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A Systematic Review Assessing Bidirectionality between Sleep Disturbances, Anxiety, and Depression

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To investigate whether sleep disturbances are bidirectionally related to anxiety and depression, and thus identify potential risk factors for each problem. A systematic review was conducted on 9 studies (8 longitudinal, 1 retrospective) that assessed bidirectionality between a sleep disturbance, and anxiety or depression. Treatment studies were excluded, along with those solely based on clinical samples or cohorts at high risk of suffering from a sleep disturbance, anxiety and depression. Eligible studies were identified by searching PubMed, PsychINFO, Embase, and Scopus databases, and reference lists of eligible studies. Publication dates ranged from the beginning of each database to December 2011. Syntheses of longitudinal studies suggested insomnia and sleep quality were bidirectionally related to anxiety and depression, and depression/anxiety, respectively. Childhood sleep problems significantly predicted higher levels of depression and a combined depression/anxiety variable, but not vice-versa. A one-way relationship was found where anxiety predicted excessive daytime sleepiness, but excessive daytime sleepiness was not associated with depression. Definitive conclusions regarding bidirectionality cannot be made for most sleep disturbances due to the small number and heterogeneity of cohort samples used across studies. Nevertheless, best available evidence suggests insomnia is bidirectionally related to anxiety and depression. Clinical and theoretical implications are discussed. Alvaro PK; Roberts RM; Harris JK. A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. SLEEP 2013;36(7):1059-1068.
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SLEEP, Vol. 36, No. 7, 2013
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
INTRODUCTION
Sleep disturbances encompass various potentially overlap-
ping symptoms and disorders including insomnia, hypersomnia,
excessive daytime sleepiness, circadian rhythm disturbance, and
extrinsic sleep disorders (related to insufcient sleep and sleep
hygiene). Sleep disturbances, anxiety and depression are common
problems that lead to neuropsychological impairment,
1-3
alcohol
and drug abuse,
4
and suicidal ideation.
5-7
Recent studies have es-
tablished high comorbidity rates between sleep disturbances (such
as insomnia, narcolepsy, sleep apnea, and circadian rhythm com-
plaints), and depression and anxiety,
8-10
rates which vary across
different anxiety disorders.
11
Longitudinal associations have also
been established between sleep disturbances, anxiety, and depres-
sion.
12,13
Nevertheless, the etiological relationship between these
problems remains unclear. Understanding the etiological relation-
ship could help determine whether the onset of one is a risk-factor
for the onset of the others, and inform public health campaigns
and clinical interventions for each disorder.
11,14,15
Research on bidirectionality can achieve such goals. Bidi-
rectionality is established by accounting for the base rate of
outcome variables,
16
considering whether a sleep disturbance
independently predicts the onset of anxiety or depression, and
whether anxiety or depression independently predicts the onset
of a sleep disturbance.
REVIEW OF SLEEP DISTURBANCES, ANXIETY AND DEPRESSION
http://dx.doi.org/10.5665/sleep.2810
A Systematic Review Assessing Bidirectionality between Sleep Disturbances,
Anxiety, and Depression
Pasquale K. Alvaro, B Psych (Honors)
1
; Rachel M. Roberts, BA (Hons), MPsych (Clinical), PhD
1
; Jodie K. Harris, BPsych (Hons), PhD (Clin Psych)
2,3,4
1
School of Psychology, University of Adelaide, South Australia;
2
Flinders University of South Australia;
3
Centre for Treatment of Anxiety and Depression
(CTAD), Adelaide Health Service;
4
University of Adelaide, South Australia
Submitted for publication June, 2012
Submitted in nal revised form February, 2013
Accepted for publication February, 2013
Address correspondence to: Pasquale Alvaro, School of Psychology, The
University of Adelaide, SA, Australia, 5005; Tel: +61 (08) 8313 3399; Fax:
+61 (08) 8303 3770; E-mail: pasquale.alvaro@adelaide.edu.au
Study Objectives: To investigate whether sleep disturbances are bidirectionally related to anxiety and depression, and thus identify potential risk
factors for each problem.
Design: A systematic review was conducted on 9 studies (8 longitudinal, 1 retrospective) that assessed bidirectionality between a sleep distur-
bance, and anxiety or depression. Treatment studies were excluded, along with those solely based on clinical samples or cohorts at high risk of suf-
fering from a sleep disturbance, anxiety and depression. Eligible studies were identied by searching PubMed, PsychINFO, Embase, and Scopus
databases, and reference lists of eligible studies. Publication dates ranged from the beginning of each database to December 2011.
Measurements and Results: Syntheses of longitudinal studies suggested insomnia and sleep quality were bidirectionally related to anxiety and
depression, and depression/anxiety, respectively. Childhood sleep problems signicantly predicted higher levels of depression and a combined
depression/anxiety variable, but not vice-versa. A one-way relationship was found where anxiety predicted excessive daytime sleepiness, but
excessive daytime sleepiness was not associated with depression.
Conclusions: Denitive conclusions regarding bidirectionality cannot be made for most sleep disturbances due to the small number and hetero-
geneity of cohort samples used across studies. Nevertheless, best available evidence suggests insomnia is bidirectionally related to anxiety and
depression. Clinical and theoretical implications are discussed.
Keywords: Anxiety, depression, insomnia, sleep disturbances, systematic review
Citation: Alvaro PK; Roberts RM; Harris JK. A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression.
SLEEP 2013;36(7):1059-1068.
Various studies have found a bidirectional relationship be-
tween sleep disturbances, anxiety and depression, suggesting
that each contribute to the development and are a consequence
of one another.
14,15,17
Other studies have suggested the existence
of two distinct cause-effect associations, where anxiety predicts
a sleep disturbance and a sleep disturbance predicts depression,
but not vice-versa.
11,18
The inconsistent nature of the research
may relate to variations of sleep disturbances that have been
assessed across studies. The statistical signicance and mag-
nitude of the relationship between sleep disturbances, anxiety
and depression differs across sleep and mental health variables
(anxiety and depression),
18-21
which in turn may affect the re-
sults of bidirectionality studies. The current study, therefore,
presents a systematic review that aims to determine whether
a variety of sleep disturbances are risk factors for the develop-
ment of anxiety or depression, and whether anxiety or depres-
sion are risk factors for the development of sleep disturbances.
METHODS
Data Sources and Study Selection
An extensive search of PubMed, PsychINFO, Embase, and
Scopus was conducted using the following search terms in com-
binations specied for each database: (anxiety OR anxious*
OR phobic OR phobia* OR psychological stress OR stress
OR stresses OR internalization OR internalization OR obses-
sive compulsive OR asthenia OR depression OR depress* OR
depressive disorder OR dysthym* OR seasonal affective dis-
order OR rumination) AND (sleep initiation and maintenance
disorders OR sleep* OR insomnia OR early awakening OR
early waking OR non-restorative OR non-restorative OR night
waking). The Embase and Scopus searches also included the
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
following terms to account for the large number of search hits:
NOT (neoplasm OR cancer OR tumor OR tumour OR carcino-
ma* OR oncolog* OR ‘cardiovascular disease’ OR dementia*
OR Alzheimer* OR parksinson* OR therapy’ OR ‘psychiatric
treatment’ OR medicat* OR treatment). Publication dates for
the searches ranged from the beginning of each database to De-
cember 2011, and all searches were limited to English and hu-
man studies. The Reference lists of retrieved studies were also
assessed for other citations.
