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Insomnia is highly co-morbid with psychiatric disorders, making it a frequent issue in treatment planning in psychiatric clinics. Research has also shown that although insomnia may originally precede or be a consequence of a psychiatric disorder, insomnia likely becomes semi-independent, and may exacerbate those disorders if it is not addressed, leading to reduced treatment response. Cognitive behavioural therapy for insomnia (CBT-I) is now recommended as the first line of treatment of primary insomnia. The research reviewed below indicates that CBT-I in patients with co-morbid depression, anxiety, post-traumatic stress disorder (PTSD), and substance abuse disorders is generally effective for insomnia and sometimes the co-morbid disorder as well. Although more research is needed before definitive recommendations can be made, it appears as though CBT-I is a viable approach to treating the patient with co-morbid insomnia and psychiatric disorders.
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Correspondence: Daniel J. Taylor, PhD, CBSM, ABSM, Department of Psychology, University of North Texas, 1155 Union Circle #311280, Denton, TX
76203-1280, USA. Tel: 940-565-2655. E-mail:
Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric
populations: A systematic review
1 Department of Psychology, University of North Texas, Denton, Texas, and
2 Department of Psychiatr y, University of Texas
Health Science Center at San Antonio, Texas, USA
Insomnia is highly co-morbid with psychiatric disorders, making it a frequent issue in treatment planning in psychiatric
clinics. Research has also shown that although insomnia may originally precede or be a consequence of a psychiatric dis-
order, insomnia likely becomes semi-independent, and may exacerbate those disorders if it is not addressed, leading to
reduced treatment response. Cognitive behavioural therapy for insomnia (CBT-I) is now recommended as the fi rst line of
treatment of primary insomnia. The research reviewed below indicates that CBT-I in patients with co-morbid depression,
anxiety, post-traumatic stress disorder (PTSD), and substance abuse disorders is generally effective for insomnia and
sometimes the co-morbid disorder as well. Although more research is needed before defi nitive recommendations can be
made, it appears as though CBT-I is a viable approach to treating the patient with co-morbid insomnia and psychiatric
Cognitive behavioural therapy for insomnia in
psychiatric populations
Insomnia is included in the diagnostic criteria for
various psychiatric disorders in the new DSM-5
(APA, 2013) including depressive disorders, anxiety
disorders post-traumatic stress disorder (PTSD),
and alcohol and substance use disorders. Research
in has generally indicated high levels of psychiatric
co-morbidity in people with insomnia (Buysse et al.,
1994; Mendelson, 1997; Ohayon et al., 1998; Taylor
et al., 2003, 2005, 2011, 2013). Current estimates
are that 80 90% of depression and anxiety patients,
70% of PTSD patients, and as many as 91% of
recovering alcoholics report signifi cant sleep disrup-
tion (Cohn et al., 2003; Ohayon & Roth, 2003;
Ohayon & Shapiro, 2000; Ohayon et al., 2000).
In the past decade, research has shown that
although insomnia may originally precede or be a
consequence of a psychiatric disorder (Ohayon &
Roth, 2003), insomnia likely becomes semi-indepen-
dent. If it is not addressed, insomnia may exacerbate
co-morbid disorders and lead to reduced treatment
response (e.g. Belleville et al., 2011b; Buysse et al.,
1997; Dew et al., 1997; Kupfer et al. , 1980, 1981,
1982; Trivedi et al., 2005; Winokur & Reynolds,
1994), and increased relapse and recurrence (Brower
et al., 2001; Drummond et al., 1998; Gillin et al.,
1994). These ndings have challenged the assump-
tion that insomnia is a symptom of or secondary
to the psychiatric disorder, which would remit once
the primary psychiatric disorders were successfully
In the past few decades, research in the treatment of
primary insomnia has coalesced to show that cogni-
tive-behavioural therapy for insomnia (CBT-I) has
comparable short-term effi cacy to pharmacotherapy,
and better long-term effi cacy (for a review see
Riemann & Perlis, 2009). Thus, CBT-I is recom-
mended as a fi rst line treatment for primary insomnia.
CBT-I is a collection of separate interventions that
target behavioural, cognitive, and physiological per-
petuating factors of insomnia which, when used
singly or in various combinations, have shown sig-
nifi cant effi cacy to varying degrees (Morin et al.,
1994, 1999, 2006; Murtagh
& Greenwood, 1995;
Smith et al., 2001). The components of CBT-I with
the strongest empirical support in primary insomnia
are stimulus control therapy, sleep restriction, relax-
ation, and cognitive therapy (Morgenthaler et al.,
2006; Morin et al., 2006). Although often included
in CBT-I, sleep hygiene alone was considered an
inadequate treatment.
What is less clear is how best to treat insomnia
when co-morbid with psychiatric disorders. Random-
ized clinical trials of CBT-I in co-morbid populations
have become much more common in the past few
decades, as has an increased focus on measuring
International Review of Psychiatry, April 2014; 26(2): 205–213
ISSN 0954– 0261 print /ISSN 1369–1627 online © 2014 Institute of Psychiatry
DOI : 10.3109/ 09540 261.2 014.902808
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206 D. J. Taylor & K. E. Pruiksma
psychiatric symptom severity in studies of primary
insomnia. The primary objective of the current sys-
tematic review was to evaluate the effects of CBT-I
on sleep in patients with co-morbid psychiatric
disorders. The secondary objective was to evaluate
the effect of CBT-I on other psychiatric outcomes in
patients with and without co-morbid psychiatric
disorders. This is an update to a review by Smith
et al. (2005) that also included medical conditions.
