ArticlePDF Available

Worry and Sleep Length Revisited: Worry, Sleep Length, and Sleep Disturbance Ascribed to Worry

Authors:

Abstract

The author administered university students (N = 222; 152 women, 70 men) the Worry Domains Questionnaire (F. Tallis, G. C. L. Davey, & A. Bond, 1994) and a newly constructed scale (the Sleep Disturbance Ascribed to Worry Scale) to measure sleep disturbance attributed to worry. To revisit previous studies (i.e., E. Hartmann, F. Baekeland, & G. R. Zwilling, 1972; S. J. H. McCann & L. L. Stewin, 1988) that suggested that sleep length was positively related to worry, the author also asked the students a question about habitual sleep length. The results indicated that worry and sleep disturbance attributed to worry were negatively related to sleep length. A regression analysis revealed that worry was significantly negatively related to habitual sleep length irrespective of sleep disturbance ascribed to worry.
296
The Journal of Genetic Psychology, 2002, 163(3), 296–304
Worry and Sleep Length Revisited: Worry,
Sleep Length, and Sleep Disturbance
Ascribed to Worry
WILLIAM E. KELLY
Department of Counseling
University of Nevada, Las Vegas
ABSTRACT. The author administered university students (N = 222; 152 women, 70 men)
the Worry Domains Questionnaire (F. Tallis, G. C. L. Davey, & A. Bond, 1994) and a newly
constructed scale (the Sleep Disturbance Ascribed to Worry Scale) to measure sleep dis-
turbance attributed to worry. To revisit previous studies (i.e., E. Hartmann, F. Baekeland,
& G. R. Zwilling, 1972; S. J. H. McCann & L. L. Stewin, 1988) that suggested that sleep
length was positively related to worry, the author also asked the students a question about
habitual sleep length. The results indicated that worry and sleep disturbance attributed to
worry were negatively related to sleep length. A regression analysis revealed that worry
was significantly negatively related to habitual sleep length irrespective of sleep distur-
bance ascribed to worry.
Key words: sleep disturbance, sleep length, worry
RECENT STUDIES of individual differences in sleep length have indicated that
individuals who habitually sleep 6 hr or fewer every 24 hr (short sleepers), as
compared with those who sleep 9 hr or more every 24 hr (long sleepers), are more
likely to be neurotic, demonstrate less creativity, report more hallucinations,
endorse more eating disorders, and perform more poorly academically (Hicks,
Guista, Schretlen, & Pellegrini, 1980; Hicks & Rozette, 1986; Kelly, Kelly, &
Clanton, 2001; Kumar & Vaidya, 1982; Soper, Kelly, & Von Bergen, 1997).
Research has also been conducted on mood states associated with sleep length.
Kumar and Vaidya (1984) administered an anxiety scale to 25 long sleepers and
25 short sleepers randomly selected from a sample of 500 undergraduate students.
The results indicated that short sleepers reported significantly more anxiety than
did long sleepers.
Address correspondence to William E. Kelly, who is now at the Graduate Department of
Counseling, George Fox University, Portland Center, 12753 S.W. 68th Avenue, Portland,
OR 97223; wkelly@georgefox.edu (e-mail).
Kelly 297
It has been posited that the lower levels of cortical arousal of short sleep-
ers may influence their psychological makeup and symptomatology (Skinner,
1983; Soper et al., 1997). Specifically, short sleepers may compensate for lower
levels of cortical arousal with increased levels of cognitive activity. This sug-
gestion has been partially supported by other research. Coursey, Buchsbaum,
and Frankel (1975) found that short sleepers demonstrated lower levels of sen-
sory response and performed worse on a perceptual-motor task, both of which
suggested lower levels of cortical arousability (Banich, 1997). The brain
appears to combat decreases in arousability by using compensatory strategies
and plasticity, or flexibility (Banich; Howard, 1994; Salthouse, 1991). This
point illustrates the resiliency of the human brain and its attempts to activate
itself in instances of damage and lower levels of activity. It seems likely, then,
that short sleepers would manifest cognitive symptoms to compensate for low
cortical arousal. Soper et al. (1997) argued that their findings of increased hal-
lucinations among short sleepers supported this hypothesis.
In addition to hallucinations, another potential cognitive symptom of lower
cortical activity is worrying. It has been posited that worry serves as a stimu-
lant during periods of decreased activity, such as boredom (Kelly & Markos,
2001). Moreover, worry has been associated with increased levels of cortical
activity (Borkovec, Shadick, & Hopkins, 1991). Thus, it is possible that short
sleepers, who have decreased levels of cortical arousal, would show an increase
in worry as their brain attempts to increase cortical activity. Interestingly, how-
ever, previous researchers have found just the opposite—that worry, loosely
defined, was reported more often by long sleepers.
Nearly 3 decades ago, Hartmann, Baekeland, and Zwilling (1972) con-
ducted a pioneer study examining individual differences among 28 short sleep-
ers and 24 long sleepers. One primary finding was that long sleepers were
described as “worriers” and were psychologically less healthy, whereas short
sleepers were psychologically healthy. The research reviewed previously using
larger, more diverse samples, however, indicated the opposite: that short sleep-
ers are psychologically less healthy than long sleepers. Only one subsequent
study has been published examining sleep length and worry. McCann and
Stewin (1988) administered to 101 undergraduate students a sleep-length pref-
erence question and a Likert-type single-item assessment of individuals’ status
as worriers. The results indicated a modest, but significant, positive correlation
between worrier status and sleep preference.