Studies were eligible if the following criteria was met: (1)
Study assessed any sleep disturbance; (2) Study assessed an
anxiety or depression variable; (3) Study reported a signi-
cance test when assessing bidirectionality; (4) Study accounted
for baseline/previous sleep, anxiety or depression variable.
Based on the recommendations of Jansson-Frojmark and Lind-
blom,
15
treatment studies, along with those who solely included
clinical samples or cohorts at high risk of suffering from a sleep
disturbance, anxiety, and depression were excluded due to the
potential bias towards a stronger association between sleep
disturbances, anxiety, and depression. The inclusion/exclusion
criteria were developed by the primary author and reviewed by
the secondary author.
Quality Assessment
The methodological quality of the eligible studies was as-
sessed by the rst author who used a modied version of a 15-
item checklist from a recent meta-analysis (Table 1).
22
Items
10 and 11 were included because the nature and magnitude of
associations between sleep disturbances, anxiety, and depres-
sion often differs across sleep and mental health variables.
18-21
Sleep disturbances, anxiety and depression have been found
to be associated with demographic information (e.g., age and
gender),
23,24
circadian rhythm disorders,
25-27
alcohol and drug
use,
4,28
exercise,
29,30
and caffeine,
31-33
thus providing the ratio-
nale behind items 12 and 13. Each item was labelled with a
(+) or (-), depending on whether the study satised the cri-
terion or not. A cutoff score was not used as a reference for
quality because such methods assign equal importance to all
criteria. However, items were not weighted: the Cochrane Col-
laboration recommend against this method due to difculties
in justifying the weights assigned to each item.
34
Instead, qual-
ity was assessed along a continuum that ranged from 0-15,
where lower scores indicated lower quality and higher scores
indicated higher quality.
Data Extraction and Synthesis
A coding form was developed to extract the following data
from each study: geographical location of the study, sample
size at baseline and follow-up, response rate, method of partici-
pant recruitment, method of data collection, use of medication,
study methodology, possible biases, prediction and outcome
variables, demographic statistics at baseline and (when avail-
able) follow-up, descriptive statistics for the variables of sleep
disturbance, anxiety and depression, statistical analyses, vari-
ables controlled for, results, interpretation of results, author(s)’
conclusions, and cited articles that were potentially relevant.
Data extraction was conducted by the rst author.
A meta-analysis could not be conducted due to the heteroge-
neous characteristics of the studies that met the inclusion cri-
teria. Therefore, the research questions were analyzed by two
narrative syntheses. The rst presented an overall representa-
tion of the ndings from each study, whereas the second com-
pared results across different sleep variables.
RESULTS
Included and Excluded Studies
The PubMed, PsychInfo, Embase, and Scopus database
searches identied 2,273, 2,352, 1,252, and 2,664 studies, re-
spectively, and one study was identied from the reference
searches. Ninety-three studies were selected as potentially rel-
evant following the application of the inclusion criteria to the
titles and abstracts. Most articles were excluded because they
did not assess bidirectionality. Five studies
13,35-38
assessed bi-
directionality but not between a sleep disturbance and anxiety
or depression; one study assessed bidirectionality but used a
cohort that was at risk for sleep disturbances, anxiety, and de-
pression
39
; and one study did not report a signicance test when
assessing bidirectionality.
18
Therefore, 10 studies were eligible
for analysis in the systematic review (12% of the original ar-
ticles). Two eligible studies contained identical samples
40,41
and
hence were collapsed into 1, leaving 9 independent studies that
were included in the systematic review (refer to Figure 1 for
a representation of the database searches and summary of the
reasons for exclusion). Eligibility of each study was assessed
by the rst author.
Table 2 displays the general characteristics of each study.
All studies were conducted between 2002 and 2010; 8 were
longitudinal and prospective, and the other study was retro-
spective and cross-sectional. Studies were set in various coun-
tries; 2 in the USA, 2 in the UK, and 1 each in Switzerland,
Sweden, Japan, South Korea, and the Netherlands. Sample
populations also differed across studies; adolescents were as-
sessed by 3 studies, adults by 2, and young adults, the elderly,
child twin pairs, and adopted children were assessed by 1
study each. Participants were recruited by random selection in
Table 1—Items for quality assessment
1. Sampling procedure of cohort described?
2. Population characteristics described?
3. Inclusion and exclusion criteria described?
4. Studied population ≥ 75% of originally selected population?
5. Information about responders vs nonresponders
6. Lost to follow-up < 20%?
7. Information about completers vs noncompleters?
8. Are insomnia, anxiety and depression assessments for clinical
diagnosis?
9. Same measures at both baseline and follow-up?
10. Are depression and anxiety assessed separately?
11. Does the sleep variable assess a specic sleep related problem?
12. Correction for potential demographic confounders?
13. Correction for circadian rhythm disorders, alcohol & drug use,
exercise or caffeine intake?
14. Correction for other potential non-demographic confounders such
as physical disorders or medication?
15. Are given data usable/transformable for meta-analysis?
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
3 studies, high schools in 2, and a 2-stage sampling procedure,
the Colorado adoption agency, birth records, and a national
registrations list of all community residents were used in 1
study each. Participants were recruited by self-report ques-
tionnaires in 4 studies (2 were postal surveys), interviews in 3
(2 were structured and 1 was semi-structured), parent report-
ed questionnaires in 1, and parent reported and self-reported
questionnaires in 1 study.
Quality Assessment
Study quality ratings ranged from 7 to 13 (M = 9.9, SD = 1.9).
All studies described the sampling procedure and population
characteristics, used the same measures at baseline and follow-
up, and gave data useable for a meta-analysis. Seven stud-
ies compared responders to non-responders and completers
to non-completers, assessed a specic sleep disturbance, and
corrected for potential demographic confounders; 6 studies re-
tained 75% of the originally selected population and assessed
depression and anxiety separately; 4 studies lost more than 20%
of participants at follow-up; 3 studies described the inclusion
and exclusion criteria, and corrected for other potential non-
demographic confounders such as physical disorders; 2 studies
assessed insomnia, anxiety, and depression according to a clini-
cal diagnosis; 1 study corrected for alcohol and exercise; and
no study corrected for circadian rhythm disorders, drug use, or
caffeine intake (Table 3).
Overall Synthesis
Table 4 presents the data for the overall synthesis. Four of the
9 independent studies supported the bidirectional theory,
14,15,17,42
2 of which assessed insomnia, anxiety, and depression,
15,17
1 as-
sessed insomnia and depression,
42
and 1 assessed insomnia and
mental health status.
14
The unidirectional theory discussed above
was supported by 3 studies.
11,43,44
One study found 2 one-way
relationships where anxiety predicted insomnia and insomnia
predicted depression but not vice-versa
11
; the other reported that
childhood sleep problems predicted depression but not vice-ver-
sa
43
; and the last suggested that childhood sleep problems pre-
dicted higher levels of depression/anxiety, but not vice-versa.
44
However, this does not necessarily support the unidirectional
theory discussed above, as the mental health variable that was
used (depression/anxiety) assessed anxiety and depression as a
single construct rather than separate variables.
Two independent studies reported mixed results.