Data sources and study selection
Published studies were identifi ed by using the
following Keywords: insomnia, cognitive, behav-
ioural, therapy, treatment, psychiatric, depression,
anxiety, post-traumatic stress disorder, alcohol, sub-
stance, and hypnotic in a search of PubMed and
PsycINFO on 1 September 2013 and updated on
12 February 2014. References in the resulting
articles were also reviewed and relevant studies were
included in this review. The intent was to capture a
broad range of randomized clinical trials that had
attempted to treat insomnia with CBT-I in psychiatric
Outcome variables
Due to the varied nature of the studies identifi ed,
outcome variables with the greatest usage between
studies and the greatest clinical utility to the practis-
ing clinician were chosen. Outcome measures were
largely dependent on the co-morbid disorder, but
included self-reported sleep effi ciency (derived from
diaries), depression, anxiety, PTSD, and hypnotic
usage. Sleep effi ciency was chosen as the primary
sleep diary outcome variable because it was the most
consistently reported variable and because it encom-
passes many of the other components of sleep often
reported. Specifi cally, sleep effi ciency total sleep
time/time in bed time in bed 100, and total sleep
time time in bed sleep onset latency wake time
after sleep onset morning wake time. Other vari-
ables of interest (e.g., insomnia severity, depression,
anxiety, PTSD, alcohol use, and hypnotic use) were
included, when a validated measure was used, in an
effort to determine whether treating insomnia alone
resulted in concomitant improvement in the co-
morbid disorders.
Calculation of effect sizes and data extraction
The current study reports effect sizes for the time
(pre-treatment versus post-treatment) X group
(treatment versus control) interaction alone. Within
subject effect sizes were not calculated because they
suffer from regression to the mean biases. Several
authors (Manber et al., 2008; Margolies et al., 2013;
Talbot et al., 2014; Taylor et al., 2010, 2014;
Ulmer et al., 2011) reported interaction effect sizes
and these were used in our calculations. More com-
monly, authors only reported within and between
subject effect sizes, signifi cant interactions only, or
no effect sizes at all. In these cases, a standardized
effect size score was calculated for each outcome
variable by using the formula ES (M1
c M2
c )
t M2
t ) / SD
c , where ES effect size, M1
pre-treatment mean, M2 post-treatment mean,
SD standard deviation, c control condition, and
t treatment condition (Cohen, 1988). When the
standard error of the mean was provided, the stan-
dard deviation was calculated (SD SEM square
root of N) for comparability between studies (Cohen,
1988). Individual effect sizes were weighted to
account for individual sample sizes. The overall
weighted effect size was calculated according to the
formula Σ (di Ni) / Σ (Ni) assuming random effects.
Cohen defi ned effect sizes of 0.2 0.3 as small, 0.5
as medium, and 0.8 as large (Cohen, 1988).
Included studies
This review returned results using a variety of
research designs (e.g. case series and randomized
clinical trials) and treatment formats (e.g. in-person,
self-help). The following analyses focused on out-
comes from randomized clinical trials that compared
active in-person CBT-I components to some sort of
control, most often a waiting list. Studies included in
this review fell into several categories. Some focused
on treating insomnia specifi cally in co-morbid
psychiatric populations (e.g. depression, anxiety,
PTSD, alcohol use, and hypnotic use), while others
focused on treating primary insomnia, but also
included psychiatric outcomes (e.g. depression and
anxiety symptoms). Studies with primarily co-
morbid medical disorders (e.g. pain, cancer, bromy-
algia) were excluded from these analyses. Studies
were also excluded if they did not report data using
a sleep diary or validated symptom questionnaire or
weekly hypnotic use.
Sixteen studies satisfi ed the criteria for entry into
this review. Studies spanned the years from 1999 to
2014 and summarized outcomes for 571 subjects.
Table 1 displays the clinical characteristics of sub-
jects by group, treatment components, number of
sessions, co-morbid disorders if applicable, and effect
sizes for reported outcomes. When multiple groups
were involved in the study, we chose to compare only
the active treatment most similar to CBT-I alone ver-
sus the control condition, which was most often a
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CBT-I in Psychiatric Populations 207
waiting list. Percentage of female participants ranged
from 10 90%. Participants were largely middle-aged,
but means ranged from 20 70. Co-morbid disorders
included depression, PTSD, alcohol dependence,
hypnotic dependence, or a variety of psychiatric dis-
orders (i.e. mixed). The mean number of therapy
sessions was six (SD 2) and included a variety of
active components, including stimulus control
(n 14), sleep restriction (n 13), cognitive therapy
(n 12), and relaxation (n 6). Several also
included sleep hygiene, but as mentioned, this is
not considered an active therapy.
Table 1. Characteristics of 16 randomized clinical trials testing cognitive behavioural therapy of insomnia in co-morbid psychiatric disorders
or on psychiatric symptoms.
Study N
(M) Female
of sessions
psychiatric disorder Variable ES
Manber et al., 2008 30 35 61% 15 15 SC, SR, CT 7 Depression SE
Margolies et al., 2013 37 38 10% 20 17 SC, SR, CT 4 PTSD SE
Talbot et al., 2014 45 37 69% 29 16 SC, SR, CT 8 PTSD SE
Ulmer et al., 2011 21 46 32% 12 9 SC, SR, CT 6 PTSD SE
Wagley et al., 2013 30 44 48 70% 20 10 SC, SR, CT 2 Mixed Dep 0.72
Arnedt et al., 2011 17 46 35% 9 8 SC, SR, CT 8 Alcohol dependence SE
Currie et al., 2004 40 43 30% 20 20 SC, SR,
5 Alcohol dependence SE
Lichstein et al., 1999 20 55 60% 10 10 RT 3 Hypnotic dependence SE
Lichstein et al., 2013 47 64 71% 24 23 SC, RT, CT 8 Hypnotic dependence SE
Morin et al., 2004 29 63 50% 24 25 SC, SR, CT 10 Hypnotic dependence SE
Okajima et al., 2013 63 47 67% 34 29 SC, SR, RT,
6 Hypnotic dependence Ins
Soeffi ng et al., 2008 47 N/A 64% 20 27 SC, RT 8 Hypnotic dependence SE 0.75
Taylor et al., 2010 46 54 54% 24 22 SR 8 Hypnotic dependence SE
Arnedt et al., 2013 30 39 90% 15 15 SC, SR, CT 8 None Dep
Germain et al., 2006 35 70 71% 17 18 SC, SR 2 None Dep
Taylor et al., (2014) 34 20 59% 17 17 SC, SR,
6 None Dep
M, mean; ES, effect size; SC, stimulus control; SR, sleep restriction; RT, relaxation therapy; CT, cognitive therapy; SE, sleep effi ciency;
Ins, Insomnia Symptom Scale; Dep, Depression Symptom Scale; PTSD, Post-Traumatic Stress Disorder Symptom Scale; Anx, Anxiety
Symptom Scale; Hyp, weekly hypnotic usage.
a Only compared two groups for all studies, even when more than one group was present, so reported N will not always equal overall N
from the parent study.
