Associations of the shared and unique as
Personality and Individual Differences xxx (2011) xxx–xxx
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
Associations of the shared and unique aspects of positive and negative
emotional factors with sleep quality
Jesse C. Stewart ⇑, Kevin L. Rand, Misty A.W. Hawkins, Jennifer A. Stines
Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA
a r t i c l e
i n f o
Article history:
Received 14 September 2010
Received in revised form 23 November 2010
Accepted 3 December 2010
Available online xxxx
Keywords:
Sleep Quality
Depression
Anxiety
Anger
Positive Affect
Rumination
a b s t r a c t
Because most studies have examined only one emotional factor at a time, it is not clear which features of
these overlapping constructs are important determinants of sleep quality. Our aims were to determine
which aspects of negative emotional factors are most strongly associated with poor sleep quality,
whether positive emotional factors are independently related to improved sleep quality, and whether
rumination explains the links between emotional factors and sleep quality. A total of 224 young men
and women completed questionnaires assessing depressive symptoms, trait anxiety, trait anger, trait
positive affect, trait rumination, and sleep quality. Structural equation models revealed that greater Negative Affect – the shared variance among the negative emotional factors – predicted poor Sleep Quality
(b = .62, p < .0001, DR2 = .38); however, unique effects of the positive and negative emotional factors were
not detected. Rumination did not account for the observed relationship. Our findings suggest that the
shared, but not unique, aspects of negative emotional factors may be key determinants of sleep quality.
Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Negative emotional factors and sleep quality
The prevalence of sleep difficulties is high and on the rise.
Approximately 64% of American adults (versus 51% in 2001) report
having experienced one or more sleep problems at least a few
nights a week (National Sleep Foundation, 2009), and about 6%
meet criteria for an insomnia diagnosis (Ohayon, 2002). Notably,
sleep dysfunction is a predictor of poor health outcomes. Both
objective and subjective measures of sleep quality have been found
to be predictive of all-cause mortality (Dew et al., 2003; Kripke,
Garfinkel, Wingard, Klauber, & Marler, 2002). Other studies have
demonstrated that poor sleep quality is associated with an increased risk of chronic diseases, such as cardiovascular disease
(Schwartz et al., 1999) and diabetes (Ayas et al., 2003). Given the
widespread, deleterious effects of poor sleep on health, a crucial
next step is to identify determinants of this emerging risk factor.
Several potential determinants of poor sleep fall in the domain
of stable, trait-like emotional factors. Individuals with depressive
disorders consistently report sleep complaints and exhibit abnormalities in sleep architecture (Tsuno, Besset, & Ritchie, 2005).
Unlike depression, investigations of anxiety have found little objective evidence of sleep architecture changes, although reports of
insomnia are common (Papadimitriou & Linkowski, 2005). Studies
examining anger suggest that it is also related to indicators of poor
sleep (Caska et al., 2009; Shin et al., 2005), and similar results have
been observed for the related constructs of hostility and aggression
(Brissette & Cohen, 2002; Ireland & Culpin, 2006).
Most previous studies, however, have examined the effect of a
single negative emotional factor on sleep quality, which considerably limits the inferences that can be drawn. Because depression,
anxiety, and anger are overlapping constructs and cluster within
individuals (Clark & Watson, 1991; Spielberger, 1988), it is not
known whether each of these emotional factors is independently
associated with sleep quality or whether one (or more) is merely
a marker for another emotional factor. To illustrate the latter possibility, anxiety could be inversely correlated with sleep quality solely due to its strong relationship with depression, which itself may
be a determinant of poor sleep. Yet another plausible model is that
the individual negative emotion-sleep quality relationships are driven by a shared underlying personality trait, such as negative
affectivity (Watson & Clark, 1984). To evaluate these competing
Abbreviations: PSQI, Pittsburgh Sleep Quality Index; BMI, body mass index; STAI,
State-Trait Anxiety Inventory; BDI-II, Beck Depression Inventory-II; STAXI, StateTrait Anger Expression Inventory; PANAS, Positive and Negative Affect Schedule;
ECQ-R, Rehearsal scale of the Emotional Control Questionnaire; SEM, structural
equation modeling; SRMR, Standardized Root Mean Squared Residual; RMSEA, Root
Mean Square Error of Approximation; CFI, Comparative Fit Index.
⇑ Corresponding author. Address: Department of Psychology, IUPUI, 402 North
Blackford Street, LD 100E, Indianapolis, IN 46202, USA. Tel.: +1 317 274 6761; fax:
+1 317 274 6756.
E-mail address: jstew@iupui.edu (J.C. Stewart).
0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2010.12.004
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
2
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
models, investigations are needed in which multiple emotional
factors are simultaneously examined as correlates or predictors
of sleep quality. Unfortunately, only a small number of these types
of studies exist (depression and anxiety: Jansson & Linton, 2006;
Johnson, Roth, & Breslau, 2006; Mallon, Broman, & Hetta, 2000;
Morphy, Dunn, Lewis, Boardman, & Croft, 2007; depression and anger: Caska et al., 2009; Shin et al., 2005), and none has examined all
three negative emotional factors.
1.2. Positive emotional factors and sleep quality
Another topic that has received limited attention is whether
positive emotional factors are associated with improved sleep
quality. Although past studies have detected a link between
psychological well-being and improved sleep (Nes, Roysamb,
Reichborn-Kjennerud, Tambs, & Harris, 2005), the multidimensional nature of this construct makes it difficult to discern the
specific influence of its positive components. Similar associations
have been detected in investigations examining more narrowly defined constructs, such as trait positive affect and life satisfaction
(Steptoe, O’Donnell, Marmot, & Wardle, 2008; Strine, Chapman,
Balluz, Moriarty, & Mokdad, 2008). However, because few studies
have adjusted for negative emotions (Gray & Watson, 2002;
Steptoe, O’Donnell, Marmot, & Wardle, 2008), it is not known
whether positive emotional factors are independent determinants
of sleep quality.
1.3. The present investigation
To address these gaps, we conducted a study of young adults in
which positive and negative emotional factors, rumination, and
sleep quality were assessed. We measured trait rumination because
relationships between this cognitive factor and both negative emotions and sleep quality have been reported (Thomsen, Mehlsen,
Christensen, & Zachariae, 2003; Zoccola, Dickerson, & Lam, 2009).
