The bidirectionality of the relationship between insomnia, anxiety and depression in adolescents: A longitudinal study Pasquale K Alvaro Bachelor of Psychology (Hons) Thesis submitted for the degree of Doctor of Philosophy School of Psychology Faculty of Health Sciences University of Adelaide August 2014
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The bidirectionality of the relationship between insomnia, anxiety and depression in adolescents: A
longitudinal study
Pasquale K Alvaro
Bachelor of Psychology (Hons)
Thesis submitted for the degree of Doctor of Philosophy
School of Psychology
Faculty of Health Sciences
University of Adelaide
August 2014
Table of contents
List of Tables ................................................................................................................... vii
List of Figures .................................................................................................................. vii
Declaration ........................................................................................................................ i
Acknowledgements ........................................................................................................... ii
2.1. The thesis .................................................................................................................................... 53
2.2. Study 1 - A Systematic Review Assessing Bidirectionality between Sleep Disturbances, Anxiety,
and Depression .................................................................................................................................. 56
2.3. Study 2 – The Independent Relationships between Insomnia, Depression, Subtypes of Anxiety,
and Chronotype during Adolescence ................................................................................................ 57
2.4. Study 3 – Bidirectional relationships between insomnia and depression, and insomnia and
subtypes of anxiety during adolescence: does chronotype effect these relationships? .................. 59
2.5. Sample size rationale for studies 2 and 3 ................................................................................... 62
3.1.1. Study Objectives ................................................................................................................................. 66
5.1.1. Study Objectives ............................................................................................................................... 122
reported that sleep disturbances are associated with clinically significant symptoms of GAD,
PD/agoraphobia, and SP, but not OCD or SAD in adolescents. Considering the evidence, it
seems that a relationship between insomnia and depression, and insomnia and anxiety
(assessed as an overall construct) during adolescence is consistently reported across
definitions and severity criteria, but not subtypes of anxiety.
1.8.1. Potential mechanisms that underlie the relationship between
insomnia, depression and anxiety
There are various mechanisms that may underlie the consistently reported
associations between insomnia and depression, and insomnia and anxiety. These disorders
may arise due to common risk-factors, or there may be a causal relationship. Both
mechanisms are likely to underlie the relationship between insomnia, depression and
anxiety, although to what extent is unclear. The following section describes common risk-
factors and causality in more detail.
1.8.1.1. Common risk-factors
Common risk-factors have been theorised to explain the relationship between
disorders in two ways. Neale and Kendler (1995) proposed that common risk-factors, all
independent and of small effect, are alternative forms of the same disorder, suggesting that
39
a disorder may manifest in different ways (e.g., via insomnia or depression). In contrast,
Staner (2010) notes that such risk-factors may result in two distinctive yet associated
disorders if risk-factors are common to both conditions.
Although both theories have merit, the evidence seems to contradict the prior theory
and support the latter theory when considering the relationships between insomnia and
depression. Various studies have reported a predictive relationship between insomnia and
depression after controlling for covariates (i.e., common risk-factors) (Baglioni, et al., 2011;
Jansson-Fröjmark & Lindblom, 2008; Meijer, Reitz, Deković, van den Wittenboer, & Stoel,
2010), suggesting that the disorders are separate constructs even when considering
common risk-factors. Furthermore, cross-sectional and longitudinal studies have reported
that anxiety and other covariates (e.g., age and gender) partially account for the relationship
between insomnia and depression, suggesting that although insomnia and depression are
separate disorders, comorbid insomnia and depression is at least partially due to common
risk-factors (i.e., anxiety, age and gender) (Alfano, et al., 2009; Gregory, et al., 2009; Jansson-
Fröjmark & Lindblom, 2008). Therefore, the current evidence indicates that common risk-
factors are a mechanism that underlies the relationship between insomnia and depression,
but other factors are also likely to explain this relationship.
The relationship between insomnia and anxiety, however, is less clear. Cross-sectional
and longitudinal studies have reported mixed findings, where covariates (e.g., depression,
gender and age) have been found to completely account for the relationship between
insomnia and anxiety in some studies (Alfano, et al., 2009), and partially account for this
relationship in other studies (Jansson-Fröjmark & Lindblom, 2008; Meijer, et al., 2010). One
reason for the contrast in results could be that anxiety is often assessed as an overall
40
variable (i.e., the different anxiety disorders are grouped as one), and studies have reported
that the relationship between insomnia and anxiety differs across anxiety subtypes (Alfano,
Ginsburg, & Kingery, 2007). Even so, it seems safe to assume that insomnia and anxiety
disorders are separate constructs given the different symptoms, diagnostic criteria and
recommended treatments for each disorder (although some do overlap) (American
Psychiatric Association, 2013). Therefore, the above evidence likely indicates that the
mechanisms underlying the relationship between insomnia and anxiety are either partially
or entirely explained by common risk-factors.
1.8.1.2. Cause-effect relationship
Insomnia, depression and anxiety may also have a causal relationship, where the
development of an insomnia disorder results in the development of a depressive or anxiety
disorder, or vice-versa. Although the notion of causality is difficult to ascertain, Hill (1965)
proposed a widely used criteria that focusses on nine aspects of a relationship by which
causal inferences can be made. Such aspects of the relationship include the magnitudes of
the effect-sizes that are reported in studies, the consistency of a significant effect across
different studies and cultures, the specificity of the observed association (is it limited to a
particular population or circumstance?), the bidirectionality of the relationship, the dose-
response linear relationship of two variables, the biological plausibility (are there biological
mechanisms that can explain the relationship?), coherence (correspond with the known
facts of the disorder), the experimental evidence available, and analogy (can a judgment be
made in similar circumstances?) (Hill, 1965).
Hill (1965) notes that these criteria do not bring irrefutable evidence for a cause-effect
relationship. In fact, the specificity, dose-response linear relationship, biological plausibility
41
and analogy criteria are features he argues cannot be demanded. Rather, such criteria aim to
assist in determining whether or not there is a more logical explanation than a cause-effect
relationship. With this in mind, the findings of the relationships between insomnia and
depression and insomnia and anxiety largely satisfy Hill’s (1965) criteria. Studies across many
cultures have consistently reported a wide range of dose-linear associations, with varying
effect-sizes, and have not seriously conflicted with the known facts about insomnia,
depression or anxiety (Gregory & O'Connor, 2002; Gregory, et al., 2009; Jansson-Fröjmark &
Lindblom, 2008; Kaneita, et al., 2009; J. M. Kim et al., 2009). Such studies have often control
for potential covariates, and hence further strengthen the notion of a cause-effect
relationship. There has also been experimental evidence based on treatment studies (Alfano,
et al., 2007; Manber et al., 2008), and various biologically plausible explanations (see
Chapters 3, 4 and 5).
