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Development and Validation of the Sleep Inertia Questionnaire (SIQ)

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ORIGINAL ARTICLE
Development and Validation of the Sleep Inertia Questionnaire
(SIQ) and Assessment of Sleep Inertia in Analogue and Clinical
Depression
Jennifer C. Kanady
1

Allison G. Harvey
1
Published online: 26 April 2015
� Springer Science+Business Media New York 2015
Abstract Sleep inertia is the transitional state from sleep to
wake. Research on sleep inertia is important in depression
because many people with depression report having diffi-
culty getting out of bed, which contributes to impairment and
can impede the implementation of interventions. The first
aim was to develop and validate the first self-report measure
of sleep inertia, the Sleep Inertia Questionnaire (SIQ). The
second aim was to compare reports of sleep inertia across
three groups: (1) No-to-Mild-Depression, (2) Analogue-
Depression, and (3) Syndromal-Depression. The SIQ
demonstrates strong psychometric properties; it has good to
excellent internal consistency, strong construct validity, and
SIQ severity is associated with less prior sleep duration.
Sleep inertia is more severe in the Analogue-Depression and
Syndromal-DepressionGroups compared to theNo-to-Mild-
Depression Group. In conclusion, the SIQ is a reliable
measure of sleep inertia and has potential for improving the
assessment of sleep inertia in clinical and research settings.
Keywords Psychometric properties � Self-report
measure � Sleep inertia � Depression
Introduction
Sleep inertia is the normal transitional state of lowered
arousal and impaired performance following sleep. Several
lines of evidence suggest that studies of sleep inertia are
particularly important in mood disorders. Most notably,
people with mood disorders often report considerable dif-
ficulty getting out of bed (Cassano et al. 2009; Ritter et al.
2012). This can be associated with significant impairment
and can impede the optimal implementation of interven-
tions for mood disorders, particularly activity scheduling
(Cuijpers et al. 2007) and behavioral activation (Hopko
et al. 2003). In addition, Ohayon and colleagues (2000)
found that over half of a sample of people reporting con-
fusion, slowed thinking/speech, and memory problems
upon awakening also reported depressed mood. Moreover,
polymorphisms in the circadian genes of NPAS2 and
CLOCK are implicated in sleep inertia severity (Gamble
et al. 2011) and polymorphisms in these same genes are
associated with mood disorders (Soria et al. 2010). Related
to this finding, people with mood disorders are more likely
to report circadian dysfunction when compared to healthy
people (see Germain and Kupfer 2008 for review) and
circadian dysfunction is associated with heightened sleep
inertia severity (Roenneberg et al. 2003). Depression is
also associated with increased daytime napping (Basta
et al. 2007; Foley et al. 2007; Peth et al. 2012) and sleep
inertia is a prevalent and problematic feature following
daytime naps (e.g., Achermann et al. 1995; Muzet et al.
1995). Finally, reduced total sleep time is a common
complaint of people with depression (Benca et al. 1992)
and reduced total sleep time is one of the strongest corre-
lates of sleep inertia (Balkin and Badia 1988; Dinges
1990). Taken together, we propose that improving our
understanding of, and our ability to assess, sleep inertia
may result in new ways to help people with depression.
To date, the primary focus of research on sleep inertia
has been to identify correlates of sleep inertia and factors
affecting sleep inertia duration and severity. A range of
sleep inertia correlates has been investigated. These can be
& Allison G. Harvey
aharvey@berkeley.edu
1
Department of Psychology, University of California,
Berkeley, 3210 Tolman Hall #1650, Berkeley,
CA 94720-1650, USA
123
Cogn Ther Res (2015) 39:601–612
DOI 10.1007/s10608-015-9686-4

grouped into four clusters: cognitive, behavioral, physio-
logical, and emotional. Sleep inertia is most commonly
characterized by cognitive and behavioral deficits such as
decreased alertness (Achermann et al. 1995; Jewett et al.
1999), slowed reaction times, increased number of errors
and lapses (Feltin and Broughton 1968; Goodenough et al.
1965; Seminara and Shavelson 1969), slowed speech
(Ohayon et al. 2000), impaired decision making (Bruck and
Pisani 1999), confusion, difficulties getting out of bed, and
the inability to resist sleep (Dinges 1990). Sleep inertia can
also be associated with physiological and emotional diffi-
culties as well. Indeed, loss of coordination and balance
(Wilkinson and Stretton 1971), hypovigilance (Jewett et al.
1999), anxiety (Hou et al. 2007), and irritability (Weiss-
bluth and Weissbluth 1992) can all be features of this
sleep-wake transitional state. The severity and duration of
the cognitive, behavioral, physiological, and emotional
correlates of sleep inertia are influenced by many factors
such as prior sleep duration (Balkin and Badia 1988;
Dinges 1990), core body temperature (Weissbluth and
Weissbluth 1992; Wilkinson and Stretton 1971), and the
sleep stage immediately prior to awakening (Feltin and
Broughton 1968; Fort and Mills 1972; Webb and Agnew
1964).
Sleep inertia has typically been assessed by performance
on cognitive and/or behavioral tasks, via self-report mea-
sures of alertness, and with electroencephalogram-record-
ings of brain activity upon awakening (Marzano et al.
2011). While there is no doubt that these approaches have
progressed the field, such methodologies are not ideal for
use in clinical practice, for longitudinal and naturalistic
studies, nor for capturing the idiosyncratic experience of
sleep inertia. Therefore, we suggest that a self-report
measure of sleep inertia is needed. Accordingly, the over-
arching goal of the present study was to develop and
validate a self-report measure of sleep inertia, the Sleep
Inertia Questionnaire (SIQ). Development and validation
of the SIQ is important because, with further validation, the
SIQ has potential to aid in easy assessment of the different
correlates of sleep inertia (i.e., cognitive, behavioral,
emotional, and physiological), it can progress research on
the important ramifications of this sleep-wake transitional
state, and will identify important and idiosyncratic sleep
inertia treatment targets.
The present study has two aims, which were addressed
using two separate samples. The first aim was to devise,
and then assess the psychometric properties of the SIQ and
revise the SIQ as necessary in order to enhance its psy-
chometric properties. The first aim was completed using an
undergraduate student sample from the University of
California, Berkeley. The second aim was to compare re-
ports of sleep inertia severity and duration across three
groups: (1) a no depression to mild depression student
group (No-to-Mild-Depression Group), (2) a non-treatment
seeking, moderate to severe depression student group
(Analogue-Depression Group), and (3) a treatment seeking,
community group with major depressive disorder (Syn-
dromal-Depression Group). Based on previous research,
we hypothesized that the Analogue-Depression and Syn-
dromal-Depression Groups would report similar levels of
sleep inertia severity and duration and that the Analogue-
Depression and Syndromal-Depression Groups would re-
port greater sleep inertia severity and longer sleep inertia
duration compared to the No-to-Mild-Depression Group.