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208 D. J. Taylor & K. E. Pruiksma
Sleep effi ciency. The primary objective of the current
systematic review was to evaluate the effects of
CBT-I on sleep in patients with co-morbid psychiat-
ric disorders. As can be seen in Table 1, 13 studies
reported sleep diary sleep effi ciency outcomes in
patients with co-morbid psychiatric disorders. These
studies varied in duration from three to eight sessions
and spanned a variety of co-morbid psychiatric dis-
orders. The mean effect size for the sleep effi ciency
outcomes was 0.758 (95% CI: 0.557 0.958), indi-
cating that CBT-I results in signifi cant improvement
in sleep effi ciency in patients with co-morbid psychi-
atric disorders, Z 7.417, p 0.001. These medium
to large effects were homogeneous across studies,
Q(12) 12.597, p 0.399.
Insomnia questionnaires. Given the added time
necessary for patients to complete prospective sleep
diaries and for clinicians to score them to develop
summary data (e.g. average sleep effi ciency), it is
likely they will not be used in most psychiatry prac-
tices. Therefore it was important to also report the
effects of CBT-I on other self-report measures of
insomnia. As can be seen in Table 1, nine studies
reported validated insomnia symptom questionnaire
outcomes in patients with co-morbid psychiatric
disorders. These studies varied in duration from
three to ten sessions and spanned a variety of co-
morbid psychiatric disorders. The mean effect size
for the insomnia symptom outcomes was 1.001 (95%
CI: 0.639 1.363), indicating that CBT-I results in
signifi cant improvement in sleep effi ciency in patients
with co-morbid psychiatric disorders, Z 5.422,
p 0.001. These relatively large effects were not
homogeneous across studies, Q(8) 12.51, p 0.0115.
Morin et al. (2004) and Lichstein et al. (2013)
appeared to be considerably lower than the other
studies. This was likely due to CBT medication
withdrawal being compared to another medication
withdrawal condition, which may have truncated the
results. If these two studies are removed from the
analysis the outcomes become homogeneous
Q(6) 3.857, p 0.696, with an effect size of 1.227
(95% CI: 0.957 1.497).
To date, only one pilot randomized trial has exam-
ined CBT-I in individuals with co-morbid depression
and insomnia (Manber et al., 2008). Participants
receiving open label escitalopram (n 30), were ran-
domized to receive concomitant CBT-I or control
treatment. The CBT-I intervention included sleep
restriction, stimulus control, stress management and
cognitive therapy over seven sessions. The CBT-I
group demonstrated signifi cantly greater baseline to
post-treatment improvements in diary measured
sleep effi ciency (p 0.05, d 0.76) and self-reported
insomnia (p 0.05, d 1.03) than the control group,
indicating that insomnia can be successfully treated
with CBT-I in patients with co-morbid depression.
This study failed to fi nd a signifi cant interaction on
depression due to the relatively small effect of d 0.25
on the 17-item Hamilton Depression Rating Scale
(Hamilton, 1960) minus the sleep items. The effect
was likely truncated somewhat by the fact that both
groups were simultaneously receiving medications
for depression. Notably, the group with CBT-I aug-
mentation had more insomnia remission (50% ver-
sus 8%) and depression remission (61.5% versus
33.3%) than the control group, which suggests that
CBT-I provides benefi t above and beyond concur-
rent depression treatment for both the insomnia and
depression outcomes.
As can be seen in Table 1, 12 additional studies
reported depression outcomes using a variety of
self-report measures. These studies varied in dura-
tion (two to eight sessions) and spanned a variety of
co-morbid psychiatric disorders as well as three in
primary insomnia patients. The effect size data were
combined to provide a meta-analytic estimate of the
effect of treating insomnia on depression outcomes.
The mean effect size for the depression outcomes
was 0.505 (95% CI: 0.313 0.696), indicating that
CBT-I results in signifi cant small to medium effects
in depression symptomatology, Z 5.159, p 0.001.
The results were homogeneous across studies,
Q(12) 10.864, p 0.541.
Anxiety, trauma and stressor-related disorders
Surprisingly, we were unable to fi nd any randomized
controlled trials that specifi cally attempted to treat
insomnia in any anxiety disorders other than PTSD
(which, as of DSM-5, is no longer considered an
anxiety disorder). A recent meta-analysis examined
the impact of CBT-I on concomitant anxiety across
a variety of trials in patients with co-morbid medical
disorders and that used self-help interventions
(Belleville et al., 2011a). They used a broad opera-
tional defi nition anxiety which included arousal,
worry, and stress. They found a combined effect size
of 0.406 (95% CI 0.318 0.493), indicating signifi -
cant small to moderate effects of CBT-I on anxiety.
They found a signifi cant amount of variability across
samples, Q(49) 131.08, p 0.001, likely due to
heterogeneity in populations and measures.
Restricting our sample to randomized clinical tri-
als of in-person CBT-I, we found six studies that
assessed anxiety symptoms (Table 1). These studies
varied in duration from three to eight sessions and
spanned a variety of co-morbid psychiatric disorders,
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CBT-I in Psychiatric Populations 209
with three that were in primary insomnia patients.
The mean effect size for the anxiety outcomes was
0.208 (95% CI: 0.084 0.499), indicating that
CBT-I did not result in signifi cant improvements in
anxiety symptomatology, Z 1.396, p 0.163.
Although the results were homogeneous across stud-
ies, Q(5) 2.513, p 0.775, Lichstein et al. (2013)
was the only study to fi nd negative results on an
anxiety measure. When that study was removed from
the analyses, the effect size increased to 0.340 (95%
CI: 0.001 0.679), indicating that CBT-I results in
signifi cant small effects in anxiety symptomatology,
Z 1.966, p 0.05, with excellent homogeneity,
Q(4) 0.259, p 0.992.