Although there is some consistency between the Hartmann et al. (1972)
and McCann and Stewin (1988) studies, there are methodological inconsis-
tencies and problems with these studies. For instance, whereas Hartmann et al.
found that long sleepers scored higher on a measure of nervousness and anx-
iety, no definition or standardized measure of worry was used. Instead, the
authors merely described nervousness and anxiety as worry. More recent find-
ings, however, indicate that worry and anxiety are separate constructs (Davey,
298 The Journal of Genetic Psychology
1992; Davey, Hampton, Farrell, & Davidson, 1991). Furthermore, the partici-
pants appear to have been limited to men recruited from newspaper advertise-
ments, nearly half of whom were eliminated by the researchers prior to data
analyses because of sleep irregularities, because they had been identified as
average sleepers (sleeping between 6 and 9 hr per night), or because they had
scored high on measures of psychopathology. Because of the quantity of par-
ticipant deletions, however, it is difficult to understand what population the
results represent.
McCann and Stewin (1988) assessed worry using only a single-item
assessment and reported data of a sleep-preference assessment as opposed to
a habitual-sleep-length assessment. Hence, the samples and sleep-length
assessments of these studies were not consistent. In addition, the samples of
both studies were small. One potential methodological problem with both of
these studies is the lack of a reliable, valid assessment of worry and a measure
of sleep disturbance related to worry. Previous studies have implicated cogni-
tive activity as a source of sleep disturbance, especially insomnia, and sleep
reduction (Nicassio, Mendlowitz, Fussell, & Petras, 1985; Watts, Coyle, &
East, 1994).
An important element in the discussion of sleep length and worry is the
lack of a cause–effect relationship regarding whether worriers are at risk of
developing sleep disturbances or having specific habitual sleep lengths, or
whether individuals with sleep disturbances or certain habitual sleep lengths are
more likely to worry. Thus, it could be argued that worriers report less sleep
because they are often unable to sleep because of worrying, rather than that
short sleepers tend to compensate for suboptimal cortical arousal by worrying.
My purpose in the present study, therefore, was to clarify the relationship
between worry and sleep length in a college-student population by investigat-
ing further the relationship between a reliable, valid measure of worry and
habitual sleep length and the possible effects on this relationship by sleep dis-
turbance ascribed to worry. For the latter question, it was necessary to devise a
scale to measure sleep disturbance attributed to worry. Therefore, I attempted
to develop a brief scale to measure sleep disturbance ascribed to worry and to
investigate the relationships among habitual sleep length, the Sleep Disturbance
Ascribed to Worry Scale (SAW), and a measure of worry.
Because of possible methodological problems with the Hartmann et al.
(1972) and McCann and Stewin (1988) studies, as well as the preponderance
of research findings indicating that short sleepers are more anxious and less
psychologically healthy (see previous review), I expected that high scores on a
reliable, valid measure of worry would be reported more by short sleepers than
by long sleepers. Considering the notion that short sleepers compensate for sub-
optimal cortical arousal through cognitive activities, I expected that individu-
als who worry more would endorse less sleep regardless of sleep difficulties
related to worry.
Kelly 299
Method
Participants
Participants included 222 students (152 women, 70 men) enrolled in under-
graduate and graduate human services courses at a midsized U.S. university.
The mean age of the sample was 24.4 years (SD = 8.0), with ages ranging from
18 to 65 years. There was no significant difference between the genders on age,
t(220) = .61, p = .54. The majority of participants identified themselves as Cau-
casian (66%). Other ethnicities included African American (10%), Latino
(10%), and Asian American (11%). Five (2%) identified their ethnicities as
“other.” Two respondents (1%) did not identify their ethnicities.
Instruments
The Sleep Disturbance Ascribed to Worry (SAW) Scale. Because no scales specif-
ically measuring sleep disturbance related to worry could be located at the time
of this study, I developed the SAW for the present study as a brief instrument to
measure sleep disturbances ascribed to worry. Previous literature indicated that
the predominant sleep disturbance associated with worry was insomnia (e.g.,
Nicassio et al., 1985; Watts et al., 1994). Hence, insomnia was the primary sleep
disturbance evaluated and used in developing this scale. I used the following pro-
cedure to develop the SAW: After reviews of the criteria for insomnia in the
Diagnostic and Statistical Manual of Mental Disorders (fourth edition; DSM–IV;
American Psychiatric Association, 1994), the pertinent literature on sleep distur-
bance and worry, and Coren’s (1988) Insomnia Scale, I developed 5 items assess-
ing the attribution of sleep disturbance to worry, effects of worry on sleep, and
frequency of sleep disturbance ascribed to worry. The result was a 5-item ques-
tionnaire that I administered by asking participants to respond to each item using
an 11-point anchored-response scale (0 = never, 10 = very often). Higher scores
on the SAW indicate more sleep disturbance ascribed to worry.
Worry Domains Questionnaire (WDQ). The original WDQ (Tallis, Eysenck, &
Mathews, 1992) included 30 items assessing six domains of worry. However, one
domain (social concerns) was found to be highly related to social desirability.