The bidirectionality of the relationships between insomnia and depression, and
insomnia and anxiety has also been assessed (Gregory & O'Connor, 2002; Jansson-Fröjmark
& Lindblom, 2008). Bidirectionality refers to whether insomnia predicts or is predicted by
anxiety and depression. The following section introduces the topic of bidirectionality,
describes the research within this area of study, and highlights the methodological issues
within this literature.
1.9. The bidirectionality of the relationship between
insomnia, depression and anxiety
Research that assesses the bidirectionality of the relationship between insomnia,
depression and anxiety aims to explore the potential overlapping developmental courses of
these problems. Anxiety may be a risk factor for insomnia, and insomnia may be a risk factor
42
for depression (Johnson, Roth, & Breslau, 2006), or each may be associated with the
development of the other (Jansson-Fröjmark & Lindblom, 2008). Such research can help
identify key aetiological factors for insomnia, depression and anxiety, establish whether the
onset of one is a risk-factor for the others, and inform public health campaigns and clinical
interventions for each disorder (Jansson-Fröjmark & Lindblom, 2008; Johnson, Roth, &
Breslau, 2006; Kaneita, et al., 2009). Bidirectionality can only be established by considering
whether insomnia is associated with the development of depression or anxiety, and whether
depression or anxiety is associated with the development of insomnia, in subsequent
analyses. A recent meta-analysis of longitudinal studies found that baseline insomnia
predicts follow up depression, but did not establish directionality because the association
between baseline depression and follow-up insomnia was not assessed simultaneously
(Baglioni, et al., 2011).
Recent evidence on bidirectionality has been limited and contentious. After controlling
for gender, race, and prior disorders of insomnia, anxiety or major depressive disorder,
Johnson, Roth, and Breslau (2006) reported significant associations between prior anxiety
disorders and onset of insomnia disorder [Hazard Ratio=3.5; the hazard in an exposed group
divided by the hazard in an unexposed group (Hernán, 2010)], and prior insomnia disorder
and onset of major depressive disorder (Hazard Ratio=3.8). However, a significant
relationship was not found between prior insomnia disorder and onset of an anxiety
disorder, or prior major depressive disorder and onset of insomnia disorder. Similarly,
Ohayon and Roth (2003) found that adults with current but no previous history of any
anxiety disorder presented with insomnia symptoms simultaneously and after the anxiety
disorder more often than before (38.6% , 43.5% and 18% respectively), whereas those
43
without a history of mood disorders presented with insomnia symptoms more often before
mood disorders than simultaneously or after (41%, 29.4% and 28.9% respectively). These
studies suggest the possibility of two separate cause-effect relationships (Figure 1), where
anxiety leads to insomnia (Figure 1a) and insomnia leads to depression (Figure 1b) (Johnson,
Roth, & Breslau, 2006), but not vice-versa. Consequently, insomnia, depression, and anxiety
are thought to have distinct natural courses of development during adolescence, from
anxiety to insomnia and insomnia to depression (Johnson, Roth, & Breslau, 2006).
Therefore, the relationship between insomnia, depression, and anxiety may predominately
follow a pathway from anxiety to insomnia to depression (Figure 1c) (Johnson, Roth, &
Breslau, 2006).
In contrast, Jansson-Frojmark and Lindblom (2008) demonstrated an association
between symptoms of anxiety and depression, and new episodes of insomnia at 1-year
follow-up (OR=4.27 and 2.28, respectively). They also reported an association between
symptoms of insomnia, and new cases of anxiety (OR=2.30) and depression (OR=3.51) at 1-
year follow-up. Similarly, after controlling for predictor variables at baseline, Morphy and
Figure 1. Aetiologically exclusive relationships between insomnia, anxiety and depression. (1a) Anxiety leads to insomnia (1b) Insomnia leads to depression (1c) Anxiety leads to insomnia, which
leads to depression
Anxiety Insomnia
Depression Insomnia
(1a)
(1b)
Anxiety Insomnia Depression
(1c)
44
colleagues (2007) reported a relationship between symptoms of anxiety and depression at
baseline, and symptoms of insomnia at 12-month follow-up , and vice-versa. Among
adolescents, Kaneita and associates (2009) found a significant relationship between poor
sleep quality present at baseline and follow-up, and mental health status
(depression/anxiety symptoms) at follow-up (OR=5.81) in participants who did not display
poor mental health at baseline. Poor mental health status present at baseline and follow-up
also predicted poor sleep quality at follow-up (OR=6.9) in participants who did not display
poor sleep quality at baseline. Gender, baseline sleep quality or mental health status, and
baseline lifestyle and contentment with daily life were used as covariates in each analysis
(Kaneita, et al., 2009). These studies suggest a bidirectional relationship between insomnia,
depression, and anxiety, where each contribute to the development of one another
(Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; Morphy, et al., 2007) (Figure 2).
Consequently, insomnia, depression and anxiety are thought to have overlapping courses of
development rather than two distinct associations, which may be facilitated by familial,
social and environmental stressors (Kaneita, et al., 2009) or abnormalities to
neurotransmitters associated with the sleep wake cycle, anxiety and depression such as
Although chronotype was assessed, other potential covariates were excluded from
the analyses for studies 2 and 3. Total sleep time and time in bed were used to tabulate data
on delayed sleep phase syndrome (see study 2). Academic achievement was not deemed to
have been measured reliably, as only two participants at baseline and one at follow-up
reported lower than average grades. The distributions were extremely skewed and the
amount of outliers exceeded 25% for exercise, caffeine intake, amount of time spent on
homework, amount of time spent working (occupation) outside of school hours, and alcohol
and drug use. These results are likely due to the decision to develop only one or two items
for the assessment of each variable that were not based on empirically validated
questionnaires. The decision to use few and non-validated items rather than empirically
validated questionnaires was based on a request from the Department for Education and
55
Child Development to limit the amount of questions that were asked to the adolescents.
Furthermore, the above variables were not the main focus of the thesis.
