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Sleep quality and stress: a literature review.


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The present literature review aims to analyze the research published between 2005 and 2015 relative to the relationship between stress and sleep quality, using the Pittsburgh Sleep Quality Index (PSQI) as an instrument to assess the sleep aspects. This review was conducted in May 2015 based on the electronic databases Web of Science and EBSCO. We used the keywords " sleep quality " and " stress " focusing our target on empirical studies. After reading the collected studies (n=1267), only those who comprised adult samples were selected, resulting in a total of 15 studies. It was found that stress is associated with several individual factors, such as age, employment status, type of work, personality, level of education, and socioeconomic status. When considering the use of the PSQI, stress also influenced the quality of sleep as a whole and in its specific components. Depression was considered important in stress relative to the sleep quality. Other relevant variables were the sociodemographic indicators and socioeconomic status. Therefore, it is essential to assess the context of stress and sleep quality so one can establish new explanations for their relationship and functions. In conclusion, it is necessary to develop thorough studies that take into consideration the importance of complementary variables, i.e., psychosocial, sociodemographic, and socioeconomic status, in the context of the quality of sleep. In this way, it will be possible to understand the effects of the quality of sleep in different samples.
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Advanced Research in Health, Education and Social Sciences: Towards a better practice
Chapter IV
1 University of Algarve, Portugal,
2 University of Algarve, Portugal,
3 University of Algarve, Portugal,
4 University of Algarve, Portugal,
5 University of Algarve, Portugal,
6 University of Granada, Spain,
Note: This paper is supported by FCT (CIEO – Research Centre for Spatial and Organizational Dynamics,
University of Algarve, Portugal)
Abstract: The present literature review aims to analyze the research published between 2005
and 2015 relative to the relationship between stress and sleep quality, using the Pittsburgh
Sleep Quality Index (PSQI) as an instrument to assess the sleep aspects. This review was
conducted in May 2015 based on the electronic databases Web of Science and EBSCO. We used
the keywords “sleep quality” and “stress” focusing our target on empirical studies. After
reading the collected studies (n=1267), only those who comprised adult samples were selected,
resulting in a total of 15 studies. It was found that stress is associated with several individual
factors, such as age, employment status, type of work, personality, level of education, and
socio-economic status. When considering the use of the PSQI, stress also influenced the quality
of sleep as a whole and in its specific components. Depression was considered important in
stress relative to the sleep quality. Other relevant variables were the sociodemographic
indicators and socioeconomic status. Therefore, it is essential to assess the context of stress and
sleep quality so one can establish new explanations for their relationship and functions. In
conclusion, it is necessary to develop thorough studies that take into consideration the
importance of complementary variables, i.e., psychosocial, sociodemographic, and
socioeconomic status, in the context of the quality of sleep. In this way, it will be possible to
understand the effects of the quality of sleep in different samples.
Key-words: literature review, PSQI, sleep quality, stress.
Sleep is a vital and complex physiological
process inherent in each individual. In the
last years, several studies (e.g., Nunes da
Silva, Martins Costa, Waquim Machado, &
Lopes Xavier, 2012) found that this process
is affected by social, cultural, and
environmental aspects. Nowadays, the
demands that an individual experiences,
especially from social and organizational
contexts, have resulted in high levels of
stress and poor sleep quality (Kurina et al.,
2011). Moreover, organic disorders have
contributed to an increase in the number of
diseases associated with sleep quality
(Carlson, Campbell, Garland, & Grossman,
2007). The risks associated with sleep
disorders may include cardiovascular
problems (Kashani, Eliasson, & Vernalis,
2012), cancer (Carlson et al., 2007), and
metabolic disorders (Luyster, Strollo, Zee, &
Walsh, 2012; Theadom & Cropley, 2008).
Sibiu, Romania, June 2015
Some studies have also established sleep as
an important element in psychiatric
conditions (Baglioni, Nanovska et al., 2014a;
Baglioni, Spiegelhalder et al., 2014b). Poor
quality of sleep and insomnia are related to
emotion, previous studies have observed the
effects of loneliness, grief, hostility,
impulsivity, stress, depression, and anxiety
on sleep (Baglioni, Spiegelhalder,
Lombardo, & Riemann, 2010; Cho et al.,
2013; Gallagher, Phillips, & Carroll, 2010;
Okun, Tolge, & Hall, 2014). Emotion and
sleep have shown a close relationship, which
is increasingly recognized as an important
area of research (Kurina et al., 2011). Recent
studies have reported some mechanisms of
sleep (Carter et al., 2012; Siegel, 2011) in an
effort to understand its behavioral
complexity and advancing beyond
pathological descriptions, trying to
understand the processes that lead to a good
quality of sleep (Hawkley, Lavelle,
Berntson, & Cacioppo, 2011; McHugh,
Casey, & Lawlor, 2011; Mellor, Waters,
Olaithe, McGowan, & Bucks, 2014; Miró,
Cano-Lozano, & Buela-Casal, 2005).
The increasing need to assess which factors
strongly influence the quality of sleep has
grown over the years. It was found that
biological traits are not always associated
with the perception of poor quality of sleep
(Hayase, Shimada, & Seki, 2014), creating
the necessity to understand these
associations. One way to assess the
subjective quality of sleep is through the
Pittsburgh Sleep Quality Index (PSQI). This
instrument provides an accurate picture of
seven different aspects of sleep: (a) sleep
duration; (b) sleep disturbance; (c) sleep
latency; (d) daytime dysfunction; (e) sleep
efficiency; (f) subjective sleep quality; and
(g) use of sleep medication (Buysse,
Reynolds, Monk, Berman, & Kupfer, 1989).
Being a self-report measure, it may be more
relevant in the clinical practice than the
objective sleep measures (Buysse, 2005;
McHugh et al., 2011).
Psychological and psychosocial factors have
contributed significantly to the
comprehension of the sleep quality (McHugh
et al., 2011). Studies have underlined the
importance of evaluating the influence of
stress on the processes concerning the proper
functioning of sleep (Cho et al., 2013;
Gamaldo et al., 2014; Kashani et al., 2012;
Ko, Chang, & Chen, 2010; Okun et al.,
2014), given that it is an essential dimension
of health (Buysse, 2014). Knowing the
importance of stress and sleep quality for
health, conducting a review on the empirical
studies addressing this topic is relevant,
particularly since there are no previous
literature reviews or meta-analysis published
about this relationship.