PTSD. Insomnia in the context of PTSD is unique
in that it is often characterized by hypervigilance at
night and fear of going to bed or to sleep (DeViva
et al., 2004; Pruiksma et al., 2014). Patients may
report increased anxiety at night related to noises
around the house, checking locks frequently, or fear
of intrusive memories or nightmares of traumatic
experiences. Thus, much of the research examining
CBT-I in PTSD has included interventions for night-
mares or has implemented insomnia treatment after
PTSD treatment. One randomized controlled trial
has examined the effi cacy of CBT-I in individuals
with PTSD (Talbot et al., 2014), and another two
have examined the effi cacy CBT-I imagery rehearsal
therapy (IRT) for nightmares (Margolies et al., 2013;
Ulmer et al., 2011).
Talbot et al. (2014) examined the effi cacy of
CBT-I compared to a waiting list control group in
45 veterans with co-morbid insomnia and PTSD.
The CBT-I intervention included stimulus control,
sleep restriction, and cognitive therapy over eight
sessions. CBT-I resulted in signifi cantly greater
baseline to post-treatment improvements in diary-
measured sleep effi ciency (p 0.001, d 1.06) than
the control group, indicating that insomnia can be
successfully treated with CBT-I alone in patients
with co-morbid PTSD. This study failed to fi nd
a signifi cant interaction on the 17-item PCL
(Blanchard et al., 1996) minus the sleep items, even
though they had a small effect (d 0.35). The effect
was likely truncated by the fact that both groups
were simultaneously receiving active PTSD treat-
ment. Both groups showed signifi cant improve-
ments in PTSD and nightmares. Notably, the CBT-I
group had more insomnia remission than the
control group (41% versus 0%), which likely had
some quality of life benefi ts for these veterans.
Ulmer et al. (2011) examined the effi cacy of three
sessions of sleep restriction, stimulus control, and
cognitive therapy, followed by three sessions of IRT
in 22 veterans with PTSD. The treatment was com-
pared to a usual care group. CBT-I IRT resulted
in signifi cantly greater baseline to post-treatment
improvements in diary measured sleep effi ciency
(p 0.01, d 1.27) than the control group. In addi-
tion, this study found a signifi cant interaction on
the 17-item PCL minus the sleep items (p 0.01,
d 1.76). The effect was likely strengthened by the
added IRT as well as the fact that most were not
simultaneously receiving active PTSD treatment.
Margolies et al. (2013) examined the effi cacy of
CBT-I IRT in 40 Operation Enduring Freedom
(OEF)/Operation Iraqi Freedom (OIF) veterans with
PTSD in comparison to a waiting list control group.
They found treatment resulted in statistically sig-
nifi cant interactions for sleep effi ciency (p 0.001,
d 1.70) and the PTSD Symptom Scale self-report
(p 0.001, d 1.64). It is important to note that
only 40% of the treatment group elected to receive
adjunctive IRT. Thus, it is possible the effects found
were likely strengthened by added IRT treatment as
well as the fact that 35% of the sample was not simul-
taneously receiving active PTSD treatment.
Overall, the research indicates insomnia can be
meaningfully improved in the context of PTSD.
There is some indication that improvements in PTSD
may occur as well. The extent to which nightmare
interventions contributed to these gains is unclear.
Although four is typically the minimum acceptable
number of studies needed to perform meta-analyses,
it was decided an exploratory analysis was justifi ed
for PTSD. The mean effect size for the PTSD
outcomes was 1.192 (95% CI: 0.210 2.173),
indicating that CBT-I results in signifi cant small to
large effects, Z 2.38, p 0.05. However, the results
were not homogeneous across studies, Q(2) 8.482,
p 0.0144. Further, it was not clear how much of
those gains are the result of the addition of IRT and
how much are the result of CBT-I alone. Further
research is needed to tease this apart. Research is also
needed to determine whether sleep disturbances
should be treated before, during, or after treatment
for PTSD.
Alcohol dependence
Two randomized clinical trials have now been
conducted examining the effi cacy of CBT-I in indi-
viduals diagnosed with alcohol dependence. Currie
et al. (2004) randomized 60 individuals recovering
from alcohol dependence to either fi ve sessions
of in-person CBT-I, a self-help intervention with
telephone support, or a waiting list control. The in-
person treatment groups included stimulus control,
sleep restriction, relaxation, and cognitive therapy.
Overall, the in-person treatment resulted in more
signifi cant gains than the waiting list group on sleep
effi ciency (p 0.05, d 0.74). Alcohol relapse rates
did not differ between groups, but the follow-up rates
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210 D. J. Taylor & K. E. Pruiksma
were exceptionally low across groups (i.e. 15%), and
long-term follow-up was less than ideal.
Arnedt et al. (2011) followed with a randomized
controlled trial of CBT-I versus a placebo control in
17 patients with co-morbid alcohol dependence. The
CBT-I included stimulus control, sleep restriction,
and cognitive therapy. Similar to the Currie et al.
(2004) study, the CBT-I group had signifi cantly
better sleep post-treatment sleep effi ciency (p 0.05,
d 0.45), but there were no differences with regard
to relapse prevention.
These studies indicate that CBT-I for insomnia
can help recovering alcoholics improve their sleep,
obviating the need for hypnotic medications. How-
ever, the treatment may not improve long-term
alcohol relapse rates. Although the small sample sizes
may account for the failure to fi nd relapse improve-
ments, it is more likely that alcohol relapse is very
complex and diffi cult to address. Two studies are not
enough to justify meta-analysis at this point.
Hypnotic dependence
Many patients (24 53%) in primary care and sleep
medicine clinics report using hypnotics for insomnia
(Hohagen et al., 1993; Rosenthal et al., 2008).
Although rarely discussed, many physicians are not
comfortable prescribing hypnotic medications long-
term (e.g. 6 12 months). Increasingly, studies are
showing that CBT-I alone or in combination with a
hypnotic tapering programme can improve both
insomnia and reduce hypnotic use, often to absti-
nence (Baillargeon et al., 2003; Dolan et al., 2010;
Lichstein & Johnson, 1993; Morgan et al., 2003;
Morin et al., 1995, 2004; Soeffi ng et al., 2008; Taylor
et al., 2010).
