Journal of Child Psychology and Psychiatry **:* (2020), pp **–**
doi:10.1111/jcpp.13288
Self-reported sleep patterns and quality amongst
adolescents: cross-sectional and prospective
associations with anxiety and depression
Faith Orchard,1
Alice M. Gregory,2 Michael Gradisar,3 and Shirley Reynolds1
1
School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK; 2Department of
Psychology, Goldsmiths, University of London, London, UK; 3College of Education, Psychology and Social Work,
Flinders University, Adelaide, SA, Australia
Background: Sleep problems are common in adolescence, and frequently comorbid with both anxiety and
depression. Research studies have suggested a bidirectional relationship between sleep and psychopathology,
which includes evidence that sleep interventions can alleviate symptoms of anxiety and depression. However, little is
known about the nature of sleep problems amongst adolescents with anxiety and depression, and whether specific
sleeping difficulties are involved in the longitudinal relationship between sleep, anxiety and depression. Method: The
sample was derived from the Avon Longitudinal Study of Parents and Children (ALSPAC), a population-based,
prospective, birth cohort study of children born in 1991–1992. Data were explored from a subset of participants who
took part in a clinical assessment at age 15, on self-report sleep patterns and quality, and diagnostic outcomes of
anxiety and depression (N = 5,033). Subsequent diagnostic and symptom severity data on anxiety and depression at
ages 17, 21 and 24 were also examined. Results: Cross-sectional and longitudinal analyses were conducted to
explore the relationship between sleep problems, anxiety and depression. Results revealed that adolescents aged 15
with depression experience difficulties with both sleep patterns and sleep quality, whereas adolescents with anxiety
only reported problems with sleep quality. A range of sleep variables at age 15 predicted the severity of anxiety and
depression symptoms and the diagnoses of anxiety and depressive disorders at age 17, 21 and 24 years.
Conclusions: The results provide further insight into the nature of sleep problems amongst adolescents with anxiety
and depression, and the prospective relationship between sleep disturbance and future psychopathology. These data
suggest that targeting sleep difficulties during adolescence may have long-term mental health benefits. Keywords:
Adolescence; longitudinal studies; sleep; depression; anxiety.
Introduction
Healthy sleep is characterised by getting enough
sleep, at appropriate times and in the absence of any
sleep disturbance (Paruthi et al., 2016). During
adolescence, a multitude of social, biological and
psychological factors make sleep particularly vulnerable (e.g. Crowley et al., 2018). Two bioregulatory
sleep processes undergo dynamic developmental
changes during this stage of life. Firstly, older
adolescents (e.g. 14 years olds) take longer to build
sleep homeostatic pressure compared to younger
adolescents (e.g. 11 years), resulting in increased
biological alertness in the late evening to early
morning (e.g. 10:30 P.M. to 2:30 A.M.) (Taylor
et al., 2005). The second biological process is a delay
in the timing of the adolescent endogenous circadian
rhythm, with sleep timing signalled later in the
evening by this ‘biological clock’. This circadian
phase delay is seen across humans and non-human
mammals (Hagenauer & Lee, 2013), substantiating
the biological basis of this developmental change.
Whilst it is feasible for adolescents to minimise and
regularise this tendency to fall asleep late and wake
late, the cessation of parent-set bedtimes (Short
Conflict of interest statement: See Acknowledgements for full
disclosures.
et al., 2011) and increased vocational, social and
cultural demands (Crowley et al., 2007; Crowley
et al., 2018; Gradisar et al., 2011) leave millions of
adolescents at risk for a range of inter-related ‘sleep
problems’, including an extended latency to sleep
onset, and restricted sleep duration on school nights
in particular (Morrison et al., 1992; Short, et al.,
2013).
The impact of poor sleep quality in adolescents is
wide-reaching (e.g. Owens, 2014), with growing
evidence that poor-quality and insufficient sleep is
linked to cognitive, emotional and behavioural dysregulation (Gregory & Sadeh, 2016; Palmer & Alfano,
2017; Sadeh et al., 2003). Inadequate sleep in
adolescence is associated with reduced executive
functioning (Beebe, 2011), poorer academic performance and reduced learning capacity (Curcio et al.,
2006; Gaultney, 2010; Lo et al., 2016), and
increased prevalence of affective disorders (e.g.
Roberts & Duong, 2014). Sleep disturbance is one
of the most common symptoms of adolescent depression (Goodyer et al., 2017; Orchard et al., 2017), and
has been linked to the severity of a depressive
episode (Liu et al., 2007) and the risk of suicidal
behaviour and self-harm (McCall et al., 2010; Singareddy et al., 2013). Similarly, high proportions of
young people with anxiety have also reported
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and
Adolescent Mental Health.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
2
Faith Orchard et al.
experiencing one or more sleep-related problems
(Alfano et al., 2007; Chase & Pincus, 2011). Recent
longitudinal data suggest that the relationship
between sleep problems and psychopathology may
be bidirectional. Although, a meta-analysis showed a
stronger direction of effect from sleep problems to
depression in adolescents, rather than the onset of
sleep problems following depression (Lovato & Gradisar, 2014). Specifically, they found that depression
appeared to be worse when adolescents spent more
time awake in bed (e.g. taking longer to fall asleep).
This meta-analysis identified ten studies that examined the longitudinal relationship between sleep and
depression. The studies included self-report and
biological measures of sleep, as well as measures of
symptom severity and diagnostic status of depression. This has enabled the examination of the
perception of problems vs objective measurement,
and symptom variance vs clinical utility. However,
none of these studies examined more than one
follow-up time point.