Therefore, the 25-item WDQ presented by Tallis, Davey, and Bond (1994) was
used for this study. The 25-item WDQ measures a tendency to worry across five
general worry domains: (a) relationships, (b) lack of confidence, (c) aimless
future, (d) work-related, and (e) financial. WDQ items were presented with a 5-
point Likert-type scale on which individuals described the extent (not at all to
extremely) to which their experience was in agreement with individual items. I
summed responses from the appropriate items to create a total WDQ score.
Higher scores denote more worry. Test–retest reliability (r = .79 after 4 weeks),
300 The Journal of Genetic Psychology
internal consistency (α = .92), and validity of the WDQ have been found to be
satisfactory (Tallis et al., 1994).
Sleep length. I provided sleep length estimates as a continuous variable by using
the method of Kumar and Vaidya (1984), whereby participants are asked to write
the amount of time, in hours and minutes, that they habitually sleep, on average,
in every 24-hr period.
Procedure
After the participants gave informed consent, they were administered the
WDQ, SAW, and sleep length question in random order. As a validity check for the
SAW, 46 of the participants were also asked to respond to the following question:
“Use the following scale to describe the general quality of your sleep.” The partic-
ipants responded to this question on an 11-point Likert-type scale ranging from
awful to great. A demographics survey that solicited information about the partic-
ipants’ age, gender, and ethnicity was also completed. The participants completed
the questionnaires in a group setting. Specific information regarding the nature of
the study was not disclosed until the participants had returned the questionnaires.
Results
Properties of the SAW
To explore the factor structure of the 5 SAW items, I calculated a principal
components factor analysis. This analysis resulted in a single factor accounting
for 62.5% (eigenvalue = 3.12) of the systematic variance. SAW items, their
respective factor loadings, and means and standard deviations are presented in
Table 1. The internal consistency of the SAW was also explored. The coefficient
alpha of the 5 items was .85. Age was not correlated with SAW scores, r = –.01,
p = .79. There was a significant difference between the genders for SAW scores,
t(220) = 3.03, p < .003, with women (M = 19.3) endorsing higher SAW scores
than men (M = 14.8). A one-way analysis of variance (ANOVA) revealed no sig-
nificant difference in SAW scores among ethnicities, F(5, 214) = 1.23, p = .29.
The correlation between the quality of sleep question and SAW scores was sig-
nificant, r = –.37, p < .01, indicating that sleep disturbance resulting from worry
negatively affects quality of sleep.
WDQ, Sleep Length, and SAW Analyses
Pearson correlations among WDQ scores, SAW scores, and sleep length
were calculated. Sleep length was significantly negatively related to both WDQ
(r = –.24, p < .01) and SAW scores (r = –.20, p < .01). SAW scores and WDQ
Kelly 301
scores were also significantly related (r = .42, p < .01). Next, gender differences
in WDQ scores and sleep length were explored. No significant gender differences
were found for WDQ scores, t(220) = 1.63, p = .12, or sleep length, t(220) = 1.29,
p = .20. Also, no significant correlations were observed between age and sleep
length (r = –.05, p = .42) or age and WDQ scores (r = –.09, p = .18).
Because of the significant gender differences found for SAW scores, it was
necessary to identify any significant Gender × SAW interactions on WDQ scores
and sleep length prior to further analyses involving these variables. Hence, I used
a median split to create a high and low group on the basis of SAW scores. I cal-
culated two separate 2 (gender) × 2 (SAW group: high or low) ANOVAs to iden-
tify significant interactions between gender and SAW scores on sleep length and
WDQ scores. The first ANOVA revealed no significant interaction between SAW
scores and gender for sleep length, F(1, 218) = .82, p = .37. The second ANOVA
also did not reveal a significant Gender × SAW score interaction for WDQ scores,
F(1, 218) = 1.05, p = .31. Thus, subsequent analyses involving SAW scores, WDQ
scores, and sleep length were analyzed irrespective of gender.
I calculated a simple regression using sleep length as the criterion and WDQ
scores as the predictor variable to test the relationship between worry and habitual
sleep length. WDQ scores accounted for 6% of the variance in sleep length, which
was significant, F(1, 220) = 13.50, p < .0001. An analysis of the beta weights indi-
cated a negative relationship between worry and sleep length (β = –.24).
I calculated a multiple regression to explore the influences of SAW scores
on the relationship between worry and sleep length, using sleep length as the
TABLE 1
Means, Standard Deviations, and Factor Loadings for SAW Items
Factor
Item MSDloading
1. How often do you awaken early from your normal
sleeping time and are completely unable to return to
sleep because of worry? 3.39 2.47 .79
2. How often are you unable to stop worrying at bedtime? 4.09 2.75 .71
3. How often can you not get to sleep for several
minutes because of worry? 4.25 2.94 .74
4. How often do you awake from sleep worrying? 2.81 2.59 .87
5. During your usual sleep time, how often do you
awaken and are unable to return to sleep for several
minutes because of worry? 3.30 2.58 .83
Total scale 17.84 10.50
Note. N = 222. SAW = Sleep Disturbance Ascribed to Worry Scale.