Methods
Development and Description of the SIQ
The initial pool of items for the SIQ was generated via two
sources: (1) a careful review of the literature and (2) by
conducting open-ended interviews with ten non-patients.
For the former, items were selected if they fitted clearly
into one of the four sleep inertia constructs highlighted in
the literature: cognitive, behavioral, physiological, or
emotional. These items were then discussed with members
of our research team that includes experts in sleep and
mood disorders. For the latter, 10-non patients were asked
the question: ‘‘Please describe the process of waking up as
you experience it’’. The ten non-patients consisted of five
graduate students and five undergraduate students within
the psychology department of UC Berkeley who responded
to a recruitment email sent by the research team. These ten
people were intentionally selected because they reported
having a hard time getting out of bed in the morning and
therefore might have an easier time generating potential
sleep inertia items. From these two sources, an initial list of
35 items was created. After compiling this list, the 35 SIQ
items and the open-ended question, ‘‘Please describe the
process of waking up as you experience it’’ was adminis-
tered electronically to 23 undergraduate students. Under-
graduates were asked to highlight any items that were not
easily understandable and to describe their process of
waking up in detail. This process did not eliminate nor
yield any additional SIQ items. Following the electronic
administration, members from the research team (N = 6)
then met to review each SIQ item. This final step resulted
in the creation of the 30-item SIQ, which was used in the
analyses for the first aim of the present study.
Each item of the SIQ is rated on a scale from 1 (not at
all) to 5 (all the time) with higher scores indicating greater
sleep inertia severity. The SIQ introduces each question
with the phrase, ‘‘On a typical morning in the last week,
after you wake up, to what extent do you…’’ The research
team carefully considered this timeframe of focus. Many
602 Cogn Ther Res (2015) 39:601–612
123

widely utilized self-report measures of sleep such as the
Insomnia Severity Index (ISI; Morin et al. 2011) and
Pittsburg Sleep Quality Index (PSQI; Carpenter and
Andrykowski 1998) ask about a typical night of sleep in the
past month. The SIQ focuses on a shorter period of recall
for two reasons. First, asking about the last week as op-
posed to the last month minimizes measurement error by
relying on more recent memory (Cansino 2009). Second,
sleep inertia is dependent on a number of variables (e.g.,
prior sleep duration, sleep stage upon awakening, timing of
sleep) and therefore, 1 week may look different than an-
other week in the same month. The SIQ also includes a
question about sleep inertia duration: ‘‘How long does it
take you to ‘come to’ in the morning (please indicate hours
or minutes)’’.
Participants
To examine the first aim, 356 undergraduate students of the
University of California, Berkeley completed the SIQ (age:
20.45 ± 2.41; 188 female). Race and ethnicity of the
sample reflected the diversity of California universities
(40.4 % Caucasian, 44.7 % Asian, 1.9 % African Amer-
ican, 2.2 % Hawaiian/Pacific Islander, 0.0 % American
Indian/Alaska Native, 9.8 % Hispanic, 0.5 % declined to
answer). A student sample was selected to examine the
psychometric properties of the SIQ because this method
allowed for efficient and economical completion of an
appropriately powered exploratory factor analysis. Indeed,
student samples are often used to establish psychometric
properties of new instruments (e.g., Dozois et al. 1998;
Fresco et al. 2007; Watson and Clark 1988). Moreover,
sleep inertia is a robust phenomenon and is prominent in
students (e.g., Tassi et al. 2006; Miccoli et al. 2008).
The second aim was examined using two different sam-
ples: the same undergraduate student sample used for the first
aim and a community sample. The undergraduate students
were allocated to one of two groups: (1) a no depression to
mild depression, student group (No-to-Mild-Depression
Group; N = 259) and (2) a non-treatment seeking, moderate
to severe depression, student group (Analogue-Depression
Group;N = 97). In order tomeet criteria for theNo-to-Mild-
Depression Group, participants had to score less than 10 on
the Quick-Inventory of Depressive Symptomatology, Self-
Report (QIDS-SR\ 10), which is a standard and valid
measure of current depressive symptoms (Rush et al. 2003).
In order tomeet criteria for the Analogue-Depression Group,
participants had to score greater than or equal to 10 on the
QIDS-SR. A score of 10 or greater in the QIDS-SR is
indicative of moderate to severe depression (Rush et al.
2003). The community sample was recruited as part of an
NIMH-funded study examining the efficacy of cognitive
therapy for treatment of depression. The community sample
comprised the third group: a treatment seeking, community
group with major depressive disorder (Syndromal-Depres-
sion Group; N = 48). Participants in the Syndromal-De-
pression Group met DSM-5 criteria for current major
depressive disorder, they scored at least a 26 on the Inventory
of Depressive Symptomatology, Self Report (IDS-SR; Rush
et al. 1996), which is equivalent to a 10 on the QIDS-SR
(Trivedi et al. 2004), and they were interested in receiving
cognitive therapy for their symptoms. In sum, for the second
aim, there were a total of three groups: (1) a no depression to
mild depression student group (No-to-Mild-Depression
Group), (2) a non-treatment seeking, moderate to severe
depression student group (Analogue-Depression Group),
and (3) a treatment seeking, community group with major
depressive disorder (Syndromal-Depression Group).
The analogue approach to studying symptoms of psy-
chopathology has been successful in a number of areas
including the role of worry in generalized anxiety disorder
(Roemer et al. 1997), key processes in social phobia (Stopa
and Clark 2001), the study of obsessive–compulsive dis-
order (Burns et al. 1995; Sternberger and Burns 1991), and
the function of personality traits in depression (Enns et al.
2001). Using analogue samples when studying sleep inertia
in depression has two important strengths: it allows for
rapid and sizeable data collection and it means that the SIQ
can be piloted in an efficient manner.
Procedure
The Committee for Protection of Human Subjects at the
University of California, Berkeley approved all procedures
described in this section. To examine the first aim, the SIQ
was administered to participants online via Qualtrics
(Qualtrics, Provo, UT). After electronically signing the
consent form, participants completed a battery of self-re-
port measures and semi-structured interviews including the
SIQ, QIDS-SR, Pittsburg Sleep Quality Index (PSQI;
Carpenter and Andrykowski 1998), Composite Scale of
Morningness (CSM; Smith et al. 1989), and sleep diaries.
Information about participant age, race, ethnicity, and
gender was also collected. To examine the second aim, the
same information from the first aim was used for the No-to-
Mild-Depression and Analogue-Depression student groups.
Data for the Syndromal-Depression Group was collected as
part of the pre-treatment stage of the NIMH-funded study.
Major depressive disorder diagnoses of the Syndromal-
Depression Group were confirmed using the Structured
Clinical Interview for DSM-IV-TR Axis I disorders (SCID;
First et al. 1995). Additionally, the SIQ along with several
other self-report measures including the PSQI and IDS-SR
were administered during the pre-treatment stage.