There was approximately 11% of data missing for the drug and alcohol use
variables, as the Department for Education and Child Development would only grant ethics
committee approval if students from public schools received a copy of the questionnaire
without the drug and alcohol items. Of those who answered the questions, approximately
90% and 98% stated they have never consumed alcohol or illicit drugs, respectively. The lack
of identification of alcohol and illicit drug consumption may have resulted from a caveat that
was placed on the information sheets stating the following: “the information regarding drug
and alcohol use collected in this study could in principle be obtained by court order: that is, it
could be required to be handed over to the police and used as evidence in a court of law
against your child. However, this is unlikely, and the researchers will make every effort to
ensure that any information gathered will remain confidential”. The Human Research Ethics
Committee of the University of Adelaide would only grant ethics approval if this caveat was
on the information sheets.
The above caveat was a rollover effect of a request from the Human Research Ethics
Committee and the requirement of Australian psychologists who conduct research with at-
risk populations to identify and follow-up participants with potential mental health disorder.
Drs Rachel Roberts and Jodie Harris are both registered clinical psychologists, and
adolescents are considered an ‘at-risk’ population. The participants were required to write
their names on the top of the questionnaire, and were informed (along with their parents) of
the reasons for this. Once data was collected, each questionnaire was locked in a secure
location, and data was de-identified as soon as possible.
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Adolescents were identified as at risk and requiring identification of follow-up if
they reported clinically significant scores in the questionnaire that assessed depression and
subtypes of anxiety AND reported thinking about death often or always (the RCADS, see
methods section of studies 2 and 3 for specific details regarding how the scores were
tabulated). Parents were contacted when students were identified as at risk. They were
informed of the findings, and sent a resources sheet containing various treatment options
and information sources via email or mail. The treatment options on each resources sheet
were based on the sector of Adelaide/South Australia the adolescent resided (e.g., north,
south, east, west or central).
2.2. Study 1 - A Systematic Review Assessing
Bidirectionality between Sleep Disturbances, Anxiety,
and Depression
Study 1 was conducted to give an overview of the current literature, along with the
shortcomings and potential future directions of research. Such a study was deemed a useful
introductory paper for this thesis, and lead into assessing the bidirectionality of the
relationship between insomnia, depression, and anxiety.
Participants of any age and variables of any sleep disturbance were included in study 1,
as only one study assessed bidirectionality between insomnia, anxiety and depression during
adolescence (Johnson, Roth, & Breslau, 2006), and five independent studies assessed
insomnia in various age groups (Buysse et al., 2008; Hasler et al., 2005; Jansson-Fröjmark &
Lindblom, 2008; Johnson, Roth, & Breslau, 2006; J. M. Kim, et al., 2009; Morphy, et al.,
2007). Another study was originally included in the systematic review that assessed the
place of chronic insomnia in the course of development of anxiety and depressive disorders,
57
and vice-versa (Ohayon & Roth, 2003). However, this study did not use significance testing,
and, after a discussion between all authors, was deemed not to adequately assess
bidirectionality.
The terms sleep disturbance encompassed any variable that was indicative of disrupted
sleep, including clinically significant and diagnosed disorders. The terms sleep problems
portrayed an overall variable consisting of items representing more than one sleep
disturbance that was assessed by a study included in the systematic review. For example, a
questionnaire that contained items about insomnia and nightmares would be considered as
sleep problems. Sleep problems was consistently referred to as childhood sleep problems, as
each sleep problem variable was assessed in children. Sleep quality was reflected by
variables that assessed how well an individual slept, and often contained insomnia items.
Insomnia was defined as troubles with sleep initiation, sleep maintenance, night-time
awakenings, and sleep latency following night-time awakenings. Some articles that assessed
insomnia also assessed daytime functioning. Although not specifically a sleep disturbance
variable, time in bed was included in the systematic review, as lower amounts of time in bed
suggests sleep loss.
2.3. Study 2 – The Independent Relationships between
Insomnia, Depression, Subtypes of Anxiety, and
Chronotype during Adolescence
Study 2 was a cross-sectional study of 318 high school students that used self-report
measures. There were two main aims; firstly, to assess the relationship between the main
variables of the thesis at baseline; and secondly, to discover whether or not chronotype
predicts insomnia, depression and subtypes of anxiety once covariates are controlled.
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Chronotype was deemed to potentially predict insomnia, depression and subtypes of
anxiety, as previous studies have found that chronotype was correlated to each variable
during adolescence (Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009; Russo, Bruni,
Lucidi, Ferri, & Violani, 2007). Study 2 originally assessed the mediation effects of
chronotype on the relationship between insomnia and depression above and beyond
anxiety, and insomnia and each subtype of anxiety above and beyond depression. However,
a reviewer for study 2 suggested that such an investigation lacked theoretical support, and
hence suggested that moderation or prediction be assessed. The authors decided on
prediction to be consistent with study 3 and the overall aim of the thesis.
The decision to use generalised linear equations to analyse the aims of study 2 was
based on the recommendation of a statistician at the University of Adelaide (statistical
justifications can be found in the methods section of the study). Furthermore, although
outliers were found, the 5% trimmed mean for insomnia, depression, subtypes of anxiety
and chronotype subscales were very similar to the actual means (differences ranged from
.09 to .26), suggesting the outliers would have a minimal effect on the results. Indeed,
sensitivity analyses showed this to be the case. Therefore, these data were retained (Pallant,
2011).
Less than 2% of the data from study 2 was missing for the insomnia, depression,
subtypes of anxiety and chronotype scales. Although some evidence suggests that
missingness may have been non-random, the sample size was large and the missing values
were very small. Therefore, on the recommendation of Tabachnick and Fidell (2007),
participants with missing data were only excluded for the analyses that assessed the
59
variables containing their missing data and retained for the analyses where all data was
present.
2.4. Study 3 – Bidirectional relationships between
insomnia and depression, and insomnia and subtypes of
anxiety during adolescence: does chronotype effect
these relationships?
Study 3, a longitudinal report of 255 high school students, was the main paper for this
thesis. The aims were to investigate the bidirectionality of the association between
insomnia, depression and subtypes of anxiety, and the effects of chronotype on these
relationships. Chronotype was used as a predictor for study 3.
Bootstrap step-wise regression analyses were used rather than generalised linear
equations in study 3 after consulting a statistician. Different analyses were needed, as study
3 was longitudinal and study 2 was cross-sectional. The statistician noted that because
mediation analyses were not conducted for study 3, deciding on the statistical analysis to
use should be based on whether cluster variables (schools in this case) have a large enough
effect on the outcome variables. He recommended not accounting for clustering if schools
explain less than 5% of the outcome variables, but if they did, further tests were to be
conducted. The amount of variance explained by schools for each outcome variable was
below 4%, with most below 2%, therefore suggesting minimal clustering.