Multiple mechanisms linking sleep, anxiety and
depression in young people have been proposed
(Blake et al., 2018; Gregory & Sadeh, 2016; Palagini
et al., 2019), and these are all likely to contribute to
this complex relationship. For example, the natural
shift towards later bedtimes and potential problems
with sleep onset latency can result in less sleep on
weeknights, due to a set time to rise for school
(Crowley et al., 2018). This chronic sleep restriction
may result in a variety of daytime symptoms, many
of which overlap with depression symptoms, including reduced positive emotions, reduced motivation,
poor concentration and fatigue/tiredness (Dahl,
1999; Lo, et al., 2016; Talbot et al., 2010).
Another potential mechanism linking sleep and
mood is a late chronotype (i.e. the mid-point of sleep
on free days; Zavada et al., 2005). During adolescent
development (10–20 years), the mid-point of sleep
dramatically drifts later and begins to reverse again
during the mid-20s (Kuula et al., 2019). Numerous
studies have shown an association between eveningness/ a later chronotype/ later circadian timing and
depression symptoms or a depressive disorder (Bauducco et al., 2020). Whilst there could be biological
and/or social reasons for these associations, these
mechanisms need to be further elucidated given that a
later chronotype and an increase in depression symptoms are prevalent during adolescence across the
world (Gradisar et al., 2011; Olds et al., 2010).
There is growing evidence that the treatment of
disrupted sleep has positive consequences for other
mental health difficulties including psychotic experiences (Bradley et al., 2018; Freeman et al., 2017)
and depression and anxiety symptoms (Gee et al.,
2018; Luik et al., 2017). These findings have led
researchers and clinicians to develop an interest in
adapting adolescent-specific sleep interventions,
with a view to improving both sleep and symptoms
of anxiety and depression (e.g. Clarke & Harvey,
2012; Orchard, et al, 2019).
The present study used data from a large longitudinal study of adolescents in the United Kingdom,
the Avon Longitudinal Study of Parents and Children
(ALSPAC). Drawing on available data of sleep, anxiety and depression, across multiple time points in
adolescence and early adulthood, two aims were
identified. The first aim was to examine crosssectional sleep habits at age 15 years and to compare self-reported sleep quality and sleep patterns of
those who met diagnostic criteria for an anxiety
disorder and/or depression to those with no anxiety
or depression. The second aim was to test the
longitudinal association between sleep patterns
and quality at 15 years and (a) diagnoses of anxiety
and depression in late adolescence and early adulthood, and (b) symptoms of anxiety and depression in
early adulthood.
Methods
Cohort study numbers
Pregnant women resident in Avon, United Kingdom, with
expected dates of delivery 1st April 1991 to 31st December
1992 were invited to take part in the study. The initial number
of pregnancies enrolled is 14,541. Of these initial pregnancies,
there was a total of 14,676 foetuses, resulting in 14,062 live
births and 13,988 children who were alive at 1 year of age.
When the oldest children were approximately 7 years of age,
an attempt was made to bolster the initial sample with eligible
cases who had failed to join the study originally. As a result,
when considering variables collected from the age of seven
onwards (and potentially abstracted from obstetric notes) there
are data available for more than the 14,541 pregnancies
mentioned above. The number of new pregnancies not in the
initial sample (known as Phase I enrolment) that are currently
represented on the built files and reflecting enrolment status at
the age of 24 is 913 (456, 262 and 195 recruited during Phases
II, III and IV, respectively), resulting in an additional 913
children being enrolled. The phases of enrolment are described
in more detail in the initial cohort profile papers and in a recent
update (Boyd et al., 2013; Fraser et al., 2012; Northstone,
et al., 2019). The total sample size for analyses using any data
collected after the age of seven is therefore 15,454 pregnancies,
resulting in 15,589 foetuses. Of these, 14,901 were alive at
1 year of age.
Participants
The present study examined data obtained from a subset of
participants who took part in a clinical assessment examining
both sleep and mental health diagnoses at age 15 years
(between October 2006 and November 2008). Of these 5,525
participants, 432 did not attend the sleep assessment session,
and a further 37 participants did not complete the diagnostic
interview. Data were also removed from 23 participants who
withdrew consent. This left a total sample of N = 5,033 with
diagnostic and sleep data at age 15. Participants were 53%
female and 98% white.
Ethical approval for the study was obtained from the
ALSPAC Ethics and Law Committee and the Local Research
Ethics Committees. Informed consent for the use of data
collected via questionnaires and clinics was obtained from
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
Sleep at 15 years predicts future anxiety and depression
participants following the recommendations of the ALSPAC
Ethics and Law Committee at the time.
Measures
The present study included measures on sleep at age 15,
diagnostic outcomes at ages 15, 17 and 24, and symptomatology at age 21. Please note that the study website contains
details of all the data that are available through a fully
searchable data dictionary and variable search tool (http://
www.bristol.ac.uk/alspac/researchers/our-data/).
Sleep. Participants completed a sleep questionnaire as part
of the Teen Focus Clinic at age 15 years that assessed various
aspects of their sleep.
Sleep patterns: Initial questions explored sleep patterns
on school days and on weekends, and participants were asked
to report on their sleep in the last 2 weeks. The variables for
analysing sleep patterns (sleep onset time and wake-up time)
were measured using a modified version of the School Sleep
Habits Survey (Wolfson et al., 2003). The main modification
that was relevant to the present analyses was the addition of
questions to establish sleep onset latency and sleep onset time,
specifically asking; ‘What time do you USUALLY start trying to
go to sleep?’ and ‘How long does it USUALLY take for you to fall
asleep?’. These additional items also made it possible to
compute total sleep time (TST), which was assessed by
calculating the difference between adolescents’ sleep onset
time and sleep offset time. This method can provide a more
accurate measurement of sleep quantity as opposed to estimating the amount of sleep one obtains (Matricciani, 2013).
Finally, chronotype was defined as the mid-point between
sleep onset and sleep offset on free days (i.e. weekends), based
on work by Zavada et al. (2005), and expressed as clock time
(i.e. the later the mid-point of sleep and the later the chronotype).