302 The Journal of Genetic Psychology
criterion and SAW and WDQ scores as predictor variables. To account for SAW
variance in the relationship between sleep length and worry, I forced SAW
scores to enter first in the equation. SAW scores accounted for 4% of the vari-
ance in sleep length, which was significant, F(1, 220) = 9.02, p < .003. The beta
weight indicated a negative relationship between sleep disturbance ascribed to
worry and sleep length (β = –.20). On Step 2, WDQ scores were entered and
accounted for an additional 3% of the variance, which was also significant, F(1,
219) = 7.02, p < .009, and remained negative (β = –.19).
Discussion
The results of the present study indicate that worry is negatively related to
habitual sleep length. In other words, individuals who sleep less tend to worry
more often. This finding is not consistent with that of previous researchers (i.e.,
Hartmann et al., 1972; McCann & Stewin, 1988) who found that worry was pos-
itively related to sleep length. However, the results of the present study are con-
sistent with those of other researchers that indicated that individuals who habit-
ually sleep less are less psychologically healthy and more anxious (Hicks &
Rozette, 1986; Kumar & Vaidya, 1982, 1984; Soper et al., 1997).
It also appears that the negative relationship between sleep length and worry
exists irrespective of sleep disturbances attributed to worry. Thus, the lower sleep
lengths of worriers do not appear to be the result of only sleep disturbance due to
worrying. As postulated by previous researchers (i.e., Skinner, 1983; Soper et al.,
1997), it may be that the less active neural systems of short sleepers tend to stim-
ulate themselves by creating additional sensory and cognitive activity through wor-
rying, but not necessarily during times of sleep. Further research is needed to sub-
stantiate this claim. Measures of excitement seeking and need for cognition as well
as neuropsychological measures might help researchers explore the cortical
arousal explanation of worry among short sleepers. Furthermore, the relatively low
percentage of variance accounted for by SAW and WDQ scores suggests that addi-
tional variables may be involved in determining habitual sleep length.
The significant relationships found between the SAW and the WDQ, sleep
length, and the sleep quality question provide some initial evidence for the valid-
ity of the SAW and, thus, provide some credence to the findings of this study
involving the SAW. Nevertheless, future researchers who use the SAW to exam-
ine sleep disturbance attributed to worry should include additional measures to
validate the SAW. Furthermore, a distinction has been drawn between transient
and chronic sleep disturbance (Giesecke, 1987). Hence, future researchers should
collect test–retest data on the SAW to determine whether it measures transient or
chronic sleep disturbance attributed to worry.
The discrepancy between the results of the present study and the results of
McCann and Stewin (1988) suggest that habitual sleep length and sleep length
preference are separate constructs. Researchers should, therefore, investigate both
Kelly 303
of these constructs in relation to worry. In addition, future research should also
include additional measures of worry. The WDQ is coined as a measure of non-
pathological worry (Tallis et al., 1992). Hence, it might be useful for researchers
to include a measure of worry such as the Penn State Worry Questionnaire
(Meyer, Miller, Metzger, & Borkovec, 1990), designed to assess pathological
worrying. There may be differences in sleep length among nonpathological and
pathological worriers. Also, sleep diaries should be considered as a measure of
sleep length in addition to single-item measures of habitual sleep length.
REFERENCES
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental
disorders (4th ed.). Washington, DC: Author.
Banich, M. T. (1997). Neuropsychology: The neural bases of mental function. New York:
Houghton Mifflin.
Borkovec, T. D., Shadick, R. N., & Hopkins, M. (1991). The nature of normal and patho-
logical worry. In R. M. Rapee & D. H. Barlow (Eds.), Chronic anxiety: Generalized
anxiety disorder and mixed anxiety-depression (pp. 29–51). New York: Guilford Press.
Coren, S. (1988). Prediction of insomnia from arousability predisposition scores: Scale
development and cross-validation. Behaviour Research and Therapy, 26(5), 415–420.
Coursey, R. D., Buchsbaum, M., & Frankel, B. L. (1975). Personality measures and evoked
response in chronic insomniacs. Journal of Abnormal Psychology, 84(3), 239–249.
Davey, G. C. L. (1992). A comparison of three worry questionnaires. Behaviour Research
and Therapy, 31(1), 51–56.
Davey, G. C. L., Hampton, J., Farrell, J., & Davidson, S. (1991). Some characteristics of
worrying: Evidence for worrying and anxiety as separate constructs. Personality and
Individual Differences, 13(2), 133–147.
Giesecke, M. E. (1987). The symptom of insomnia in university students. Journal of Amer-
ican College Health, 35(5), 215–221.
Hartmann, E., Baekeland, F., & Zwilling, G. R. (1972). Psychological differences between
long and short sleepers. Archives of General Psychiatry, 26, 463–468.
Hicks, R. A., Guista M., Schretlen, D., & Pellegrini, R. J. (1980). Habitual duration of
sleep and divergent thinking. Psychological Reports, 46, 426.
Hicks, R. A., & Rozette, E. (1986). Habitual sleep duration and eating disorders in college
students. Perceptual and Motor Skills, 62, 209–210.
Howard, P. J. (1994). The owner’s manual for the brain: Everyday applications from mind-
brain research. Austin, TX: Bard Press.
Kelly, W. E., Kelly, K. E., & Clanton, R. C. (2001). The relationship between sleep length
and grade-point average among college students. College Student Journal, 35(1),84–86.
Kelly, W. E., & Markos, P. A. (2001). The role of boredom in worry: An empirical inves-
tigation with implications for counsellors. Guidance and Counselling, 16(3), 81–85.