Lichstein et al. (1999) performed one of the fi rst
randomized clinical trials examining the effi cacy of
insomnia interventions in 20 individuals with insom-
nia and hypnotic dependency. They compared three
sessions of relaxation therapy hypnotic withdrawal
to hypnotic withdrawal alone. They reported that the
addition of relaxation therapy to hypnotic withdrawal
resulted in signifi cant improvements in sleep effi -
ciency (p 0.05, d 0.26). Although both groups
showed signifi cant reductions in hypnotic use, the
hypnotic withdrawal only group actually had greater
reductions in hypnotics from pre- to post-treatment
than the group with relaxation therapy augmentation
(d 0.62).
Morin et al. (2004) examined the effi cacy of 10
sessions of CBT-I in 29 older adults with chronic
insomnia and prolonged benzodiazepine medication
use (M 19.3 years). Patients were randomly
assigned to either medication withdrawal, CBT-I, or
CBT-I medication withdrawal. Comparing only the
CBT-I medication withdrawal versus the medication
withdrawal only, to determine the additive effect of
CBT-I, the CBT-I withdrawal resulted in a larger
improvement in sleep effi ciency (d 0.42), but this
small to medium effect did not reach statistical sig-
nifi cance. Similar, but smaller results were seen for
hypnotic withdrawal (d 0.30).
Taylor et al. (2010) examined the effi cacy of sleep
restriction therapy versus a sleep hygiene control in
46 hypnotic using patients with insomnia. These
authors found that sleep restriction resulted in sig-
nifi cant improvements in sleep effi ciency (p 0.003,
d 1.16). They also found signifi cant reductions in
hypnotic usage (p 0.001, d 1.05).
Okajima et al. (2013) compared the effi cacy of
CBT-I behaviour analysis to treatment as usual in
63 people with insomnia resistant to pharmaco-
logical treatment. The CBT-I intervention included
sleep restriction, stimulus control, relaxation ther-
apy, and cognitive therapy over six sessions. CBT-I
resulted in signifi cantly greater baseline to post-
treatment improvements on insomnia severity
(p 0.01, d 0.88) and hypnotic usage (p 0.01,
d 1.01).
Lichstein et al. (2013) examined CBT-I drug
withdrawal, placebo biofeedback with drug with-
drawal, and drug withdrawal only in 70 older adults.
The CBT-I intervention included relaxation training,
stimulus control, sleep hygiene and also integrated
cognitive therapy. Comparing only the CBT-I
medication withdrawal versus the placebo
withdrawal only, to determine whether CBT-I was
better than a credible control, we found that CBT-I
resulted in a larger improvement in sleep effi ciency
(d 0.63), but this effect did not reach statistical sig-
nifi cance. Similar, but smaller results were seen for
hypnotic withdrawal and insomnia symptom severity
(d 0.20). Although both groups showed signifi cant
reductions in depression, anxiety, and hypnotic use,
the placebo group actually had greater reductions
(d 0.33).
We performed a meta-analyses of these fi ve ran-
domized clinical trials that reported the quantity of
hypnotics taken at pre- and post-treatment (Table 1).
These studies varied in duration from three to eight
sessions and spanned a variety of co-morbid psychi-
atric disorders. The mean effect size for the hypnotic
quantity outcomes was 0.333 (95% CI: 0.287
0.953). The results were non-signifi cant, Z 1.053,
p 0.293, largely due to the heterogeneity,
Q(4) 19.633, p 0.0006. Similar to the anxiety
analyses, only the Lichstein et al., studies (1999,
2013) reported negative results. If those studies
are removed from the equation, the effect size of
CBT-I on hypnotic reduction increased to 0.802
(95% CI: 0.402 1.202), which was signifi cant,
Z 1.62, p 0.001, and homogeneous, Q(2) 2.863,
p 0.239. Thus, it appears that the addition of CBT-I
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CBT-I in Psychiatric Populations 211
alone is signifi cantly better than waiting-list controls
at reducing hypnotic reduction, but does not add
much over tapering alone.
CBT-I is now considered a fi rst-line intervention for
primary insomnia (National Institutes of Health,
2005; Wilson et al., 2010). The results presented here
provide clear evidence that CBT-I is also highly effec-
tive in improving sleep in patients with co-morbid
psychiatric disorders. To our knowledge, no contrain-
dications have been reported for the use of CBT-I in
patients with depression, anxiety, PTSD, alcohol
dependence, or hypnotic medication dependence. In
addition, CBT-I signifi cantly improves depression
and anxiety symptoms in both co-morbid and pri-
mary insomnia populations. Although there are some
promising results regarding the effi cacy of CBT-I in
reducing PTSD symptoms and hypnotic withdrawal,
more studies are needed before defi nitive conclu-
sions can be drawn.
The evidence base does appear strong enough to
begin using CBT-I in clinical practice to augment
treatment for patients with co-morbid depression,
anxiety, PTSD, and substance abuse disorders. It
seems clear this augmentation should, at the very
least, result in improved sleep as well as depression
and anxiety symptoms. Several excellent manuals are
available describing in detail how to deliver these
interventions (e.g. Edinger & Carney, 2008; Morin
& Espie, 2003; Perlis et al. , 2005, 2010), including
one developed specifi cally for treating insomnia
in patients with depression or anxiety (Carney &
Manber, 2009).
One of the primary diffi culties with performing
the above meta-analysis was the inconsistency of
reporting means, standard deviations, interaction
effects and what types of analyses they use even
within the same study. A new movement within ran-
domized clinical trials is to performed mixed models
analyses, eliminating the need for imputation of
missing data, and reporting effect sizes for all inter-
actions within tables. This is a positive change that
will allow for greater assimilation of results across
studies in the future. In addition, more recent
studies are beginning to take heed of the recom-
mendations of Buysse et al., (2006) to include a
larger compendium of outcome measures, including
depression and anxiety.
We would like to offer special thanks to Katherine
Marczyk, who helped in compiling the large list of
manuscripts and in building Table 1.
Declaration of interest: The authors report no
confl icts of interest. The authors alone are respon-
sible for the content and writing of the paper.
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... Similarly, Casement and Swanson (2012) identified 13 studies in their review and found that sleep-specific psychological treatments (IRT or IRT + CBT-I, or ERRT, or EERT + CBT-I) significantly reduced nightmare frequency (ES = 0.59), significantly improved sleep quality (ES = 0.69) and significantly reduced PTSD symptoms with a large effect size (ES = 0.67) for the treatment groups in comparison to the control groups. Other reviews reported comparable results (Taylor & Pruiksma, 2014;Wu et al., 2015). ...