The amount of missing data for study 3 was a little higher than that of study 2, but still
below 3% for depression, each anxiety subtype and chronotype, and below 7% for insomnia
(approximately 6.6%). The evidence suggested that missing data was random, while the
60
sample size was large and the amount of missing values was very low. Therefore,
participants with missing data were only excluded from the analyses when they had missing
data for all major variables (i.e., insomnia, anxiety subtypes, depression and chronotype)
(Tabachnick & Fidell, 2007).
Outliers were also found in study 3, and the 5% trimmed means for the outcome
variables were slightly higher than those found in study 2. However, as suggested by Pallant
(2011), the outliers were retained, as the differences between the 5% trimmed means and
original means were still very low (ranging from 0.12 to 0.45). These findings, paired with the
sensitivity analyses, suggested that the outliers had a negligible impact on the results.
Originally, there was a debate regarding the length of time between baseline and
follow-up for study 3. Bidirectionality studies have typically used 12 or 24 month intervals
between baseline and follow-up (Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; J.
M. Kim, et al., 2009; Meijer, et al., 2010), but the current study used a six month interval.
This makes for a more reliable study, as potential confounders such as schools, classrooms,
friends and lifestyle (e.g., driving and dating) is more likely to remain constant. It could be
argued that seasonal affective disorders are more likely to bias the results when using a six
month follow-up, as baseline and follow-up would occur at opposing seasons. However,
studies have found low prevalence rates of winter seasonal affective disorders within
Australia [3.65% (Kasof, 2009), 1.7% (Morrissey, Raggatt, James, & Rogers, 1996), 0.7% and
0.5% (Murray, 2004)], suggesting that seasonal affective disorders are not expected to
greatly impact the results. Furthermore, such prevalence rates are likely to be exaggerated;
the Seasonal Pattern Assessment Questionnaire was used by each study to assess seasonal
affective disorders, which has been found to overestimate the prevalence rate of seasonal
61
affective disorders compared to clinical assessments (for a discussion see Mersch,
Middendorp, Bouhuys, Beersma, & Van Den Hoofdakker, 1999). Finally, summer seasonal
affective disorders are likely to occur in tropical climates (Morrissey, et al., 1996) and
therefore should not affect the study population, because South Australian weather
resembles a Mediterranean climate.
The choice of a 6-month follow-up had a repercussion on the statistical analyses.
Although study 1 argued that outcome variables must be controlled at baseline to accurately
assess bidirectional relationships, it was deemed inappropriate to control for baseline
outcome variables due to the 6-month follow-up. Firstly, symptoms and disorders of
insomnia, depression and subtypes of anxiety are often chronic (see chapter 1 for a review
of the chronicity of each disorder). Secondly, 6 months is unlikely to be enough time for
many new cases of clinically significant insomnia and mental health problems to develop
either independently or as a result of one another, although some new cases may arise.
Indeed, Pearson’s correlation analyses between baseline and follow-up variables showed r
values that ranged between .66 and .78 for insomnia, depression, generalised anxiety
disorder, separation anxiety disorder, and chronotype, and an r value of .61 for obsessive
compulsive disorder and .59 for panic disorder. Furthermore, paired samples t-tests did not
find a significant difference between baseline and follow-up insomnia, depression or anxiety
subtypes, except for PD. However, the Cohen’s d statistic was small in magnitude (d= 0.12)
(Cohen, 1992), suggesting the change was unlikely to be meaningful. Moreover, Baglioni, et
al. (2011) stated that a long-term evaluation of the relationship between insomnia and
depression should have a follow-up of no less than a 12 month. Thirdly, a minimum criterion
of GAD is the presentation of symptoms more often than not for at least 6 months. The
62
current thesis did assess clinically significant symptoms of GAD, but this assessment was
based on, and has been validated against (see chapters 4 and 5 for a review of the validation
literature) the DSM-IV diagnostic criteria (American Psychiatric Association, 2000).
Therefore, the development of clinically significant GAD symptoms is likely to reflect the
development of GAD that is diagnosed according to the DSM-IV diagnostic criteria. Indeed,
Mathyssek et al. (2013) psychometric evaluation of the Revised Children’s Anxiety and
Depression Scale (RCADS, the inventory that was used to assessed symptoms of anxiety
subtypes in this research project) suggests that measured stability or changes of anxiety
symptoms over time are highly indicative of true stability or changes in anxiety levels over
time. Nevertheless, the authors recognise the validity of the argument for controlling
baseline outcome variables, and have therefore presented such the results in an appendix in
chapter 7.
2.5. Sample size rationale for studies 2 and 3
Tabachnick and Fidell (2007) recommended the following equation to attain a sample
size that would yield adequate power (α = .05, β= .20), as dependent variables were
expected to be skewed (which was the case) and possibly small in magnitude:
N ≥ (8/f2) + (m – 1)
Where f2= R2/(1 – R2), and m= number of predictors.
An examination of the recent literature found that effect sizes (f2) generated by studies
that assessed the bidirectionality of the relationship between anxiety, depression and sleep
disturbances ranged from 0.02 - 0.33 (Gregory & O'Connor, 2002; Gregory, et al., 2009;
Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; J. M. Kim, et al., 2009), with the
63
majority between .04 and .08. An f2 statistic of .03, considered to be a small effect size
(Cohen, 1992), was used to calculate the target sample size to be conservative.
N ≥ (8/.03) + (4 – 1)
N ≥ 228.57 + 3
N ≥ 231.57
Therefore, approximately 232 participants were needed at follow up to achieve
adequate power. A further 25% was added to the target sample size at baseline to account
for dropouts. Therefore, the target sample size was 290 at baseline.
2.6. Summary
The current chapter described information that was important to but beyond the
scope of the manuscripts due to journal word limits. The reasons for conducting each study
were discussed, along with the methodological overlap and differences between the
manuscripts, and reasons for important decisions such as sample size.
The next chapter presents the first study entitled ‘A systematic review assessing
bidirectionality between sleep disturbances, anxiety and depression’. It follows a normal
structure for a systematic review, which begins with an abstract, followed by an
introduction, methods, results and discussion. The main difference between the structure of
a systematic review and an original article is in the methods section, where a systematic
review outlines the assessment of quality for each study that is included in the analyses.
64
Chapter 3: Study 1
A Systematic Review assessing Bidirectionality between Sleep Disturbances, Anxiety and Depression.
Alvaro, P.K., Roberts, R.M., & Harris, J.K.
School of Psychology, University of Adelaide
Published: 2013, Sleep, 36 (7), 1059 – 1068.