Kumar, A., & Vaidya, A. K. (1982). Neuroticism in short and long sleepers. Perceptual
and Motor Skills, 54, 962.
Kumar, A., & Vaidya, A. K. (1984). Anxiety as a personality dimension of short and long
sleepers. Journal of Clinical Psychology, 40, 197–198.
McCann, S. J. H., & Stewin, L. L. (1988). Worry, anxiety, and preferred sleep length. The
Journal of Genetic Psychology, 149(3), 413–418.
Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and
validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy,
28, 487–495.
304 The Journal of Genetic Psychology
Nicassio, P. M., Mendlowitz, D. R., Fussell, J. J., & Petras, L. (1985). The phenomenolo-
gy of the pre-sleep state: The development of the pre-sleep arousal scale. Behaviour
Research and Therapy, 23(3), 263–271.
Salthouse, T. A. (1991). Expertise as the circumvention of human processing limitations.
In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects
and limits (pp. 286–300). New York: Cambridge University Press.
Skinner, N. F. (1983). Neuroticism, extraversion, and sex differences in short and long
sleepers. Psychological Reports, 53, 669–670.
Soper, B., Kelly, W. E., & Von Bergen, C. W. (1997). A preliminary study of sleep length
and hallucinations in a college student population. College Student Journal, 31(2),
272–275.
Tallis, F., Davey, G. C. L., & Bond, A. (1994). The Worry Domains Questionnaire. In G.
Davey & F. Tallis (Eds.), Worrying: Perspectives on theory, assessment and treatment
(pp. 286–297). New York: Wiley.
Tallis, F., Eysenck, M., & Mathews, A. (1992). A questionnaire for the measurement of
nonpathological worry. Personality and Individual Differences, 13, 161–168.
Watts, F. N., Coyle, K., & East, M. P. (1994). The contribution of worry to insomnia.
British Journal of Clinical Psychology, 33(2), 211–220.
Received July 5, 2001
... There are many models indicating the symptoms of insomnia, which are worry, rumination, destructive thoughts regarding sleep, selective attention to some thoughts, and behavior management strategies for sleep failure. These cognitive activities are responsible for insomnia when entering sleep (Kelly, 2002;Tang & Harvey, 2004;Yildirim et al., 2018a). ...
Article
Full-text available
Individuals suffer from insomnia through the presence of extraneous thoughts. Furthermore, there are cognitive and behavioral aspects, which afflict a person when they have insomnia. In this context, there are strategies to address these thoughts. The Thought Control Questionnaire: Insomnia-Revised (TCQI-R) is a self-reporting instrument that aims to evaluate the strategies used by individuals to control their thoughts when suffering from insomnia. This study aimed to translate and adapt the TCQI-R to the Jordanian society. In this study, the questionnaire was applied to a sample of 361 participants aged between 19 and 61 years, of which 210 were women (58%). The principal component analysis determined five components: reappraisal, cognitive distraction, aggressive suppression, worry, and behavioral distraction. Each of the total scores of the questionnaire (α = 0.90) and its five components (Cronbach’s α between 0.75 and 0.83) demonstrated high internal consistency. The results of the triple analysis of variance established that the instrument can distinguish between depressed and non-depressed people, anxious and non-anxious people, and people who suffer from insomnia and those who do not. Additionally, the results revealed significant statistical correlations between each of the total scores of the questionnaire and its five components as well as with the related scales. Finally, multiple regression analysis demonstrated the ability of the TCQI-R to predict depression, anxiety, and insomnia, and the prediction ratio for the overall score were 32.4%, 36.6%, and 42.6%, respectively. This indicates that worry and cognitive distraction were the most powerful strategies in dealing with insomnia. This instrument has the ability to assess and diagnose intrusive thoughts and adjust the strategies used to overcome insomnia.
... As a major component of anxiety, worry refers to anticipating and expecting unpleasant events. Anxiety causes hidden psychological threats to different social groups, especially pregnant women [9]. Some evidence claim that clinical anxiety during pregnancy can result in fetal brain and nervous system development [10,11], mother-infant emotional interactions [12], preterm labor [13], and increased risk of postpartum depression [1]. ...
Article
Background: The rapid spread of COVID-19 and the time needed to develop a vaccine or definitive treatment for the disease have caused great anxiety in communities, especially in pregnant women whose high levels of distress may have short and/or long-term maternal and fetal consequence. This study was conducted to investigate the relationship between anxiety induced by COVID-19 and perceived social support in Iranian pregnant women. Methods: This online cross-sectional study was conducted on 801 pregnant women from all over Iran in 2020. The data were collected using a demographic questionnaire, the multidimensional scale of perceived social support, and the coronavirus disease anxiety scale. A standard multiple linear regression model was used to identify the association between perceived social support and anxiety, controlling for possible confounding variables. Partial r was used as an estimate of effect size. Results: The mean anxiety score was 10.7 ± 8.0, in which its level was moderate in 122 (15.2%; 95% confidence interval (CI): 12.8–17.9%)) of the participants, and severe in 28 (3.4%; 95% CI: 2.3–5.0%) of them. The mean score of perceived social support was 48.2 ± 7.6. 6.9% (95% CI: 5.2–8.8%) and 93.1% (95% CI: 91.2–94.8%) of the participants reported mild and moderate levels of perceived social support, respectively. The results of the multiple linear model showed a significant negative correlation between perceived social support and anxiety levels in a way that for every 10 units increase in the perceived social support score, the anxiety level of pregnant women was decreased by 0.8 units (B= −0.08, t= −2.08, p = 0.037), which was a small effect size (partial r = −0.07). Conclusion: There was a small significant relationship between the perceived social support and COVID-19 anxiety. Further studies are required to identify associated factors of anxiety level during COVID-19 in pregnant women.