... SE encompasses a number of sleep variables collected through sleep diaries. SE = total sleep time/time in bed multiplied by 100; and total sleep time (TST) = time in bed-sleep onset latency-waking time after sleep onset-morning waking time (Taylor & Pruiksma, 2014). ISI scores (Bastien et al., 2001), were also reported where appropriate. ...
... Our findings from the four selected RCTs showed that CBT-I treatment alone or combined with either IRT and ERRT, led to a reduction in nightmare frequency in individuals presenting with nightmares and post-traumatic stress symptoms with medium to large effect sizes (Germain et al., 2012;Rhudy et al., 2010;Talbot et al., 2014;Walters et al., 2020). The findings of this review are in line with previous research that has shown that CBT-I and ERRT alone or combined are effective in reducing nightmares in people with post-traumatic stress symptoms (Casement & Swanson, 2012;Taylor & Pruiksma, 2014). ...
... Given healthcare workers 57 increased professional and personal duties during the COVID-19 pandemic, sleep loss or 58 poor sleep quality may be exacerbated by added stress over time [3]. However, our 59 knowledge of the sleep quality of healthcare workers during the pandemic comes mainly 60 from cross-sectional studies. A major limitation of the cross-sectional design is that it can- 61 not be used to infer causality because it does not determine the temporal link between the 62 outcome and the exposure, as both are examined at the same time. ...
... Perhaps a practical therapeutic approach for reducing psychological distress 403 among individuals, especially healthcare workers, during the COVID-19 pandemic could 404 be CBTi. Clinical studies of CBTi have shown decreases in anxiety and depression symp-405 toms in patients experiencing both insomnia and psychological distress when used to treat 406 insomnia or shortened sleep duration in non-pandemic conditions [59]. It is possible that 407 treating insomnia is vital because it has been suggested that reduced sleep duration dur-408 ing the COVID-19 pandemic may increase the chance of long-term negative psychological 409 effects [60]. ...
The purpose of the study was to measure changes in sleep quality and perceived stress 28 and their interrelationships in a sample of healthcare workers two years post the COVID-19 pan-29 demic. Using a cohort design, data were collected from frontline healthcare workers (FLHCW, n = 30 70) and non-frontline healthcare workers (NFLHCW, n = 74) in April 2020 (T1) and in February 2022 31 (T2). The Pittsburgh Sleep Quality Index (PSQI) and the Perceived Stress Scale (PSS-10) were ad-32 ministered at both time points. There were no differences in sleep quality or perceived stress be-33 tween FLHCW and NFLHCW at either timepoint. For the entire sample, the PSQI scores at T2 were 34 significantly higher than at T1 (7.56 ± 3.26 and 7.25 ± 3.29, respectively) p = 0.03, Cohen's d = 0.18. 35 PSS-10 scores at T2 were significantly lower than at T1 (19.85 ± 7.73 and 21.13 ± 7.41, respectively) p 36 = 0.001, Cohen's d = 0.78. Baseline sleep quality PSQI (T1) was a significant predictor for changes in 37 sleep quality. During the initial months of the outbreak of the COVID-19 pandemic, poor sleep 38 quality and perceived stress were common for healthcare workers. Two years into the pandemic, 39 perceived stress was reduced, but sleep quality worsened. 40 41
... Second, the importance of sleep underscores our findings. Effective sleep therapies (i.e., cognitive behavioral therapy for insomnia) have demonstrated utility for improving sleep in the presence of comorbid psychiatric concerns (Taylor & Pruiksma, 2014), including among veterans with PTSD (Talbot et al., 2014). Given these findings, residential treatment programs should consider incorporating sleep treatments into the program or even providing such interventions before the start of PTSD treatment to optimize treatment response. ...
Full-text available
Residential posttraumatic stress disorder (PTSD) research in military samples generally shows that in aggregate, PTSD symptoms significantly improve over the course of treatment but can remain at elevated levels following treatment. Identifying individuals who respond to residential treatment versus those who do not, including those who worsen, is critical given the extensive resources required for such programs. This study examined predictors of treatment response among 282 male service members who received treatment in a U.S. Department of Defense residential PTSD program. Using established criteria, service members were classified as improved, indeterminate (referent), or worsened in terms of self‐reported PTSD symptoms. Multinomial logistic regression results showed that for PTSD symptoms, higher levels of pretreatment PTSD symptom severity were associated with significantly lower odds of being in the improved group, adjusted odds ratio (aOR) = 0.955, p = .018. In addition, service members who completed treatment were significantly more likely to be in the improved group, aOR = 2.488, p = .048. Longer average pretreatment nightly sleep duration, aOR = 1.157, p = .035, and more severe pretreatment depressive symptoms, aOR = 1.109, p = .014, were associated with significantly higher odds of being in the improved group. These findings reveal clinical characteristics better suited for residential PTSD treatment and highlight implications for comorbid conditions.
Understanding patient responsiveness, a component of fidelity, is essential as it impacts treatment outcome and ongoing use of treatment elements. This study evaluated patient responsiveness—operationalized as receptivity to treatment modules and ratings of the usefulness and the utilization of treatment elements—to the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) in a sample of adults with serious mental illness (SMI) and sleep/circadian dysfunction. Adults with SMI and sleep/circadian dysfunction (N=104) received TranS-C in a community mental health setting. Independent raters rated TranS-C sessions to assess receptivity. At post-treatment and 6-month follow-up, participants completed the Usefulness Scale, Utilization Scale, PROMIS Sleep Disturbance (PROMIS-SD) and Sleep-Related Impairment (PROMIS-SRI) scales, DSM-5 Cross Cutting Measure (DSM-5-CC), and Sheehan Disability Scale (SDS). Receptivity was rated as somewhat to fully understood, and predicted a reduction on the DSM-5-CC. On average, participants rated TranS-C as moderately useful and utilized treatment elements occasionally. Ratings of usefulness were associated with the PROMIS-SD, PROMIS-SRI, and DSM-5-CC at post-treatment, but not with the SDS. Ratings of utilization were not associated with outcome. The findings add to the literature on patient responsiveness, an implementation outcome, and provide data on the utility of TranS-C within a community mental health setting.