Pasquale Alvaro (PhD Candidate)
I collected data, performed each synthesis, interpreted data, wrote the manuscript and
acted as the corresponding author. I also made the decisions on what data and arguments to
present in this paper. I later made the revisions to the paper based on the reviewers’
comments.
XPasquale Alvaro
PhD Candidate
Date: 25/08/14
65
Rachel Roberts (Co-Author)
I was the primary supervisor for the research project that led to this paper, so was involved
in the design of the study described in the paper and discussions of results, particularly in
the case of adolescent literature. Mr Alvaro was responsible for writing the paper, and I was
responsible for providing editorial comments and advice on making changes following
review. I hereby give my permission for this paper to be included in Mr Alvaro’s submission
for the degree of PhD at the University of Adelaide.
Jodie Harris (Co-Author)
I was the co-supervisor for the research project that led to this paper. I was involved in the
design of the study described in the paper and discussions of results, particularly where the
sleep variables were concerned. I also provided editorial comments and advice on making
changes following review. I hereby give my permission for this paper to be included in Mr
Alvaro’s submission for the degree of PhD at the University of Adelaide.
XRachel Roberts
Co-author
XJodie Harris
Co-author
Date: 25/08/14
Date: 25/08/14
66
A Alvaro, P.K., Roberts, R.M. & Harris, J.K. (2013) A systematic review assessing bidirectionality between sleep disturbances, anxiety and depression. Sleep, v. 36(7), pp. 1059-1068
NOTE:
This publication is included on pages 66-92 in the print copy of the thesis held in the University of Adelaide Library.
It is also available online to authorised users at:
http://doi.org/10.5665/sleep.2810
94
Chapter 4: Study 2
The independent relationships between insomnia, depression, subtypes of anxiety, and chronotype during adolescence.
report questionnaire that is an adaption of the Spence Children’s Anxiety Scale (Spence,
1997) and corresponds to diagnostic categories of DSM-IV. Each item is scored on a 0 to 3
Likert scale, with higher scores corresponding to more severe depression or anxiety.
Subscales are provided for Generalised Anxiety Disorder (GAD, 6 items, scores ranging from
0 to 18), Panic Disorder (PD, 9 items, scores ranging from 0 to 27), Obsessive Compulsive
Disorder (OCD, 6 items, scores ranging from 0 to 18), Separation Anxiety Disorder (SAD, 7
items, scores ranging from 0 to 21), Social Phobia (SP, 9 items, scores ranging from 0 to 27),
and Major Depressive Disorder (MDD, 10 items, scores ranging from 0 to 30). An overall
scale for anxiety (37 items, scores ranging from 0 to 111) is also provided. Scales are
103
calculated by the sum of each item. The RCADS users guide [available on the University of
California, Los Angeles website (Chorpita, 2011)] converted raw scores into standardised T
scores, where T scores between 65 and 69 indicate borderline clinical threshold and T scores
≥ 70 indicate above clinical threshold. A recent study reported the following Cronbach’s α in
Hawaiian adolescents: SP, α=0.81; PD, α=0.85; GAD, α=0.80; MDD, α=0.76; SAD, α=0.78; and
OCD, α=0.71 (Chorpita, et al., 2000). The same study provided strong support for the
structural, convergent and discriminant validity of the RCADS. The RCADS has also been
validated in Australian adolescents; De Ross and colleagues (2002) provided support for the
internal consistency for MDD, and anxiety overall, and anxiety subscales. Good convergent
validity was also demonstrated (2002) with moderate to strong correlations between the
subscales of RCADS with scores on the Revised Manifest Anxiety Scale (RCMAS) (Reynolds &
Richmond, 1985) and the Children’s Depression Inventory (CDI) (Kovacs, 1981). Indeed, the
RCADS has been extensively used in the adolescent population (Austin & Chorpita, 2004;
Weems & Costa, 2005), including in studies that have assessed the relationship between
sleep problems, anxiety and depression (Alfano, et al., 2009).
4.3.3. Procedure
Every secondary school within a 40km radius of the Adelaide CBD that was listed by
the Department for Education and Child Development (DECD) in South Australia or in the
2010 Annual Report of the Advisory Committee on Non-Government Schools in South
Australia (n= 71 within catchment area) was approached to participate in the study. Each
principal was sent a letter that invited the school to participate and outlined the study
details, along with the relevant documents including the questionnaire, information letters
to students and parents, and consent forms. Principals who did not respond to the letter
104
received a follow-up phone call or email. Once principals agreed to participate, information
letters and consent forms for the students and parents were given to each student in
selected grades. Students were asked to return consent forms to their home-group teachers,
and those who did not were unable to participate. Questionnaires were completed in class
time under the supervision of teachers. Eight secondary schools in South Australia
participated in the current study.
4.3.4. Statistical analyses
Descriptive statistics were reported using means and standard deviations, or numbers
and percentages. Spearman’s rho (Spearman, 1910) was used to calculate correlations to
account for the slightly skewed data. Direct effects were assessed when a predictor variable
was correlated with an outcome variable.
Two analyses were conducted to assess the independent effect of insomnia on
depression or subtype of anxiety, and vice-versa, and the unique effect of chronotype on
insomnia, depression and subtypes of anxiety. One analysis used insomnia, chronotype, and
depression, an anxiety subtype or anxiety overall as the predictor variables, and an anxiety
subtype or depression as the outcome variable (Figure 5). The other analysis used insomnia
as the outcome variable, and depression or an anxiety subtype, chronotype and anxiety
overall or depression as predictor variables (Figure 5). When anxiety subtypes were
analysed, chronotype and depression were assessed as predictors. When depression was
analysed, chronotype and an anxiety overall variable were used as predictors. The overall
anxiety variable was used to control for all anxiety symptoms.
105
Figure 5. Steps for analyses.
Insomnia
Depression
Chronotype
Anxiety
overall
Insomnia
Anxiety
Subtype
Chronotype
Depression
Depression
Insomnia
Chronotype
Anxiety
overall
Anxiety
subtype
Insomnia
Chronotype
Depression
106
Generalised Estimating Equations (GEE) were used to assess the above analyses. GEE
accounts for clustered and skewed data (Galbraith, Daniel, & Vissel, 2010) that commonly
occur when data are collected from schools. GEE also produces regression coefficients (β)
and confidence intervals (CI). β represents the magnitude of the effects of X on Y, and is
used to generate the coefficients of each unique independent effect. The independent effect
of X on Y is represented by the β that is calculated after each predictor is accounted for CIs
are used for significance testing, where CIs that contain 0 fail to reject the null hypothesis.