... This further illustrates how excessive worrying has been associated with impaired sleep ( Åkerstedt et al., 2007 ;McGowan et al., 2016a ). In turn, sleep problems, particularly a short duration of sleep (Kelly, 2002) , can lead to more worrying, because lying awake offers an obvious opportunity to think about concerns, and because people may start to worry about their lack of sleep. Taken together, these mechanisms may lead to a mutually reinforcing process Harvey (2002) . ...
Article
Full-text available
Objectives Little is known about the daily associations between affect, worry, and sleep problems, and previous studies did not distinguish differences between persons from differences within persons. We examined bidirectional associations of daily unpleasant affect (UA), pleasant affect (PA), and worry with sleep problems at both the between- and the within-persons level. Methods The data came from a web-based diary study called “HowNutsAreTheDutch”, in which 1,165 respondents filled out an online questionnaire 3 times a day, for 30 consecutive days. Daily levels of affect and worry were calculated by averaging the morning, afternoon, and evening scores. Sleep problems were assessed in the morning, with regard to the previous night. Bidirectional associations between affect, worry, and sleep problems were tested using Dynamic Structural Equation Modeling (DSEM). Results High UA, low PA, high worry, and poor sleep were strongly associated at the between-person level. At the within-person level, better-than-usual sleep at night significantly predicted lower UA (β = -0.31, p<.001) and worry (β = -0.16, p<.001) and higher PA (β = 0.29, p<.001) during the subsequent day. The effects from daytime affect and worry to sleep the subsequent night were also significant, but considerably weaker. Limitations Women and highly educated individuals were overrepresented in our sample. Conclusions: Persons who sleep worse than usual at night are likely to experience less PA and more UA and worry the following day. Daytime UA, PA, and worry also predict sleep problems during the following night, but to a lesser extent than the reverse effects.
... Both worries about adversities and experience of adversities are types of stressors [30]. The stress of experiencing adversities has been shown to impair sleep [31][32][33], while the stress of worrying about life events has been associated with shorter sleep length and greater sleep disturbance [34,35]. Numerous biological studies have focused on the pathways underlying these effects, including disruption of HPA axis activity, increased cortisol production, and bidirectional changes between hormonal variation and circadian rhythm [36,37]. ...
Article
Full-text available
Background There are concerns that both the experience of adversities during the COVID-19 pandemic and worries about experiencing adversities will have substantial and lasting effects on mental health. One pathway through which both experience of and worries about adversity may impact health is through effects on sleep. Methods We used data from 46,284 UK adults in the COVID-19 Social Study assessed weekly from 01/04/2020-12/05/2020 to study the association between adversities and sleep quality. We studied six categories of adversity including both worries and experiences of: illness with COVID-19, financial difficulty, loss of paid work, difficulties acquiring medication, difficulties accessing food, and threats to personal safety. We used random-effect within-between models to account for all time-invariant confounders. Results Both the total number of adversity experiences and total number of adversity worries were associated with lower quality sleep. Each additional experience was associated with a 1.16 (95% CI = 1.10, 1.22) times higher odds of poor quality sleep while each additional worry was associated with a 1.20 (95% CI = 1.17, 1.22) times higher odds of poor quality sleep. When considering specific experiences and worries, all worries and experiences were significantly related to poorer quality sleep except experiences relating to employment and finances. Having a larger social network offered some buffering effects on associations but there was limited further evidence of moderation by other social or psychiatric factors. Conclusion Poor sleep may be a mechanism by which COVID-19 adversities are affecting mental health. This highlights the importance of interventions that support adaptive coping strategies during the pandemic.
... One pathway through which both experience of and worries about adversity may impact health is through effects on sleep 28 . Studies have related adversity to psychosocial stress 29 , which is known to impair sleep [30][31][32] , while worrying has also been associated with shorter sleep length and greater sleep disturbance 33,34 . Impaired sleep is in turn related to worsened health outcomes, such as cardiovascular disease, weight gain, and mortality 35,36 . ...
Preprint
Full-text available
There are concerns that both the experience of adversities during the COVID-19 pandemic and worries about experiencing adversities will have substantial and lasting effects on mental health. One pathway through which both experience of and worries about adversity may impact health is through effects on sleep. We used data from 48,723 UK adults in the COVID-19 Social Study assessed weekly from 01/04/2020-12/05/2020 to study the association between adversities and sleep quality. We studied six categories of adversity including both worries and experiences of: illness with COVID-19, financial difficulty, loss of paid work, difficulties acquiring medication, difficulties accessing food, and threats to personal safety. We used random-effect within-between models to account for all time-invariant confounders. Both the total number of adversity experiences and total number of adversity worries were associated with lower quality sleep. Each additional experience was associated with a 1.16 (95% CI = 1.10, 1.22) times higher odds of poor quality sleep while each additional worry was associated with a 1.20 (95% CI = 1.17, 1.22) times higher odds of poor quality sleep. When considering specific experiences and worries, all worries and experiences were significantly related to poorer quality sleep except experiences relating to employment and finances. Having a larger social network offered some buffering effects on associations but there was limited further evidence of moderation by social or psychiatric factors. Poor sleep may be a mechanism by which COVID-19 adversities are affecting mental health. This highlights the importance of interventions that support adaptive coping strategies during the pandemic.