40,41,45
One
study found no relationship between excessive daytime sleepi-
ness and major depressive episodes or “pure insomnia” (in-
somnia without concurrent depression) and “pure depression”
(depression without concurrent insomnia), a bidirectional rela-
tionship between insomnia and major depressive episodes, and
a one-way relationship where anxiety predicted excessive day-
time sleepiness.
40,41
The other found a bidirectional relationship
between sleep quality and depression/anxiety and a one-way
relationship between time in bed and depression/anxiety, where
less time in bed at baseline predicted higher levels of depres-
sion/anxiety, but not vice-versa.
45
Therefore, 6 independent
studies supported the bidirectional theory,
14,15,17,40-42,45
5 sup-
ported a unidirectional relationship,
11,40,41,43-45
and 1 study did
not nd a signicant relationship between excessive daytime
sleepiness and major depressive episodes or “pure insomnia”
and “pure depression.
40,41
Figure 2 depicts the number of stud-
ies and analyses that supported each type of relationship.
Synthesis of Sleep Variables
Insomnia was the most commonly assessed sleep vari-
able,
11,15,17,40,42
followed by childhood sleep problems,
43,44
sleep
quality,
14,45
excessive daytime sleepiness,
41
and time in bed
(Table 5).
45
Studies that assessed bidirectionality between anxi-
ety, depression, and other common sleep disturbances such as
sleep apnea were not found.
For insomnia, 3 of the 5 studies supported the bidirectional
theory.
15,17,42
One study supported the unidirectional theory,
where anxiety disorders predicted insomnia, which predicted
major depressive disorder.
11
The remaining independent study
reported several analyses across 6 time points over 20 years. No
relationship was found in either direction between “pure insom-
nia” and “pure depression,” along with a bidirectional relation-
ship between insomnia and insomnia comorbid with depression
across 5 separate bidirectional analyses over 20 years.
40
The
same study also found conicting results across these 5 analy-
ses when assessing bidirectionality between “pure depression”
and insomnia comorbid with depression (2 bidirectional rela-
tionships, a one-way relationship where insomnia comorbid
with depression predicted pure depression but not vice-versa,
and 2 relationships where no association was found in either
direction). Finally, insomnia without concurrent depression
predicted the development of depression at the interview di-
rectly following baseline and interviews at any given follow-
up, whereas depression without concurrent insomnia predicted
insomnia at the interview at any given time-point only.
40
Regarding sleep quality, both studies found a bidirectional
relationship, one with depression/anxiety
45
and the other with
mental health status (a variable that contains a depression/anxiety
scale).
14
Childhood sleep problems predicted depression and de-
pression/anxiety but not vice-versa.
43,44
One study found that less
time in bed at baseline is associated with more severe symptoms
of depression/anxiety at follow-up, but not vice-versa.
45
Finally,
Figure 1—Summary of the reasons for exclusion.
93 potentially relevant articles from initial searches
10 articles eligible for the systematic review
Reasons for exclusion
• 76 studies did not assess directionality
• 5 studies did not assess directionality
of sleep related problems, anxiety or
depression
• 1 study assessed an “at risk”
population
• 1 study did not control for relevant
baseline variables
2 articles assessed the same population
9 articles included in the systematic review
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
one study found a one-way relationship where anxiety predicted
excessive daytime sleepiness, but no relationship between exces-
sive daytime sleepiness and depression (i.e., excessive daytime
sleepiness did not predict depression and vice-versa).
41
DISCUSSION
Each study included in the systematic review reported at
least one signicant relationship between a sleep disturbance,
anxiety and/or depression, which further consolidates the no-
tion of an association between these problems.
8,23,44
Interest-
ingly, one study
41
found that anxiety predicted later excessive
daytime sleepiness but no relationship between excessive day-
time sleepiness and major depressive disorder symptoms, sug-
gesting that a bidirectional relationship is not consistent across
mental health problems. This is surprising given the overlap
of abnormalities in neurotransmitters and brain structures in-
volved in problems with the sleep wake cycle, anxiety, and
depression,
46-53
but Hasler and colleagues
41
suggested that the
association between excessive daytime sleepiness and depres-
sion may be mediated by sleep disorder symptoms and co-
morbid anxiety. More studies are needed to draw a denitive
conclusion on whether excessive daytime sleepiness, anxiety,
and depression are bidirectionally related.
Best available evidence suggests that insomnia is bidirec-
tionally related to anxiety and depression. One retrospective
study reported a one-way relationship where anxiety predicted
Table 2—General Characteristics of Each Study Included in the Systematic Review
Study
Sample
size
Study
design
City/
Country Population
Age
range at
baseline
Method of participant
recruitment
Method of
participant
assessment
N time
points
(N years
between
follow
up)
40/41 457 Longitudinal,
Prospective
Switzerland Young adults,
representative,
stratied
sample
20-21 Two stage sampling procedure:
(1) Participants were screened
for health problems, (2) Those
at risk for psychiatric syndromes
were identied using a stratied
sampling procedure and selected
for interviews. Males recruited
through conscription, females
recruited through electoral
register. Males were investigated
in groups of 10, and females
received postal surveys
Semi-structured
interview
6 (2, 5, 2,
5, 6)
44 360 Longitudinal,
Prospective
USA Adopted
children
4 Participants recruited from the
Colorado Adoption Project
Parent reported
questionnaires
2 (11)
43 500 Longitudinal,
Prospective
Wales &
England
Child twin pairs 8 Twin pairs identied through birth
records. Parents were contacted
by the UK Ofce for National
Statistics after screening for
infant mortality.
Questionnaires.
Parent reported
at baseline, and
Self-report at
follow up
2 (2)
15 1,498 Longitudinal,
Prospective
Sweden Adult 20-60 Random selection, identied via
public register
Self-report
questionnaires,
postal
2 (1)
11 1,014 Cross-
sectional,
Retrospective
USA Adolescents,
metropolitan
13-15 Random selection, Youth-parents
pairs identied from a 400,000
member Health Maintenance
Organisation
Structured
interviews
1 (n/a)
14 516 Longitudinal,
Prospective
Japan Adolescents 13-15 High schools from metropolitan
Tokyo
Self-report
questionnaires
2 (2)
42 909 Longitudinal,
Prospective
South Korea Elderly, general
population
65+ All community residents within
2 geographic catchment areas
(one urban, one rural) were
identied and approached
through national registration list.
Structured
interview, home
based
2 (2)
45 493 Longitudinal,
Prospective
Netherlands Adolescents 12-15 High schools Self-report
questionnaires
3 (1)
17 1,589 Longitudinal,
Prospective
UK Adults 18+ Random selection, adults
registered to 1 of 5 general
practices from urban and rural
areas
Self-report
questionnaires,
postal
2 (1)
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
depression, but not vice-versa.
11
Conversely, four longitudinal
studies found a bidirectional relationship between insomnia,
anxiety and/or depression.
15,17,40,54
Due to the retrospective na-
ture of, and hence the memory bias apparent in retrospective
methodology,
55
longitudinal study designs allow for more ac-
curate causal inferences and reliable assessment of bidirec-
tionality, and are therefore the preferred methodology for
bidirectional studies.