The high prevalence and severe consequences of poor sleep following acquired brain injury emphasises the need for an effective treatment. However, treatment studies are scarce. The present study evaluates the efficacy of blended online cognitive behavioural therapy for insomnia (eCBT‐I) developed specifically for people with acquired brain injury. In a multicentre prospective, open‐label, blinded end‐point randomised clinical trial, 52 participants with insomnia and a history of a stroke or traumatic brain injury were randomised to 6 weeks of guided eCBT‐I or treatment as usual, with a 6‐week follow‐up. The primary outcome measure was the change in insomnia severity between baseline and after treatment, measured with the Insomnia Severity Index. Results showed that insomnia severity improved significantly more with eCBT‐I than with treatment as usual compared to baseline, both at post‐treatment (mean [SEM] 4.0 [1.3] insomnia severity index points stronger decrease, d = 0.96, p < 0.003) and at follow‐up (mean [SEM] 3.2 [1.5] insomnia severity index points, d = −0.78, p < 0.03). In conclusion, our randomised clinical trial shows that blended CBT is an effective treatment for insomnia, and feasible for people with acquired brain injury, regardless of cognitive and psychiatric complaints. Online treatment has major advantages in terms of availability and cost and may contribute to the successful implementation of insomnia treatment for people with acquired brain injuries.
Purpose Sleep is essential for our overall health and wellbeing. Unfortunately, stroke often induces insomnia, which has been shown to impede rehabilitation and recovery of function. Cognitive behavioral therapy for insomnia (CBT-I) is the treatment of choice for insomnia in the general population and is efficacious both when delivered face-to-face or online. The primary aim of this study was to evaluate efficacy of blended CBT-I (eCBT-I) in five poststroke participants with insomnia according to DSM-5 criteria. Methods A randomized multiple baseline design was used to evaluate improvements in total sleep time, sleep onset latency, sleep efficiency, nocturnal awakenings and sleep quality. The intervention included six weeks of eCBT-I combined with two face-to-face sessions. Results All participants completed the intervention. One participant stopped using the diary, while the other four completed it fully. All five sleep diary measures improved, significantly so for nocturnal awakenings. Moreover, after completion of the treatment, four out of five participants no longer fulfilled DSM-5 criteria for insomnia disorder Conclusions This is the first study to show that blended CBT-I is potentially effective in participants with post-stroke insomnia. The findings justify extension to a randomized controlled trial.
Insomnia is a prevalent sleep disorder. Insomnia results in distress and daytime impairment and is often comorbid with other sleep, medical, and mental health disorders. This chapter describes best practices for the treatment of insomnia disorder. Practices for non-medication and medication treatments are described, including the first line treatment, Cognitive Behavioral Therapy for Insomnia (CBT-I). Treatment recommendations are presented in the context of common comorbidities with insomnia. Additionally, recommendations are provided in the context of first line treatment non-responders. The risks associated with non-medication treatments and medication treatments and strategies to mitigate potential harms are described. The goals of the current chapter are to provide sleep medicine clinicians with (1) knowledge of evidence-based treatments for insomnia disorder and (2) a decision-making framework for the delivery of preparatory, concurrent, and/or supplemental treatments for patients who experience insomnia disorder.
Objectives: Cognitive Behavioural Therapy (CBT) is an effective psychological intervention for sleep difficulties and has been used successfully in individuals with psychosis. However, access is restricted due to lack of resources and staff training. Delivering CBT for sleep problems using smartphone technology may facilitate wider access. This study aimed to evaluate the feasibility, acceptability and potential usefulness of a guided, smartphone-based CBT intervention targeting sleep disturbance for individuals with psychosis. Design: Participants with psychosis spectrum diagnoses were recruited to a single-arm, uncontrolled study and engaged with the seven-module programme via smartphone app for six weeks with therapist support. Method: Feasibility was assessed by rates of referral, recruitment and completion. Acceptability was assessed by app usage, a satisfaction questionnaire and qualitative analysis of participants' semi-structured interview. Pre- and post-intervention assessment of sleep, psychotic experiences, mood, well-being and functioning was conducted. Mean change confidence intervals were calculated and reported as an indication of usefulness. Results: Fourteen individuals consented to participation, and eleven completed the post-intervention assessment. On average, each participant engaged with 5.6 of 7 available modules. Qualitative feedback indicated the intervention was considered helpful and would be recommended to others. Suggested improvements to app design were provided by participants. Potential treatment benefits were observed for sleep difficulties, and all outcomes considered, except frequency of hallucinatory experiences. Conclusions: It is feasible and acceptable to deliver therapist-guided CBT for sleep problems by smartphone app for individuals with psychosis. This method provides a low-intensity, accessible intervention, which could be offered more routinely. Further research to determine treatment efficacy is warranted.
Sleep and circadian problems are intertwined with serious mental illness (SMI). Thus, optimizing treatments that target comorbid sleep and circadian problems and SMI is critical. Among adults with sleep and circadian problems and SMI, the present study conducted a secondary data analysis of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C). TranS-C targets a range of sleep and circadian problems and SMI with 15 modules, 7 of which are optional. In a real-world sample recruited from a community setting (N = 121, 52.07% female, 42.97% African American or Black), we aimed to (1) elucidate patterns of sleep and circadian problems that met criteria for full diagnoses and subdiagnostic symptoms across (a) the full sample and (b) SMI diagnoses; and (2) determine whether TranS-C optional modules were delivered as intended based on participants’ sleep and circadian problems. Results indicated that most participants (> 85.0%) had full diagnoses or subdiagnostic symptoms of two or more sleep and circadian problems. Further, participants exhibited heterogeneous comorbidities between sleep and circadian problems and SMI diagnoses. Specifically, participants with a schizophrenia spectrum disorder (n = 50), bipolar disorder (n = 35), and major depressive disorder (n = 26) exhibited 25, 24, and 21 patterns of sleep and circadian comorbidity, respectively. Notably, most participants with insomnia, hypersomnia, and an advanced or delayed circadian rhythm phase disorder did not receive the intended TranS-C optional modules designed to target these problems. Results underscore sleep and circadian and SMI diagnostic complexity in the community. Additionally, findings reveal discrepancies between intended and real-world use of treatment modules. Future research investigating clinician decision-making—particularly when treating patients with comorbidities or using modularized treatments—is needed.