Cogn Ther Res (2015) 39:601–612 603
123

Statistical Analyses
Aim One: Assessment of Psychometric Properties
To examine the psychometric properties of the SIQ, an
exploratory factor analysis (EFA) was conducted followed
by analyses of scale reliability, construct validity, and
convergent validity. Following each analysis, SIQ items
were removed as necessary in order to enhance the psy-
chometric properties of the SIQ.
Selection of Items for the Exploratory Factor Analysis
Items were selected for the exploratory factor analysis
based on the following criteria (Ree et al. 2005): (1) Items
were deleted if they were not easily understandable. We
deleted one item, ‘To what extent do you feel terrible upon
awakening’ because of the ambiguity of the descriptor
‘‘terrible’’ in this question. We did not eliminate any ad-
ditional items based on this criterion. (2) Items were
deleted if they did not employ the full response range of the
scale. No items were deleted based on this criterion. (3)
Items were deleted if they were often skipped. The per-
centage of missing values for each item was examined. No
item had a missing value rate of above 5 %. Therefore, no
items were discarded due to high rates of missing values.
(4) Items were deleted if they were overly redundant. To
assess for item redundancy, the correlation matrix of the 30
items was inspected. Items that showed a correlation of
above 0.60 with other items were examined to determine if
this could be explained by highly similar content (Rapee
et al. 1996; Ree et al. 2005). This resulted in the deletion of
one item, ‘To what extent do you think about how you
could get more sleep’ since this item was highly similar to
the item, ‘To what extend to you wish you could sleep
more’. Although other items showed a correlation of above
0.60, these items were not considered redundant and were
therefore not removed from subsequent analyses.
Exploratory Factor Analysis Since a factor analysis has
not yet been performed on the SIQ, it was considered ap-
propriate to perform an exploratory factor analysis rather
than a confirmatory factor analysis on the data. A max-
imum likelihood exploratory factor analysis with oblique
(Direct Quartimin) rotation and Kaiser Normalization was
performed on the 28 SIQ items, as the variables were ex-
pected to be correlated. Missing values were replaced with
the mean value for that item. Several items did not load
highly on any factor and therefore were excluded from
subsequent analyses. These items included: (1) To what
extend do you feel frustrated about having to wake up, (2)
To what extent do you feel fatigued, (3) To what extent do
you fall back asleep after getting out of bed, (4) To what
extent do you need caffeine to ‘wake up’, (5) To what
extent do you think about or assess your energy level, (6)
To what extent do you feel irritable, and (7) To what extent
do you calculate the amount of sleep you actually got? We
did not include the question asking about sleep inertia
duration (i.e., ‘‘How long does it take you to ‘come to’ in
the morning?’’) in the exploratory factor analysis because
this question was not created to assess the duration of a
particular sleep inertia correlate and instead is meant to
assess the duration of sleep inertia broadly. Thus, we did
not expect this question to load on any particular factor.
However, we suggest this question be included in the SIQ
as information about the duration of sleep inertia will likely
inform whether there is need for intervention.
Scale Reliability To examine the internal consistency of
the factors (e.g., scale reliability), Cronbach alpha coeffi-
cients were calculated for the SIQ total score as well as the
four factors of the SIQ determined by the EFA. Factors that
did not incur a Cronbach alpha of at least 0.70 were con-
sidered for exclusion.
Construct Validity Construct validity was assessed using
Pearson correlations, which examined the relationship be-
tween the four SIQ factors and SIQ total score. Given that
each of these factors is measuring the same construct—
sleep inertia—high correlations were expected.
Convergent Validity For convergent validity, Pearson
correlations were performed. Correlation coefficients were
inspected in order to investigate the association between the
factors and total score of the SIQ and measures of average
total sleep time (TST) from the week prior, morningness,
and sleep quality. Average TST was assessed using sleep
diaries, morningness was assessed using the total score from
the CSM, and sleep quality was assessed using the total
score from the PSQI. We chose these particular measures
because sleep inertia is more severe following sleep depri-
vation (Balkin and Badia 1988; Dinges 1990), the CSM has
questions pertaining to the process of waking up and sleep
inertia is negatively associated with eveningness (Roen-
neberg et al. 2003), and sleep inertia is more severe when
sleep is more disturbed (Tassi and Muzet 2000).
Aim Two: Comparison Across Groups
The second aim was to compare reports of sleep inertia
severity and duration across three groups: (1) No-to-Mild-
Depression, (2) Analogue-Depression Group and (3) Syn-
dromal-Depression. Before examining group differences in
sleep inertia severity and duration, group differences in
gender, age, average TST, and average QIDS-SR and PSQI
scores were examined using a one-way analysis of variance
(ANOVA). In order to examine differences in QIDS-SR
604 Cogn Ther Res (2015) 39:601–612
123

scores, the IDS-SR scores of the Syndromal-Depression
Group were first converted to QIDS-SR scores based on the
Trivedi et al. (2004) guidelines. Age and PSQI scores
significantly differed between the groups; participants in
the Syndromal-Depression Group were significantly older
and had higher PSQI scores than the Analogue-Depression
and No-to-Mild-Depression Groups. However, we did not
conduct an analysis of covariance to control for baseline
differences based on Miller and Chapman’s (2001) rec-
ommendation against this practice. Next, to examine be-
tween-group differences in sleep inertia severity, an
omnibus ANOVA was performed for each of the four SIQ
factors. As the SIQ is a novel self-report measure and we
were interested in examining idiosyncratic sleep inertia
differences across groups, we performed a follow-up
ANOVA for each SIQ item for the significant SIQ factors.
To account for multiple comparisons, post hoc Bonferroni
corrections were applied by dividing alpha of 0.05 by the
total number of comparisons performed (i.e., 0.05721 =
0.002; p = 0.002). We also calculated partial eta squared
(g
p
2
) as a index of effect size and interpreted g
p
2
based on
Ferguson’s (2009) guidelines (i.e., small effect & 0.04;
moderate effect & 0.25; large effect & 0.64). To examine
between-group differences in sleep inertia duration, an
ANOVA was again performed.
Results
Aim One: Assessment of Psychometric Properties
An exploratory factor analysis with oblique rotation and
Kaiser Normalization was performed on the SIQ. Inspec-
tion of the break in slope on the scree plot indicated that
four factors should be retained in the final solution. These
four factors accounted for 58.54 % of the total variance and
were labeled as follows: physiological, responses to sleep
inertia, cognitive, and emotional. Factor loadings are listed
in Table 1.