14
Interestingly, insomnia could predict depression more con-
sistently than depression predicts insomnia. Buysse and col-
Table 3—Amount of studies (n) that met each criterion of quality assessment
Criterion Number (n) +, n/a Studies
Sampling procedure of cohort described? 9 11, 14, 15, 17, 40, 42-45
Population characteristics described? 9 11, 14, 15, 17, 40, 42-45
Inclusion and exclusion criteria described? 3 11, 15, 42
Studied population ≥ 75% of originally selected population? 6 14, 40, 42-45
Information about responders vs nonresponders 7 11, 15, 17, 40, 42, 44, 45
Lost to follow-up < 20%? 4 11, 15, 43, 45
Information about completers vs noncompleters? 7 11, 17, 40, 42-45
Are insomnia, anxiety and depression assessments for clinical diagnosis? 2 11, 42
Same measures at both baseline and follow-up? 9 11, 14, 15, 17, 40, 42-45
Are depression and anxiety assessed separately? 6 11, 15, 17, 40, 42, 44
Does the sleep variable assess a specic sleep related problem? 7 11, 14, 15, 17, 40, 42, 45
Correction for potential demographic confounders? 7 11, 14, 15, 17, 42, 44, 45
Correction for circadian rhythm disorders, alcohol & drug use, exercise, or caffeine intake? 1 42
Correction for other potential non-demographic confounders, such as physical disorders or medication? 3 14, 17, 42
Are given data usable/transformable for meta-analysis? 9 11, 14, 15, 17, 40, 42-45
Figure 2—Summary of the total number of studies (analyses) that supported each type of relationship between sleep disturbances, anxiety and depression.
1(2)
Unidirectional relationships
No relationships
Bidirectional relationships
Sleep disturbance
Sleep disturbance
Sleep disturbance
Depression/Anxiety
Depression
Insomnia/Depression
3(3)
Sleep disturbance/
Depression
2(2)
4(4)
2(2)
1(2)
3(3)
Anxiety
Sleep disturbance
Sleep disturbance/
Depression
Sleep disturbance
Sleep disturbance
Anxiety
Depression
Depression
Depression/Anxiety
1(6)
Sleep disturbance
Depression Depression
2(2)
1(5)
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
leagues
40
assessed bidirectionality over a 20-year period across
six time-points. They found consistent bidirectional relation-
ships between “pure insomnia” and insomnia comorbid with
depression, yet conicting relationships between “pure depres-
sion” and insomnia comorbid with depression. Such results
suggest that insomnia at baseline is a stronger and more persis-
tent predictor of follow-up depression than baseline depression
is of follow-up insomnia.
Various types of mechanisms may explain the bidirectional
association between insomnia, anxiety and depression. Com-
Table 4—Main ndings from each study
Study
Sleep variable
(instrument name)
Mental health
variables (instrument
name) Baseline/past variables accounted for Results
40, 41 Insomnia – immediate
follow up only (SPIKE
63
)
MDE – immediate follow
up only (SPIKE
63
)
Concurrent MDE when predicting insomnia, and vice-versa.
Insomnia immediate follow up
depression.
Insomnia – any follow
up (SPIKE
63
)
MDE – any follow up
(SPIKE
63
)
Concurrent MDE when predicting insomnia, and vice-versa. Bidirectional: baseline variable and
follow up at any time
“Pure insomnia”
(SPIKE
63
)
“Pure depression”
(SPIKE
63
)
Only participants who reported no baseline symptoms of the outcome
variable were included in the analysis.
5 analyses, all no relationship
“Pure insomnia”
(SPIKE
63
)
Insomnia comorbid with
depression (SPIKE
63
)
“Pure insomnia” refers to those without concurrent depression 5 analyses, all bidirectional
Insomnia comorbid with
depression (SPIKE
63
)
“Pure depression”
(SPIKE
63
)
“Pure depression” refers to those without concurrent insomnia 5 analyses: 2 bidirectional, 2 no
relationship, 1insomnia + depression
depression
EDS (SPIKE
63
) Cumulative MDE
(SPIKE
63
), Cumulative
Anxiety (SPIKE
63
)
Baseline EDS when assessing anxiety and depression as predictors, and
vice-versa. Gender and stratied sampling for both analyses.
Anxiety EDS. No relationship
between EDS and MDE
44 Childhood sleep
problems (CBC
64
)
Depression/Anxiety
(CBC
64
)
Baseline depression/anxiety, when assessing childhood sleep problems
as a predictor, and vice-versa. Adoptive status and child sex for both
analyses.
Childhood sleep problems
Depression/Anxiety
43 Childhood sleep
problems (CSHQ
65
)
Depression (CDI
66
) When predicting depression: the effects of depression at age 8 on
depression at age 10 and the association between sleep and depression
at age 8. When predicting childhood sleep problems: the effects of
childhood sleep problems at age 8 on childhood sleep problems at age 10
and the association between sleep and depression at age 8.
Childhood sleep problems
Depression
15 Insomnia (BNSQ
67
&
USI
68
)
Depression (HADS
69
),
Anxiety (HADS
69
)
Only participants who reported low or no symptoms of the outcome
variable at baseline were included in analyses, e.g., if assessing anxiety
as an outcome variable, only participants with low scores of anxiety were
used. Age and gender were used as predictor variables for all analyses.
Bidirectional
11 Insomnia (DSM-IV)
70
* Depression (DSM-
IV)
70
*, Any Anxiety
Disorder(DSM-IV)
70
*
Prior depression was controlled for in the insomnia – anxiety disorders
models and prior anxiety disorders in the insomnia – depression models.
Gender and race/ethnicity were also controlled for in each analysis.
Anxiety Insomnia Depression
14 Sleep quality (PSQI
71
) Mental Health Status
(GHQ-12)
72
Only participants who did not report symptoms of the outcome variable at
baseline were included in the analyses, e.g. only those without poor sleep
quality were used when assessing sleep quality as an outcome variable.
Predictor variables, gender, and lifestyle and contentment with daily life at
baseline were used as covariates.
Bidirectional
42 Insomnia
(10/66DDCRG)
73
*
Depression (GMS B3)
74
* Only participants who did not report symptoms of the outcome variable
at baseline were included in the analyses, e.g. those without insomnia at
baseline were used when assessing insomnia as an outcome variable.
Number of physical disorder, age, gender, education, housing, past
occupation, current employment, living area, life events, social decit,
physical activity, organicity, anxiety, and daily drinking were controlled for
in all analyses.
Bidirectional
45 Sleep quality (SQS)
75
Depression/Anxiety
(YSR)
76
Time is included as a covariate, along with time 1, sleep quality, and mean
time in bed.
Bidirectional
Time in Bed (TBS)
45
Depression/Anxiety
(YSR)
76
Time is included as a covariate, along with time 1, sleep quality, and mean
time in bed.
Less time in bed Depression/
Anxiety
17 Insomnia (ISRS)
77
Depression (HADS
69
),
Anxiety (HADS
69
)
Only participants who did not report symptoms of the outcome variable
at baseline were included in analyses, e.g., if assessing insomnia as an
outcome variable, only participants who were not categorised as having
insomnia were used. Age and gender were used as predictor variables for
all analyses. Baseline covariates such as age, sex, social class and pain
areas were also controlled for.