Furthermore, rumination and related constructs are identified as
key factors in current models of insomnia (Harvey, 2002). Thus,
rumination is one mechanism through which emotional factors
may have an impact on sleep quality. Our aims were to determine
(a) which aspects of overlapping negative emotional factors (shared
versus unique) are most strongly related to poor sleep quality, (b)
whether positive emotional factors are independently associated
with improved sleep quality, and (c) whether trait rumination
explains the emotional factor-sleep quality associations.
2. Method
2.1. Participants
Table 1
Characteristics of participants (N = 224).
Demographic factors
Age (years)
Gender, % female
Race-ethnicity, % non-white
23.4 ± 6.1
75.0
26.3
Health-relevant factors
Smoking status, % current smokers
Daily alcohol intake (g/day)
Body mass index (kg/m2)
16.1
8.9 ± 15.5
24.9 ± 5.4
Emotional factors
Beck Depression Inventory-II (range: 0–60)a
Trait Anxiety Scale (range: 20–80)
Trait Anger Scale (range: 10–40)
PANAS-Positive Affect subscale (range: 10–50)
ECQ-Rehearsal subscale (range: 0–14)
9.6 ± 7.8
41.7 ± 10.0
17.7 ± 4.4
33.5 ± 7.5
5.9 ± 3.3
Sleep Quality
Pittsburgh Sleep Quality Index (range: 0–19)
Sleep quality status, % poor sleepers
6.7 ± 3.2
60.7
Note: Values are means ± standard deviations for continuous variables and percentages for categorical variables. PANAS = Positive and Negative Affect Schedule;
ECQ = Emotion Control Questionnaire.
a
Item 16 omitted.
Anxiety scale of the STAI (Spielberger, 1983), and Trait Anger scale
of the STAXI (Spielberger, 1988), respectively. The BDI-II is a 21item, self-report measure of depressive symptom severity that
has been shown to have high internal consistency, test–retest reliability, and construct validity (Beck et al., 1996). We did not include Item 16 (changes in sleeping pattern) due to concerns that
it might artificially inflate the correlation between the BDI-II and
PSQI. The Trait Anxiety scale is a 20-item, self-report measure that
provides an assessment of stable individual differences in the propensity to experience anxiety. This scale has high internal consistency and test–retest reliability, and it correlates strongly with
other trait anxiety measures (Spielberger, 1983). The Trait Anger
scale is a 10-item, self-report measure that assesses stable individual differences in tendency to experience anger. It has also been
found to be internally consistent, reliable over time, and valid
(Spielberger, 1988).
2.2.2. Positive emotional factors
We assessed trait positive affect with the 10-item Positive Affect
(PA) scale of the widely used PANAS (Watson, Clark, & Tellegen,
1988). Participants were asked to report the extent to which they
had experienced ten positive emotions during the past few weeks.
The PANAS-PA scale has been shown to be internally consistent
and stable over time, and it correlates with other self-report and
peer-rated measures of positive affect (Watson & Clark, 1994).
Our sample consisted of 224 young adults enrolled in courses at
a large urban university. This study was approved by the institutional review board at Indiana University-Purdue University Indianapolis. Besides the requirement of age P 18 years, there were no
inclusion criteria. Of the 257 enrolled students, we excluded individuals who reported current use of psychotropic medication
(n = 18), did not complete all of the PSQI items (n = 10), were missing data for BMI or smoking status (n = 4), or did not complete the
STAI Trait Anxiety scale (n = 1). The characteristics of the final sample are shown in Table 1.
2.2.3. Trait rumination
The Rehearsal scale of the ECQ (Roger & Najarian, 1989) was
administered to measure trait rumination. It is a 14-item, true–
false instrument that provides an index of one’s general tendency
to repeatedly think about negative events (e.g., ‘‘I find it hard to
get thoughts about things that have upset me out of my mind.’’).
This scale has satisfactory internal consistency and test–retest reliability (Roger & Najarian, 1989), and it has been found to be correlated with other rumination measures (Siegle, Moore, & Thase,
2004).
2.2. Measures
2.2.4. Demographic and health-relevant factors
The questionnaire battery included items assessing age, gender,
race-ethnicity, and smoking status. Because few participants
identified their race-ethnicity as Asian/Pacific Islander (n = 14),
Hispanic/Latino (n = 11), Native American/Eskimo/Aleut (n = 1), or
2.2.1. Negative emotional factors
To assess depressive symptoms, trait anxiety, and trait anger,
we administered the BDI-II (Beck, Steer, & Brown, 1996), Trait
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
3
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
Other (n = 5), race-ethnicity was coded as white versus non-white.
BMI was calculated from self-reported height and weight. Daily
alcohol intake was computed using a quantify-frequency method
(Garg, Wagener, & Madans, 1993) and was log transformed to reduce positive skew.
2.2.5. Sleep quality
The PSQI (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) is a
19-item measure of subjective sleep quality during the past month.
The seven component scores (subjective sleep quality, sleep latency,
sleep duration, habitual sleep efficiency, sleep disturbances, use of
sleep medication, and daytime dysfunction) are summed to calculate a global score. A global score of >5 is indicative of severe impairment in P2 areas or moderate impairment in P3 areas; individuals
with scores >5 have been classified as ‘‘poor sleepers’’ (Buysse et al.,
1989). The PSQI has adequate internal consistency and test–retest
reliability and is able to distinguish between groups with varying
degrees of sleep disturbance – i.e., sleep-disordered patients,
depressed patients, and controls (Buysse et al., 1989).
2.3. Procedure
Participants attended a 1-h assessment session held in a computer laboratory, during which they completed the questionnaire
battery on a secure website. To ensure privacy, individuals were
separated by at least one empty terminal. Participants were
awarded course credit at the end of the session.
2.4. Data analysis
We first performed correlational analyses to quantify the degree
of overlap among the emotional measures and to examine the
associations of the emotional factors with sleep quality. Next, we
used SEM with maximum likelihood estimation using LISREL 8.8
(Joreskog & Sorbom, 2008) to test the hypothesized latent-variable
models. To assess model fit, we examined absolute (model v2-statistic and SRMR), parsimonious (RMSEA), and incremental (CFI) fit
indices (Hu & Bentler, 1999).