Stress may conduce to negative health
implications, including increasing the
likelihood of cardiovascular disease, directly
affecting the nervous system, as well as
increasing the probability of involvement in
risk behaviors, such as smoking and
excessive alcohol consumption, which will
propitiate a poor quality of sleep (Hawkley,
Masi, Berry, & Cacioppo, 2006; McHugh &
Lawlor, 2013).
Therefore, our aim is to review the studies
on sleep quality using the PSQI, one of the
main self-report instruments on sleep
evaluation, in order to understand how this
construct relates to stress. With the collected
information, we hope to contribute directly
and indirectly to the increase of individuals’
subjective perception of well-being, health,
and quality of life.
This review was conducted in May 2015 in
the electronic databases Web of Science
(WoS) and EBSCO. The keywords used
were “sleep quality” and “stress”. The
collected studies should have been published
between 2005 and 2015. The research was
divided in four phases (Figure 1): (a) 1267
references were found using the previously
chosen keywords; (b) the relevance of the
studies was based in the following criteria:
(b1) studies published in scientific journals;
(b2) empirical study; (b3) presence of
enough data to analyse “what has been
studied” and “how it was studied”; and (b4)
the use of the Pittsburgh Sleep Quality Index
(PSQI). Thus, after the second phase, the
Advanced Research in Health, Education and Social Sciences: Towards a better practice
number of studies registered was 125; (c)
considering the use of two research sources,
some studies were repeated and, therefore,
excluded, resulting in 76 studies; and (d) in
this last phase, only studies that were
composed by adult samples were considered,
which resulted in 15 articles.
After the fourth phase (i.e., phase d), the 15
selected studies were assessed regarding the
following information: (a) authors; (b) year
of publication; (c) type of sample; (d)
instruments used; and (e) obtained results.
The taxonomy of Montero and León was
applied in the classification of these studies.
Figure 1. Phases of the literature review. In each phase are presented the number (n) of studies that
remained in the sample.
The theoretical perspective was confirmed
by a selection of studies with identical
subjects (i.e., adults). The characterization of
the “sleep” variable had the same approach,
although there are some differences
regarding sleep quality. The “stress” variable
was evaluated in different perspectives, for
instance (a) perceived stress; (b) symptoms
of stress; (c) mood states; and (d) biological
traits. These perspectives were not
discriminated, given that they rely on self-
evaluation methods. The methodological
approach was common to all studies (i.e.,
quantitative approach) and pointed to works
where the PSQI was the instrument used to
evaluate sleep quality in adult samples.
Table 1 displays the authors, year of
publication, type of sample, age/status, and
total number of participants. The total
number of participants was 11025, the
majority were females (8797) while 1777
were males. Moreover, in the studies of Cho
et al. (2013) and Cohrs et al. (2012) a higher
percentage of female participants,
respectively 4966 (about 36% of total
participants) and 1340 (about 10% of total
participants), was registered comparatively
to the other evaluated studies. Regarding the
type of sample, three studies were composed
by pregnant women (Hayase et al., 2014; Ko
et al., 2010; Okun et al., 2014).
Table 2 shows the main results of the studies
conducted on sleep quality and stress.
Table 1. Articles Included in the Literature Review and Participant Characterization (N = 15)
Authors/year Type of sample N
Age/Status Total M/F
Carlson et al. (2007) Adults/Women with breast cancer 66 0/66
Sibiu, Romania, June 2015
Cho et al. (2013) Adults/Women workers 4966 0/4966
Cohrs et al. (2012) Adults/Smokers 2314 974/1340
Costa, Zummer, and Fitzcharles
Adults/Spondyloarthropathy 125 58/67
Eliasson, Kashani, Dela Cruz,
and Vernalis (2012)
Adults/Soldiers 265 236/29
Gallagher et al. (2010) Adults/Parents caring for children with
developmental disabilities
109 26/83
Gamaldo et al. (2014) Adults/Older blacks 606 153/449
Hayasse et al. (2014) Adults/Pregnant women 56 0/56
Kashani et al. (2012) Adults/ Cardiovascular disease
prevention program
350 138/212
Ko et al. (2010) Adults/Pregnant women 600 0/600
McHugh and Lawlor (2013) Older adults/General population 447 -
Mellor et al. (2014) Adults/General population 582 154/428
Okun et al. (2014) Adults/Pregnant women 170 0/170
Rocha and Martino (2010) Adults/Hospital nurses 203 24/179
Theadom and Cropley (2008) Adults/Fibromyalgia 166 14/152
Total 11025 1777/8797
Note. M = Male, F = Female.
Table 2. Main Results of the Studies Conducted on Sleep Quality and Stress (N = 15)
Authors/year Instruments Results
Carlson et al. (2007) SOSI1; CES-D2; STAI3;
Women with breast cancer had significantly
higher levels of disorder on all
psychological indicators, but there were no
differences between the groups on any of
the biological measures.
Cho et al. (2013) PSQI5; KOSS-SF7; CES-D2
The depressive symptoms of female workers
were closely related to their job stress and
sleep quality. In particular, the lack of
rewards and subjective sleep factors had the
greatest impact.
Cohrs et al. (2012) PSQI
Direct aspects related to smoking seem to
have a strong effect on sleep quality.
Costa, Zummer, and
Fitzcharles (2009)
Higher perceived stress was an independent
contributor of poor sleep quality.
Eliasson, Kashani,
Dela Cruz, and
Vernalis (2012)
PSQI5; PSS12; ESS16; FS17;
MDQ18; BQ19; SQSD20
Soldiers with high stress, depression, poor
sleep quality, and sleep apnea are at
increased long-term risk for cardiovascular
Gallagher et al.
(2010) PSQI5; QRSF21; SDQ22; SFS23
Parental stress is associated with poor sleep
quality in parents of children with
developmental disabilities.
Gamaldo et al.
(2014) PSQI5; CVRFs24; CES-D2
Perceived stressors, including current
financial hardship or hardship experienced
for an extended period throughout the
lifespan, may influence sleep later in life.
Hayasse et al. (2014) PSQI5; PSS12; BM6 Pregnant women with pregnancy-induced
hypertension and gestational diabetes
Advanced Research in Health, Education and Social Sciences: Towards a better practice
mellitus experience higher stress levels than
the non-pregnant women and healthy
pregnant women. Further, the results
indicated that sleep quality worsens during
the third trimester compared with the
Kashani et al. (2012) PSS12; PSQI5; ESS16; FS17;
High stress was associated with significant
disorders in sleep duration and quality.
Stress levels also correlated with daytime
consequences of disturbed sleep. The stress-
sleep relationship may be an important
mediator in the association between stress
and cardiovascular disease.
