceptions distinguishable from magnitude of risk and cancer worry, (ii) may be an important predictor of daily prevention behavior, and (iii) remained elevated 1 year following genetic counseling only for participants who received a positive melanoma genetic test result.
Associations have been found between perseverative cognition (PC: worry and rumination) and somatic markers of ill-health. Further studies have reported associations between sleep and both PC and poorer health. As such, sleep disturbance may represent a pathway between PC and ill-health. Therefore, studies assessing the relationship between PC and sleep in non-clinical populations were synthesized. Meta-analyses (k = 55) revealed small- to medium-sized associations between higher PC and poorer sleep quality (SQ, r = -0.28), shorter total sleep time (TST, r = -0.15) and longer sleep onset latency (SOL, r = -0.16). Variations included associations between SQ and rumination (r = -.33) and worry (r = -.23). Associations were stronger in studies measuring SQ via self-report rather than actigraphy, and where SOL and TST outcomes were cross-sectional. Associations with SOL were stronger when outcomes were from non-diary studies and when trait, rather than state PC, was measured, but weaker where studies incorporated more measures of PC. Effect sizes were generally larger where studies were higher quality and being female may act as a protective factor between PC and longer SOL. Therefore, there is a consistent association between PC and sleep which may partially explain the link between PC and ill-health.
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Bedtime procrastination is an important predictor of sleep insufficiency in the general population (Kroese et al., 2014b), but little is known about the determinants of this self-undermining behavior. As the phenomenon has been conceptualized in the literature as a form of self-regulation failure (Kroese et al., 2014a), we hypothesized that people’s self-regulatory resources in the evening would be predictive of going to bed later than they intended. Specifically, we examined whether the cumulative effect of resisting desires, a measure of self-regulatory resource depletion (Hofmann et al., 2012b), relates to bedtime procrastination. Participants (N = 218) reported how many desires they had tried to resist during the previous day and the extent of their bedtime procrastination. Results show that people who attempted to resist more desires were more likely to engage in bedtime procrastination, suggesting that people may be less likely to stick to their intended bedtime after a particularly taxing day. Implications for intervention strategies are discussed.
Objective: This article investigated whether work-to-family conflict (WFC) and work-to-family enrichment (WFE) were associated with employee sleep quality. WFC and WFE reflect the potential for experiences at work to negatively and positively influence nonworking life respectively, and may have implications for sleep quality. In this article, we examined whether WFC and WFE were linked with sleep quality via hedonic balance (i.e., positive affect relative to negative affect). Participants: The sample included 3,170 employed Australian parents involved in the Household Income and Labour Dynamics in Australia (HILDA) Survey. Methods: Information on WFC, WFE, hedonic balance, sleep quality, and relevant covariates was collected through a structured interview and self-completion questionnaire. Results: WFC was associated with poorer sleep quality (β = .27, p < .001), and this relationship was stronger in males than females and in dual parent-single income families. WFC was also found to be indirectly associated with poor sleep quality via a lower hedonic balance (β = .17, 99% confidence interval [.14, .20]). WFE was not directly associated with sleep quality, but was indirectly associated with better sleep quality via a higher hedonic balance (β = -.04 [-.07, -.02]). Conclusions: These results indicate that aspects of the work-family interface are associated with employee sleep quality. Furthermore, affective experiences were found to link WFC and WFE with sleep quality. Workplace interventions that target WFC and WFE may have implications for employee sleep.
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Background Depression in cardiac patients has gained importance due to increased mortality. Although sleep disturbances are a core symptom of depression, the prevalence and patterns of sleep disturbances in heart disease have hardly been examined regarding depression. Purpose This cross-sectional study aims to examine sleep disturbances and depressive symptoms in consecutively admitted cardiac patients and depressed patients. Methods Two hundred four inpatients (113 male, 91 female) were examined: 94 cardiac inpatients (mean age 49.3 ± 14.3 years) with different heart diseases and 110 psychiatric inpatients (mean age 41.6 ± 13.0 years) with depressive disorders (DP). A depressive episode according to International Classification of Diseases (ICD)-10 was also diagnosed in 14 of the cardiac patients (DCP). The Pittsburgh Sleep Quality Index (PSQI) and the Beck Depression Inventory (BDI) were used to assess subjective sleep quality and severity of depressive symptoms. Results Poor sleep quality (PSQI > 5) was reported in all comorbid DCP (PSQI 12.00 ± 3.53, BDI 17.86 ± 4.28), in 60% of the 80 non-DCP (PSQI 5.59 ± 3.73, BDI 4.47 ± 3.07), and in 86.4% of the DP (PSQI 11.76 ± 4.77, BDI 27.11 ± 10.54). The cardiac inpatients showed a significant correlation between increased depressive symptoms and the PSQI components subjective sleep quality (r = 0.40) and daytime dysfunction (r = 0.34). Both sleep components were significant predictors of self-rated depression (R² = 0.404). Conclusions Most cardiac patients experience poor sleep quality. Self-reported sleep disturbances in heart disease could serve as predictors of clinical or subclinical comorbid depression outside of a psychiatric setting in cardiology and other fields, and such patients should be referred to consultation-liaison psychiatry or polysomnography where sleep disorders like sleep apnea are suspected.
Job satisfaction is often described as an affective response to one's job, but is usually measured largely as a cognitive evaluation of job features. This paper explores several hypothesized relationships between real time affect while working and standard measures of job satisfaction. Experience sampling methodology was used to obtain up to 50 reports of immediate mood and emotions from 121 employed persons over a two week period. As expected, real time affect is related to overall satisfaction but is not identical to satisfaction. Moment to moment affect is more strongly related to a faces measure of satisfaction than to more verbal measures of satisfaction. Positive and negative emotions both make unique contributions to predicting overall satisfaction, and affect accounts for variance in overall satisfaction above and beyond facet satisfactions. Frequency of net positive emotion is a stronger predictor of overall satisfaction than is intensity of positive emotion. It is concluded that affect while working is a missing piece of overall job attitude, as well as a phenomenon worthy of investigation in its own right. Implications for further research and for improving the conceptualization and measurement of job satisfaction are discussed. Copyright © 2000 John Wiley & Sons, Ltd.
The Oxford Handbook of Positive Psychology studies the burgeoning field of positive psychology, which, in recent years, has transcended academia to capture the imagination of the general public. The book provides a roadmap for the psychology needed by the majority of the population-those who don't need treatment, but want to achieve the lives to which they aspire. The articles summarize all of the relevant literature in the field, and each is essentially defining a lifetime of research. The content's breadth and depth provide a cross-disciplinary look at positive psychology from diverse fields and all branches of psychology, including social, clinical, personality, counseling, school, and developmental psychology. Topics include not only happiness-which has been perhaps misrepresented in the popular media as the entirety of the field-but also hope, strengths, positive emotions, life longings, creativity, emotional creativity, courage, and more, plus guidelines for applying what has worked for people across time and cultures.
Brief measurement devices can alleviate respondent burden and lower refusal rates in surveys. This article reports on a field test of two shorter forms of the Center for Epidemiological Studies Depression (CES-D) symptoms index in a multisite survey of persons 65 and older. Factor analyses demonstrate that the briefer forms tap the same symptom dimensions as does the original CES-D, and reliability statistics indicate that they sacrifice little precision. Simple transformations are presented to show how scores from the briefer forms can be compared to those of the original.