To estimate the internal consistency of the factors,
Cronbach alpha coefficients were calculated and are pre-
sented in Table 2. The total SIQ score, physiological
Table 1 SIQ items and factor
loadings of the SIQ items
Factor/item Factor loadings
123 4
Factor 1: Physiological
Notice that it is difficult to keep your balance 0.85
Bump into and drop things 0.75
Notice that you get winded more easily 0.63
Notice your arms and/or legs feeling tired or heavy 0.61
Notice your eyes feeling heavy, sore, or itchy 0.53
Notice that you move more slowly 0.53
Notice that your mind feels groggy, fuzzy, or hazy 0.47
Notice that you feel tense 0.47
Factor 2: Responses to sleep inertia
Need an alarm to wake up 0.70
Wish you could sleep more 0.69
Notice that you feel sleepy 0.63
Hit the snooze button on the alarm 0.54
Have problems getting out of bed 0.46
Factor 3: Cognitive
Find that you think more slowly -1.04
Find that you react more slowly -0.77
Have difficulty concentrating -0.69
Find that you make more mistakes/errors -0.67
Have difficulty getting your thoughts together -0.59
Factor 4: Emotional
Feel anxious about the upcoming day -0.63
Dread starting your day -0.56
Can’t imagine being able to wake up -0.41
Loadings below 0.40 not shown
Cogn Ther Res (2015) 39:601–612 605
123

factor, and cognitive factor demonstrated excellent internal
consistency with an alpha of above 0.90. With an alpha of
above 0.80, the responses to sleep inertia factor and emo-
tional factor demonstrated good internal consistency
(Gliem and Gliem 2003). Pearson correlation coefficients
between the SIQ factors and the total SIQ score are also
presented in Table 2. The scale intercorrelations on the SIQ
were moderate to very strong, indicating good construct
validity.
To assess convergent validity, Pearson correlations were
performed examining the relationship between the SIQ and
measures of average TST, morningness, and sleep quality.
Results are presented in Table 3. Total SIQ score, the
physiological factor, and the cognitive factor were sig-
nificantly correlated with average TST. More specifically,
decreases in TST were associated with increases in SIQ
scores, indicating more severe sleep inertia. The responses
to sleep inertia factor and emotional factor were not sig-
nificantly associated with prior sleep duration. The SIQ
total score and SIQ factors were not significantly correlated
with CSM or PSQI scores. The final version of the SIQ can
be found in the Appendix.
Aim Two: Comparison Across Groups
Prior to examining differences in SIQ items, differences
between demographic information and self-report measures
across the three groups were assessed. Participant demo-
graphics and self-report measures for the No-to-Mild-De-
pression, Analogue-Depression, and Syndromal-Depression
Groups are presented in Table 4. The three groups didn’t
differ on gender or average TST. There were significant
differences for age, PSQI and QIDS-SR scores. More
specifically, the Syndromal-Depression Group was sig-
Table 2 Construct validity and
internal consistency
SIQ total Physiological Responses to SI Cognitive Emotional Cronbach a
SIQ total 1 0.95
Physiological 0.94*** 1 0.92
Responses to SI 0.70*** 0.48*** 1 0.81
Cognitive 0.92*** 0.84*** 0.51*** 1 0.94
Emotional 0.86*** 0.77*** 0.47*** 0.78*** 1 0.86
*** p\ 0.001
Table 3 Pearson correlations between SIQ factors and measures of TST, morningness, and sleep quality
SIQ total Physiological Responses to SI Cognitive Emotional
Average TST for the week (N = 353) -0.15** -0.13* -0.08 -0.17* -0.08
CSM total (N = 345) 0.03 0.05 0.07 0.06 0.05
PSQI total (N = 337) -0.03 -0.03 -0.03 0.01 0.04
*** p\ 0.001; ** p\ 0.01; * p\ 0.05
Table 4 Participant demographics and self-report measures
(1) No-to-Mild-
Depression (N = 259)
(2) Analogue-
Depression (N = 97)
(3) Syndromal-
Depression (N = 48)
v
2
Direction
b
Gender 51.0 % female 57.7 % female 54.2 % female 2.53
F
(2,401)
a
Mean age (SD) 20.39 (2.60) 20.63 (1.77) 44.27 (10.97) 608.32*** 3[ 1 and 2
Mean QIDS-SR score (SD) 6.23 (4.13) 17.59 (2.23) 16.48 (3.67) 217.64*** 2 and 3[ 1
Mean PSQI score (SD) 4.45 (2.72) 4.24 (2.48) 9.31 (3.55) 64.10*** 3[ 1 and 2
Average TST: hours (SD) 7.14 (1.13) 6.81 (1.42) 7.00 (1.90) 2.25
a
* p\ 0.05; ** p\ 0.01; *** p\ 0.001
b
Indicates the direction of the relationship. 1 = No-to-Mild-Depression; 2 = Analogue-Depression; 3 = Syndromal-Depression
606 Cogn Ther Res (2015) 39:601–612
123

nificantly older and reported worse sleep quality, indexed by
the PSQI, than the No-to-Mild-Depression and Analogue-
Depression Groups. Moreover, the Analogue-Depression
and Syndromal-Depression Groups had significantly higher
scores on the QIDS-SR, thus more severe depressive
symptoms, than the No-to-Mild-Depression Group.
To examine group differences in sleep inertia severity,
an omnibus ANOVA was performed for each SIQ factor.
All SIQ factors—physiological (F = 53.44; p\ 0.001;
g
p
2
= 0.21), responses to sleep inertia (F = 3.64; p = 0.03;
g
p
2
= 0.02), cognitive (F = 61.02; p\ 0.001; g
p
2
= 0.23),
and emotional (F = 51.09; p\ 0.001; g
p
2
= 0.20)—were
significantly different across groups. The Analogue-De-
pression and Syndromal-Depression Groups reliably re-
ported comparable sleep inertia severity to each other and
greater sleep inertia severity than the No-to-Mild-Depres-
sion Group across the four factors. A follow-up ANOVA
with post hoc Bonferroni tests was used to examine group
differences between each SIQ item. Scores on every item
except the items ‘Need an alarm to wake up’, ‘Wish you
could sleep more’, and ‘Hit the snooze button on the
alarm’, were significantly different across groups with the
Analogue-Depression and Syndromal-Depression consis-
tently reporting more severe sleep inertia than the No-to-
Mild-Depression Group. Effect sizes for each analysis
ranged from small to moderate. An ANOVA was also used
to examine group differences in sleep inertia duration. The
Analogue-Depression Group reported significantly longer
sleep inertia duration than the No-to-Mild-Depression
Group. Results are presented in Table 5.