3. Results
3.1. Correlational analyses
Bivariate correlations revealed that there was substantial overlap among the emotional factors, as they shared 3–55% of the variance (see Table 2). In addition, partial correlations between the
emotional measures and the PSQI were all significant, and most
were moderate in size. The BDI-II, Trait Anxiety, and Trait Anger
scores were associated with poorer sleep quality, whereas the
PANAS-PA score was related to better sleep quality.
3.2. Structural equation models
We first created a measurement model consisting of the firstorder latent variables of Depression, Anxiety, Anger, Positive Affect,
and Sleep Quality. We aggregated items into three parcels per scale
because modeling a large set of measured indicators adversely affects model fit (Little, Cunningham, Shahar, & Widaman, 2002). To
rule out potential confounders, we adjusted the PSQI parceled indicators for age, gender, race-ethnicity, smoking, daily alcohol intake,
and BMI.1 We also modeled a second-order latent variable of Nega1
We residualized the PSQI parceled indicators instead of including the demographic and health-relevant factors in the models as covariates, as the latter would
have yielded a ratio of participants to estimated parameters below the acceptable
lower limit of 5:1 (Kline, 2005)
Table 2
Correlations among measures of emotional factors, trait rumination, and sleep
quality.
PSQI
BDI-II
Trait anxiety
Trait anger
PANAS-PA
ECQ-R
PSQI
BDI-II
Trait anxiety
Trait anger
PANAS-PA
–
.46*
.46*
.18*
.24*
.34*
–
.74*
.31*
.46*
.47*
–
.42*
.50*
.59*
–
.17*
.35*
–
.30*
Note: N = 224. Correlations involving PSQI are partial correlations controlling for
age, gender, race-ethnicity, body mass index, cigarette smoking, and daily alcohol
intake. PSQI = Pittsburgh Sleep Quality Index, BDI-II = Beck Depression Inventory-II,
PANAS-PA = Positive and Negative Affect Schedule-Positive Affect subscale, and
ECQ-R = Emotion Control Questionnaire-Rehearsal subscale.
*
p < .05.
tive Affect as driving the first-order latent variables of Depression,
Anxiety, and Anger. In the measurement model, Negative Affect,
Positive Affect, and Sleep Quality were freed to correlate. This model
showed acceptable fit to the data, v2(84, N=224) = 143.36 (p = 0.0006),
RMSEA = .051, CI90: (0.034, 0.067), SRMR = 0.047, CFI = .98. As can be
seen in Panel A of Fig. 1, the correlations among Negative Affect,
Positive Affect, and Sleep Quality were significant (all ps < .0001).
To determine which aspects of the negative emotional factors
were most strongly associated with sleep quality, we first freed a
structural path from Negative Affect to Sleep Quality. The significant path (b = .62, p < .0001) indicates that shared aspects of the
negative emotional factors strongly predicted poorer sleep quality
(see Fig. 1, Panel B), accounting for 38% of the variance. To illustrate
this effect, we computed the percentage with a PSQI global score
>5 for each tertile of the Negative Affect latent variable score,
which revealed that 31%, 68%, and 84% of those in the lower, middle, and upper tertile, respectively, were poor sleepers. We then
freed the structural paths from each of the first-order latent variables to Sleep Quality, one at a time, to examine whether there
were unique effects of depression, anxiety, and anger. Individually
freeing the paths from Depression [Dv2(1,N=224) = 0.40 (p = 0.53)],
Anxiety [Dv2(1, N=224) = 0.02 (p = 0.89)], and Anger [Dv2(1, N=224) =
1.13 (p = 0.29)] did not improve model fit. Furthermore, the structural paths from Depression (b = .13, p = .48), Anxiety (b = .13,
p = .90), and Anger (b = .09, p = .30) were all nonsignificant. Taken
together, these results indicate that only the shared aspects of the
negative emotional factors influence sleep quality. To evaluate
whether the positive emotional factor was associated with better
sleep quality, we next freed the structural path from Positive Affect
to Sleep Quality. The nonsignificant path (b = .04, p = 0.69) suggests
that trait positive affect may not exert an independent, protective
effect on sleep quality.
Finally, we included the first-order latent variable of Rumination, which was modeled using three parceled indicators from
the ECQ-R. Negative Affect, Positive Affect, Rumination, and Sleep
Quality were freed to correlate. This model showed acceptable
fit, v2(126, N=224) = 198.61 (p = 0.0009), RMSEA = .044, CI90: (0.029,
0.058), SRMR = 0.047, CFI = .99. We then estimated three structural
paths. The paths from Negative Affect to Sleep Quality (b = .59,
p < .0001) and Negative Affect to Rumination (b = .69, p < .0001)
were significant, whereas the Rumination to Sleep Quality path
was not (b = .03, p = .81). These results are inconsistent with trait
rumination being a mediator of the negative affect–sleep quality
relationship.
4. Discussion
Our study is the first to examine the associations of the shared
and unique aspects of negative and positive emotional factors with
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
4
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
A
.14
.34
.19
.36
.26
.24
.30
.30
.51
BDI-II 1
BDI-II 2
BDI-II 3
Trait
Anxiety 1
Trait
Anxiety 2
Trait
Anxiety 3
Trait
Anger 1
Trait
Anger 2
Trait
Anger 3
.80
.86
.93
.81
.90
.25
Depression
.87
Anxiety
.84
.70
Anger
.07
.87*
.84
.78
.46*
.96*
Negative
Affect
.62
1.0*
1.0*
-.58
.54
Sleep
Quality
1.0*
B
.87
PANASPA 1
PANASPA 2
PANASPA 3
.18
.24
.32
.19
.36
.26
.24
.30
.30
.51
BDI-II 1
BDI-II 2
BDI-II 3
Trait
Anxiety 1
Trait
Anxiety 2
Trait
Anxiety 3
Trait
Anger 1
Trait
Anger 2
Trait
Anger 3
Depression
.90
.80
.25
.71
PSQI 3
.77
PSQI 1
.21
PSQI 2
.71
PSQI 3
.77
.82
.34
.81
PSQI 2
-.34
.14
.93
.21
.48
Positive
Affect
.90
PSQI 1
.89
.87
.86
Anxiety
.84
Anger
.07
.87*
.84
.70
.78
.46*
.96*
Negative
Affect
.62
.89
1.0*
.62*
-.58
1.0*
.87
.54
.48
Positive
Affect
.90
Sleep
Quality
.02
.82
PANASPA 1
PANASPA 2
PANASPA 3
.18
.24
.32
Fig. 1. (A) Measurement model of Depression, Anxiety, Anger, Negative Affect, Positive Affect, and Sleep Quality. (B) Structural model of Negative Affect as a predictor of Sleep
Quality. Values associated unidirectional arrows between variables are standardized regression coefficients, bidirectional arrows between variables are Pearson correlation
coefficients, and unidirectional arrows pointing at a single variable represent error variances. Paths with significant values are solid, whereas paths with nonsignificant values
are dashed. BDI-II = Beck Depression Inventory-II. PANAS-PA = Positive and Negative Affect Schedule-Positive Affect subscale. PSQI = Pittsburgh Sleep Quality Index. N = 224.