Sleep quality: Perceptions of sleep quality were examined
with a range of questions. These included sleep onset latency
(mins) on school nights and weekends (Ohayon et al., 2017),
and how many times a night participants usually woke up on a
four-point scale (1 = never; 2 = once; 3 = two or three times;
4 = >three times). Additionally, problems with daytime sleepiness were examined on a five-point Likert scale (1 = no
problem; 2 = a little problem; 3 = more than a little problem;
4 = a big problem; 5 = a very big problem). Ease of getting up
was also examined on a four-point Likert scale (1 = very easy;
2 = easy; 3 = not easy; 4 = hard). Finally, participants were
asked how often they believed they got enough sleep on a fivepoint Likert scale (1 = always; 2 = usually; 3 = sometimes;
4 = rarely; 5 = never).
Anxiety and depression diagnoses. Anxiety and
depression diagnoses were assessed at focus clinics at ages
15, 17 and 24 years.
(n = 74), and met diagnostic criteria for depression (n = 78).
Adolescents with comorbid anxiety and depression (n = 22)
were included in the depressed group. This decision was made
for two reasons, (a) the numbers of comorbid participants were
too small to analyse independently, and (b) depression is more
commonly comorbid with anxiety, than vice versa (Cummings
et al., 2014); hence, the ‘depressed’ group will be more
generalisable. In order to ensure that this did not result in
the depressed group showing differences due to being more
severe, sensitivity analyses were conducted without comorbid
participants.
Externalising disorders were also assessed using the
DAWBA. Participants with externalising disorders were not
removed from the three groups. Thus, differences between the
three groups are more likely to be explained by the presence of
anxiety and depression only.
Diagnostic assessment at age 17 and 24: Anxiety
and depression diagnoses were assessed on a subset of
participants using the Clinical Interview Schedule – Revised
(CIS-R; Lewis et al., 1992) at the Teen Focus Clinic at age
17 years (n = 5,081) and the Focus@24 Clinic at age 24 years
(n = 4026). The CIS-R is a psychiatric interview designed to
assess symptoms of depression and anxiety in non-clinical
populations, and is administered by non-clinicians. At age 17,
n = 3,528 participants also had sleep and diagnostic data
available at age 15 and hence could be included in the
longitudinal analyses, and at age 24, n = 2,853 participants
also had sleep and diagnostic data at age 15. Frequencies of
participants with anxiety and depression at ages 17 and 24 are
summarised in Table 1, along with the percentages of adolescents in each of the diagnostic groups at age 15, to demonstrate how many adolescents had persistent vs new diagnoses.
When participants were 22 years and older, data were
collected and managed using REDCap electronic data capture
tools hosted at University of Bristol (Harris et al., 2019; Harris
et al., 2009). REDCap (Research Electronic Data Capture) is a
secure, web-based software platform designed to support data
capture for research studies, providing (a) an intuitive interface
for validated data capture; (b) audit trails for tracking data
manipulation and export procedures; (c) automated export
procedures for seamless data downloads to common statistical
packages; and (d) procedures for data integration and interoperability with external sources.
Anxiety and depression symptoms. Anxiety and
depression symptoms were measured by self-report questionnaires at age 21 (n = 3,463). Of these participants, n = 2,459
also had sleep and diagnostic data available at age 15, and
hence could be included in the longitudinal analyses.
Anxiety symptoms were measured using the Generalised
Anxiety Disorder Assessment (GAD-7; Spitzer et al., 2006), a
seven-item questionnaire that is used to measure the severity
Table 1 Percentages of anxiety and depression diagnoses at
ages 17 and 24, and percentages of age 15 adolescents with
persistent vs. new diagnoses
Diagnostic assessment at age 15: Anxiety and
depression diagnoses were assessed at the Teen Focus Clinic
at age 15 years, using the Development and Well-Being
Assessment (DAWBA; Goodman et al., 2000). This assessment
is a self-report measure that enquires about psychiatric
symptoms and generates International Classification of Diseases – 10th Revision diagnoses (World Health Organization;
WHO, 1992).
For the purpose of the cross-sectional analyses, participants
were grouped, on the basis of the DAWBA outcome, into one of
three groups: no diagnosis of anxiety or depression (n = 4,921),
met diagnostic criteria for one or more anxiety disorders
3
Diagnostic
groups at
age 15
No anxiety/
depression
Anxiety
Depression
Diagnostic data at age
17
(N = 3,528)
Diagnostic data at age
24
(N = 2,853)
Anxiety
(n = 317;
9%)
Anxiety
(n = 270;
9%)
Depression
(n = 259,
7%)
Depression
(n = 291;
10%)
8.1%
6.8%
8.7%
9.5%
35.8%
40.0%
20.8%
30.9%
37.8%
30.0%
37.8%
26.0%
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
4
Faith Orchard et al.
of generalised anxiety disorder. The individual is asked to rate
the severity of each item over the past two-week period.
Responses are scored on a four-point Likert scale from ‘not at
all’ to ‘nearly every day’. The measure has been found to have
good reliability and validity (L€
owe et al., 2008; Ruiz et al.,
2011; Spitzer et al., 2006). Total scores are computed to give a
measure of severity and can vary from 0 to 21.
Depression symptoms were measured using the adult
version of the Short Mood and Feelings Questionnaire (SMFQ;
Angold et al., 1995), a 13-item questionnaire that is used to
measure the severity of depression over the past two-week
period. Responses are scored on a three-point Likert scale from
‘not true’ to ‘true’. The measure has been found to have good
reliability and validity (Angold et al., 1995; Messer et al., 1995).
Total scores are computed to give a measure of severity and
can vary from 0 to 26.