Table 5 The Sleep Inertia Questionnaire: means, standard deviations, and differences across groups
Questions
a
Groups
(1) No-to-Mild
Depression
(N = 259)
(2) Analogue-
Depression
(N = 97)
(3) Syndromal-
Depression
(N = 48)
F
(2,401)
b
g
p
2
Direction
c
Mean (SD) Mean (SD) Mean (SD)
Factor 1: Physiological
Notice that it’s difficult to keep your balance? 1.66 (0.94) 2.64 (1.29) 2.29 (1.40) 30.81* 0.13 2 and 3[ 1
Bump into and drop things? 1.73 (1.00) 2.58 (1.19) 2.29 (1.24) 24.19* 0.12 2 and 3[ 1
Notice that you get winded more easily? 1.82 (1.02) 3.04 (1.27) 2.77 (1.39) 47.28* 0.19 2 and 3[ 1
Notice your arms/legs feeling tired or heavy? 2.12 (1.15) 3.15 (1.26) 2.94 (1.39) 29.48* 0.13 2 and 3[ 1
Notice your eyes feel heavy, sore or itchy? 2.38 (1.19) 3.19 (1.33) 3.38 (1.24) 23.45* 0.11 2 and 3[ 1
Notice that you move more slowly? 2.22 (1.13) 2.95 (1.14) 3.17 (1.14) 23.90* 0.11 2 and 3[ 1
Notice that your mind feels groggy/fuzzy/hazy? 2.52 (1.16) 3.40 (1.20) 3.50 (1.20) 28.41* 0.13 2 and 3[ 1
Notice that you feel tense? 1.91 (1.10) 3.18 (1.26) 3.21 (1.03) 58.88* 0.23 2 and 3[ 1
Factor 2: Responses to sleep inertia
Need an alarm to wake up? 4.01 (1.27) 4.07 (1.19) 3.44 (1.43) 4.67
Wish you could sleep more? 3.81 (1.23) 3.91 (1.16) 4.12 (1.04) 1.50
Notice that you feel sleepy? 3.11 (1.12) 3.64 (1.13) 3.81 (0.94) 13.60* 0.06 2 and 3[ 1
Hit the snooze button on the alarm? 3.41 (1.50) 3.88 (1.28) 3.09 (1.60) 5.57
Have problems getting out of bed? 2.93 (1.17) 3.38 (1.22) 3.56 (1.18) 8.92* 0.04 2 and 3[ 1
Factor 3: Cognitive
Find that you think more slowly? 2.28 (1.14) 3.42 (1.27) 3.27 (1.32) 39.35* 0.16 2 and 3[ 1
Find that you react more slowly? 2.23 (1.14) 3.38 (1.26) 3.15 (1.34) 37.73* 0.16 2 and 3[ 1
Have difficulty concentrating? 2.46 (1.10) 3.66 (1.14) 3.54 (1.01) 51.76* 0.21 2 and 3[ 1
Find that you make more mistakes/errors? 2.13 (1.09) 3.32 (1.24) 3.06 (1.41) 42.21* 0.17 2 and 3[ 1
Have difficulty getting your thoughts together? 2.08 (1.04) 3.40 (1.17) 3.35 (1.26) 64.46* 0.24 2 and 3[ 1
Factor 4: Emotional
Feel anxious about the upcoming day? 2.29 (1.18) 3.40 (1.26) 3.60 (1.03) 46.76* 0.19 2 and 3[ 1
Dread starting your day? 2.01 (1.17) 3.09 (1.32) 3.25 (1.31) 40.13* 0.17 2 and 3[ 1
Can’t imagine being able to wake up? 1.82 (1.06) 2.76 (1.28) 2.46 (1.24) 26.64* 0.12 2 and 3[ 1
Sleep inertia duration (min) 35.71 (49.94) 66.05 (77.93) 52.09 (47.14) 10.68* 0.05 2[ 1
a
SIQ items were introduced with the phrase, ‘‘On a typical morning in the last week, after you wake up, to what extent do you…’’
b
Bonferroni corrections were applied. * a significance level of p\ 0.002
c
Indicates the direction of the relationship. 1 = No-to-Mild-Depression; 2 = Analogue-Depression; 3 = Syndromal-Depression
Cogn Ther Res (2015) 39:601–612 607
123

Discussion
The overarching goal of the present study was to develop
and validate a self-report measure of sleep inertia to assess
the cognitive, behavioral, physiological, and emotional
correlates of this sleep-wake transitional state. By im-
proving our understanding of, and our ability to assess,
sleep inertia we hope the field may be better positioned to
decrease sleep inertia among people with depression. The
first aim was to assess the psychometric properties of the
SIQ and to revise the SIQ in order to enhance the psy-
chometric properties. An exploratory factor analysis re-
vealed four factors accounting for 58.54 % of the total
variance: (1) physiological, (2) responses to sleep inertia,
(3) cognitive, and (4) emotional. Notably, these four factors
corresponded with the four sleep inertia correlates used to
generate the SIQ items (i.e., cognitive, behavioral,
physiological, and emotional), with the exception of the
responses to sleep inertia factor. Most of the items in the
responses to sleep inertia factor are behavioral in nature.
However, two of the items, ‘Notice that you feel sleepy’
and ‘Wish you could sleep more’ appear to be measuring
sleepiness. It is possible that the responses to sleep inertia
factor is capturing a sleep inertia construct that is a com-
bination of both behavioral correlates of sleep inertia and
related sleepiness.
The internal consistency of the four factors varied from
excellent (physiological and cognitive factors) to good
(responses to sleep inertia and emotional factors). In
terms of construct validity, we expected high intercorre-
lations because the factors of the SIQ are assessing the
same construct: sleep inertia. Indeed, the scale intercor-
relations of the SIQ were moderate to very strong, indi-
cating good construct validity. Finally, convergent
validity of the SIQ varied. All SIQ factors except the
responses to sleep inertia and emotional factor demon-
strated a significant negative correlation with TST from
the week prior. These findings are broadly consistent with
the literature, which suggests that sleep inertia is more
severe following periods of reduced sleep (Balkin and
Badia 1988; Dinges 1990). The responses to sleep inertia
and emotional factors did not correlate with prior sleep
duration. This result may be due to the limited number of
items in these factors (n = 5 and n = 3, respectively).
Another possible explanation is that TST in this sample is
relatively high (approximately 7 h) and the relationship
between sleep inertia and prior sleep duration is stronger
when sleep deprivation is more severe (Balkin and Badia
1988; Dinges 1990). Contrary to the original prediction,
the SIQ factors did not significantly correlate with the
CSM or PSQI, raising the possibility that sleep inertia
is a construct that is at least partially independent of
eveningness and sleep quality. Notably, the relationship
between sleep inertia and eveningness and sleep inertia
and sleep quality has been less extensively studied than
the relationship between sleep inertia and sleep duration.
Therefore, future research is needed to further parse out
these relationships.
The second aim was to compare reports of sleep inertia
severity and duration between three groups: (1) No-to-
Mild-Depression, (2) Analogue-Depression, and (3) Syn-
dromal-Depression. All items of the SIQ, with the excep-
tion of the items, ‘Need an alarm to wake up’, ‘Wish you
could sleep more’, and ‘Hit the snooze button on the
alarm’, demonstrated significant between group differ-
ences, with Analogue-Depression and Syndromal-Depres-
sion Groups consistently reporting greater sleep inertia
severity than the No-to-Mild-Depression Group. These
results are consistent with our hypothesis. Also consistent
with our hypothesis, the Analogue-Depression Group re-
ported significantly longer sleep inertia duration than the
No-to-Mild-Depression Group. However, there was not a
significant difference between the Syndromal-Depression
Group and the No-to-Mild-Depression Group. One possible
explanation for this unexpected finding is that the Ana-
logue-Depression Group reported the lowest amount of
average TST (6.81 h) and is younger than the Syndromal-
Depression Group. Hence, their sleep need is likely to be
greater (e.g., Tonetti et al. 2008).