⁄
Coefficient fixed to 1.0 prior to standardization.
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
sleep quality. We report three important findings that address
existing gaps. First, we found that the shared aspects of depression,
anxiety, and anger were more strongly related to sleep quality than
were the unique aspects. Structural equation models revealed that
Negative Affect – a latent variable representing the shared variance
among the negative emotions – was associated with poorer sleep
quality, whereas depressive symptoms, trait anxiety, and trait anger were not in the presence of the Negative Affect variable. The
observed relationship may be clinically meaningful, as 84% of
adults with Negative Affect scores in the upper tertile were identified as poor sleepers versus only 31% of those with scores in the
lower tertile. Our results raise the possibility that a common
underlying factor may account for the associations of individual
negative emotional factors with sleep quality detected in past
studies. Furthermore, the failure to take into account the overlap
among negative emotional factors could explain previous inconsistent results (cf. Jansson & Linton, 2006 and Johnson et al., 2006).
Whether or not a particular negative emotion is found to be associated with sleep quality in a given sample may depend on the extent to which the emotional measure taps this underlying common
factor.
Defined as a pervasive disposition to experience negative emotions across situations (Watson & Clark, 1984), negative affectivity
has recently been examined as a determinant of sleep quality, and
an inverse association has been detected (Danielsson, JanssonFröjmark, Linton, Jutengren, & Stattin, 2010; Fortunato & Harsh,
2006; Gau, 2000; Gray & Watson, 2002; Williams & Moroz,
2009). In contrast to our investigation (which employed SEM to extract a measure of negative affectivity), most previous studies have
used neuroticism subscales of personality inventories to assess
negative affectivity and did not simultaneously examine the effects
of individual emotional factors. Critically, the results of the existing
neuroticism-sleep studies are ambiguous, given that any one of the
negative emotional factors (i.e., depression, anxiety, or anger)
could have been driving the observed relationships.
The second noteworthy finding was that the positive emotional
factor did not exert an independent, protective effect on sleep quality. Although the bivariate correlation between trait positive affect
and sleep quality was significant, no association was observed in
the SEM model. Thus, our results suggest that positive emotion measures may be related to sleep quality merely because they are markers of the absence of negative affect. Results of another investigation
support this conclusion (Brissette & Cohen, 2002). In two other studies, however, high positive emotionality was associated with improved sleep quality, even after controlling for negative emotions
(Gray & Watson, 2002; Steptoe et al., 2008). A possible explanation
for these conflicting findings is that our latent Negative Affect
variable may overlap with positive affect measures to a greater
extent than the global assessments used in past studies.
Our third important finding was that trait rumination did not
play a role, as a mediator or a confounder, in the negative affect–
sleep quality relationship. Negative Affect remained related to
Sleep Quality in the presence of Rumination, even though Rumination was correlated with the PSQI in the univariate analyses and
was associated with Negative Affect in the SEM analyses. Our results contrast with the finding that negative emotions and rumination are independent correlates of sleep quality (Thomsen et al.,
2003). While our findings suggest that rumination may not be part
of the causal chain linking negative affect and sleep, they should
not be overinterpreted. First, we may have detected mediation if
we had examined other forms of perseverative cognition, such as
trait worry (Brosschot, Gerin, & Thayer, 2006), or momentary cognitive activity, such as state rumination or worry (Brosschot, Van
Dijk, & Thayer, 2007). Second, rumination may be associated with
some aspects of sleep quality, such as sleep latency, but not others
(Zoccola et al., 2009).
5
4.1. Limitations
One limitation of our study is the cross-sectional design, which
precludes us from drawing directional inferences. It is possible that
chronically experiencing negative emotions contributes to the
onset of insomnia (Jansson & Linton, 2006; Johnson et al., 2006)
or that insomnia is a risk factor for emotional disturbances
(Danielsson et al., 2010; Johnson et al., 2006). Another limitation
is that we used a self-report measure to assess sleep quality. In a
study comparing subjective and objective sleep assessments
(Means, Edinger, Glenn, & Fins, 2003), individuals with insomnia
tended to underestimate their sleep duration relative to controls,
a finding that raises concerns regarding the accuracy of self-reports
of sleep. Although evidence linking subjective sleep quality to
health outcomes attests to its importance (Kripke et al., 2002), future studies should explore whether the same pattern of results is
found when sleep quality is assessed by actigraphy or polysomnography. A third limitation is that we derived our measure of negative affectivity from scales assessing depression, anxiety, and
anger only. Future studies should obtain a broader assessment by
including measures of other facets of negative affectivity, such as
disgust and scorn. A final limitation is that our participants were
healthy, young adults, although an advantage of such a sample is
that there is a low probability that medical conditions are operating as confounders. Additional investigations are needed to determine whether our findings extend to middle-aged or older adults
and to various patient groups, such as those with mood, anxiety,
or sleep disorders.
4.2. Conclusions
In sum, we show for the first time that the shared, but not the
unique, aspects of depression, anxiety, and anger may be important determinants of sleep quality. From a research perspective,
our results underscore the importance of simultaneously examining overlapping emotional constructs and teasing apart the influence of their common and unique features. Had we examined the
emotional factors one at a time only, we would have reached vastly
different conclusions. From a clinical perspective, our findings lead
us to speculate that including a module designed to reduce negative affectivity may enhance the potency of psychological interventions for insomnia. Similarly, the efficacy of interventions designed
to prevent or treat depressive and anxiety disorders might be enhanced by concurrently addressing sleep quality.