Bidirectional
*Diagnosis. EDS, excessive daytime sleepiness; MDE, major depressive episode; SPIKE, The Structured Psychopathological Interview and Rating of Social Consequences of Psychic
Disturbances for Epidemiology; CBC, Child Behavior Checklist; CSHQ, Child Sleep Habits Questionnaire; CDI, Child Depression Inventory; BNSQ, Basic Nordic Sleep Questionnaire; USI,
Uppsala Sleep Inventory; HADS, Hamilton Anxiety Depression Scale; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders-IV; PSQI, Pittsburgh Sleep Quality Index; General Health
GHQ, Questionnaire; 10/66DDCRG, 10/66 Dementia in Developing Countries Research Group; GMS B3, Geriatric Mental State diagnostic schedule; SQS, Sleep Quality Scale; YSR, Youth
Self-report; TBS, Time in Bed Scale; ISRS, Insomnia Self-report Scale.
SLEEP, Vol. 36, No. 7, 2013
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
mon neurobiological underpinnings (i.e., neurotransmitters
and brain structures, discussed above) have been associated
with insomnia, anxiety, and depression.
46-53
Batterham and col-
leagues
56
hypothesize there may also be biological factors such
as increased inammatory dysregulation in response to sleep
disturbances
57
that are associated with anxiety
58
and depres-
sion.
59
Three other potential mechanisms discussed by Kaneita
and colleagues,
14
which may coexist, include common factors
(e.g., genetic, familial, social, or environmental) that indepen-
dently contribute to the development of insomnia, anxiety and
depression; insomnia, anxiety, and depression may be related,
in which only the order of appearance of symptoms may alter;
and insomnia, anxiety, and depression may be independent, but
mutually inuencing disorders.
A denitive conclusion is difcult to make regarding whether
sleep disturbances, anxiety, and depression are bidirectionally
related. Firstly, the small number of studies that assessed each
sleep disturbance variable prevents an accurate representation
of the true relationship between sleep disturbances, anxiety,
and depression. Further longitudinal studies are needed to un-
derstand whether there is a bidirectional relationship between
sleep disturbances, anxiety, and depression.
Secondly, sleep disturbance variables that were assessed by
the studies in this systematic review were limited and some-
times problematic. Prevalent sleep disorders such as obstruc-
tive sleep apnea and circadian rhythm sleep disorders were not
investigated, while insomnia symptoms such as problems with
sleep latency, nighttime awakenings, and daytime functioning
decits as a result of sleep were incorporated in the denition of
sleep quality
14,45
and childhood sleep problems.
43,44
The deni-
tion of childhood sleep problems also incorporated other sleep
disturbances such as nightmares, sleep duration, overtiredness,
bedtime resistance, sleep anxiety, parasomnias, sleep disor-
dered breathing, and daytime sleepiness. Therefore, implying
differences in ndings across sleep variables is problematic be-
cause distinctions between sleep variables are obscured and the
denition of childhood sleep problems was overly inclusive.
Future bidirectionality studies should focus on a broader range
of clearly dened sleep disturbances that contain reasonable
conceptual overlap.
Variables of anxiety and depression were also sometimes de-
ned using conceptually overlapping and overly inclusive vari-
ables, along with non-diagnostic self-report questionnaires, and
may variably reect current diagnostic criteria. Such cases in-
clude a combined depression/anxiety variable, which assessed
depression and anxiety as one construct. These inventories
could mask potential differences in bidirectionality across vari-
ables, and are problematic because bidirectional inferences can-
not be made about the relationship between sleep disturbances.
Indeed, only two studies assessed diagnosed insomnia, anxiety,
and depressive disorders.
11,42
Future studies should refrain from
using overly inclusive variables, and use denitions based on
established diagnostic criteria.
Moreover, bidirectionality between anxiety, depression,
and different types of insomnia symptoms (i.e., sleep onset in-
somnia, early morning awakening) was not assessed. Recent
Table 5—Comparison of study ndings between sleep variables
Sleep variables Study
Mental health
variable(s) Study design Results
Insomnia 40, 41 MDE Longitudinal
(immediate only)
Insomnia immediate follow up depression
40, 41 MDE Longitudinal (any
follow up)
Bidirectional
40, 41 “Pure depression” Longitudinal All no relationship
40, 41 Insomnia comorbid with
depression
Longitudinal All bidirectional
15 Depression, Anxiety Longitudinal Bidirectional
42 Depression* Longitudinal Bidirectional
17 Depression, Anxiety Longitudinal Bidirectional
11 Depression*, Anxiety* Retrospective
Anxiety Insomnia Depression
Insomnia comorbid
with depression
40, 41 “Pure depression” Longitudinal
2 bidirectional, 2 no relationship, 1 insomnia + depression
depression
EDS 40/41 MDE, Anxiety Longitudinal
Anxiety EDS, No relationship between EDS & major depression
Childhood sleep
problems
44 Depression/Anxiety Longitudinal
Childhood sleep problems Depression/Anxiety
43 Depression Longitudinal
Childhood sleep problems Depression
Sleep quality 14 Mental Health Status Longitudinal Bidirectional
45 Depression/Anxiety Longitudinal Bidirectional
Time in bed 45 Depression/Anxiety Longitudinal
Less time in bed Depression/Anxiety
*Diagnosis. EDS, Excessive Daytime Sleepiness; MDE, Major Depressive Episode.
SLEEP, Vol. 36, No. 7, 2013
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Review of Sleep Disturbances, Anxiety and Depression—Alvaro et al
studies have suggested that the association between insomnia,
anxiety and depression may differ across specic insomnia
symptoms.
19,20
Therefore, it remains unclear whether anxiety
and depression are bidirectionally related across insomnia
symptoms. Further contributions to the eld may consider
different insomnia proles.
Also, studies did not consider bidirectionality across different
types of anxiety disorders. Johnson et al.,
11
found that comor-
bidity rates of insomnia vary across different subtypes. Future
research can assess whether sleep disturbances are consistently
bidirectionally associated with different anxiety proles.
Furthermore, the samples used by the included studies were
heterogeneous, assessing children, adolescents, young adults,
adults, and the elderly. Recent evidence has suggested age
may be related to types and prevalence rates of sleep distur-
bances,
60-62
rendering a comparison between studies potentially
awed at this time. The heterogeneous samples across studies
also prevented the use of meta-analytic techniques to perform
quantitative analyses.
Finally, important potential covariates were not controlled
for. One study corrected for alcohol and exercise, but no study
accounted for variables that have been associated with sleep dis-
turbances, anxiety, and depression such as lifestyle factors, med-
ical conditions, and other sleep disorders.
4,25-33
Bidirectionality
is difcult to infer without controlling for such variables. Future
research should account for variables such as circadian rhythm
disorders, drug and alcohol use, caffeine intake, and exercise.
Although few conclusions can be made, this study contrib-
utes to the burgeoning understanding of the etiological relation-
ship between, and prevention of anxiety, depression and various
sleep disturbances. At the very least, patients who present with
a sleep disturbance should also be assessed for anxiety and de-
pression, and vice-versa.
Bidirectionality was found between insomnia, anxiety, and de-
pression, suggesting that insomnia predicts and is predicted by
anxiety and depression. Therefore, successful treatment of in-
somnia may prevent the onset of subsequent or exacerbation of
comorbid anxiety or depression, and vice-versa.
14
Future research
should consider the most appropriate and cost-effective targets of
prevention and intervention for insomnia, anxiety and depression.
The unidirectional relationship between childhood sleep
problems and depression suggests that some sleep disturbances
may signicantly contribute to depression, at least in children,
but not vice-versa. Such conclusions should be treated with
caution due to the limited research in this area, the overly inclu-
sive nature of the childhood sleep problems variable, and the
conceptual overlap with insomnia. Further research is needed
to identify the specic sleep disturbance proles that indepen-
dently predict anxiety and depression.