Ko et al. (2010) PSQI5; EPDS25; PSS12
The sleep quality of pregnant women was
related to stress and depression, and
comparatively to the non-pregnant women
they tend to have a poor sleep quality.
McHugh and Lawlor
The impact of emotional loneliness on sleep
quality in older adults is partly because of
the stress experienced as a result of feeling
Mellor et al. (2014) PSQI5; DASS-2128; BQ19
Sleep-related risk factors, such as gender,
psychological symptoms, and risk of sleep-
disorder breathing, although related to sleep
quality, did not have an impact on the
relation between age and sleep quality.
Okun et al. (2014) PSQI5; MMA29; SDD30;
PSS12; IDS31
Perceived stress and financial strain
attenuated the socioeconomic status-sleep
association, indicating that psychological
situations preceding pregnancy are also
important to consider.
Rocha and Martino
(2010) PSQI5; BSSm32
There is a significant correlation between
stress and sleep. Nurses working in the
morning shifts showed higher stress levels
and poorer sleep quality.
Theadom and
Cropley (2008)
DBAS-1033; PSS12; PSQI1;
FAS34; SF-3635
Beliefs about sleep and perceived stress play
a significant role in the sleep quality of
patients with fibromyalgia.
Note. 1Symptoms of Stress Inventory (SOSI); 2Centre for Epidemiological Studies – Depression Inventory
(CES-D); 3Spielberger State-Trait Anxiety Inventory (STAI); 4Profile of Mood States (POMS); 5Pittsburgh
Sleep Quality Index (PSQI); 6Biological Measures (BM); 7The Korean Occupational Stress Scale-Short
Form (KOSS-SF); 8The Fagerström Test of Nicotine Dependence (FTND); 9Questionnaire of Smoking
Urges (QSU); 10Beck Depression Inventory (BDI); 11Alcohol Use Disorders Identification Test (AUDIT);
12Perceived Stress Scale (PSS); 13Bath Ankylosing Spondylitis Disease (BASDAI); 14Bath Ankylosing
Spondylitis Functional Index (BASFI); 15Aerobics Center Longitudinal Study Physical Activity
Questionnaire (ACLS-PAQ); 16Epworth Sleepiness Scale (ESS); 17Fatigue Scale (FS); 18Mediterranean
Diet Questionnaire (MDQ); 19Berlin Questionnaire (BQ); 20Single Question to Screen for Depression
(SQSD); 21Questionnaire on Resources and Stress Freidrich Short Form (QRSF); 22Strenghs and
Dificulties Questionnaire (SDQ); 23Support Functions Scale (SFS); 24Cardiovascular Risk Factor
Composite Score (CVRFs); 25Edinburgh Postnatal Depression Scale (EPDS); 26Jong Gierveld Scale of
Lonelines (JGSL); 27Charlson Co-morbidity Index (NACI); 28Depression, Anxiety, and Stress Scale
(DASS-21); 29Mini Mitter Actiwatch-64 (MMA); 30Sleep Diary Data (SDD); 31Inventory for Depressive
Symptoms (IDS); 32Bianchi Stress Scale Modified (BSSm); 33Dysfunctional Beliefs and Attitudes About
Sleep Scale (DBAS-10); 34Fatigue Assessment Scale (FAS); 35Short-Form Medical Outcomes
Questionnaire (SF-36).
Sibiu, Romania, June 2015
The present study reviewed the papers that
evaluated the relationship between stress and
sleep quality in adults, using the PSQI as a
measure to assess the sleep aspects. All the
studies were based on self-report ratings,
mainly studying the subjective quality of
The type of sample existent in the analyzed
studies (i.e., adults) presents a heterogeneity
level that enables us to verify aspects of
sleep quality and stress in diverse contexts.
Variety is important because nowadays the
quality of sleep and stress are capable of
affecting health both in individuals suffering
from any kind of medical condition as in
individuals from the general population. In
this review we covered samples with
particular characteristics: (a) pregnant
women; (b) smokers; (c) workers; (d)
soldiers; (e) older blacks; (f) parents of
children with developmental disabilities; and
(g) nurses.
We selected the PSQI as an assessment tool
for sleep quality because it addresses seven
aspects of sleep and it is widely used in the
research of this topic. The research on sleep
quality has been carried out with self-report
instruments. On one hand, they are limited
regarding objective evidence, however they
are able to show individual perceptions, thus
becoming useful in the clinical setting
(Buysse, 2005). Moreover, research has
shown that objective assessments (i.e.,
biological traits) do not always address the
psychological disorders that can be evaluated
by subjective measures (i.e., self-reports)
(Hawkley, Lavelle, Berntson, & Cacioppo,
Our review underlined the influence of stress
on the perceived quality of sleep (Cho et al.,
2013; Gallagher et al., 2010; Gamaldo et al.,
2014; Hayase et al., 2014; Kashani et al.,
2012; Ko et al., 2010; McHugh & Lawlor,
2013; Okun et al., 2014; Rocha & Martino,
2010; Theadom & Cropley, 2008), which
emphasizes the importance of considering
the inclusion of this variable in research on
sleep quality. The high levels of stress in
pregnant women (Hayase et al., 2014; Ko et
al., 2010) and individuals with chronic
diseases (Costa et al., 2009; Theadom &
Cropley, 2008) further worsen their sleep
quality. It is necessary to pay special
attention to pregnant women or people
suffering from any medical condition,
especially if chronic, performing periodic
evaluations. The first reason is the fact that
these individuals are more likely to have
compromised the quality of sleep at some
level (Hayase et al., 2014; Ko et al., 2010;
Okun et al., 2014; Sayar, Arikan, & Yontem,
2002; Wilcox et al., 2000). The second
reason is the possibility of establishing
adequate preventive interventions to avoid
the negative effects of poor sleep quality and
stress. In addition, stress variables, such as
depression (Cho et al., 2013; Eliasson et al.,
2012; Gallagher et al., 2010; Ko et al., 2010;
Okun et al., 2014; Rocha & Martino, 2010),
anxiety, fatigue, confusion (Carlson et al.,
2007; Da Costa et al., 2009; Theadom &
Cropley, 2008), loneliness (McHugh &
Lawlor, 2013), age (Mellor et al., 2014), race
(Gamaldo et al., 2014), socioeconomic status
(Lallukka et al., 2012; Okun et al., 2014;
Patel, Grandner, Xie, Branas, & Gooneratne,
2010), and smoking (Cohrs et al., 2012), can
directly or indirectly influence the self-
perceived quality of sleep.