Sample size analysis for the GEE was based on multiple regression, as both apply to the
class of generalised linear models and produce β coefficients. The difference between GEE
and multiple regression is that GEE generates robust standard errors that account for
clustered data (Galbraith, et al., 2010), and hence should have more power to detect an
effect when analysing clustered data.
4.4. Results
4.4.1. Descriptive statistics
Tables 6, 7 and 8 report descriptive statistics for demographic information, sleep and
mental health variables, and frequencies of clinically significant and sub-threshold mental
health and insomnia cases. Approximately 25% of adolescents reported suffering from past
sleep or mental health problems, while approximately 17% reported previous treatment for
sleep or mental health problems. Approximately 20% reported other medical issues, while
14% reported previous or ongoing treatment for such issues. Insomnia was the most
frequently identified clinically significant problem (11.19%), whereas MDD was the most
common mental health problem (8.39%).
107
Table 6 Demographic and personal history
Variable Group Number Percent
Self-report past mental health symptoms Depression 22 6.9 Anxiety 21 6.6 Sleep problems 25 7.9 Depression, Anxiety 4 1.3 Depression, Anxiety and Insomnia 5 1.6 Other 8 2.5 No 233 73.3 Self-report past treatment for symptoms of depression, anxiety, sleep problems Psychologist 11 3.5 Counsellor 24 7.5 GP 5 1.6 Other 20 6.2 None 263 82.7 Permanent disabilities or illnesses Asthma 40 12.6 Chronic pain 3 0.9 Other 19 5.8 None 255 80.2 Medication No 272 85.5 Asthma medication e.g., Ventolin puffer 32 10.1 ADHD medication 1 0.3 Sleep medication 1 0.3 Prescribed antidepressants 0 0
Non-prescribed antidepressants or sleep medication 1 0.3
Other 12 3.7 Alcohol use Yes 30 80.2 No 255 9.4 Missing data 33 10.4 Drug use Yes 5 1.6 No 279 87.7 Missing data 34 10.7 Subjective sleep sufficiency Too little sleep 143 44.9 Enough sleep 170 53.4 Too much sleep 5 1.6 Frequency of sufficient sleep Never 7 2.2 Rarely 50 15.7 Sometimes 112 35.2 Usually 133 41.8 Always 16 5.0 Chronotype Eveningness 37 11.64 Neither 252 79.25 Morningness 26 8.18 Missing 3 0.94
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Table 7
Descriptive statistics for sleep and mental health variables
Consequently, insomnia, anxiety and depression may have overlapping courses of
development, and hence contribute to the development of and result from one another
(Jansson-Fröjmark & Lindblom, 2008; Kaneita, et al., 2009; Morphy, et al., 2007).
Psychological and social factors that are common during adolescence such as increased
autonomy, and psychosocial (e.g., peer groups), familial and educational stressors (Kaneita,
et al., 2009; Lipton, et al., 2008) may also predispose adolescents to the development of
insomnia, anxiety and depression.
This study also showed that while associations between insomnia, OCD, SAD and SP
were evident, the relationships were no longer significant when depressive symptoms were
controlled for. In contrast, a previous study reported an association between insomnia and
SP (Alfano, et al., 2007), and suggested that depression has a large yet partial mediation
effect when social phobia predicts insomnia (Buckner, Bernert, Cromer, Joiner, & Schmidt,
2008). These studies used different methodologies; the items used in the current study, as
opposed to the other (Buckner, et al., 2008), are based on DSM-IV (American Psychiatric
Association, 1994) criteria and hence may better represent the symptoms that are assessed
for a clinical diagnosis of insomnia, depression and subtypes of anxiety. Furthermore, the
113
previous study used an overall sleep construct that assessed insomnia via one item (Alfano,
et al., 2007), whereas the current study used a validated 7-item instrument that is specific to
insomnia. An explanation for the current study’s findings could be that symptoms of
depression and GAD may lead to significant and persistent distress and cognitive arousal at
night, whereas symptoms of PD, SAD and SP are triggered by particular stimuli that may not
be present nocturnally (American Psychiatric Association, 1994).
The findings from this study also suggest that depression has a stronger role in either
the development or maintenance of sleep and anxiety symptoms than insomnia. Depression
and not insomnia (or chronotype) predicted each anxiety variable. Furthermore, insomnia
predicted each anxiety subtype before but not after depression was entered into the
models, suggesting that depression may explain the relationship between insomnia and
anxiety subtypes. Nevertheless, this study improves upon current clinical theories by
indicating that insomnia has a stronger relationship with depression than anxiety subtypes,
insomnia is independently related to some but not other anxiety subtypes, and depression
may be a mediating factor between insomnia and subtypes of anxiety. Such findings, paired
with the high correlations between insomnia and all anxiety subtypes before covariates were
controlled further consolidate the notion of a complex and intertwined relationship between
insomnia, anxiety and depression (Jansson-Fröjmark & Lindblom, 2008).
The second aim of this study was to investigate the effect of chronotype on insomnia,
depression, and subtypes of anxiety that is unique from other predictors. The current study
found that an evening chronotype predicted insomnia and depression after controlling for
other predictors, which replicates the findings from previous studies that reported an
association between chronotype and sleep problems, and chronotype and depression
114
(Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009; Russo, et al., 2007). However, in
contrast to previous research (Ferber, 1990; Giannotti, et al., 2002; Randler, et al., 2009;
Russo, et al., 2007), chronotype did not predicted OCD, PD, SAD and SP. This result is likely
due to the effect of insomnia, as chronotype was significantly correlated with OCD, PD, SAD
and SP, but did not predict either anxiety subtype after insomnia was controlled for. Also,
the results regarding PD may better reflect the large sample size, as the effect size was small
and confidence intervals approached zero. Together, these results suggest that chronotype
may have a more direct association with insomnia and depression than with anxiety
subtypes, and that the relationship between chronotype and anxiety subtypes may be
largely due to the presence of insomnia.
The independent effect of chronotype on insomnia and depression may be explained
by symptoms that are common to depression, insomnia and an evening preference during
adolescence such as persistent sleep deprivation, sleep displacement, difficulty adjusting to
social constraints and an alternating lifestyle are also related to poor sleep and mental
health (Giannotti, et al., 2002). Carskadon (Carskadon, et al., 2004) and Kaneita (Kaneita, et
al., 2009) suggested that daily pressures of life accumulate during adolescence and hence
promote later bedtimes, poorer sleep and more mental health problems in older
adolescents. Furthermore, Russo and colleagues (Russo, et al., 2007) found that later bed
and rise times occur with older age on the weekends, indicating that rise times are dictated
by school schedule. Consequently, total sleep time on school nights decreases with age but
remains constant on the weekend, suggesting that sleep deprivation may occur during the
week (Russo, et al., 2007). The results of this study showed a similar pattern.