Data preparation and analytic plan
Continuous data were screened in relation to the assumptions
of parametric tests (Tabachnick & Fidell, 2007). Where
assumptions were violated, confirmatory analyses were conducted by running analyses with 1,000 bootstrap samples. All
results were consistent, suggesting that the original analyses
were robust to the violations of assumptions. For simplicity
and comparability with other research, results based on the
original (non-bootstrapped) analyses are presented below.
For cross-sectional analyses, continuous sleep variables
were analysed using multiple analysis of variance (MANOVA),
with diagnostic group as the independent variable, with three
levels (no depression or anxiety, depressed and anxious). For
prospective analyses, multiple regression models and multiple
logistic regression models were conducted investigating how
well sleep patterns and sleep quality at age 15 predicted
symptoms of anxiety and depression at 21, and diagnoses of
anxiety and depression at ages 17 and 24, after controlling for
age 15 anxiety and depression.
Results
Cross-sectional between-group differences in sleep
patterns at age 15
Between-group differences for sleep patterns were
examined using a MANOVA where the independent
variable was diagnostic group (depressed, anxious
and no anxiety/depression) and the dependent variables were sleep onset time and wake-up time on
school days and weekends, total sleep time on school
days and weekends, and chronotype. Mean sleep
times, between-subjects analyses and Bonferronicorrected pairwise comparisons are presented in
Table 2.
There was a significant main effect of group on
sleep patterns, V = 0.01, F(8, 9570) = 7.11,
p < .001, ɳ2 = .01. Between-subjects effects revealed
significant group differences for sleep onset time on
school days and weekends, wake-up time on school
days, total sleep time on school days and weekends,
and chronotype. There was no significant difference
for wake-up time on weekends. Corrected pairwise
comparisons for sleep onset times indicated that the
depressed group reported going to sleep significantly
later than the no anxiety/depression group (35 min
later) and the anxious group (33 min later) on school
nights and that the depressed group reported going
to sleep significantly later than the no anxiety/
depression group on weekends (30 min later). Corrected pairwise comparisons did not reveal significant differences for wake-up times.
Corrected pairwise comparisons for total sleep
time revealed that the depressed group had significantly less sleep on school nights than the no
anxiety/depression group and the anxious group
(42 min less and 34 min less; respectively) and that
the depressed group also had significantly less
sleep on weekends than the no anxiety/depression
group and the anxious group (31 min less and
36 min less; respectively). Corrected pairwise comparisons did not reveal significant differences for
chronotype.
To ensure that differences between the depressed
and anxiety groups were not being driven by the
depressed group including participants with comorbid anxiety and depression, sensitivity analyses were
conducted without comorbid participants, and the
results did not change.
Cross-sectional between-group differences in
subjective sleep quality
Group differences between perceived sleep quality
variables were examined using a MANOVA where the
independent variable was diagnostic group (depressed, anxious and no anxiety/depression) and
the dependent variables were seven sleep quality
items: sleep onset latency (school nights and weekends), night wakening, ease to get up (school days
and weekends), daytime sleepiness and frequency of
sufficient sleep. Mean sleep ratings, between-group
analyses and Bonferroni-corrected pairwise comparisons are presented in Table 3.
There was a significant main effect of group,
V = 0.04, F(14, 9344) = 13.92, p < .001, ɳ2 = .02,
with significant between-group differences for all
subjective sleep quality variables. Corrected pairwise
comparisons revealed many characteristics specific
to individual disorders and across both disorders,
compared to adolescents with no anxiety/depression.
Adolescents with anxiety and depression took
significantly longer to fall asleep on school nights
and weekends compared to adolescents with no
anxiety/depression. Depressed adolescents also
reported longer sleep onset latency than the anxious
group on school nights. Adolescents reported more
difficulty getting up on school days if they were
depressed (35% reported that it was ‘hard’ to get up)
or anxious (31% reported that it was ‘hard’ to get up),
compared to adolescents with no anxiety/depression
(18% reported that it was ‘hard’ to get up). But only
depressed adolescents reported significantly worse
problems on weekends when compared to adolescents with no anxiety/depression (11%, compared to
3%).
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
Sleep at 15 years predicts future anxiety and depression
5
Table 2 Subjective sleep patterns amongst those with and without anxiety and depression diagnoses at age 15 (n = 4,790)
Mean (SD)
No depression or anxiety
(n = 4,658)
Sleep onset time (hr:min)
School nights
Weekend nights
Wake-up time (hr:min)
School days
Weekend days
Total sleep time (hr:min)
School nights
Weekend nights
Chronotype (hr:min)
Anxiety
(n = 65)
Depression
(n = 67)
22:58 (77.78)a
00:01 (109.85)a
23:00 (79.28)a
00:13 (111.79)ab
23:33 (112.95)b
00:31 (131.55)b
17.84***
7.58**
07:05 (49.05)
09:42 (131.25)
06:59 (60.20)
09:59 (146.98)
06:57 (66.08)
09:41 (163.62)
3.02*
1.53
08:07 (0.82)a
09:41 (1.27)a
04:52 (1.03)
07:59 (0.84)a
09:46 (1.46)a
05:07 (1.08)
07:25 (1.27)b
09:10 (1.78)b
05:07 (1.19)
F (2, 4787)
24.05***
5.40**
3.48*
*p < .05; **p < .01; and ***p < .001; Superscript letters have been used to indicate significant differences, where letters are the same
across variables there is no difference, and where letters differ (i.e. a and b) this denotes between-group Bonferroni-corrected
significance.