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quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
Associations of the shared and unique aspects of positive and negative
emotional factors with sleep quality
Jesse C. Stewart ⇑, Kevin L. Rand, Misty A.W. Hawkins, Jennifer A. Stines
Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA
a r t i c l e
i n f o
Article history:
Received 14 September 2010
Received in revised form 23 November 2010
Accepted 3 December 2010
Available online xxxx
Keywords:
Sleep Quality
Depression
Anxiety
Anger
Positive Affect
Rumination
a b s t r a c t
Because most studies have examined only one emotional factor at a time, it is not clear which features of
these overlapping constructs are important determinants of sleep quality. Our aims were to determine
which aspects of negative emotional factors are most strongly associated with poor sleep quality,
whether positive emotional factors are independently related to improved sleep quality, and whether
rumination explains the links between emotional factors and sleep quality. A total of 224 young men
and women completed questionnaires assessing depressive symptoms, trait anxiety, trait anger, trait
positive affect, trait rumination, and sleep quality. Structural equation models revealed that greater Negative Affect – the shared variance among the negative emotional factors – predicted poor Sleep Quality
(b = .62, p < .0001, DR2 = .38); however, unique effects of the positive and negative emotional factors were
not detected. Rumination did not account for the observed relationship. Our findings suggest that the
shared, but not unique, aspects of negative emotional factors may be key determinants of sleep quality.
Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Negative emotional factors and sleep quality
The prevalence of sleep difficulties is high and on the rise.
Approximately 64% of American adults (versus 51% in 2001) report
having experienced one or more sleep problems at least a few
nights a week (National Sleep Foundation, 2009), and about 6%
meet criteria for an insomnia diagnosis (Ohayon, 2002). Notably,
sleep dysfunction is a predictor of poor health outcomes. Both
objective and subjective measures of sleep quality have been found
to be predictive of all-cause mortality (Dew et al., 2003; Kripke,
Garfinkel, Wingard, Klauber, & Marler, 2002). Other studies have
demonstrated that poor sleep quality is associated with an increased risk of chronic diseases, such as cardiovascular disease
(Schwartz et al., 1999) and diabetes (Ayas et al., 2003). Given the
widespread, deleterious effects of poor sleep on health, a crucial
next step is to identify determinants of this emerging risk factor.
Several potential determinants of poor sleep fall in the domain
of stable, trait-like emotional factors. Individuals with depressive
disorders consistently report sleep complaints and exhibit abnormalities in sleep architecture (Tsuno, Besset, & Ritchie, 2005).
Unlike depression, investigations of anxiety have found little objective evidence of sleep architecture changes, although reports of
insomnia are common (Papadimitriou & Linkowski, 2005). Studies
examining anger suggest that it is also related to indicators of poor
sleep (Caska et al., 2009; Shin et al., 2005), and similar results have
been observed for the related constructs of hostility and aggression
(Brissette & Cohen, 2002; Ireland & Culpin, 2006).
Most previous studies, however, have examined the effect of a
single negative emotional factor on sleep quality, which considerably limits the inferences that can be drawn. Because depression,
anxiety, and anger are overlapping constructs and cluster within
individuals (Clark & Watson, 1991; Spielberger, 1988), it is not
known whether each of these emotional factors is independently
associated with sleep quality or whether one (or more) is merely
a marker for another emotional factor. To illustrate the latter possibility, anxiety could be inversely correlated with sleep quality solely due to its strong relationship with depression, which itself may
be a determinant of poor sleep. Yet another plausible model is that
the individual negative emotion-sleep quality relationships are driven by a shared underlying personality trait, such as negative
affectivity (Watson & Clark, 1984). To evaluate these competing
Abbreviations: PSQI, Pittsburgh Sleep Quality Index; BMI, body mass index; STAI,
State-Trait Anxiety Inventory; BDI-II, Beck Depression Inventory-II; STAXI, StateTrait Anger Expression Inventory; PANAS, Positive and Negative Affect Schedule;
ECQ-R, Rehearsal scale of the Emotional Control Questionnaire; SEM, structural
equation modeling; SRMR, Standardized Root Mean Squared Residual; RMSEA, Root
Mean Square Error of Approximation; CFI, Comparative Fit Index.
⇑ Corresponding author. Address: Department of Psychology, IUPUI, 402 North
Blackford Street, LD 100E, Indianapolis, IN 46202, USA. Tel.: +1 317 274 6761; fax:
+1 317 274 6756.
E-mail address: jstew@iupui.edu (J.C. Stewart).
0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2010.12.004
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
2
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
models, investigations are needed in which multiple emotional
factors are simultaneously examined as correlates or predictors
of sleep quality. Unfortunately, only a small number of these types
of studies exist (depression and anxiety: Jansson & Linton, 2006;
Johnson, Roth, & Breslau, 2006; Mallon, Broman, & Hetta, 2000;
Morphy, Dunn, Lewis, Boardman, & Croft, 2007; depression and anger: Caska et al., 2009; Shin et al., 2005), and none has examined all
three negative emotional factors.
1.2. Positive emotional factors and sleep quality
Another topic that has received limited attention is whether
positive emotional factors are associated with improved sleep
quality. Although past studies have detected a link between
psychological well-being and improved sleep (Nes, Roysamb,
Reichborn-Kjennerud, Tambs, & Harris, 2005), the multidimensional nature of this construct makes it difficult to discern the
specific influence of its positive components. Similar associations
have been detected in investigations examining more narrowly defined constructs, such as trait positive affect and life satisfaction
(Steptoe, O’Donnell, Marmot, & Wardle, 2008; Strine, Chapman,
Balluz, Moriarty, & Mokdad, 2008). However, because few studies
have adjusted for negative emotions (Gray & Watson, 2002;
Steptoe, O’Donnell, Marmot, & Wardle, 2008), it is not known
whether positive emotional factors are independent determinants
of sleep quality.
1.3. The present investigation
To address these gaps, we conducted a study of young adults in
which positive and negative emotional factors, rumination, and
sleep quality were assessed. We measured trait rumination because
relationships between this cognitive factor and both negative emotions and sleep quality have been reported (Thomsen, Mehlsen,
Christensen, & Zachariae, 2003; Zoccola, Dickerson, & Lam, 2009).