Improving the quality of sleep is an action
that can be, in some cases, carried out by
manipulating these variables without
necessarily administering medication for
specific purposes. In accordance, studies
should indicate actions to reduce stress and
depression in order to improve the sleep
quality (Ko et al., 2010), for example
massage or relaxation (Bastani, Hidarnia,
Kazemnejad, Vafaei, & Kashanian, 2005),
music therapy, meditation or yoga
(Narendran, Nagarathna, Narendran,
Gunasheela, & Nagendra, 2005), and
psychosocial approaches (McHugh et al.,
Several mechanisms have been proposed to
explain the relationship between stress and
quality of sleep, including physiological
arousal (Freedman & Sattler, 1982; Van
Reeth et al., 2000) and poor coping
Advanced Research in Health, Education and Social Sciences: Towards a better practice
mechanisms to adaptively manage stress,
evidenced by findings from Morin,
Rodrigue, and Ivers (2003) in a sample of
healthy adults suffering from primary
insomnia. This leads to the understanding
that stress is an important part of one of
several variables that influence the quality of
sleep, particularly concerning subjective
evaluation. Regarding biological traits, the
variations are not always significant, neither
in the matter of the quality of sleep nor in the
matter of stress (Carlson et al., 2007; Hayase
et al., 2014).
In conclusion, it is necessary to develop
further studies considering the importance of
complementary variables, i.e., psychosocial,
sociodemographic indicators, and
socioeconomic status, in the context of the
quality of sleep. Also, it is necessary to
understand the relationship between the
quality of sleep and stress, enabling, in the
future, a proper understanding of the effects
on sleep quality in different samples.
Baglioni, C., Nanovska, S., Regen, W.,
Spiegelhalder, K., Feige, B., Nissen, C.,
Riemann, D. (2014a). Sleep and psychiatric
disorders: A meta-analysis of the last 20
years of research. Journal of Sleep Research,
23, 80-80.
Baglioni, C., Spiegelhalder, K., Lombardo,
C., & Riemann, D. (2010). Sleep and
emotions: A focus on insomnia. Sleep
Medicine Reviews, 14(4), 227–38.
Baglioni, C., Spiegelhalder, K., Regen, W.,
Feige, B., Nissen, C., Lombardo, C.,
Riemann, D. (2014b). Insomnia disorder is
associated with increased amygdala
reactivity to insomnia-related stimuli. Sleep,
37(12), 1907–17. doi:10.5665/sleep.4240
Bastani, F., Hidarnia, A., Kazemnejad, A.,
Vafaei, M., & Kashanian, M. (2005). A
randomized controlled trial of the effects of
applied relaxation training on reducing
anxiety and perceived stress in pregnant
women. Journal of Midwifery & Women’s
Health, 50(4), e36-40. doi
Buysse, D. J. (2005). Diagnosis and
assessment of sleep and circadian rhythm
disorders. Journal of Psychiatric Practice,
11(2), 102–115. doi:10.1097/00131746-
Buysse, D. J. (2014). Sleep health: Can we
define it? Does it matter?. Sleep, 37(1), 9–
17. doi:10.5665/sleep.3298
Buysse, D. J., Reynolds, C. F., Monk, T. H.,
Berman, S. R., & Kupfer, D. J. (1989). The
Pittsburgh sleep quality index: A new
instrument for psychiatric practice and
research. Psychiatry Research, 28(2), 193–
213. doi:10.1016/0165-1781(89)90047-4
*Carlson, L. E., Campbell, T. S., Garland, S.
N., & Grossman, P. (2007). Associations
among salivary cortisol, melatonin,
catecholamines, sleep quality and stress in
women with breast cancer and healthy
controls. Journal of Behavioral Medicine,
30(1), 45–58. doi:10.1007/s10865-006-9082-
Carter, M. E., Brill, J., Bonnavion, P.,
Huguenard, J. R., Huerta, R., & de Lecea, L.
(2012). Mechanism for hypocretin-mediated
sleep-to-wake transitions. Proceedings of the
National Academy of Sciences of the United
States of America, 109(39), E2635–2644.
*Cho, H. S., Kim, Y. W., Park, H. W., Lee,
K. H., Jeong, B. G., Kang, Y. S., & Park, K.
S. (2013). The relationship between
depressive symptoms among female workers
and job stress and sleep quality. Annals of
Occupational and Environmental Medicine,
25(1), 12. doi:10.1186/2052-4374-25-12
*Cohrs, S., Rodenbeck, A., Riemann, D.,
Szagun, B., Jaehne, A., Brinkmeyer, J.,
Winterer, G. (2012). Impaired sleep quality
and sleep duration in smokers: Results from
the German multicenter study on nicotine
dependence. Addiction Biology, 19(3), 486–
496. doi:10.1111/j.1369-1600.2012.00487.x
*Costa, D., Zummer, M., & Fitzcharles, M.
A. (2009). Determinants of sleep problems
in patients with spondyloarthropathy.
Musculoskeletal Care, 7(3), 143–161.
*Eliasson, A., Kashani, M., Dela Cruz, G., &
Vernalis, M. (2012). Readiness and
associated health behaviors and symptoms in
Sibiu, Romania, June 2015
recently deployed army national guard
solders. Military Medicine, 177(11), 1254–
1260. doi:10.7205/MILMED-D-11-00242
Freedman, R. R., & Sattler, H. L. (1982).
Physiological and psychological factors in
sleep-onset insomnia. Journal of Abnormal
Psychology, 91(5), 380–389.
*Gallagher, S., Phillips, A. C., & Carroll, D.
(2010). Parental stress is associated with
poor sleep quality in parents caring for
children with developmental disabilities.
Journal of Pediatric Psychology, 35(7), 728–
737. doi:10.1093/jpepsy/jsp093
*Gamaldo, A. A., Gamaldo, C. E., Allaire, J.
C., Aiken-Morgan, A. T., Salas, R. E.,
Szanton, S., & Whitfield, K. E. (2014). Sleep
complaints in older blacks: Do demographic
and health indices explain poor sleep quality
and duration?. Journal of Clinical Sleep
Medicine, 10(7), 725–731.
Hawkley, L. C., Lavelle, L. A., Berntson, G.