Table 3 Subjective sleep quality amongst those with and without anxiety and depression diagnoses at age 15 (n = 4,680)
Mean (SD)
Sleep onset latency (min)
School nights
Weekend nights
Number of times wakes at night
Ease to get up
School days
Weekend days
Daytime sleepiness
Frequency of enough sleep
No depression or anxiety
(n = 4,548)
21.02 (16.28)a
17.38 (14.12)a
1.83 (1.15)a
2.71
2.04
1.72
2.38
(0.83)a
(0.70)a
(0.73)a
(0.84)a
Anxiety
(n = 67)
Depression
(n = 65)
29.76 (27.23)b
21.94 (17.07)b
2.07 (1.13)ab
37.60 (35.67)c
26.03 (21.62)b
2.55 (1.17)b
3.01
2.22
2.18
2.87
(0.86)b
(0.78)ab
(1.00)b
(1.09)b
3.06
2.35
2.58
3.14
(0.90)b
(0.84)b
(1.07)c
(0.90)b
F (2, 4677)
39.24*
14.89*
18.83*
10.21*
8.75*
55.92*
36.59*
*p < .001; Superscript letters have been used to indicate significant differences, where letters are the same across variables there is
no difference, and where letters differ (i.e. a and b) this denotes between-group Bonferroni-corrected significance; ‘Ease to get up’
measured on 4-point scale where 1 = easy and 4 = hard; ‘Daytime sleepiness’ measured on a 5-point scale from 1 = no problem to
5 = very big problem; ‘Frequency of enough sleep’ measured on 5-point scale where 1 = always and 5 = never.
Adolescents also reported more problems with
daytime sleepiness if they had depression (19%
reported ‘big’ or ‘very big’ problem) or anxiety (10%
reported ‘big’ or ‘very big’ problem), compared to
adolescents with no anxiety/depression (3%
reported ‘big’ or ‘very big’ problem). Depressed adolescents reported significantly more daytime sleepiness than anxious adolescents. Night-time waking
was significantly worse for depressed participants
than young people who were anxious or not
depressed/anxious. Finally, adolescents were less
likely to think that they get enough sleep if they were
depressed (38% reported ‘rarely’ or ‘never’ getting
enough sleep) or anxious (31% reported ‘rarely’ or
‘never’ getting enough sleep), compared to adolescents with no anxiety/depression (11% reported
‘rarely’ or ‘never’ getting enough sleep).
To ensure that differences between the depressed
and anxious groups were not being driven by
participants with comorbidity in the depressed
group, sensitivity analyses were conducted without
comorbid participants. The only difference that did
not persist was depressed participants reporting
more problems with daytime sleepiness than anxious participants.
Prospective relationship between sleep habits at
15 years and diagnoses of anxiety and depression at
17 and 24 years
To test the hypothesis that sleep patterns and sleep
quality would significantly predict a diagnosis of
depression or anxiety at age 17 and 24, we conducted multiple logistic regression models with
diagnostic status as the dependent variable. Due to
issues with multicollinearity, and to reduce the
number of models being analysed, the only sleep
pattern variables included in prospective analyses
were total sleep time (school and weekend nights)
and chronotype.
Four models were analysed for each age (i.e. 17
and 24): (1) sleep patterns predicting anxiety diagnoses, (2) sleep quality predicting anxiety diagnoses,
(3) sleep patterns predicting depression diagnoses
and (4) sleep quality predicting depression diagnoses. Diagnoses of anxiety and depression at age 15
were entered as a predictor in the first step, and
sleep variables were entered in the second step. This
was to establish whether sleep habits at age 15
predicted diagnoses of anxiety or depression, over
and above the presence of these difficulties at age 15.
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
6
Faith Orchard et al.
As expected, diagnoses of anxiety and depression
at 15 years were significant predictors of diagnoses
at 17 and 24 in the first step of all models conducted
(p < .001). To simplify the reporting of results, these
first steps of the models are not included in the
tables. Sleep variable statistics for logistic regression
models at age 17 can be found in Table 4, and sleep
variable statistics for logistic regression models at
age 24 can be found in Table 5.
Less total sleep time on school nights at age 15 was
a significant predictor of anxiety and depression at
ages 17 and 24. Total sleep time on weekends and
chronotype was not significant predictors of future
anxiety or depression. Three sleep quality variables
measured at 15 years consistently predicted anxiety
and depression diagnoses at age 17 and 24: daytime
sleepiness, night-time waking and perception of
getting enough sleep. Sleep onset latency was also
a significant predictor of age 24 anxiety diagnoses
only.
Prospective relationship between sleep habits at
15 years and symptoms of anxiety and depression at
21 years
To test the hypothesis that sleep patterns and sleep
quality would significantly predict symptoms of
depression or anxiety at age 21, we conducted
hierarchical multiple regression models with symptoms of anxiety or depression as the dependent
variable. Due to issues with multicollinearity, and to
reduce the number of models being analysed, the
only sleep pattern variables included in prospective
analyses were total sleep time (school and weekend
nights) and chronotype.
Four models were analysed: (1) sleep patterns
predicting anxiety symptoms, (2) sleep quality predicting anxiety symptoms (3) sleep patterns predicting depression symptoms and (4) sleep quality
predicting depression symptoms. Diagnoses of anxiety and depression at age 15 were entered as a
predictor in the first step, and sleep variables were
entered in the second step. This was to establish
whether sleep habits at age 15 predicted symptoms
of anxiety or depression, over and above the presence of these difficulties at age 15.
As expected, diagnoses of anxiety and depression
at 15 years were significant predictors of symptoms
at age 21 in the first step of all models conducted
(p < .001). To simplify the reporting of results, these
first steps of the model are not included in tables.
Sleep variable statistics for linear regression models
at age 21 can be found in Table 6.
Less total sleep time on school nights at age 15 was
a significant predictor of both anxiety and depression symptoms at age 21. Total sleep time on
weekends and chronotype was not significant predictors. Four of the sleep quality variables measured
at 15 years predicted anxiety and depression symptoms at age 21: sleep onset latency on school nights,
daytime sleepiness, night-time waking and perception of getting enough sleep.