In conclusion, due to the small number of studies in this area
and the heterogeneity of cohort samples used across studies,
few denitive conclusions about the bidirectionality of most
sleep disturbances can be made. Best available evidence to
date suggests depression bidirectional relationship between in-
somnia, anxiety, and depression, and further consolidates the
association found between sleep disturbances, anxiety, and de-
pression. Therefore, treatment of insomnia may prevent the de-
velopment of anxiety and depressive disorders, and vice-versa.
Future research should consider whether insomnia, anxiety,
or depression should be targeted to ensure the most efcient
and cost-effective method for prevention and intervention
of these disorders.
ACKNOWLEDGMENTS
Authors did not have conict of interests, including specic
nancial interests and relationships and afliations relevant to
the subject of this manuscript. The principal author “had full
access to all of the data in the study and takes responsibility for
the integrity of the data and the accuracy of the data analysis.”
DISCLOSURE STATEMENT
This was not an industry supported study. The authors have
indicated no nancial conicts of interest.
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... [6][7][8] Bucciarelli et al systematically summarized gender differences in the relationship between depressive symptoms and CVD. 9 Both sleep disorders and depressive symptoms have been recognized as risk factors for CVD, and a systematic review indicated that sleep disorders were bidirectionally related to depression. 10 Therefore, there may be pathways between depression and sleep disorders that have a common effect on CVD, which may greatly increase the risk of CVD in people who suffer from the both diseases. However, previous studies have focused on depression and sleep disorders as independent predictors for CVD. ...
... The measurement of depressive symptoms was conducted through the Patient Health Questionnaire 9 (PHQ-9). 13 The severity of depressive symptoms was classified into five categories according to the PHQ-9 scores, including no depressive symptoms (0-4), mild depressive symptoms (5-9), moderate depressive symptoms (10)(11)(12)(13)(14), moderately-severe depressive symptoms (15)(16)(17)(18)(19), and severe depressive symptoms (20)(21)(22)(23)(24)(25)(26)(27). 14 In this study, depressive symptoms were defined as PHQ-9 scores ≥10. ...
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Purpose: Depressive symptoms and sleep disorders were independent risk factors for cardiovascular diseases (CVD). However, few studies have examined the combined effects of depressive symptoms and sleep disorders on CVD. We aimed to evaluate the association between depressive symptoms, sleep disorders and CVD occurrence. Methods: Data on 30,398 participants were extracted from the National Health and Nutritional Examination Survey (NHANES) database (2005-2018). Univariate and multivariate analyses were used for assessing the association of depressive symptoms, sleep disorders, and CVD occurrence. Three indexes, including the relative excess risk of interaction (RERI), attributable proportion of interaction (API), and synergy index (SI), were used to analyze the interaction. Results: Of 30,398 participants, 11,544 (37.98%) participants had CVD and 18,854 (62.02%) did not. Except for gender, the differences were significant between CVD and non-CVD participants in all variables (all P<0.001). Depressive symptoms [odds ratio (OR)=1.73; 95% confidence intervals (CI):1.57-1.91] and sleep disorders (OR=1.76; 95% CI:1.65-1.88) were associated with an increased risk of CVD after adjusting all confounders. Patients with both depressive symptoms and sleep disorders (OR=2.64; 95% CI:2.32-3.00) had a higher risk of CVD than those without. There may be a synergistic interaction between depression and sleep disorders on the CVD occurrence (SI=1.763; 95% CI:1.299-2.394), and the proportion of CVD caused by this interaction was 26.9% (API=0.269; 95% CI:0.148-0.389). In addition, only moderate depressive symptoms may interact with sleep disorders in the occurrence of CVD. Conclusion: There may be a synergistic interaction between depressive symptoms and sleep disorders, and the synergistic interaction may increase the occurrence of CVD.
... A Chinese study showed a positive association between poor sleep quality and symptoms of anxiety; this association was significant among both males and females while the strongest positive associations were found among people aged 60 or older, smokers and people with a low level of physical activity, obesity and type 2 diabetes 37 . However, the mechanistic relationship between depression, anxiety, and sleep has not been completely understood yet, and has been considered complex and probably bidirectional 35,38 . ...
... Recently, insomnia has been identified as a disorder and as a symptom of psychiatric and medical disorders. Insomnia and anxiety often coexisted; especially when there is a clear overlap of symptoms 38 . An early morning awakening is considered a hallmark symptom of depression, and resolution of insomnia is a predictor of a favorable response to depression treatment 44 . ...
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Objectives: To associate the effects of the social outbreak with insomnia and daytime sleepiness according to the distance from the riots. Material and Methods: Cross-sectional analytical study; a non-probabilistic sampling was carried out at a national level. The Google Forms tool was used; a document was submitted using a national database. The instrument consisted of four sections: socio-demographic data, biopsychosocial symptoms, insomnia severity index (ISI), and the Epworth sleepiness scale (ESS). The data were analyzed using descriptive statistics and the zero-inflated negative binomial model. Results: Of a total of 2,532 surveyed people, 29% were male; 43% was younger than 30 years old. The 50% of the sample suffers from sleepiness and 71% shows some type of insomnia. The marginal effects of the zero-inflated negative binomial model show that women, people aged 51 or older, who are neither studying a healthcare degree nor working in the healthcare sector, that are exposed to 4 or more hours per day to the news and that live in areas near or very near the riots, have significantly higher ISI (marginal effect 1.356, SE 0.381, p-value 0.000) and ESS scores (marginal effect 0.693, SE 0.320, p-value 0.030). To live/work in rioting areas has the greater marginal effect compared to other determinants. Finally, neither employment status nor educational level are associated with significant effects in the aforementioned scales. Conclusion: The riots occurred during the social outbreak of October 2019 in Chile had an effect on insomnia and daytime sleepiness. Particularly, to live/work in rioting areas has the greater marginal effect compared to other determinants.
... Consequently, it can influence sleep duration and sleep-wake cycles, which is evident in the present study. Furthermore, mental health problems will be caused by disrupted circadian rhythms and sleep patterns (Alvaro, Roberts, & Harris, 2013;Freeman et al., 2017;Lovato & Gradisar, 2014). ...
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Background School closures and home confinement due to the ongoing COVID-19 pandemic may lead to disrupted sleep patterns. Consequently, it could increase the risk of children and adolescents’ mental health disorders. Methods In this prospective study, we randomly selected ten schools in Shanghai and conducted cluster sampling of students from each school. The first wave of the survey was conducted between January 3 and 21, 2020. Approximately two months after the COVID-19 outbreak declared, a second wave of the survey was conducted. In total, 2,427 individuals were surveyed in both waves using the same sampling method. Participants’ mental health status (depression, anxiety and stress), sleep patterns and other demographic information were measured in both waves. Multivariate regression analysis was used to examine the associations between sleep patterns and mental health status. Results During the COVID-19 pandemic, a total of 873 participants (19.9%), 1,100 participants (25.1%), and 670 participants (15.3%) reported depression, anxiety, and stress symptoms, respectively. Significant changes of both sleep duration and sleep-wake cycle patterns were observed before and during the COVID-19 pandemic. Moreover, shorter sleep duration and late to rise patterns (including early to bed late to rise and late to bed late to rise) were found to be associated with higher odds of having mental illnesses during the pandemic. Conclusions These results suggest there is a pressing need to monitor children's and adolescents’ health behaviour and mental health and develop timely evidence‐based strategies and interventions to mitigate adverse behavioural and psychological impacts caused by these unprecedented challenges.