Taken together, the results from the second aim have
several important implications. First, these findings suggest
that students with moderate to severe symptoms of de-
pression (i.e., the Analogue-Depression Group) and adults
with a diagnosis of major depressive disorder (i.e., the
Syndromal-Depression Group) experience severe and long
periods sleep inertia. This may partially explain why peo-
ple with depression have a hard time getting out of bed in
the morning (Cassano et al. 2009) and the idiosyncratic
experience of sleep inertia may be an important treatment
target in depression. In particular, targeting the experience
of sleep inertia may help prevent people with depression
from staying in bed for extended periods of time (Cassano
et al. 2009; Ritter et al. 2012) and may improve adherence
with cognitive-behavioral therapy interventions such as
activity scheduling (Cuijpers et al. 2007) and behavioral
activation (Hopko et al. 2003). Further, intervention for
sleep inertia may also help people more fully engage in
their lives (e.g., return to fulltime employment, be able to
attend early classes). Second, using a student sample with
moderate to severe symptoms of depression (i.e., Ana-
logue-Depression Group) appears to be a valid analogue
strategy for examining sleep inertia in depression as the
results from the Analogue-Depression sample were com-
parable to those from the community group with major
608 Cogn Ther Res (2015) 39:601–612
123

depressive disorder. The advantage of analogue research is
that it facilitates piloting, allows complex experimental
designs that are not always feasible with community
populations, and it allows key questions to be addressed
more quickly and inexpensively.
The results from this study should be interpreted in
light of several limitations. First, the data from the stu-
dent sample were collected online. Collecting data in this
manner poses several potential problems, including the
possibility of lower response rates and greater measure-
ment errors and possible technical difficulties. Indeed,
several participants neglected to complete certain aspects
of this study. However, these participants represented a
very small percentage of the total (\6.0 %). Also, less
than 5 % of participants skipped items on the SIQ and
every SIQ item employed the full range of the scale,
suggesting that measurement error in this study is mini-
mal. It is perhaps worth noting that collecting data online
also poses several benefits, including reduced participant
response time and burden, lowered cost, ease of data
entry, and participant acceptance of the format (Granello
and Wheaton 2004). Despite this, future studies should
consider using other formats. Second, we did not collect
the time of day for when the SIQ was completed. Hence,
we and are not able to control for time of day across the
three groups. It is possible that time of day may influ-
ence how participants responded to the SIQ. Determining
whether the SIQ is affected by time of day and/or con-
trolling for time of day across groups will be an im-
portant addition to future studies using this measure.
Third, validity of the SIQ would have been strengthened
by comparing SIQ scores to performance on behavioral
and/or cognitive tasks upon awakening. Notably, tradi-
tional cognitive and behavioral tasks often only assess
one factor of the sleep inertia experience, whereas the
SIQ is meant to capture multiple sleep inertia correlates.
Regardless, this type of analysis will be an important
next step for SIQ validation. Fourth, given the method
employed in the present study we cannot be certain that
the SIQ is indeed measuring sleep inertia in people with
depression without verifying this assessment with stan-
dard polysomnographic procedures (Marzano et al. 2011)
and traditional cognitive and behavioral tasks. However,
clinical observation is a critical component of research
and treatment development (Salkovskis 2002) and the
present study was initially motivated by our clinical
observation that many patients with a mood disorder
have what appears to be crippling sleep inertia. Although
the validity of the SIQ would be strengthened by
comparing ratings on the SIQ with an electroencephalo-
gram-recording of brain activity upon awakening and/or
performance on cognitive and behavioral tasks, we be-
lieve the SIQ in its current form is an important step and
has the potential to advance cognitive-behavioral therapy
interventions for people with depression (Salkovskis
2002).
Despite these limitations, this is the first study to de-
velop and validate a self-report measure of sleep inertia,
the Sleep Inertia Questionnaire. Development and
validation of the SIQ is important because the SIQ allows
for easy assessment of the cognitive, behavioral, emo-
tional, and physiological correlates of sleep inertia in a
clinical context and can also progress research on why
certain populations (e.g., people with a mood disorder)
have trouble getting out of bed and performing during the
early morning hours. The present study found that the
SIQ demonstrates strong psychometric properties; it has
good to excellent internal consistency, demonstrates
strong construct validity and is associated with sleep
duration, one of the strongest correlates of sleep inertia.
This study also demonstrates that sleep inertia is more
severe and lasts longer in students with moderate to
severe symptoms of depression and adults with major
depressive disorder when compared to a No-to-Mild De-
pression student group. These results may have treatment
implications. First, reducing sleep inertia in people with
depression may be an important target for intervention.
Second, reducing sleep inertia may improve response to
cognitive behavioral therapy for depression, particularly
response to activity scheduling and behavioral activation.
The first step towards researching these possibilities is to
establish a measure of sleep inertia. The SIQ has potential
for fulfilling this role.
Acknowledgments This research was partially supported by the
National Institute of Mental Health Grants R34 MH094535. Pre-
liminary data pertaining to this manuscript was presented during a
poster session at the 2013 Associated Professionals Sleep Societies
conference in Baltimore.
Conflict of Interest Jennifer C. Kanady and Allison G. Harvey
report no conflicts of interest.
Informed Consent All procedures were in accordance with the
ethical standards of the responsible committee on human ex-
perimentation (institutional and national) and with the Helsinki
Declaration of 1964. Informed consent was obtained from all par-
ticipants before being included in the study.
Animal Rights No animal studies were carried out by the authors
for this study.
Cogn Ther Res (2015) 39:601–612 609
123

Appendix
References
Achermann, P., Werth, E., Dijk, D. J., & Borbely, A. A. (1995).
Time course of sleep inertia after nighttime and daytime
sleep episodes. Archives Italiennes de Biologie, 134(1),
109–119.
Balkin, T. J., & Badia, P. (1988). Relationship between sleep inertia
and sleepiness: Cumulative effects of four nights of sleep
disruption/restriction on performance following abrupt nocturnal
awakenings. Biological Psychology, 27(3), 245–258.
Basta, M., Lin, H., Pejovic, S., Sarrigiannidis, A., Bixler, E. O., &
Vgontzas, A. N. (2007). Lack of regular exercise, depression,
and degree of apnea are predictors of excessive daytime
sleepiness in patients with sleep apnea: Sex differences. Journal
of Clinical Sleep Medicine, 4(1), 19–25.
Benca, R. M., Obermeyer, W. H., Thisted, R. A., & Gillin, J. C.
(1992). Sleep and psychiatric disorders: A meta-analysis.
Archives of General Psychiatry, 49(8), 651–668.