Discussion
This study addressed cross-sectional and longitudinal relationships between self-reported sleep patterns and sleep quality at age 15 and subsequent
symptoms and diagnoses of anxiety and depression.
The first research question addressed whether there
would be differences between diagnostic groups at
age 15 in sleep patterns and sleep quality. Some
significant between-group differences were identified
for sleep patterns, including sleep onset time (during
the school week and at the weekend) and total sleep
time (school days and weekend). Specifically, adolescents with depression had later sleep onset time
on school nights, and less total sleep on school
nights and weekends, than the anxiety and the no
anxiety/depression groups. Adolescents with
depression also had later sleep onset time on weekends than the participants with no anxiety or
depression. Although the depressed group included
comorbid participants with anxiety diagnoses, sensitivity analyses without these participants revealed
no change in results.
For sleep quality, adolescents with depression
reported greater difficulties than adolescents with
no anxiety/depression on all variables (i.e. time to
fall asleep on school nights and weekends, difficulty
getting up on school nights and weekends, waking in
the night, daytime sleepiness and not getting enough
sleep). Adolescents with depression also reported
greater difficulties falling asleep on school days, and
with daytime sleepiness, than adolescents with anxiety. Finally, adolescents with an anxiety disorder
did not report any sleep problems to be worse than
the depressed group but did report worse sleep
quality on most indices than the adolescents with no
anxiety/depression. Sensitivity analyses without
comorbid participants in the depressed group
revealed that depressed adolescents no longer differed from anxiety participants on daytime sleepiness.
The second research question addressed whether
sleep patterns and sleep quality at age 15 would
predict symptoms and diagnoses of anxiety and
depression later in adolescence (age 17) and in early
adulthood (age 21 and 24). The first analysis examined whether sleep patterns at 15 years predicted
anxiety and depression diagnoses at ages 17 and 24.
Anxiety and depression diagnosis at age 17 and 24
was significantly predicted by less total sleep time on
school nights at age 15, but not by weekend total
sleep time or chronotype. This finding is consistent
with the literature which hypothesises that low total
sleep time on school nights would predict future
anxiety and depression, because late sleep onset and
fixed waking times on school days create chronic
sleep restriction during adolescence, and that
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
Sleep at 15 years predicts future anxiety and depression
7
Table 4 Summary of sleep variables at age 15 in logistic regression models predicting anxiety and depression diagnoses at age 17
(n = 3,347)
Age 15
Sleep patterns
Sleep quality
Age 15
Sleep patterns
Sleep quality
Anxiety at 17 years
b
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
0.28***
0.06
0.10
0.01
0.00
0.06
0.05
0.40***
0.15**
0.19*
Depression at 17 years
b
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
0.36***
0.04
0.03
1.01
0.00
0.06
0.17
0.51***
0.21**
0.31*
Odds ratio (95% CI)
0.76
0.94
1.11
1.01
1.00
1.06
1.05
1.49
1.16
1.20
(0.64–0.89)
(0.85–1.05)
(0.97–1.26)
(1.00–1.02)
(0.99–1.01)
(0.90–1.25)
(0.88–1.26)
(1.26–1.77)
(1.05–1.29)
(1.02–1.42)
Odds ratio (95% CI)
0.70
0.96
1.03
1.01
1.00
0.94
1.19
1.67
1.23
1.36
(0.59–0.84)
(0.86–1.08)
(0.89–1.19)
(1.00–1.02)
(0.99–1.01)
(0.79–1.12)
(0.98–1.44)
(1.39–1.00)
(1.11–1.38)
(1.14–1.63)
R2
Model
.02 (Cox&Snell)
.05 (Nagelkerk)
X2(3) = 26.65***
.04 (Cox&Snell)
.09 (Nagelkerk)
X2(7) = 74.38***
R2
Model
.02 (Cox&Snell)
.04 (Nagelkerk)
X2(3) = 26.49***
.04 (Cox&Snell)
.10 (Nagelkerk)
X2(7) = 103.11***
Step 1 age 15 anxiety and depression not included in table; SOL, sleep onset latency; TST, total sleep time; *p < .05; **p < .01; and
***p < .001.
adolescents with delayed sleep are particularly vulnerable (Crowley et al., 2018). However, it does not
support the hypothesis that a late chronotype may
be a mechanism linking sleep and mental health (e.g.
Bauducco et al., 2020).
The second analysis examined whether sleep quality at 15 years predicted depression and anxiety
diagnoses at 17 and 24 years. Analyses revealed
significant predictors to be daytime sleepiness, night
waking and perception of whether sleep quantity was
enough. These predictors were the same for each
diagnosis and at 17 and 24 years. Whilst a model
proposed by Lovato and Gradisar (2014) emphasises
sleep onset latency and wake after sleep onset as
predicting later depression, their model also found
self-reported sleep quality to be the third predictor of
depression. Although there is a consensus about
objective measures of sleep quality (Ohayon et al.,
2017), there is not yet a consensus about subjective
measures. Conceptually, daytime sleepiness and
night wakings are likely to also be perceived as
reflecting inadequate sleep and poor-quality sleep.
Thus, these aspects of sleep quality hold promise for
future research, particularly when investigating links
with anxiety and depression. In contrast, anxiety
diagnoses at age 24 were predicted by sleep onset
latency on school nights. This may not be surprising
as sleep onset latency on school nights may be more
prone to problems with pre-sleep arousal or worries,
which could, in turn, be associated with the development of anxiety, although this would not explain
why this finding was not also present at age 17.
Finally, sleep patterns and sleep quality at age 15
were examined as predictors of anxiety and depression symptoms at age 21. The same variables were
predictive of both anxiety and depression symptoms,
and were, overall, consistent with results from the
models predicting diagnoses. Total sleep time on
school nights was the only significant sleep pattern
predictor of symptoms of anxiety and depression.