Furthermore, rumination and related constructs are identified as
key factors in current models of insomnia (Harvey, 2002). Thus,
rumination is one mechanism through which emotional factors
may have an impact on sleep quality. Our aims were to determine
(a) which aspects of overlapping negative emotional factors (shared
versus unique) are most strongly related to poor sleep quality, (b)
whether positive emotional factors are independently associated
with improved sleep quality, and (c) whether trait rumination
explains the emotional factor-sleep quality associations.
2. Method
2.1. Participants
Table 1
Characteristics of participants (N = 224).
Demographic factors
Age (years)
Gender, % female
Race-ethnicity, % non-white
23.4 ± 6.1
75.0
26.3
Health-relevant factors
Smoking status, % current smokers
Daily alcohol intake (g/day)
Body mass index (kg/m2)
16.1
8.9 ± 15.5
24.9 ± 5.4
Emotional factors
Beck Depression Inventory-II (range: 0–60)a
Trait Anxiety Scale (range: 20–80)
Trait Anger Scale (range: 10–40)
PANAS-Positive Affect subscale (range: 10–50)
ECQ-Rehearsal subscale (range: 0–14)
9.6 ± 7.8
41.7 ± 10.0
17.7 ± 4.4
33.5 ± 7.5
5.9 ± 3.3
Sleep Quality
Pittsburgh Sleep Quality Index (range: 0–19)
Sleep quality status, % poor sleepers
6.7 ± 3.2
60.7
Note: Values are means ± standard deviations for continuous variables and percentages for categorical variables. PANAS = Positive and Negative Affect Schedule;
ECQ = Emotion Control Questionnaire.
a
Item 16 omitted.
Anxiety scale of the STAI (Spielberger, 1983), and Trait Anger scale
of the STAXI (Spielberger, 1988), respectively. The BDI-II is a 21item, self-report measure of depressive symptom severity that
has been shown to have high internal consistency, test–retest reliability, and construct validity (Beck et al., 1996). We did not include Item 16 (changes in sleeping pattern) due to concerns that
it might artificially inflate the correlation between the BDI-II and
PSQI. The Trait Anxiety scale is a 20-item, self-report measure that
provides an assessment of stable individual differences in the propensity to experience anxiety. This scale has high internal consistency and test–retest reliability, and it correlates strongly with
other trait anxiety measures (Spielberger, 1983). The Trait Anger
scale is a 10-item, self-report measure that assesses stable individual differences in tendency to experience anger. It has also been
found to be internally consistent, reliable over time, and valid
(Spielberger, 1988).
2.2.2. Positive emotional factors
We assessed trait positive affect with the 10-item Positive Affect
(PA) scale of the widely used PANAS (Watson, Clark, & Tellegen,
1988). Participants were asked to report the extent to which they
had experienced ten positive emotions during the past few weeks.
The PANAS-PA scale has been shown to be internally consistent
and stable over time, and it correlates with other self-report and
peer-rated measures of positive affect (Watson & Clark, 1994).
Our sample consisted of 224 young adults enrolled in courses at
a large urban university. This study was approved by the institutional review board at Indiana University-Purdue University Indianapolis. Besides the requirement of age P 18 years, there were no
inclusion criteria. Of the 257 enrolled students, we excluded individuals who reported current use of psychotropic medication
(n = 18), did not complete all of the PSQI items (n = 10), were missing data for BMI or smoking status (n = 4), or did not complete the
STAI Trait Anxiety scale (n = 1). The characteristics of the final sample are shown in Table 1.
2.2.3. Trait rumination
The Rehearsal scale of the ECQ (Roger & Najarian, 1989) was
administered to measure trait rumination. It is a 14-item, true–
false instrument that provides an index of one’s general tendency
to repeatedly think about negative events (e.g., ‘‘I find it hard to
get thoughts about things that have upset me out of my mind.’’).
This scale has satisfactory internal consistency and test–retest reliability (Roger & Najarian, 1989), and it has been found to be correlated with other rumination measures (Siegle, Moore, & Thase,
2004).
2.2. Measures
2.2.4. Demographic and health-relevant factors
The questionnaire battery included items assessing age, gender,
race-ethnicity, and smoking status. Because few participants
identified their race-ethnicity as Asian/Pacific Islander (n = 14),
Hispanic/Latino (n = 11), Native American/Eskimo/Aleut (n = 1), or
2.2.1. Negative emotional factors
To assess depressive symptoms, trait anxiety, and trait anger,
we administered the BDI-II (Beck, Steer, & Brown, 1996), Trait
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
3
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
Other (n = 5), race-ethnicity was coded as white versus non-white.
BMI was calculated from self-reported height and weight. Daily
alcohol intake was computed using a quantify-frequency method
(Garg, Wagener, & Madans, 1993) and was log transformed to reduce positive skew.
2.2.5. Sleep quality
The PSQI (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) is a
19-item measure of subjective sleep quality during the past month.
The seven component scores (subjective sleep quality, sleep latency,
sleep duration, habitual sleep efficiency, sleep disturbances, use of
sleep medication, and daytime dysfunction) are summed to calculate a global score. A global score of >5 is indicative of severe impairment in P2 areas or moderate impairment in P3 areas; individuals
with scores >5 have been classified as ‘‘poor sleepers’’ (Buysse et al.,
1989). The PSQI has adequate internal consistency and test–retest
reliability and is able to distinguish between groups with varying
degrees of sleep disturbance – i.e., sleep-disordered patients,
depressed patients, and controls (Buysse et al., 1989).
2.3. Procedure
Participants attended a 1-h assessment session held in a computer laboratory, during which they completed the questionnaire
battery on a secure website. To ensure privacy, individuals were
separated by at least one empty terminal. Participants were
awarded course credit at the end of the session.
2.4. Data analysis
We first performed correlational analyses to quantify the degree
of overlap among the emotional measures and to examine the
associations of the emotional factors with sleep quality. Next, we
used SEM with maximum likelihood estimation using LISREL 8.8
(Joreskog & Sorbom, 2008) to test the hypothesized latent-variable
models. To assess model fit, we examined absolute (model v2-statistic and SRMR), parsimonious (RMSEA), and incremental (CFI) fit
indices (Hu & Bentler, 1999).
3. Results
3.1. Correlational analyses
Bivariate correlations revealed that there was substantial overlap among the emotional factors, as they shared 3–55% of the variance (see Table 2). In addition, partial correlations between the
emotional measures and the PSQI were all significant, and most
were moderate in size. The BDI-II, Trait Anxiety, and Trait Anger
scores were associated with poorer sleep quality, whereas the
PANAS-PA score was related to better sleep quality.