... According to the allostatic model (for a summary and review, see [31]), mental health problems and fatigue can result from excessive, prolonged and/or repeated exposure to stressors, such as high job demands, stressful life events and chronic negative experiences [4,30]. In addition, mental health problems and fatigue can mutually affect and reinforce each other [3,32,35,46]. ...
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While commercial shipping remained vital for maintaining global supply chains during the COVID-19 pandemic, measures imposed to control the spread of infection have disrupted crew changes and impacted interactions with port personnel and among crew members on board. Initial reports indicate that this affected work and life on board, the length of seafarers’ time on board as well as seafarers’ employment and family concerns. However, the consequences for seafarers’ well-being are not well understood. The purpose of this study was to examine the effects of the COVID-19 pandemic on seafarers’ mental health and chronic fatigue, and to analyze the role of potential mitigating factors, notably onboard peer support, external support and Internet quality. Survey responses from 622 seafarers on international commercial vessels were analyzed using structural equation modeling. Findings suggested that the impact of the pandemic increased seafarers’ fatigue and mental health problems. However, they also indicated ways of mitigating the negative impact of the pandemic and increasing resilience by enhancing support from fellow crew members on board, ensuring the availability of external support and providing fast and reliable Internet access.
... Emerging research is highlighting the critical role that sleep plays in overall health. Poor sleep health has been associated with several negative health outcomes, including increased risk of cardiovascular disease, 1,2 obesity, 3 depression, 4 and neurodegenerative disorders. 5 Despite these well-known health risks, we are in the middle of a sleep crisis, with more than 70 million Americans experiencing sleep related problems 6 and with less than 20% of patients estimated to be properly diagnosed and treated for sleep disorders. ...
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Study Objectives: Validate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA). Methods: The algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP). Results: CinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusions: Results demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.
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Neurological disorders related to neuroinfections are highly prevalent in Sub-Saharan Africa (SSA), constituting a major cause of disability and economic burden for patients and society. These include epilepsy, dementia, motor neuron diseases, headache disorders, sleep disorders, and peripheral neuropathy. The highest prevalence of human immunodeficiency virus (HIV) is in SSA. Consequently, there is a high prevalence of neurological disorders associated with HIV infection such as HIV-associated neurocognitive disorders, motor disorders, chronic headaches, and peripheral neuropathy in the region. The pathogenesis of these neurological disorders involves the direct role of the virus, some antiretroviral treatments, and the dysregulated immune system. Furthermore, the high prevalence of epilepsy in SSA (mainly due to perinatal causes) is exacerbated by infections such as toxoplasmosis, neurocysticercosis, onchocerciasis, malaria, bacterial meningitis, tuberculosis, and the immune reactions they elicit. Sleep disorders are another common problem in the region and have been associated with infectious diseases such as human African trypanosomiasis and HIV and involve the activation of the immune system. While most headache disorders are due to benign primary headaches, some secondary headaches are caused by infections (meningitis, encephalitis, brain abscess). HIV and neurosyphilis, both common in SSA, can trigger long-standing immune activation in the central nervous system (CNS) potentially resulting in dementia. Despite the progress achieved in preventing diseases from the poliovirus and retroviruses, these microbes may cause motor neuron diseases in SSA. The immune mechanisms involved in these neurological disorders include increased cytokine levels, immune cells infiltration into the CNS, and autoantibodies. This review focuses on the major neurological disorders relevant to Africa and neuroinfections highly prevalent in SSA, describes the interplay between neuroinfections, immune system, neuroinflammation, and neurological disorders, and how understanding this can be exploited for the development of novel diagnostics and therapeutics for improved patient care.
Article
Background The lockdown measures implemented to face the 2019 Coronavirus Disease (COVID-19) first wave deeply modified the lifestyle of the Italian population. Despite its efficacy in limiting the number of infections, forced home confinement was paralleled by sleep/wake cycle disruptions, psychological distress and maladaptive coping strategies (i.e., unhealthy behaviours, such as tobacco and alcohol consumption). Under these unprecedented stress conditions, we explored a possible association between poor sleep quality and increased likelihood of engaging in an unhealthy lifestyle. Methods A cross-sectional study was conducted by disseminating an online survey via social networks and e-mail. We collected information on demographics, COVID-19-related data, sleep quality, chronotype, circadian misalignment, and lifestyle before and during the lockdown (i.e., consumption of cigarettes, alcoholic beverages, coffee, hypnotics, comfort food and fresh food; practice of physical activity). A global healthiness score was computed to assess participants’ modifications in lifestyle since the beginning of the lockdown. Results 1297 respondents were included in the study: 414 (31.9%) from Northern Italy, 723 (55.8%) from Central Italy, 160 (12.3%) from Southern Italy. The following variables were found to be significant predictors of the adoption of an unhealthy lifestyle since the beginning of the lockdown: poor sleep quality, high BMI and considering the measures adopted by the government to fight the pandemic as excessive. Living in Northern Italy, instead, was associated with healthier habits compared to living in Central Italy. Conclusions Poor sleepers may represent the share of the general population who paid the highest price for social isolation. Further investigations are required to explore the role of sleep quality assessment in the identification of individuals vulnerable to unhealthy behaviours under stressful conditions.
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Without the structure and schedule of traditional activities such as in-person school and socialization, evidence is emerging of pediatric sleep changes during the COVID-19 pandemic. A narrative review was conducted of the sleep literature during the pandemic for preschoolers, school-aged children, and adolescents. Changes in sleep and risk and protective factors for sleep heath during the COVID-19 pandemic are reviewed along with real-life clinical case examples for each developmental period. Given the high rates of pediatric sleep disturbance, clinicians, researchers, and policymakers should refine screening strategies and facilitate referrals for behavioral interventions to support sleep health during pandemics and other natural disasters.
Article
Objective Aim of this study was to examine the association between infant temperament and sleep characteristics and postpartum depressive symptoms among mothers. Study design Research data were collected at the baseline (2nd -4th days postpartum) and the follow-up (6-8 weeks postpartum), Slovak version of the (EPDS) was used, along with questions focused on perceived sleeping problems of an infant, and temperament Linear regression models were employed. Setting Two public hospital sites in Slovakia. Participants 204 women participated in both time points (mean age 30.9±4.8, age range: 20-44; 78.9% vaginal births; 56.9% primiparas). Results Significant differences in the EPDS scores were found according to infant sleeping problems (p≤0.05) and duration of infant night sleep (p≤0.01). Both night sleep and day sleep duration were significant predictors for the level of postpartum depression symptoms at the 6-8 weeks follow-up in the linear regression model after adjusting for confounding variables (β= -0.13; 95%CI: -3.04;-0.01; β= -0.15; 95%CI: -3.02;-0.28, total explained variance 39.0%). Infant temperament characteristics have not been found significant predictors of postpartum depression symptoms in this study. Key conclusions Significant differences in depression levels were found among postpartum women according to perceived sleeping problems of infant, and the duration of infant night sleep. It is important to focus more attention on the role of infant sleeping problems as a possible risk factor for the increased occurrence of postpartum depressive symptoms.