Bruck,D.,&Pisani, D. L. (1999). The effects of sleep inertia on decision-
making performance. Journal of Sleep Research, 8(2), 95–103.
The Sleep Inertia Questionnaire (SIQ)
On a typical morning in the past week, after you wake up, to what extent do you . . .
1. Have problems getting out of bed? 1 2 3 4 5
2. Need an alarm to wake up? 1 2 3 4 5
3. Hit the snooze button on the alarm? 1 2 3 4 5
4. Bump into and drop things? 1 2 3 4 5
5. Notice that you move more slowly? 1 2 3 4 5
6. Notice that you feel sleepy? 1 2 3 4 5
7. Notice your eyes feeling heavy, sore, or itchy? 1 2 3 4 5
8. Notice your arms and/or legs feeling tired or heavy? 1 2 3 4 5
9. Notice that your mind feels groggy, fuzzy or hazy? 1 2 3 4 5
10. Notice that you get winded more easily? 1 2 3 4 5
11. Notice that it is difficult to keep your balance? 1 2 3 4 5
12. Notice that you feel tense? 1 2 3 4 5
13. Feel anxious about the upcoming day? 1 2 3 4 5
14. Dread starting your day? 1 2 3 4 5
15. Wish you could sleep more? 1 2 3 4 5
16. Have difficulty concentrating? 1 2 3 4 5
17. Find that you think more slowly? 1 2 3 4 5
18. Find that you think react more slowly? 1 2 3 4 5
19. Find that you make more mistakes/errors? 1 2 3 4 5
20. Can’t imagine being able to wake up? 1 2 3 4 5
21. Have difficulty getting your thoughts together? 1 2 3 4 5
22. How long does it take you to ‘come to’ in the morning? _____________ minutes.
22b. How many days per week is this the case? _______________.
Please use the following scale to answer the questions below:
1 = Not at all 2 = A little 3 = Somewhat 4 = Often 5 = All the time
610 Cogn Ther Res (2015) 39:601–612
123

Burns, G. L., Formea, G. M., Keortge, S., & Sternberger, L. G.
(1995). The utilization of nonpatient samples in the study of
obsessive–compulsive disorder. Behavior Research and Ther-
apy, 33(2), 133–144.
Cansino, S. (2009). Episodic memory decay along the adult lifespan:
A review of behavioral and neurophysiological evidence.
International Journal of Psychophysiology, 7(1), 84–89.
Carpenter, J. S., & Andrykowski, M. A. (1998). Psychometric
evaluation of the Pittsburgh Sleep Quality Index. Journal of
Psychosomatic Research, 45(1), 5–13.
Cassano, G. B., Benvenuti, A., Miniati, M., Calugi, S., Mula, M.,
Maggi, L., et al. (2009). The factor structure of lifetime
depressive spectrum in patients with unipolar depression.
Journal of Affective Disorders, 115(1–2), 87–99.
Cuijpers, P., van Straten, A., & Warmerdam, L. (2007). Behavioral
activation treatments of depression: A meta-analysis. Clinical
Psychology Review, 27(3), 318–326.
Dinges, D. F. (1990). Are you awake? Cognitive performance and
reverie during the hypnopompic state. In R. Bootzin, J.
Kihlstrom, & D. Schacter (Eds.), Sleep and cognition. Wash-
ington, DC: American Psychological Association.
Dozois, D. J., Dobson, K., & Ahnberg, J. A. (1998). Psychometric
evaluation of the Beck Depression Inventory-II. Psychological
Assessment, 10(2), 83–89.
Enns, M. W., Cox, B. J., & Borger, S. C. (2001). Correlates of
analogue and clinical depression: A further test of the phe-
nomenological continuity hypothesis. Journal of Affective
Disorders, 66(2–3), 175–183.
Feltin, M., & Broughton, R. J. (1968). Differential effects of arousal
from slow wave sleep and REM sleep. Psychophysiology, 5(2),
231.
Ferguson, C. J. (2009). An effect size primer: A guide for clinicians
and researchers. Professional Psychology, Research and Prac-
tice, 40(5), 532–538.
First, M. B., Spitzer, M. B., Gibbon, M., & Williams, J. B. (1995).
Structured clinical interview for DSM-IV Axis-I Disorders—
Patient Edition (SCID-I/P, Version 2.0). New York, NY:
Biomedics Research Department, New York State Psychiatric
Institute.
Foley, D. J., Vitiello, M. V., Bliwise, D. L., Ancoli-Israel, S., Monjan,
A. A., & Walsh, J. K. (2007). Frequent napping is associated
with excessive daytime sleepiness, depression, pain, and nocturia
in older adults: Findings from the National Sleep Foundation
‘2003 Sleep in America’ poll. The American Journal of Geriatric
Psychiatry, 15(4), 344–350.
Fort, A., & Mills, J. N. (1972). Influence of sleep, lack of sleep and
circadian rhythms on short psychometric tests. In W.
P. Colquhoun (Ed.), Aspects of human efficiency. London: The
English University Press.
Fresco, D. M., Moore, M. T., Manfred, H. M., van Dulmen, M. H. M.,
Segal, Z. V., Ma, S. H., et al. (2007). Initial psychometric
properties of the experiences questionnaire: Validation of a self-
report measure of decentering.Behavior Therapy, 38(3), 234–246.
Gamble, K. L., Motsinger-Reif, A. A., Hida, A., Borsetti, H. M.,
Servick, S. V., Ciarleglio, C. M., et al. (2011). Shift work in
nurses: Contribution of phenotypes and genotypes to adaptation.
PLoS One, 6(4), e18395.
Germain, A., & Kupfer, D. J. (2008). Circadian rhythm disturbances
in depression. Human Psychopharmacology, 23(7), 571–585.
Gliem, J. A. & Gliem, R. R. (2003). Calculating, interpreting, and
reporting Chronbach’s alpha reliability coefficient for Likert-
type scales. In: Ohio State University (Ed.) Midwest research to
practice conference in adult, continuing, and community educa-
tion. Columbus, Ohio: Ohio State University.
Goodenough, D. R., Lewis, H. B., Shapiro, A., Jaret, L., & Sleser, I.
(1965). Dream report following abrupt awakening from different
kinds of sleep. Journal of Personality and Social Psychology,
2(2), 170–179.
Granello, D. G., & Wheaton, J. E. (2004). Online data collection:
Strategies for research. Journal of Counseling and Development,
82(4), 387–393.
Hopko, D. R., Lejuez, C. W., Ruggiero, K. J., & Eifert, G. H. (2003).
Contemporary behavioral activation treatments for depression:
Procedures, principles, and progress. Clinical Psychology Re-
view, 23(5), 699–717.
Hou, Y., Huangfu, E., Zhang, L., & Miao, D. (2007). Changes in
cognition and mood due to sleep inertia after 30-hour sleep
deprivation. The Internet Journal of Mental Health, 4(1).
Jewett, M. E., Wyatt, J. K., Ritz-De Cecco, A., Khalsa, S. B., Dijk, D.