115
The current study has public health and clinical implications. Given that depression and
GAD independently predict insomnia, prevention and treatment plans for insomnia may also
focus more broadly on depression and GAD. Similarly, given that insomnia independently
predicts depression, prevention and treatment plans for depression might also consistently
focus on improving sleep. Eaton, Badawi and Melton(Eaton, Badawi, & Melton, 1995)
estimated that 47% of cases of depression could have been prevented had sleep problems
been successfully treated one year prior. Furthermore, Ohayon and Roth found that
insomnia was a precursor for relapse of anxiety and depression (Ohayon & Roth, 2003). Also,
given that chronotype and anxiety predicted insomnia and depression, prevention and
treatment plans for depression and insomnia could simultaneously focus on anxiety and the
eveningness chronotype. Finally, given the importance of depression, prevention and
treatment plans for various subtypes of anxiety disorders should also consider depression.
Therefore, interventions that focus on mental health, sleep and circadian rhythms could
prevent the development or help alleviate symptoms of insomnia, depression and the
subtypes of anxiety.
4.5.1. Limitations
The current study contained some limitations. First, directionality could not be inferred
due to the cross-sectional methodology used, and knowledge about the aetiological aspect
of the relationship between these problems remains unclear. This is particularly relevant to
the finding that GAD predicted insomnia, but insomnia did not predict depression. Such a
finding can only infer a relationship, and longitudinal studies are needed for a clearer
understanding of the direction of this relationship. Future studies, then, could assess the
longitudinal relationships between insomnia and subtypes of anxiety, and insomnia and
116
depression after accounting for potential covariates, thereby expanding the current
knowledge base of the direction and hence aetiological relationship between these
problems.
Second, this study is based on self-reported sleep and mental health symptoms rather
than methods that allow a clinical diagnosis. Regarding mental health problems,
comprehensive interviews are required to detect the presence of other disorders that may
confound the results, such as adjustment or conduct disorder. Nevertheless, the RCADS has
been extensively used in the adolescent population (Austin & Chorpita, 2004; Weems &
Costa, 2005), including in studies that have assessed the relationship between sleep
problems, anxiety and depression (Alfano, et al., 2009). Furthermore, recent studies have
shown inconsistencies between subjective and objective reports of poor sleep in paediatric
populations diagnosed with Major Depressive Disorder (Bertocci et al., 2005; Forbes et al.,
2008) and anxiety disorders (Forbes, et al., 2008). The same studies also showed that
objective and subjective reports of sleep were more similar in the general population than in
youths diagnosed with anxiety or depression (Bertocci, et al., 2005; Forbes, et al., 2008).
Indeed, the current study assessed the general population, and prevalence rates of clinically
significant insomnia, depression and SP were similar to those found in other studies using
the general population (Australian Bureau of Statistics, 2008a; Costello, et al., 2011;
Johnson, Roth, & Breslau, 2006). The RCADS and ISI are also measures of mental health
disorders and insomnia that are based on the DSM-IV diagnostic criteria (American
Psychiatric Association, 1994). Future studies could use objective measures and/or clinical
methods of assessment for insomnia, depression and subtypes of anxiety.
117
Moreover, other sleep problems were not assessed. However, disorders such as
obstructive sleep apnoea are unlikely to confound the results due to the relatively low
prevalence rates [range from 0.4% (Johnson & Roth, 2006) - 2.9% (Sánchez-Armengol et al.,
2001)] in paediatric populations. In any case, future studies could use polysomnography and
sleep diaries to detect other sleep disorders, and even assess biological measures that are
directly relevant to chronotype and therefore could better detect delayed sleep phase
syndrome such as core body temperature and dim light melatonin onset.
4.5.2. Conclusions
In conclusion, this study adds to the current literature by assessing the independent
relationship between insomnia and depression, and insomnia and anxiety across subtypes of
anxiety, while investigating the effects of chronotype on insomnia, anxiety and depression
above and beyond potential confounders. The general adolescent population was the
targeted sample, and psychometrically sound measures based on the DSM-IV (American
Psychiatric Association, 1994) criteria for insomnia, subtypes of anxiety and depression were
used. The results suggested that insomnia is independently related to symptoms of
depression and GAD, but not the other subtypes of anxiety. Furthermore, an evening
preference uniquely predicted insomnia and depression, but not GAD, PD, OCD, SAD or SP.
Prevention and treatment efforts for insomnia and depression should potentially consider
and concurrently focus on mental health, sleep and the eveningness chronotype, whereas
prevention and treatment efforts for anxiety subtypes may consider also focussing on
insomnia and depression.
118
Acknowledgements
Dr Stewart Howell contributed to the statistical analyses.
119
Chapter 5: Study 3
Bidirectional relationships between insomnia and depression, and insomnia and subtypes of anxiety during adolescence: does chronotype affect these relationships?
Alvaro, P.K., Roberts, R.M., Harris, J.K., & Bruni, O.
School of Psychology, University of Adelaide
Prepared for submission
Pasquale Alvaro (PhD Candidate)
I collected data, performed each analysis, interpreted data, wrote the manuscript and acted
as the corresponding author. I also made the decisions on what data and arguments to
present in this paper.
XPasquale Alvaro
PhD Candidate
Date: 25/08/14
120
Rachel Roberts (Co-Author)
I was the primary supervisor for the research project that led to this paper, so was involved
in the design of the study described in the paper and discussions of results, particularly in
the case of adolescent literature. Mr Alvaro was responsible for writing the paper, and I was
responsible for providing editorial comments. I hereby give my permission for this paper to
be included in Mr Alvaro’s submission for the degree of PhD at the University of Adelaide.
Jodie Harris (Co-Author)
I was the co-supervisor for the research project that led to this paper. I was involved in the
design of the study described in the paper, the write up of the introduction, and discussions
of results, particularly where the sleep variables were concerned. I hereby give my
permission for this paper to be included in Mr Alvaro’s submission for the degree of PhD at
the University of Adelaide.
XRachel Roberts
Co-author
XJodie Harris
Co-author
Date: 25/08/14
Date: 25/08/14
121
Oliviero Bruni (Co-Author)
I was responsible for Pasquale Alvaro during his academic visit to La Sapienza Unverity of
Rome. As a co-author of this paper, I was heavily involved in editing the write-up, and
statistical analyses. I also contributed to the design of the study, the introduction, and the
discussions of results, particularly where the adolescent sleep research was concerned. I
hereby give my permission for this paper to be included in Mr Alvaro’s submission for the
degree of PhD at the University of Adelaide.