... Distress is the cognitive response to stressors (5) indicating the hidden psychological threats among various social groups. These pressures can be higher in certain classes of society; and the pregnant women are among these groups (6). ...
Article
Full-text available
Background: pregnancy is one of the most stressful periods a woman experiences in her life. This study was done to determine the perceived stress and prenatal distress in pregnancy and its related factors. Methods: The is cohort study was carried out on 110 pregnant women whit gestational age of 24 to 28 weeks who referred to Reference laboratory in Miandoab city in 2016-2017. The sample was selected based on availability. The Cohen Perceived Stress Scale (PSS) and Prenatal Distress Questionnaire (PDQ) and demographical information were used. All obtained data were analyzed in SPSS- 23 using t test, Paired-t test and person test. Significant level was considered less than 0.05. Results: The results of study show perceived stress and prenatal distress scores in 32-36 weeks have been uptrend than 24-28 weeks. Also, there was a significant relationship between wanted pregnancy with the mean stress score in 24-28 weeks (p=0.04). Also, between perceived stress score in 32-36 weeks and maternal education level (p=0.045) and between the distress score in 24-28 weeks pregnant and the wanting the sex of the fetus by the women. (p=0.045) Conclusions: According to the results, stress and anxiety of pregnant women increase with approaching delivery time; caregivers should be taken into consideration by pregnant women. In addition, the results of the study show the importance of holding educational classes and raising women’s' awareness of pregnancy and childbirth and having a planned pregnancy to reduce stress and distress.
Article
Study objectives: Cognitive behavioral treatment for insomnia is performed under the premise that feedback provided by evaluation of sleep diaries written by patients will result in good sleep. The sleep diary is essential for behavior therapy and sleep hygiene education. However, limitations include subjectivity and laborious input. We aimed to develop an artificial intelligence sleep prediction model and to find factors associated with good sleep using a wrist-worn actigraphy device. Methods: We enrolled 109 participants who reported having no sleep disturbances. We developed a sleep prediction model using 733 days of actigraphy data of physical activity and light exposure. Twenty-four sleep prediction models were developed based on different data sources (actigraphy alone, sleep diary alone, or combined data), different durations of data (1 or 2 days), and different analysis methods (extreme gradient boosting, convolutional neural network, long short-term memory, logistic regression analysis). The outcome measure of "good sleep" was defined as ≥90% sleep efficiency. Results: Actigraphy model performance was comparable to sleep diary model performance. Two-day models generally performed better than 1-day models. Among all models, the 2-day, combined (actigraphy and sleep diary), extreme gradient boosting model had the best performance for predicting good sleep (accuracy=0.69, area under the curve=0.70). Conclusions: The findings suggested that it is possible to develop automated sleep models with good predictive performance. Further research including patients with insomnia is needed for clinical application.
Article
Full-text available
This study aimed to assess the structural relationships between metacognition, emotional flexibility, affective styles, and worry in a nonclinical Persian sample. Two hundred seventy-seven undergraduate students were selected by convenience sampling and then completed the Pennsylvania State Worry Questionnaire (PSWQ), Metacognitive Questionnaire-30 (MCQ-30), Affective Styles Questionnaire (ASQ), and Emotional Flexibility Questionnaire (EFQ). Data were analyzed using structural equation modeling (SEM) based on Linear Structural relationships (LISREL). The model examination indicated that the proposed theoretical model had the goodness of fit with the measurement model (SMSEA = 0.04; SRMR = 0.04; NFI = 0.97; GFI = 0.97; CFI = 0.99; IFI = 0.99). Also, the results of SEM presented the significant direct impact of metacognition on worry (β = 0.40; P < 0.05). The relationships between metacognition and worry are mediated by emotional flexibility (β = 0.120; P < 0.05) and affective styles (β = 0.121; P < 0.05). According to the finding, the maladaptive affective styles and emotional inflexibility are good predictors of worry and should be considered in treating pathological worry.
Article
Research suggests that psychological stress is associated with insomnia, but there is limited research on vulnerabilities that might amplify this association, particularly in college students. Based on a sample of 507 undergraduates, the current study demonstrates that the observed positive correlation between self‐perceived stress and insomnia severity is moderated by the tendency to engage in repetitive negative thinking (RNT) at bedtime. Additionally, separate analyses of those who scored below/above the threshold for insomnia (non‐insomniacs vs. insomniacs) revealed that the interaction between stress and these negative bedtime cognitions differed qualitatively between the two groups. In insomniacs, the stress‐insomnia relationship was dampened for those with lower levels of bedtime RNT, but amplified for those with higher levels. For non‐insomniacs, the stress‐insomnia relationship was stronger for those with minimal bedtime RNT, while higher levels of bedtime RNT appeared to overshadow this association. To develop a better understanding of the contribution of stress and RNT to clinically‐relevant levels of insomnia, future studies should take into account the dissimilar patterns of moderation seen in non‐insomniacs and insomniacs, either through prospective screening or separate analyses. Findings from the current study suggest that insomnia treatments that can simultaneously reduce stress and address bedtime RNT may be optimal. This article is protected by copyright. All rights reserved.