3.2. Structural equation models
We first created a measurement model consisting of the firstorder latent variables of Depression, Anxiety, Anger, Positive Affect,
and Sleep Quality. We aggregated items into three parcels per scale
because modeling a large set of measured indicators adversely affects model fit (Little, Cunningham, Shahar, & Widaman, 2002). To
rule out potential confounders, we adjusted the PSQI parceled indicators for age, gender, race-ethnicity, smoking, daily alcohol intake,
and BMI.1 We also modeled a second-order latent variable of Nega1
We residualized the PSQI parceled indicators instead of including the demographic and health-relevant factors in the models as covariates, as the latter would
have yielded a ratio of participants to estimated parameters below the acceptable
lower limit of 5:1 (Kline, 2005)
Table 2
Correlations among measures of emotional factors, trait rumination, and sleep
quality.
PSQI
BDI-II
Trait anxiety
Trait anger
PANAS-PA
ECQ-R
PSQI
BDI-II
Trait anxiety
Trait anger
PANAS-PA
–
.46*
.46*
.18*
.24*
.34*
–
.74*
.31*
.46*
.47*
–
.42*
.50*
.59*
–
.17*
.35*
–
.30*
Note: N = 224. Correlations involving PSQI are partial correlations controlling for
age, gender, race-ethnicity, body mass index, cigarette smoking, and daily alcohol
intake. PSQI = Pittsburgh Sleep Quality Index, BDI-II = Beck Depression Inventory-II,
PANAS-PA = Positive and Negative Affect Schedule-Positive Affect subscale, and
ECQ-R = Emotion Control Questionnaire-Rehearsal subscale.
*
p < .05.
tive Affect as driving the first-order latent variables of Depression,
Anxiety, and Anger. In the measurement model, Negative Affect,
Positive Affect, and Sleep Quality were freed to correlate. This model
showed acceptable fit to the data, v2(84, N=224) = 143.36 (p = 0.0006),
RMSEA = .051, CI90: (0.034, 0.067), SRMR = 0.047, CFI = .98. As can be
seen in Panel A of Fig. 1, the correlations among Negative Affect,
Positive Affect, and Sleep Quality were significant (all ps < .0001).
To determine which aspects of the negative emotional factors
were most strongly associated with sleep quality, we first freed a
structural path from Negative Affect to Sleep Quality. The significant path (b = .62, p < .0001) indicates that shared aspects of the
negative emotional factors strongly predicted poorer sleep quality
(see Fig. 1, Panel B), accounting for 38% of the variance. To illustrate
this effect, we computed the percentage with a PSQI global score
>5 for each tertile of the Negative Affect latent variable score,
which revealed that 31%, 68%, and 84% of those in the lower, middle, and upper tertile, respectively, were poor sleepers. We then
freed the structural paths from each of the first-order latent variables to Sleep Quality, one at a time, to examine whether there
were unique effects of depression, anxiety, and anger. Individually
freeing the paths from Depression [Dv2(1,N=224) = 0.40 (p = 0.53)],
Anxiety [Dv2(1, N=224) = 0.02 (p = 0.89)], and Anger [Dv2(1, N=224) =
1.13 (p = 0.29)] did not improve model fit. Furthermore, the structural paths from Depression (b = .13, p = .48), Anxiety (b = .13,
p = .90), and Anger (b = .09, p = .30) were all nonsignificant. Taken
together, these results indicate that only the shared aspects of the
negative emotional factors influence sleep quality. To evaluate
whether the positive emotional factor was associated with better
sleep quality, we next freed the structural path from Positive Affect
to Sleep Quality. The nonsignificant path (b = .04, p = 0.69) suggests
that trait positive affect may not exert an independent, protective
effect on sleep quality.
Finally, we included the first-order latent variable of Rumination, which was modeled using three parceled indicators from
the ECQ-R. Negative Affect, Positive Affect, Rumination, and Sleep
Quality were freed to correlate. This model showed acceptable
fit, v2(126, N=224) = 198.61 (p = 0.0009), RMSEA = .044, CI90: (0.029,
0.058), SRMR = 0.047, CFI = .99. We then estimated three structural
paths. The paths from Negative Affect to Sleep Quality (b = .59,
p < .0001) and Negative Affect to Rumination (b = .69, p < .0001)
were significant, whereas the Rumination to Sleep Quality path
was not (b = .03, p = .81). These results are inconsistent with trait
rumination being a mediator of the negative affect–sleep quality
relationship.
4. Discussion
Our study is the first to examine the associations of the shared
and unique aspects of negative and positive emotional factors with
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
4
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
A
.14
.34
.19
.36
.26
.24
.30
.30
.51
BDI-II 1
BDI-II 2
BDI-II 3
Trait
Anxiety 1
Trait
Anxiety 2
Trait
Anxiety 3
Trait
Anger 1
Trait
Anger 2
Trait
Anger 3
.80
.86
.93
.81
.90
.25
Depression
.87
Anxiety
.84
.70
Anger
.07
.87*
.84
.78
.46*
.96*
Negative
Affect
.62
1.0*
1.0*
-.58
.54
Sleep
Quality
1.0*
B
.87
PANASPA 1
PANASPA 2
PANASPA 3
.18
.24
.32
.19
.36
.26
.24
.30
.30
.51
BDI-II 1
BDI-II 2
BDI-II 3
Trait
Anxiety 1
Trait
Anxiety 2
Trait
Anxiety 3
Trait
Anger 1
Trait
Anger 2
Trait
Anger 3
Depression
.90
.80
.25
.71
PSQI 3
.77
PSQI 1
.21
PSQI 2
.71
PSQI 3
.77
.82
.34
.81
PSQI 2
-.34
.14
.93
.21
.48
Positive
Affect
.90
PSQI 1
.89
.87
.86
Anxiety
.84
Anger
.07
.87*
.84
.70
.78
.46*
.96*
Negative
Affect
.62
.89
1.0*
.62*
-.58
1.0*
.87
.54
.48
Positive
Affect
.90
Sleep
Quality
.02
.82
PANASPA 1
PANASPA 2
PANASPA 3
.18
.24
.32
Fig. 1. (A) Measurement model of Depression, Anxiety, Anger, Negative Affect, Positive Affect, and Sleep Quality. (B) Structural model of Negative Affect as a predictor of Sleep
Quality. Values associated unidirectional arrows between variables are standardized regression coefficients, bidirectional arrows between variables are Pearson correlation
coefficients, and unidirectional arrows pointing at a single variable represent error variances. Paths with significant values are solid, whereas paths with nonsignificant values
are dashed. BDI-II = Beck Depression Inventory-II. PANAS-PA = Positive and Negative Affect Schedule-Positive Affect subscale. PSQI = Pittsburgh Sleep Quality Index. N = 224.