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Objectives: The present study examined the daily, within-individual associations of anxiety with sleep quality and sleep duration and the moderating effects of alexithymia on these associations in community-dwelling young adults. It was hypothesized that daily anxiety and sleep parameters would be bidirectionally related and alexithymia would moderate these relationships. Method: Participants completed morning and evening diaries assessing daily anxiety and sleep parameters for 30 consecutive days. They also completed questionnaires assessing baseline sleep parameters, anxiety, and alexithymia. Multilevel modeling was used to evaluate the within-individual associations between daily anxiety and sleep parameters and whether between-individual differences in alexithymia moderated these associations. Results: Higher anxiety relative to personal averages across the study period was associated with shorter sleep duration at night. Poorer sleep quality and shorter sleep duration relative to personal averages were associated with higher next-day anxiety. A significantly stronger association between poorer sleep quality and higher next-day anxiety was observed in individuals with higher levels of alexithymia. Conclusion: Daily anxiety and sleep quantity are bidirectionally associated within individuals in community-dwelling young adults. Poorer sleep quality was associated with higher next-day anxiety but not vice versa. Individuals with higher levels of alexithymia might be more vulnerable to the effects of poor sleep on next-day anxiety.
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Study Objectives To present psychometric data on a comprehensive, parent-report sleep screening instrument designed for school-aged children, the Children's Sleep Habits Questionnaire (CSHQ). The CSHQ yields both a total score and eight subscale scores, reflecting key sleep domains that encompass the major medical and behavioral sleep disorders in this age group. Design Cross-sectional survey. Setting Three elementary schools in New England, a pediatric sleep disorders clinic in a children's teaching hospital. Participants Parents of 469 school-aged children, aged 4 through 10 years (community sample), and parents of 154 patients diagnosed with sleep disorders in a pediatric sleep clinic completed the CSHQ. Interventions N/A Measurements and Results The CSHQ showed adequate internal consistency for both the community sample (=0.68) and the clinical sample (=0.78); alpha coefficients for the various subscales of the CSHQ ranged from 0.36 (Parasomnias) to 0.70 (Bedtime Resistance) for the community sample, and from 0.56 (Parasomnias) to 0.93 (Sleep-Disordered Breathing) for the sleep clinic group. Test-retest reliability was acceptable (range 0.62 to 0.79). CSHQ individual items, as well as the subscale and total scores were able to consistently differentiate the community group from the sleep-disordered group, demonstrating validity. A cut-off total CSHQ score of 41 generated by analysis of the Receiver Operator Characteristic Curve (ROC) correctly yielded a sensitivity of 0.80 and specificity of 0.72. Conclusions The CSHQ appears to be a useful sleep screening instrument to identify both behaviorally based and medically-based sleep problems in school-aged children.
Article
Study Objectives To establish the direction and etiology of longitudinal associations between sleep problems and depression symptoms in children. Design Data on twins aged 8 and 10 years were obtained. At assessments, parents completed the Child Sleep Habits Questionnaire, and twins completed the Children’s Depression Inventory. Setting Participants were mainly interviewed at the Institute of Psychiatry, London. Patients or Participants Three hundred twin pairs initially enrolled in the study. Interventions N/A. Measurements and Results A genetically informative cross-lagged model examined links between sleep and depression. Sleep problems at age 8 predicted depression at age 10 (partial regression coefficient [95% confidence intervals] = 0.10 [0.01-0.18]). The converse was not found. Stability of sleep problems across time was mainly due to genes (46% of the genetic influence on sleep at 10 was due to the same genetic influence on sleep aged 8). Stability of depression was mainly due to nonshared environmental influences (19% of the nonshared environmental influence on depression at 10 was due to the same nonshared environmental influence on depression at age 8). The cross-lagged association between sleep problems at 8 and depression at 10 years was largely due to genes, although this finding was nonsignificant. Conclusions This study adds to our understanding of the temporal precedence of sleep problems and depression and the risks underlying their associations. There are implications regarding the value of specifying genes linked to sleep problems and potential opportunities for informing early intervention strategies in high-risk groups at key points in the progression to developing more serious problems.
Article
Study Objectives (1) To describe the prevalence and prospective course of insomnia in a representative young-adult sample and (2) to describe the cross-sectional and longitudinal associations between insomnia and depression. Design Longitudinal cohort study. Setting Community of Zurich, Switzerland. Participants Representative stratified population sample. Interventions None. Measurements and Results The Zurich Study prospectively assessed psychiatric, physical, and sleep symptoms in a community sample of young adults (n = 591) with 6 interviews spanning 20 years. We distinguished 4 duration-based subtypes of insomnia: 1-month insomnia associated with significant distress, 2- to 3-week insomnia, recurrent brief insomnia, and occasional brief insomnia. The annual prevalence of 1-month insomnia increased gradually over time, with a cumulative prevalence rate of 20% and a greater than 2-fold risk among women. In 40% of subjects, insomnia developed into more chronic forms over time. Insomnia either with or without comorbid depression was highly stable over time. Insomnia lasting 2 weeks or longer predicted major depressive episodes and major depressive disorder at subsequent interviews; 17% to 50% of subjects with insomnia lasting 2 weeks or longer developed a major depressive episode in a later interview. “Pure” insomnia and “pure” depression were not longitudinally related to each other, whereas insomnia comorbid with depression was longitudinally related to both. Conclusions This longitudinal study confirms the persistent nature of insomnia and the increased risk of subsequent depression among individuals with insomnia. The data support a spectrum of insomnia (defined by duration and frequency) comorbid with, rather than secondary to, depression.
Book
As soldiers and combat veterans have returned from the wars in Iraq and Afghanistan traumatic brain injury (TBI) has been identified as the "signature injury" of those wars. TBI is also in the news on a daily basis due to sports injuries. This new edition has been revised and updated from the 2005 first edition to reflect the exponential expansion of research and clinical data amassed in the intervening years. Each chapter was written and reviewed by the foremost authorities in neuropsychiatry, neurology, rehabilitation medicine, and the other specialties who assess, diagnose, and treat these patients. The revisions and additions to this comprehensive volume were made to ensure that the scope and coverage is both up-to-date and down-to-earth. Key features include: New chapters on epidemiology, neuropathology, and genetics of TBI; new chapter on TBI in the military emphasizing the unique feature of blast injury; A new chapter on posttraumatic stress disorder (PTSD), which emphasizes the common co-occurrence of TBI and PTSD in both combat and other settings; Enhanced coverage of psychopharmacology and psychotherapy for the psychiatric symptoms associated with TBI; Information on the social ramifications of TBI so that clinicians will better understand and help their patients cope with the complex legal, financial, and insurance-based struggles their patients who have sustained TBI encounter; Each chapter concludes with essential points and key references to focus attention and consolidate learning; A foreword written by Bob Woodruff (the ABC World News correspondent who sustained a TBI while covering the war in Iraq) and his wife, Lee Woodruff, who underscore that although this volume is intended to be read primarily by professionals, patients and families may also find the information in the textbook to be of keen interest and practical application.
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Textbook of Traumatic Brain Injury is a comprehensive collection of information on the important topic of traumatic insult to the central nervous system. It is an expanded and enriched version of Neuropsychiatry of Traumatic Brain Injury, published in 1994.