G., & Cacioppo, J. T. (2011). Mediators of
the relationship between socioeconomic
status and allostatic load in the Chicago
health, aging, and social relations study
(CHASRS). Psychophysiology, 48(8), 1134–
1145. doi:10.1111/j.1469-
Hawkley, L. C., Masi, C. M., Berry, J. D., &
Cacioppo, J. T. (2006). Loneliness is a
unique predictor of age-related differences in
systolic blood pressure. Psychology and
Aging, 21(1), 152–164. doi:10.1037/0882-
*Hayase, M., Shimada, M., & Seki, H.
(2014). Sleep quality and stress in women
with pregnancy-induced hypertension and
gestational diabetes mellitus. Women and
Birth, 27(3), 190–195.
*Kashani, M., Eliasson, A., & Vernalis, M.
(2012). Perceived stress correlates with
disturbed sleep: A link connecting stress and
cardiovascular disease. Stress: The
International Journal on the Biology of
Stress, 15(1), 45–51. doi:
*Ko, S. H., Chang, S. C., & Chen, C. H.
(2010). A comparative study of sleep quality
between pregnant and nonpregnant
Taiwanese women. Journal of Nursing
Scholarship, 42(1), 23–30.
Kurina, L. M., Knutson, K. L., Hawkley, L.
C., Cacioppo, J. T., Lauderdale, D. S., &
Ober, C. (2011). Loneliness is associated
with sleep fragmentation in a communal
society. Sleep, 34(11), 1519–1526.
Lallukka, T., Sares-Jaske, L., Kronholm, E.,
Saaksjarvi, K., Lundqvist, A., Partonen, T.,
Knekt, P. (2012). Sociodemographic and
socioeconomic differences in sleep duration
and insomnia-related symptoms in Finnish
adults. BMC Public Health, 12, 565.
Luyster, F. S., Strollo, P. J., Zee, P. C., &
Walsh, J. K. (2012). Sleep: A health
imperative. Sleep, 35(6), 727–34.
McHugh, J. E., Casey, M. M., & Lawlor, B.
A. (2011). Psychosocial correlates of aspects
of sleep quality in community-dwelling Irish
older adults. Aging & Mental Health, 15(6),
*McHugh, J. E., & Lawlor, B. A. (2013).
Perceived stress mediates the relationship
between emotional loneliness and sleep
quality over time in older adults. British
Journal of Health Psychology, 18(3), 546–
555. doi:10.1111/j.2044-8287.2012.02101.x
*Mellor, A., Waters, F., Olaithe, M.,
McGowan, H., & Bucks, R. S. (2014). Sleep
and aging: Examining the effect of
psychological symptoms and risk of sleep-
disordered breathing. Behavioral Sleep
Medicine, 12(3), 222–234.
Miró, E., Cano-Lozano, M. C., & Buela-
Casal, G. (2005). Sleep and quality of life.
Revista Colombiana de Psicología, 14, 11-
Morin, C. M., Rodrigue, S., & Ivers, H.
(2003). Role of stress, arousal, and coping
skills in primary insomnia. Psychosomatic
Medicine, 65(2), 259–267.
Narendran, S., Nagarathna, R., Narendran,
V., Gunasheela, S., & Nagendra, H. R. R.
Advanced Research in Health, Education and Social Sciences: Towards a better practice
(2005). Efficacy of yoga on pregnancy
outcome. Journal of Alternative and
Complementary Medicine, 11(2), 237–244.
Nunes da Silva, J. M., Martins Costa, A. C.,
Waquim Machado, W., & Lopes Xavier, C.
(2012). Avaliação da qualidade de sono em
idosos não institucionalizados. ConScientiae
Saúde, 11(1), 29-36. doi:10.5585/
*Okun, M. L., Tolge, M., & Hall, M. (2014).
Low socioeconomic status negatively affects
sleep in pregnant women. Journal of
Obstetric, Gynecologic, and Neonatal
Nursing : JOGNN / NAACOG, 43(2), 160–
167. doi:10.1111/1552-6909.12295
Patel, N. P., Grandner, M. A., Xie, D. W.,
Branas, C. C., & Gooneratne, N. (2010).
"Sleep disparity" in the population: Poor
sleep quality is strongly associated with
poverty and ethnicity. BMC Public Health,
11, 10:475. doi:10.1186/1471-2458-10-475
*Rocha, M. C. P. da, & Martino, M. M. F.
(2010). O estresse e qualidade de sono do
enfermeiro nos diferentes turnos
hospitalares. Revista da Escola de
Enfermagem Da USP, 44(2), 280–286.
Sayar, K., Arikan, M., & Yontem, T. (2002).
Sleep quality in chronic pain patients.
Canadian Journal of Psychiatry-Revue
Canadienne de Psychiatrie, 47(9), 844–848.
Siegel, J. M. (2011). REM sleep: A
biological and psychological paradox. Sleep
Medicine Reviews, 15(3), 139–42.
*Theadom, A., & Cropley, M. (2008).
Dysfunctional beliefs, stress and sleep
disturbance in fibromyalgia. Sleep Medicine,
9(4), 376–81. doi:10.1016/
Van Reeth, O., Weibel, L., Spiegel, K.,
Leproult, R., Dugovic, C., & Maccari, S.
(2000). Interactions between stress and
sleep: From basic research to clinical
situations. Sleep Medicine Reviews, 4(2),
201–219. doi:10.1053/smrv.1999.0097
Wilcox, S., Brenes, G. A., Levine, D.,
Sevick, M. A., Shumaker, S. A., & Craven,
T. (2000). Factors related to sleep
disturbance in older adults experiencing knee
pain or knee pain with radiographic evidence
of knee osteoarthritis. Journal of the
American Geriatrics Society, 48(10), 1241–
... Prolonged sleep disruptions are likely to interfere with mental restitution 15,16 and mood 17 , and several studies have shown that poor sleep plays an important etiological role in the development of poor mental health working through changed emotional regulation and neuro-biological interaction [18][19][20] . Poor sleep quality has shown to either affect or exacerbate feelings of perceived stress 21 , which may over a longer period develop into depressive symptoms 22 . Further, sleep deprivation may also hamper daily functioning and the ability and energy to engage in meaningful activities, which are likely to affect overall life satisfaction and feelings of being socially connected. ...