J., & Czeisler, C. A. (1999). Time course of sleep inertia
dissipation in human performance and alertness. Journal of Sleep
Research, 8(1), 1–8.
Marzano, C., Ferrara, M., Moroni, F., & Gennaro, L. D. (2011).
Electroencephalographic sleep inertia of the awakening brain.
Neuroscience, 176, 308–317.
Miccoli, L., Versace, F., Koterle, S., & Cavallero, C. (2008).
Comparing sleep-loss sleepiness and sleep inertia: Lapses make
the difference. Chronobiology International, 25(5), 725–744.
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of
covariance. Journal of Abnormal Psychology, 110(1), 40–48.
Morin, C. M., Belleville, G., Belanger, L., & Ivers, H. (2011). The
Insomnia Severity Index: Psychometric indicators to detect insom-
nia cases and evaluate treatment response. Sleep, 34(5), 601–608.
Muzet, A., Nicolas, A., Tassi, P., & Dewasmes, G. (1995).
Implementation of napping in industry and the problem of sleep
inertia. The Journal of Sleep Research, 4(2), 67–69.
Ohayon, M. M., Priest, R. G., Zulley, J., & Smirne, S. (2000). The
place of confusional arousals in sleep and mental disorders:
Findings in a general population sample of 13,057 subjects. The
Journal of Nervous and Mental Disease, 188(6), 340–348.
Peth, J., Regen, F., Bajbouj, M., Heuser, I., Anghelescu, I., &
Hornung, O. P. (2012). The influence of daytime napping versus
controlled activity on the subjective well-being of patients with
major depression. Psychiatry Research, 200, 368–373.
Rapee, R. M., Craske, M. G., Brown, T. A., & Barlow, D. H. (1996).
Measurement of perceived control over anxiety-related events.
Behavior Therapy, 27(2), 279–293.
Ree,M. J., Harvey, A. G., Blake, R., Tang, N.K., & Shawnee-Taylor,M.
(2005). Attempts to control unwanted thoughts in the night:
Development of the thought control questionnaire-insomnia revised
(TCQI-R). Behavior Research and Therapy, 43(8), 985–998.
Ritter, P. S., Marx, C., Lewtschenko, N., Pfeiffer, S., Leopold, K.,
Bauer, M., & Pfennig, A. (2012). The characteristics of sleep in
patients with manifest bipolar disorders, subjects at high risk of
developing the disease and healthy controls. Journal of Neural
Transmission, 119(10), 1173–1184.
Roemer, L., Molina, S., & Borkovec, T. D. (1997). An investigation
of worry content among generally anxious individuals. Journal
of Nervous and Mental Disease, 185(5), 314–319.
Roenneberg, T., Wirz-Justice, A., & Merrow, M. (2003). Life
between clocks: Daily temporal patterns of human chronotypes.
Journal of Biological Rhythms, 18(1), 80–90.
Rush, A. J., Gullion, C. M., Basco, M. R., Jarrett, R. B., & Trivedi, M.
H. (1996). The Inventory of Depressed Symptomatology (IDS):
Psychometric properties. Psychological Medicine, 26(3),
477–486.
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow,
B., Klein, D. N., et al. (2003). The 16-item Quick Inventory of
Depressive Symptomatology (QIDS), clinican rating (QIDS-C),
and self-report (QIDS-SR): A psychometric evaluation in
patients with chronic major depression. Biological Psychiatry,
54(5), 573–583.
Cogn Ther Res (2015) 39:601–612 611
123

Salkovskis, P. M. (2002). Empirically grounded clinical interventions:
Cognitive-behavioral therapy progresses through a multi-dimen-
sional approach to clinical science. Behavioural and Cognitive
Psychotherapy, 30(1), 3–9.
Seminara, J. L., & Shavelson, R. J. (1969). Effectiveness of space
crew performance and subsequent to sudden sleep arousal.
Aerospace Medicine and Human Performance, 40(7), 723–727.
Smith, C. S., Reilly, C., & Midkiff, K. (1989). Evaluation of three
circadian rhythm questionnaires with suggestions for an im-
proved measure of morningness. Journal of Applied Psychology,
74(5), 728–738.
Soria, V., Martinez-Amoros, E., Escaramis, G., Perez-Egea, R.,
Garcia, C., Gutierrez-Zotes, A., et al. (2010). Differential
association of circadian genes with mood disorders: CRY1 and
NPAS2 are associated with unipolar major depression and
CLOCK and VIP with bipolar disorder. Neuropsychopharma-
cology, 35(6), 1279–1289.
Sternberger, L. G., & Burns, G. L. (1991). Obsessive–compulsive
disorder: Symptoms and diagnosis in a college sample. Behavior
Therapy, 22(4), 569–576.
Stopa, L., & Clark, D. M. (2001). Social phobia: Comments on the
viability and validity of an analogue research strategy and British
norms for the Fear of Negative Evaluation Questionnaire.
Behavioural and Cognitive Psychotherapy, 29(4), 423–430.
Tassi, P., Bonnefond, A., Engasser, O., Hoeft, A., Eschenlauer, R., &
Muzet, A. (2006). EEG spectral power and cognitive performance
during sleep inertia: The effect of normal sleep duration and
partial sleep deprivation. Physiology & Behavior, 87(1), 177–184.
Tassi, P., & Muzet, A. (2000). Sleep inertia. Sleep Medicine Reviews,
4(4), 341–353.
Tonetti, L., Fabbri, M., & Natale, V. (2008). Sex differences in sleep
time preference and sleep need: A cross-sectional survey among
Italian pre-adolescents, adolescents, and adults. Chronobiology
International, 25(5), 745–759.
Trivedi, M. H., Rush, A. J., Ibrahim, H. M., Carmody, T. J., Biggs, M.
M., Suppes, T., et al. (2004). The Inventory of Depressive
Symptomatology, Clinician Rating (IDS-C) and Self-Report
(IDS-SR), and the Quick Inventory of Depressive Symptoma-
tology, Clinical Rating (QIDS-C) and Self-Report (QIDS-SR) in
public sector patients with mood disorders: A psychometric
evaluation. Psychological Medicine, 34(1), 73–82.
Watson, D., & Clark, L. A. (1988). Development and validation of
brief measures of positive and negative affect: The PANAS
scales. Journal of Personality and Social Psychology, 54(6),
1063–1070.
Webb, W. B., & Agnew, H. (1964). Reaction time and serial response
efficiency on arousal from sleep. Perceptual and Motor Skills,
18, 783–784.
Weissbluth, M., & Weissbluth, L. (1992). Colic, sleep inertia,
melatonin, and circannual rhythms. Medical Hypothesis, 38(3),
224–228.
Wilkinson, R. T., & Stretton, M. (1971). Performance after awaken-
ing at different times of night. Psychonomic Science, 23(4),
283–285.
612 Cogn Ther Res (2015) 39:601–612
123