Article
Introduction: Latinx college students are at high risk of suffering from depressive symptoms. A factor posited to influence depressive symptoms among Latinx college students is racial/ethnic discrimination. However, the mechanisms which link racial/ethnic discrimination to depressive symptoms are not well understood. This study examined the mediating role of racism-related vigilance and sleep-related factors (i.e., sleep quality, sleep efficiency) on the relationship between perceived intergroup racial/ethnic discrimination and depressive symptoms. Methods: Participants were 194 Latinx college students enrolled at a Midwestern university designated as a Hispanic-Serving Institution. Path analysis was conducted to investigate whether racism-related vigilance and sleep-related factors (i.e. sleep quality, sleep efficiency) are potential pathways in the relationship between intergroup racial/ethnic discrimination and depressive symptoms. Results: Path analysis revealed that racism-related vigilance and sleep quality sequentially mediated the effect of perceived intergroup racial/ethnic discrimination on depressive symptoms. Sleep efficiency did not mediate the relationship between racial/ethnic discrimination and depressive symptoms. Discussion: This study is among the first to document that intergroup racial/ethnic discrimination is negatively related to mental health through both cognitive and behavioral mechanisms. This research has important implications for understanding how discrimination may influence mental health outcomes among Latinx college students.
Article
Full-text available
The current study investigated the relationship between worry and boredom and suggests implications for counsellors. Students enrolled in introductory psychology classes completed the Worry Domains Questionnaire (WDQ) and the Boredom Proneness Scale (BPS). Regression analysis indicated that higher boredom scores significantly predicted higher worry scores. A multiple regression found that the Perception of Time and Affective Responses subscales of the BPS accounted for most worry variance. The Aimless Future sub-scale of the WDQ accounted for most boredom variance. Implications for counsellor education are addressed.
Article
Full-text available
Differences have been reported between short sleepers (those who typically sleep 6 or fewer hours per right) and long sleepers (those who sleep 9 or more hours). One explanation for differences in sleep time is that short sleepers have less than optimal levels of cortical arousal. Given this conjecture, and given that sleep amount has been related to hallucinations, it was hypothesized that short sleepers might augment their arousal with hallucinations. A college student sample's self-reported, preferred, sleep length and experiences of hallucinations were explored. It was found that over half the short sleepers reported hallucinations, while average and long sleepers reported significantly fewer hallucinations. Directions for further research are offered.
Article
Full-text available
Previous research indicated that short sleepers (those who typically sleep 6 or fewer hours out of every 24) report more symptoms of psychological maladjustment than do long sleepers (those who sleep more than 9 hours). The presence of psychological maladjustment symptoms have been found to negatively affect academic performance. Hence, it was hypothesized that short sleepers would report lower grade-point averages than those classified as long sleepers. A college student sample's self-reported typical sleep length and grade-point averages were explored. It was found that short sleepers reported significantly lower overall grade-point averages than did long sleepers. Directions for future research are offered.
Article
Eighteen chronic insomniacs, whose sleep problems were confirmed by all night EEG recordings, were matched with normal sleepers on age, sex and education. Insomniacs were significantly more depressed than normals on Zung's Self Rating Depression Scale and the Minnesota Multiphasic Personality Inventory (MMPI) D scale. Second, insomniacs showed more anxious worrying behavior (high on the MMPI Pt, Hy, Hs, Taylor Manifest Anxiety Scale, and Eysenck's Neuroticism scale, toward the sensitization end on Byrne's Repression Sensitization Scale, and overly concerned about the past and future on the Time Competence Scale). Third, insomniacs appeared to be sensory reducers, as evidenced by lower evoked potential responses to sound and by low scores on Zuckerman's Sensation Seeking Scale. They were less proficient in perceptual motor skills on the Wechsler Adult Intelligence Scale. A stepwise regression analysis showed that the sensation avoiding dimension accounted for the most variance in predicting an EEG sleep efficiency criterion.
Article
This is an investigation of the psychological characteristics of males who normally obtain over nine or under six hours of sleep per 24 hours, and who function well with these unusual amounts of sleep. The study includes results from detailed sleep histories, sleep logs, psychiatric interviews, and a number of psychological tests. Short sleepers were found generally to be smooth, efficient persons with a tendency towards handling stress by keeping busy and by denial. Long sleepers were "worriers" and were chronically somewhat depressed or anxious; they scored higher than the short sleepers on most tests of pathology. This group also included some creative persons. It is suggested that the differences in sleep need may be a response to the above differences in life-style and personality and that sleep, and especially D-sleep, may have a function in restoring the brain and psyche after stress or psychic pain.
Article
Existing measures of worry content were designed to ascertain levels of worry in special groups, particularly children and the elderly. In the present study, cluster analysis techniques were employed to develop a measure of worry suitable for use on a nonclinical adult population. The Worry Domains Questionnaire (WDQ) yields a global score which is calculated by summing scores on 5 subscales: these subscales, or domains, are labelled (1) Relationships (2) Lack of Confidence (3) Aimless Future (4) Work Incompetence and (5) Financial. Content differences between pathological and nonpathological worry are considered.