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Frequent nighttime smartphone use can disturb healthy sleep patterns and may adversely affect mental health and wellbeing. This study aims at investigating whether nighttime smartphone use increases the risk of poor mental health, i.e. loneliness, depressive symptoms, perceived stress, and low life satisfaction among young adults. High-dimensional tracking data from the Copenhagen Network Study was used to objectively measure nighttime smartphone activity. We recorded more than 250,000 smartphone activities during self-reported sleep periods among 815 young adults (university students, mean age: 21.6 years, males: 77%) over 16 weekdays period. Mental health was measured at baseline using validated measures, and again at follow-up four months later. Associations between nighttime smartphone use and mental health were evaluated at baseline and at follow-up using multiple linear regression adjusting for potential confounding. Nighttime smartphone use was associated with a slightly higher level of perceived stress and depressive symptoms at baseline. For example, participants having 1–3 nights with smartphone use (out of 16 observed nights) had on average a 0.25 higher score (95%CI:0.08;0.41) on the Perceived stress scale ranging from 0 to 10. These differences were small and could not be replicated at follow-up. Contrary to the prevailing hypothesis, nighttime smartphone use is not strongly related to poor mental health, potentially because smartphone use is also a social phenomenon with associated benefits for mental health.
... Many different factors contribute to sleep quality, ranging from stress, physical activity, and physical health, to various psychological factors [3,16]. Since many people experience a lot of stress during the pandemic, it is logical to expect their sleep quality to decline. ...
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The pandemic caused by the coronavirus disease 2019 (COVID-19) had a huge impact on public mental health. This was also reflected in dreams. Not only did people start to remember more dreams, but dream content changed as themes like sickness, confinement, and—in the English-speaking world—even bugs began to dominate. This also led to an increase in nightmare frequency. There are various factors that contributed to this change in the dream landscape. Some people have started to sleep more and hereby spend more time in REM sleep, which is known to increase dream recall and further lead to bizarre and vivid dreams. On the other hand, stress and poor mental health had an impact on sleep, and sleep quality thus dropped in many individuals. Poor sleep quality can also lead to an increase in dream recall. Dreams are known to regulate mood, so the rise in dreams and the change in dream content could also reflect a reaction to the overall rise in stress and decline in mental health. Recent studies have shown that as the pandemic progresses, further changes in mental health, dream recall, and dream content arise, but data are still scarce. Further research could help understand the impact the pandemic still has on mental health and dreams, and how this impact is changing over the course of the pandemic.
... In the present clinical study, we evaluated the effect of the dietary supplement Mg-Teadiola versus a placebo in stressed, healthy individuals scoring at least 14 points on a clinically validated, self-reported measure-the Depression Anxiety Stress Scale (DASS) [14]. We also explored the effects of Mg-Teadiola on a variety of QoL parameters related to stress: anxiety, depression [15,16], quality of sleep [17,18], and perception of pain [19,20]. ...
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The effect of a combination of magnesium, vitamins B6, B9, B12, rhodiola and green tea/L-theanine (Mg-Teadiola) on stress was evaluated in chronically stressed, otherwise healthy individuals. Effects on stress-related quality-of-life parameters (sleep and perception of pain) were also explored. Adults with stress for ≥1 month, scoring ≥14 points on the Depression Anxiety Stress Scale (DASS)-42 questionnaire, were randomized (1:1) to receive oral Mg-Teadiola (n = 49) or a placebo (n = 51), for 28 days, with a follow-up assessment on Day 56 (NCT04391452). The primary endpoint was the change in the DASS-42 stress score from baseline to Day 28 with Mg-Teadiola versus placebo. The DASS-42 stress scores significantly decreased from baseline to Day 28 with Mg-Teadiola versus placebo (effect size, 0.29; 95% CI [0.01, 0.57]; p = 0.04). Similar reductions were observed on Day 14 (p = 0.006) and Day 56 (p = 0.02). A significant reduction in sensitivity to cold pain (p = 0.01) and a trend for lower sensitivity to warm pain was observed (p = 0.06) on Day 28. Improvements in daytime dysfunction due to sleepiness (Pittsburgh Sleep Quality Index-7 component score) were reported on Day 28, and were significant on Day 56 (p < 0.001). Mg-Teadiola is effective in managing stress in otherwise healthy individuals. Its beneficial effects on sleep and pain perception need further investigation.
... Finally, it can be inferred from the results that sleep parameters, both objective and subjective, could constitute a light indicator to indirectly recognize the emotional state of athletes since it correlates negatively and significantly with emotional states perceived as negative (anxiety, depression, stress, fatigue, tension, and anger). As previously explained, sleep is a very sensitive factor to any sign of stress (80). ...
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The COVID-19 outbreak has affected the sports field unprecedentedly. The emergency alert has deprived athletes of training in a suitable environment, as they are faced with cancellations of relevant events in their sports careers. This situation can cause stress levels and other emotional disorders similar to those experienced by athletes during periods of injury. Since the relationship between psychological factors and sports injuries is well-studied, the Global Psychological Model of Sports Injury (MGPLD) is applied to this historical situation for athletes. The purpose of this study was to analyze the relationships between perfectionism and trait anxiety with indicators of mental health (mood, depression, state anxiety, and stress) in high-performance athletes during confinement due to the COVID-19 pandemic, as well as to explore the coping strategies that athletes have applied and whether they are perceived as useful for managing negative emotional states. A cross-sectional study was conducted through online questionnaires during April 2020, adapting the Psychological Assessment Protocol of the High-Performance Sports Center of Murcia (Spain), to assess the psychological effects of confinement in a cross-cultural sample of 310 athletes (141 women and 169 men) from different countries in Europe, Asia, and America, and from diverse sports disciplines. The protocol comprised six instruments that test perfectionism, trait anxiety, mood states, stress, depression, coping strategies, and sleep. It was answered online via Google Forms. The results show that maladaptive perfectionism was related to all the indicators of athletes' mental health. However, athletes' levels of anxiety, stress, and depressive symptoms are relatively low, and the use of coping strategies such as cognitive restructuring and emotional calm was associated with lower levels of negative emotional states. Besides, the Iceberg Profile, a suitable fit for the mental health model, is observed in the mood of athletes, both in men and in women, although women showed higher levels of anxiety, stress, and depression than men. A strong relationship was observed between maladaptive perfectionism and martial arts sports discipline, superior to other sports. In short, it can be concluded that high-performance athletes in the Leguizamo et al. High-Performance Athletes' Psychological Impact of COVID-19 studied sample showed negative emotional state values below the expected average. Finally, the proposals for practical applications of the results collected are discussed.
... The result of this study is supported by Shariat et al. [12] that there is a relationship between anxiety experienced by pregnant women and sleep quality. Stress is one of several variables that affect sleep quality, especially regarding subjective evaluations [34]. The stress can increase the cortisol awakening response and hyperactive hypothalamus-pituitary-adrenal (HPA) that influence sleep [35]. ...