Significant sleep quality predictors mirrored those
found in the diagnostic analyses, whereby daytime
sleepiness, night waking and perception of whether
sleep quantity was enough, significantly predicted
symptoms. However, as in the model of predicting
anxiety diagnoses at 24, sleep onset latency on
school nights was also a significant predictor of
symptoms of both anxiety and depression at age 21.
This slight discrepancy between the models of diagnostic outcomes compared to symptoms may be
reflective of the greater variance that can be examined when analysing continuous variables compared
to categorical variables. This finding also strengthens the likelihood that sleep onset latency on school
nights is an important factor in influencing mental
health outcomes later in life, as predicted by Lovato
and Gradisar (2014).
Anxious participants reported challenges with
sleep quality but did not report any sleep patterns
to be worse than adolescents with no anxiety/
depression. Similar results have been reported when
comparing objective and subjective measures of
sleep amongst anxious children. Alfano et al.
(2015) reported that parent and child reported sleep
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
8
Faith Orchard et al.
Table 5 Summary of sleep variables at age 15 in logistic regression models predicting anxiety and depression diagnoses at age 24
(n = 2,719)
Age 15
Sleep patterns
Sleep quality
Age 15
Sleep patterns
Sleep quality
Anxiety at 24 years
B
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
0.30**
0.02
0.01
0.01**
0.00
0.08
0.09
0.34***
0.15**
0.22*
Depression at 24 years
B
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
0.20*
0.04
0.07
0.01
0.00
0.01
0.06
0.32***
0.12*
0.22*
Odds ratio (95% CI)
0.74
0.99
0.99
1.01
1.00
1.08
1.09
1.40
1.17
1.25
(0.62–0.88)
(0.88–1.10)
(0.86–1.15)
(1.00–1.02)
(0.99–1.01)
(0.91–1.29)
(0.90–1.32)
(1.17–1.67)
(1.05–1.30)
(1.05–1.49)
Odds ratio (95% CI)
0.82
0.97
1.07
1.01
1.00
0.99
1.07
1.38
1.13
1.25
(0.69–0.97)
(0.87–1.08)
(0.93–1.23)
(1.00–1.02)
(0.99–1.01)
(0.83–1.17)
(0.89–1.28)
(1.16–1.64)
(1.02–1.26)
(1.05–1.48)
R2
Model
.02 (Cox&Snell)
.03 (Nagelkerk)
X2(3) = 15.64**
.04 (Cox&Snell)
.08 (Nagelkerk)
X2(7) = 73.78***
R2
Model
.01 (Cox&Snell)
.02 (Nagelkerk)
X2(3) = 10.80*
.03 (Cox&Snell)
.06 (Nagelkerk)
X2(7) = 51.57***
Step 1 age 15 anxiety and depression not included in table; SOL: sleep onset latency; TST: total sleep time; *p < .05; **p < .01; and
***p < .001.
problems were worse in anxious compared to control
children, but no between-group differences were
found for actigraphy-based sleep patterns. Furthermore, research examining objective measures of
sleep in depressed and anxious young people found
that anxious participants had less slow-wave sleep
than depressed participants (Forbes et al., 2008),
which may explain the presence of tiredness even in
the absence of quantity disturbance. Anxious young
people reported that these difficulties were only
present on school nights, which may indicate heightened anxiety or worry related to school (e.g. worries
about academic performance or social interactions)
is interfering with good quality sleep. Alternatively,
heightened anxiety may increase dysfunctional
beliefs about sleep (e.g. Gregory et al., 2009), and
this would be consistent with the finding that
adolescents with anxiety did not report different
sleep patterns from those with no anxiety or depression. To help better understand the data on sleep
difficulties in anxious young people, future research
would benefit from using objective measures of sleep
(e.g. actigraphy or polysomnography) as well as
assessing beliefs and biases about sleep.
Although difficulties with sleep patterns were not
characteristic in young people who had anxiety
disorders at 15 years, they seem to be a potential
risk factor for the development of both anxiety and
depression. This may indicate a potential benefit to
treating early sleep difficulties in adolescence, irrespective of existing psychological disorders. There is
not only growing interest in the effectiveness of sleep
interventions on sleep but also on anxiety and
depression (e.g. Gee et al., 2018; Luik et al., 2017).
The findings of this study also suggest that treatment of sleep should be examined for prevention of
future anxiety or depression. Prevention activities
may be well suited to delivery in schools, where large
numbers of young people with early sleep problems
are likely to be present. To date, the evidence seems
to suggest that interventions are most effective at
improving short-term anxiety and depression when
they use CBT-I programmes (rather than sleep
education), and when they target at-risk participants
(i.e. presenting with symptoms of anxiety and
depression) (Blake & Allen, 2019).
Young people with anxiety and/or depression may
also benefit from treatment that targets sleep difficulties either by including sessions on sleep in a
multi-modal intervention (e.g. Clarke et al., 2015), or
even by targeting only sleep problems (e.g. Gee et al.,
2018; Orchard et al., 2019). Importantly, given the
robust findings that sleep quality predicted later
anxiety and depression, robust randomised controlled trials of sleep programmes in adolescents
should include measures of sleep quality as well as
anxiety and depression symptoms.