⁄
Coefficient fixed to 1.0 prior to standardization.
Please cite this article in press as: Stewart, J. C., et al. Associations of the shared and unique aspects of positive and negative emotional factors with sleep
quality. Personality and Individual Differences (2011), doi:10.1016/j.paid.2010.12.004
J.C. Stewart et al. / Personality and Individual Differences xxx (2011) xxx–xxx
sleep quality. We report three important findings that address
existing gaps. First, we found that the shared aspects of depression,
anxiety, and anger were more strongly related to sleep quality than
were the unique aspects. Structural equation models revealed that
Negative Affect – a latent variable representing the shared variance
among the negative emotions – was associated with poorer sleep
quality, whereas depressive symptoms, trait anxiety, and trait anger were not in the presence of the Negative Affect variable. The
observed relationship may be clinically meaningful, as 84% of
adults with Negative Affect scores in the upper tertile were identified as poor sleepers versus only 31% of those with scores in the
lower tertile. Our results raise the possibility that a common
underlying factor may account for the associations of individual
negative emotional factors with sleep quality detected in past
studies. Furthermore, the failure to take into account the overlap
among negative emotional factors could explain previous inconsistent results (cf. Jansson & Linton, 2006 and Johnson et al., 2006).
Whether or not a particular negative emotion is found to be associated with sleep quality in a given sample may depend on the extent to which the emotional measure taps this underlying common
factor.
Defined as a pervasive disposition to experience negative emotions across situations (Watson & Clark, 1984), negative affectivity
has recently been examined as a determinant of sleep quality, and
an inverse association has been detected (Danielsson, JanssonFröjmark, Linton, Jutengren, & Stattin, 2010; Fortunato & Harsh,
2006; Gau, 2000; Gray & Watson, 2002; Williams & Moroz,
2009). In contrast to our investigation (which employed SEM to extract a measure of negative affectivity), most previous studies have
used neuroticism subscales of personality inventories to assess
negative affectivity and did not simultaneously examine the effects
of individual emotional factors. Critically, the results of the existing
neuroticism-sleep studies are ambiguous, given that any one of the
negative emotional factors (i.e., depression, anxiety, or anger)
could have been driving the observed relationships.
The second noteworthy finding was that the positive emotional
factor did not exert an independent, protective effect on sleep quality. Although the bivariate correlation between trait positive affect
and sleep quality was significant, no association was observed in
the SEM model. Thus, our results suggest that positive emotion measures may be related to sleep quality merely because they are markers of the absence of negative affect. Results of another investigation
support this conclusion (Brissette & Cohen, 2002). In two other studies, however, high positive emotionality was associated with improved sleep quality, even after controlling for negative emotions
(Gray & Watson, 2002; Steptoe et al., 2008). A possible explanation
for these conflicting findings is that our latent Negative Affect
variable may overlap with positive affect measures to a greater
extent than the global assessments used in past studies.
Our third important finding was that trait rumination did not
play a role, as a mediator or a confounder, in the negative affect–
sleep quality relationship. Negative Affect remained related to
Sleep Quality in the presence of Rumination, even though Rumination was correlated with the PSQI in the univariate analyses and
was associated with Negative Affect in the SEM analyses. Our results contrast with the finding that negative emotions and rumination are independent correlates of sleep quality (Thomsen et al.,
2003). While our findings suggest that rumination may not be part
of the causal chain linking negative affect and sleep, they should
not be overinterpreted. First, we may have detected mediation if
we had examined other forms of perseverative cognition, such as
trait worry (Brosschot, Gerin, & Thayer, 2006), or momentary cognitive activity, such as state rumination or worry (Brosschot, Van
Dijk, & Thayer, 2007). Second, rumination may be associated with
some aspects of sleep quality, such as sleep latency, but not others
(Zoccola et al., 2009).
5
4.1. Limitations
One limitation of our study is the cross-sectional design, which
precludes us from drawing directional inferences. It is possible that
chronically experiencing negative emotions contributes to the
onset of insomnia (Jansson & Linton, 2006; Johnson et al., 2006)
or that insomnia is a risk factor for emotional disturbances
(Danielsson et al., 2010; Johnson et al., 2006). Another limitation
is that we used a self-report measure to assess sleep quality. In a
study comparing subjective and objective sleep assessments
(Means, Edinger, Glenn, & Fins, 2003), individuals with insomnia
tended to underestimate their sleep duration relative to controls,
a finding that raises concerns regarding the accuracy of self-reports
of sleep. Although evidence linking subjective sleep quality to
health outcomes attests to its importance (Kripke et al., 2002), future studies should explore whether the same pattern of results is
found when sleep quality is assessed by actigraphy or polysomnography. A third limitation is that we derived our measure of negative affectivity from scales assessing depression, anxiety, and
anger only. Future studies should obtain a broader assessment by
including measures of other facets of negative affectivity, such as
disgust and scorn. A final limitation is that our participants were
healthy, young adults, although an advantage of such a sample is
that there is a low probability that medical conditions are operating as confounders. Additional investigations are needed to determine whether our findings extend to middle-aged or older adults
and to various patient groups, such as those with mood, anxiety,
or sleep disorders.
4.2. Conclusions
In sum, we show for the first time that the shared, but not the
unique, aspects of depression, anxiety, and anger may be important determinants of sleep quality. From a research perspective,
our results underscore the importance of simultaneously examining overlapping emotional constructs and teasing apart the influence of their common and unique features. Had we examined the
emotional factors one at a time only, we would have reached vastly
different conclusions. From a clinical perspective, our findings lead
us to speculate that including a module designed to reduce negative affectivity may enhance the potency of psychological interventions for insomnia. Similarly, the efficacy of interventions designed
to prevent or treat depressive and anxiety disorders might be enhanced by concurrently addressing sleep quality.
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