... Sleep is an important factor for recovering from fatigue and reducing stress. Therefore, usual and natural sleep monitoring at home can contribute to healthcare and stress management [1,2]. The sleep quality is objectively assessed by sleep cycles representing time-course change of sleep stages such as rapideye movement (REM) and non-REM sleep at the clinical sites. ...
Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects’ body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and five parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these five parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed sufficient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
... Mindfulness interventions are posited to affect not only stressrelated biological pathways but also stress-related health behaviors (40). Although it is well known that stress is associated with negative health behaviors such as greater tobacco use, increased difficulty with smoking cessation, increased likelihood of smoking relapse (59,60), poorer diet and eating behaviors (60,61), and impaired sleep quality (62), less is known about how mindfulnessbased interventions can impact these behaviors. Some of the strongest evidence from RCTs in this area suggests that mindfulness interventions can reduce substance-use behaviors in at-risk populations, such as cigarette use among heavy smokers (63), drug relapse and alcohol consumption among substance-abusing individuals (64), and opioid misuse among adults suffering from chronic pain (21). ...
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Objective: There has been substantial research and public interest in mindfulness interventions, biological pathways, and health over the past two decades. This article reviews recent developments in understanding relationships between mindfulness interventions and physical health. Methods: A selective review was conducted with the goal of synthesizing conceptual and empirical relationships between mindfulness interventions and physical health outcomes. Results: Initial randomized controlled trials (RCTs) in this area suggest that mindfulness interventions can improve pain management outcomes among chronic pain populations, and there is preliminary evidence for mindfulness interventions improving specific stress-related disease outcomes in some patient populations (i.e., clinical colds, psoriasis, IBS, PTSD, diabetes, HIV). We offer a stress buffering framework for the observed beneficial effects of mindfulness interventions and summarize supporting biobehavioral and neuroimaging studies that provide plausible mechanistic pathways linking mindfulness interventions with positive physical health outcomes. Conclusion: We conclude with new opportunities for research and clinical implementations to consider in the next two decades.
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Introduction: Sleep is a major contributor to the physical and mental recovery of the body. Sleeping well enables people to perform well in their daily activities. The quality of sleep is directly linked to their daily production, but many seniors do not consider this data. Objectives: To assess the quality of sleep of non-institutionalized elderly in Teresina (PI) and identify problems related to sleep. Methods: It is a cross-sectional, descriptive and observational study held at Peace Village Subdivision Teresina (PI) with 65 elderly, where specific instruments were used for evaluation of sleep quality (Pittsburgh Sleep Quality Index -- PSQI) and instruments developed by researchers to socio-demographic, lifestyle and clinical history of the sample. Results: Out of the total number of participants, 63% (n=41) had poor sleep quality. Conclusion: The quality of sleep is an important factor for health, well-being of the elderly in general.
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Recently, workers' mental health has become important focus in the field of occupational health management. Depression is a psychiatric illness with a high prevalence. The association between job stress and depressive symptoms has been demonstrated in many studies. Recently, studies about the association between sleep quality and depressive symptoms have been reported, but there has been no large-scaled study in Korean female workers. Therefore, this study was designed to investigate the relationship between job stress and sleep quality, and depressive symptoms in female workers. From Mar 2011 to Aug 2011, 4,833 female workers in the manufacturing, finance, and service fields at 16 workplaces in Yeungnam province participated in this study, conducted in combination with a worksite-based health checkup initiated by the National Health Insurance Service (NHIS). In this study, a questionnaire survey was carried out using the Korean Occupational Stress Scale-Short Form(KOSS-SF), Pittsburgh Sleep Quality Index(PSQI) and Center for Epidemiological Studies-Depression Scale(CES-D). The collected data was entered in the system and analyzed using the PASW (version 18.0) program. A correlation analysis, cross analysis, multivariate logistic regression analysis, and hierarchical multiple regression analysis were conducted. Among the 4,883 subjects, 978 subjects (20.0%) were in the depression group. Job stress(OR=3.58, 95% CI=3.06-4.21) and sleep quality(OR=3.81, 95% CI=3.18-4.56) were strongly associated with depressive symptoms. Hierarchical multiple regression analysis revealed that job stress displayed explanatory powers of 15.6% on depression while sleep quality displayed explanatory powers of 16.2%, showing that job stress and sleep quality had a closer relationship with depressive symptoms, compared to the other factors. The multivariate logistic regression analysis yielded odds ratios between the 7 subscales of job stress and depressive symptoms in the range of 1.30-2.72 and the odds ratio for the lack of reward was the highest(OR=2.72, 95% CI=2.32-3.19). In the partial correlation analysis between each of the 7 subscales of sleep quality (PSQI) and depressive symptoms, the correlation coefficient of subjective sleep quality and daytime dysfunction were 0.352 and 0.362, respectively. This study showed that the depressive symptoms of female workers are closely related to their job stress and sleep quality. In particular, the lack of reward and subjective sleep factors are the greatest contributors to depression. In the future, a large-scale study should be performed to augment the current study and to reflect all age groups in a balanced manner. The findings on job stress, sleep, and depression can be utilized as source data to establish standards for mental health management of the ever increasing numbers of female members of the workplace.
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Este estudo teve como objetivo analisar a relação entre estresse e qualidade do sono de enfermeiros que atuam em diferentes setores hospitalares, dos turnos diurnos e noturnos. Foi realizado em uma instituição hospitalar da cidade de Campinas, São Paulo. Utilizou-se para a coleta de dados: Escala Bianchi de Stress modificada (EBSm) e o Índice de Qualidade do Sono de Pittsburgh (PSQI). Participaram 203 enfermeiros com faixa etária predominante de 40 a 49 anos de idade. Os resultados indicaram que houve uma correlação significativa entre estresse e sono (correlação de Spearman; r= 0,21318; p= 0,0026) e entre níveis elevados de estresse e qualidade de sono ruim para os enfermeiros do turno da manhã (p=0,030; Teste Qui-Quadrado). Concluiu-se que o nível de estresse pode ser um fator diretamente correlacionado com o sono, visto que quanto maior o nível de estresse dos enfermeiros, pior é a qualidade de sono.