This study improved on previous research in the
field by addressing a number of questions (a)
exploring both cross-sectional and prospective relationships between sleep, and anxiety and depression, (b) exploring both sleep patterns and perceived
sleep quality, (c) considering prospective relationships with categorical and continuous approaches to
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
Sleep at 15 years predicts future anxiety and depression
9
Table 6 Summary of sleep variables at age 15 in multiple regression models predicting anxiety and depression symptoms at age 21
(n = 2,363)
Age 15
Anxiety at 21 years
F
df
R2
DR2
B
SE B
b
Sleep patterns
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
23.10***
4, 2348
0.04
0.01***
26.05***
8, 2246
0.09
0.06***
0.54
0.02
0.16
0.03
0.01
0.07
0.07
0.70
0.35
0.54
0.13
0.08
0.11
0.01
0.01
0.12
0.13
0.14
0.08
0.13
0.10***
0.00
0.04
0.11***
0.04
0.01
0.01
0.12***
0.09***
0.10***
18.50***
4, 2358
0.03
0.02***
19.60***
8, 2256
0.07
0.05***
0.68
0.01
0.23
0.03
0.01
0.13
0.01
0.80
0.33
0.52
0.16
0.10
0.13
0.01
0.01
0.15
0.16
0.17
0.10
0.16
0.10***
0.00
0.04
0.10**
0.03
0.02
0.00
0.11***
0.07**
0.08**
Sleep quality
Age 15
Depression at 21 years
Sleep patterns
TST school
TST weekend
Chronotype
SOL school
SOL weekend
Ease getting up school
Ease getting up weekend
Daytime sleepiness
Night waking
Perception of enough sleep
Sleep quality
Step 1 age 15 anxiety and depression not included in table; SOL: sleep onset latency; TST: total sleep time; *p < .05; **p < .01; and
***p < .001.
mental health measurement, and (d) at multiple time
points across adolescence and in adulthood. The
analysis of this cohort dataset enabled access to a
large sample and the ability to examine individuals
with and without diagnoses of anxiety and depression, established with diagnostic assessments. There
are however some important limitations. Firstly,
participants were mostly White British, limiting the
extent to which the study can be generalised to other
demographic groups. Secondly, the diagnostic
groups were small at age 15, with depression and
anxiety identified in approximately 1% of the sample,
and this is lower than reported point prevalence of
depression and anxiety in young people (2.6% and
6.5% respectively; Polanczyk, Salum, Sugaya, Caye,
& Rohde, 2015). The relative scarcity of participants
who met diagnostic criteria at 15 years may be an
artefact of the DAWBA, which has been noted to be a
conservative measurement of depression and anxiety (Angold et al., 2012). At age 17 and 24, when
anxiety and depression were assessed using the CISR, rates of anxiety and depression were higher (at
17 years, 9.0% anxiety and 7.3% depression; at
24 years, 9.5% anxiety and 10.2% depression).
However, this limitation is somewhat alleviated by
the inclusion of prospective models predicting severity of anxiety and depression symptoms reported
with self-report questionnaires, especially as these
models provide internal replication for the majority
of findings.
It is also important to note a couple of limitations
related to the measurement of sleep. Sleep was
measured subjectively, and thus could be influenced
by reporting and memory biases. However, the
longitudinal relationships indicate that even if sleep
problems reported at age 15 are not objective, these
subjective ratings are related to future mental health
problems and therefore warrant further investigation
or intervention. Secondly, not all facets of sleep were
assessed. One key area that was not measured was
‘wake after sleep onset’ (WASO). This may be important because WASO may reduce total sleep time
values, and therefore, total sleep time may have been
overestimated. Finally, diagnoses of depression and
some anxiety disorders do include assessment of
sleep difficulties, meaning that there is some overlap
in measurement.
Although there were significant differences in sleep
between young people who had anxiety disorders
and depression, the predictors of future anxiety and
depression in adolescence and adulthood were
largely the same across the two difficulty areas. This
fits with research examining other predictors, such
as genetics, where strong genetic overlap is often
flagged between depression and anxiety (Nivard
et al., 2015), as well as sleep phenotypes (Barclay
& Gregory, 2013). The data from this study do imply
that managing the amount, quality and perception of
sleep during adolescence may have long-term implications for the development of anxiety and depression later in adolescence and in early adulthood.
Future work should begin to examine this, providing
support for sleep behaviours in adolescence, and
conducting long-term follow-ups to examine the
resulting effect on the development of anxiety and
depression.
© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health.
10
Faith Orchard et al.
Acknowledgements
The UK Medical Research Council and Wellcome (Grant
ref: 102215/2/13/2) and the University of Bristol
provide core support for ALSPAC. This publication is
the work of the authors, and F.O. will serve as guarantor for the contents of this paper. A comprehensive list
of grants funding is available on the ALSPAC website.
This research was specifically funded by NIH
(5R01MH073842-04; PI Tom O’Connor) and Wellcome
Trust and MRC (076467/Z/05/Z; PI George Davey
Smith). The authors are extremely grateful to all the
families who took part in this study, the midwives for
their help in recruiting them and the whole ALSPAC
team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.
A.G. is an advisor for a project sponsored by Johnson’s
Baby. She has written two books (Nodding Off,
Bloomsbury Sigma, 2018; The Sleepy Pebble, Flying
Eye Books, 2019). She is a regular contributor to BBC
Focus Magazine and has contributed to numerous
other outlets (such as The Conversation and The
Guardian). She has been interviewed by magazines
and commercial websites. She has provided a talk for
business (Investec) and is occasionally sent trial products from commercial companies (e.g. blue light-blocking glasses). M.G. has current consultancies with Nanit
and Polar Electro Oy, and a book contract with
Hachette. The remaining authors have declared that
they have no competing or potential conflicts of interest.
Correspondence
Faith Orchard, School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6
6AL, UK; Email: f.orchard@reading.ac.uk
Key points
Sleep problems are common in adolescence, and frequently co-occur with depression and anxiety.
Adolescents with depression reported problems with sleep patterns and sleep quality, and adolescents with
anxiety reported problems with sleep quality only.
Sleep quality and total sleep time at age 15 were predictive of anxiety and depression diagnoses and
symptoms at ages 17, 21 and 24 years.
Adolescents with anxiety and depression may benefit from additional support with sleep habits.
Future research should address whether providing support with sleep habits in adolescence may prevent
anxiety and depression from developing later in life.
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Accepted for publication: 27 May 2020
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