XOliviero Bruni
Co-author
Date: 20/08/14
122
5.1. Abstract
5.1.1. Study Objectives
To assess (1) the bidirectionality of the relationship between insomnia and various
subtypes of anxiety, and insomnia and depression after controlling for confounders; (2) the
independent predictive effects of chronotype on insomnia, depression, and each subtype of
anxiety during adolescence.
5.1.2. Design
Prospective, longitudinal study with a 6-month follow-up. Assessment of insomnia,
subtypes of anxiety, depression and chronotype was made via self-report questionnaires.
5.1.3. Settings
Community sample from eight high schools in Adelaide, South Australia.
5.1.4. Participants
The study was completed at baseline and follow-up by 255 high-school students aged
12 – 18 (M= 14.96, SD= 1.34), attending school grades 7 to 11 at baseline.
5.1.5. Measurement and Results
Participants completed the Insomnia Severity Index, the Revised Child Anxiety and
Depression Scale to assess subtypes of anxiety and depression, and the Morningness-
Eveningness Scale to assess chronotype. After accounting for covariates, insomnia was
bidirectionally related to depression (covariates were baseline age, gender, chronotype and
anxiety overall) and Generalised Anxiety Disorder (GAD, covariates were baseline age,
123
gender, chronotype and depression), but was not related to other subtypes of anxiety
(covariates were baseline age, gender, chronotype and depression). Chronotype predicted
insomnia after accounting for covariates (baseline age, gender, depression and anxiety), but
not depression (covariates were baseline age, gender, insomnia and anxiety overall) or any
anxiety subtypes (covariates were baseline age, gender, insomnia and depression).
5.1.6. Conclusion
Evidence suggests that insomnia is related to depression and GAD above and beyond
other factors, and vice-versa, but not to other anxiety subtypes. An eveningness chronotype
independently predicts the development of insomnia above and beyond other factors, but
not depression or anxiety subtypes. Eveningness, then, predisposes an individual to the
development of insomnia, which is a risk-factor for depression or GAD.
124
5.2. Introduction
Insomnia, anxiety and depression are common disorders during adolescence, with
Sleep medication 1 (0.39) 0 (0) Valerian 1 (0.39) 0 (0) Other 7 (2.75) 8 (3.15) None 221 (87.01) 225 (88.58) Alcohol consumption during the last two weeks Yes 24 (9.45) 31 (12.20) No 201 (79.13) 192 (75.59) Missing data 29 (11.42) 31 (12.20) Drug use during the last two weeks Yes 3 (1.18) 3 (1.18) No 221 (87.01) 220 (86.61) Missing data 30 (11.81) 31 (12.20) Subjective opinion on amount of sleep Too little sleep 113 (44.5) 105 (41.3) Enough sleep 137 (53.9) 146 (57.5) Too much sleep 4 (1.6) 2 (0.79) Missing data 0 (0) 1 (0.39)
139
Table 10 cont…
Variable Baseline Follow-up
Subjective opinion of sufficient sleep Never 4 (1.57) 4 (1.57) Rarely 40 (15.74) 36 (14.17) Sometimes 87 (34.25) 92 (36.22) Usually 110 (43.30) 103 (40.55) Always 13 (5.12) 18 (7.09) Missing data 0 (0) 1 (0.39)
Table 11
Descriptive statistics for sleep and mental health symptoms at baseline and follow-up
GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation Anxiety Disorder, SP= Social Phobia, TST WD= Total sleep time weekday, TST WE= Total sleep time weekend
Table 12
Amount of cases of clinically significant and sub-threshold insomnia, subtypes of anxiety and depression, and morningness, neutral and eveningness chronotype at baseline and follow-up
Note. Bootstrapped correlations between baseline data (n= 245 – 252) are presented in the top left-hand corner, bootstrapped correlations between baseline and follow-up (n= 234 – 249) are presented in the bottom left hand corner, and bootstrapped correlations between follow-up data (n= 232 – 250) are presented in the bottom right hand corner. GAD= Generalised Anxiety Disorder, PD= Panic Disorder, OCD= Obsessive Compulsive Disorder, SAD= Separation Anxiety Disorder, SP= Social Phobia, **= Significant correlation according to the confidence intervals
142
5.4.1. Step-wise regression analyses
Insomnia at baseline predicted depression and GAD in steps 1, 2, 3, and 4, and
depression and GAD predicted insomnia across each step. Insomnia did not predict other
subtypes of anxiety once depression was entered into the model (i.e., step 3). Similarly,
other subtypes of anxiety did not predict insomnia once depression was entered into the
model (i.e., step 2 or 3, depending if chronotype was assessed as a predictor). Chronotype
predicted insomnia in steps 1, 2, 3, and 4 across each analysis, but did not predict depression
or subtypes of anxiety. Age at baseline predicted follow-up depression, but no other
outcome variables. Gender did not predict any outcome variables. Table 15 reports the final
step of the regression analyses where insomnia was used as the outcome variable, while
Table 16 reports the final step of the regression analyses where insomnia was used as a
predictor variable. The full step-wise regression analyses can be found in the appendix1.
1 Note there are two separate appendices, one in section 5.5 and one in section 7. The former is a part of the third study that will be submitted for publication, so we decided to keep it in the same section. The latter adds information that is beyond the scope of study 3. Therefore, we decided to separate the two appendices.
143
Table 15 Regression analyses with insomnia as the outcome variable
Model Predictor β (Bootstrap) CI Lower CI Upper R2adjusted
Note. Bootstrapped regression analyses for when depression and subtypes of anxiety at baseline were used to predict insomnia at follow-up after controlling for insomnia at baseline. 5000 bootstrapped were computed. β= regression co-efficient, CI= Bca 95% Confidence Interval, GAD= Generalised anxiety disorder, OCD= Obsessive compulsive disorder, PD= Panic disorder SAD= Separation anxiety disorder, SP= Social phobia.
192
Table 18 Regression analyses with insomnia as the predictor variable
Note. Bootstrapped regression analyses for when insomnia at baseline was used to predict depression and subtypes of anxiety at follow-up after controlling predicted variable at baseline. 5000 bootstrapped were computed. GAD= Generalised anxiety disorder, OCD= Obsessive compulsive disorder, PD= Panic Disorder, SAD= Separation anxiety disorder, SP= Social phobia, CI= Bca 95% Confidence Interval
193
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