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To examine major factors affecting readiness in the Army National Guard (ARNG), 265 soldiers of the Pennsylvania ARNG redeploying in 2010 from Iraq and Afghanistan were evaluated with validated questionnaires during their first unit formation. The questionnaires assessed demographic information, health habits, levels of perceived stress, mood, diet, sleep, and exercise habits, and included a screening question for depression. Our analysis revealed no negative effects of multiple deployments in this cohort of ARNG soldiers. There was no apparent impact on readiness attributable to soldiers' living locations; there did not appear to be an urban-rural divide. There were, however, numerous opportunities to improve health behaviors, including smoking (prevalence of 41%), poor dietary choices and sleep habits, as well as management of stress and mood disorders. A striking prevalence of sleep apnea exists in these ARNG soldiers (40%), approximately double that previously measured in the general U.S. population. Soldiers with high stress, depression, poor sleep quality, and sleep apnea are at increased long-term risk for cardiovascular complications and deserve focused interventions to encourage lifestyle behavior change.
Objective: To examine the relationship between measures of sleep quality and the presence of commonly encountered comorbid and sociodemographic conditions in elderly Black subjects. Method: Analyses included participants from the Baltimore Study of Black Aging (BSBA; n = 450; mean age 71.43 years; SD 9.21). Pittsburgh Sleep Quality Index (PSQI) measured overall sleep pattern and quality. Self-reported and objective measures of physical and mental health data and demographic information were collected for all participants. Results: Sociodemographic and comorbid health factors were significantly associated with sleep quality. Results from regression analyses revealed that older age, current financial strain, interpersonal problems, and stress were unique predictors of worse sleep quality. Sleep duration was significantly correlated with age, depressive affect, interpersonal problems, and stress; only age was a unique significant predictor. While participants 62 years or younger had worse sleep quality with increasing levels of stress, there was no significant relationship between sleep quality and stress for participants 81 years and older. Conclusions: Several potential mechanisms may explain poor sleep in urban, community dwelling Blacks. Perceived stressors, including current financial hardship or hardship experienced for an extended time period throughout the lifespan, may influence sleep later in life.
Background: Pregnant women with complications including pregnancy-induced hypertension (PIH) and gestational diabetes mellitus (GDM) often experience disrupted sleep patterns because of activation of the sympathetic nervous system. These pathologies are aggravated by sympathetic nervous system activation and may be related to stress. The present study aimed to clarify the characteristics of and changes in sleep quality and stress in pregnant women with PIH and GDM during the second and third trimesters. Methods: We enrolled 56 women in their second or third trimesters who were diagnosed with PIH or GDM. Participants completed questionnaires, including the Pittsburgh Sleep Quality Index (PSQI) and the Perceived Stress Scale (PSS). Secretory immunoglobulin A (SlgA) concentrations were measured as a biological indicator of stress. Results: PSS scores and subjective stress parameters were significantly higher than those reported from previous studies of healthy pregnant women (15.2 points and 15.1 points for the second and third trimesters, respectively). Mean one-day values for SIgA were 168.3 and 205.7 μg/mL for the second and third trimesters, respectively. During the second and third trimesters, SIgA scores were higher than those reported for healthy pregnant women in previous studies. The PSQI component scores sleep disturbance (C5) and sleep duration (C3) in follow up case were significantly higher in the third trimester than in the second trimester. Discussion: This investigation suggests that pregnant women with PIH and GDM experience higher stress levels than do non-pregnant women and healthy pregnant women. Further, our results indicate that sleep quality worsens during the third trimester compared with the second trimester.
To evaluate the effect of socioeconomic status on measures of sleep quality, continuity, and quantity in a large cohort of pregnant women. Prospective, longitudinal study. One hundred seventy (170) pregnant women at 10-20 weeks gestation. Sleep quality was assessed with the Pittsburgh Sleep Quality Index. Sleep duration and continuity (sleep fragmentation index [SFI]) were assessed with actigraphy at 10-12, 14-16, and 18-20 weeks gestation. Because sleep did not significantly differ across time, averages across all three time points were used in analyses. Socioeconomic status (SES) was defined by self-reported annual household income. Linear regression analyses were used to model the independent associations of SES on sleep after adjusting for age, race, parity, marital status, body mass index (BMI), perceived stress, depressive symptoms, and financial strain. On average, women reported modestly poor sleep quality (M = 5.4, SD = 2.7), short sleep duration (391 [55.6] min) and fragmented sleep (SFI M = 33.9, SD = 10.4. A household income < $50,000/year was associated with poorer sleep quality (β = -.18, p < 0.05) and greater sleep fragmentation (β = -.18, p < 0.05) following covariate adjustment. Low SES was associated with poorer sleep quality and fragmented sleep, even after statistical adjustments. Perceived stress and financial strain attenuated SES-sleep associations indicating that psychosocial situations preceding pregnancy are also important to consider.
Good sleep is essential to good health. Yet for most of its history, sleep medicine has focused on the definition, identification, and treatment of sleep problems. Sleep health is a term that is infrequently used and even less frequently defined. It is time for us to change this. Indeed, pressures in the research, clinical, and regulatory environments require that we do so. The health of populations is increasingly defined by positive attributes such as wellness, performance, and adaptation, and not merely by the absence of disease. Sleep health can be defined in such terms. Empirical data demonstrate several dimensions of sleep that are related to health outcomes, and that can be measured with self-report and objective methods. One suggested definition of sleep health and a description of self-report items for measuring it are provided as examples. The concept of sleep health synergizes with other health care agendas, such as empowering individuals and communities, improving population health, and reducing health care costs. Promoting sleep health also offers the field of sleep medicine new research and clinical opportunities. In this sense, defining sleep health is vital not only to the health of populations and individuals, but also to the health of sleep medicine itself. Buysse DJ. Sleep health: can we define it? Does it matter? SLEEP 2014;37(1):9-17.
Acute stress is a fundamental adaptive response which enables an organism to cope with daily threatening environmental stimuli. If prolonged and uncontrollable, the stress response may become inadequate and ultimately result in health damage. Animal models of stress in rodents indicate that both acute and chronic stressors have pronounced effects on sleep architecture and circadian rhythms. One major physiological response elicited by stress is activation of the hypothalamo-pituitary-adrenal axis. In both animals and humans, the hypothalamo-pituitary-adrenal axis plays an important role in sleep–wake regulation and in alterations of the sleep–wake cycle secondary to exposure to acute or chronic stressors. In humans, dysfunction of the neuroendocrine regulation of sleep can lead to severe sleep disturbances. The progressive decay of the hypothalamo-pituitary-adrenal axis in elderly people, which mimics chronic exposure to stress, may contribute to fragmented and unstable sleep in ageing. Shift workers, chronic insomniacs or patients suffering from mental disorders show abnormal hypothalamo-pituitary-adrenal secretory activity and concomitant sleep disturbances. Those sleep disorders and possible underlying mechanisms are briefly reviewed.