Introduction: Cognitive behavior therapy for insomnia (CBTi), delivered face-to-face or digitally, can improve the mental health of adults. Although insomnia is common in adolescents, the effects of digital CBTi on adolescent mental health have seldom been investigated. Objectives: The aims of this study were to explore: (i) the acceptability of a digital CBTi intervention, Sleepio, as a first-step intervention for adolescents referred to specialist mental health services (CAMHS), (ii) the impact on sleep and mental health and (iii) subsequent CAMHS interventions. Method: Sleepio is a computerized CBTi intervention comprised of six sequentially delivered sessions. Digital Sleepio was offered to new referrals to CAMHS with poor sleep and mental health problems. Results. Of the 75 eligible adolescents, 70 (93%; 95% CI: 85% to 98%) accepted Sleepio with 59 starting the programme and consenting to participate in the study. Of these, 37 (63%; 95% CI: 49% to 75%) completed at least half of the programme. There were post-intervention improvements in sleep, mood, and anxiety; the improvement in sleep was greater for those who completed at least half the programme compared to those who did not. Of those who completed all the programme, 55% (15/29) did not need any subsequent specialist CAMHS input. Of the 11 adolescents who accepted but never started Sleepio, none engaged with other CAMHS interventions and were subsequently discharged. Conclusion: Our study has a number of limitations, in particular the absence of a control group and the loss of follow-up data for programme drop-outs. Nonetheless, these results suggest that digital CBTi may offer a novel and acceptable way of improving the sleep and mental health of adolescents with insomnia. A fully powered randomized controlled trial is required to obtain definitive estimates of the effects of the intervention.
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Fear of sleep may play a significant role in sleep disturbances in individuals with posttraumatic stress disorder (PTSD). This report describes a psychometric study of the Fear of Sleep Inventory (FoSI), which was developed to measure this construct. The psychometric properties of the FoSI were examined in a non-clinical sample of 292 college students (Study I) and in a clinical sample of 67 trauma-exposed adults experiencing chronic nightmares (Study II). Data on the 23 items of the FoSI were subjected to exploratory factor analyses (EFA) to identify items uniquely assessing fear of sleep. Next, reliability and validity of a 13-item version of the FoSI was examined in both samples. A 13-item Short-Form version (FoSI-SF) was identified as having a clear 2-factor structure with high internal consistency in both the non-clinical (α = 0.76-0.94) and clinical (α = 0.88-0.91) samples. Both studies demonstrated good convergent validity with measures of PTSD (0.48-0.61) and insomnia (0.39-0.48) and discriminant validity with a measure of sleep hygiene (0.19-0.27). The total score on the FoSI-SF was significantly higher in the clinical sample (mean = 17.90, SD = 12.56) than in the non-clinical sample (mean = 4.80, SD = 7.72); t 357 = 8.85 p < 0.001. Although all items are recommended for clinical purposes, the data support the use of the 13-item FoSI-SF for research purposes. Replication of the factor structure in clinical samples is needed. Results are discussed in terms of limitations of this study and directions for further research. Pruiksma KE, Taylor DJ, Ruggero C, Boals A, Davis JL, Cranston C, DeViva JC, Zayfert C. A psychometric study of the Fear of Sleep Inventory-short form (FoSI-SF). J Clin Sleep Med 2014;10(5):551-558.
This paper reviews the evidence regarding the efficacy of nonpharmacological treatments for primary chronic insomnia. It is based on a review of 48 clinical trials and two meta-analyses conducted by a task force appointed by the American Academy of Sleep Medicine to develop practice parameters on non-drug therapies for the clinical management of insomnia. The findings indicate that nonpharmacological therapies produce reliable and durable changes in several sleep parameters of chronic insomnia sufferers. The data indicate that between 70% and 80% of patients treated with nonpharmacological interventions benefit from treatment. For the typical patient with persistent primary insomnia, treatment is likely to reduce the main target symptoms of sleep onset latency and/or wake time after sleep onset below or near the 30-min criterion initially used to define insomnia severity. Sleep duration is also increased by a modest 30 minutes and sleep quality and patient's satisfaction with sleep patterns are significantly enhanced. Sleep improvements achieved with these behavioral interventions are sustained for at least 6 months after treatment completion. However, there is no clear evidence that improved sleep leads to meaningful changes in daytime well-being or performance. Three treatments meet the American Psychological Association (APA) criteria for empirically-supported psychological treatments for insomnia: Stimulus control, progressive muscle relaxation, and paradoxical intention; and three additional treatments meet APA criteria for probably efficacious treatments: Sleep restriction, biofeedback, and multifaceted cognitive-behavior therapy. Additional outcome research is needed to examine the effectiveness of treatment when it is implemented in clinical settings (primary care, family practice), by non-sleep specialists, and with insomnia patients presenting medical or psychiatric comorbidity.
Relaxation therapy was given to 3 groups of older women (N = 57): (a) hypnotically medicated insomniacs, (b) nonhypnotically medicated insomniacs, and (c) noninsomniacs. Groups b and c were receiving antihypertensives. Self-reported sleep and medication data were collected for 1 week at pretreatment (except relaxation), posttreatment, and 6-weeks follow-up. Three relaxation sessions, a nondemanding, hybrid method, were administered with the rationale of helping insomnia or high blood pressure. Substantial sleep improvement occurred only for nonhypnotically medicated insomniacs. Substantial sleep medication reduction (47%) occurred only for hypnotically medicated insomniacs. This relaxation approach proved valuable, but the nature of the treatment effect was dependent on the medication status of the insomniac.
Sleep is a major component of good mental and physical health, yet over 40 million Americans suffer from sleep disorders. Edited by three prominent clinical experts, this volume is the first reference to cover all of the most common disorders (insomnia, sleep apnea, restless legs syndrome, narcolepsy, parasomnias, etc) and the applicable therapeutic techniques. The volume adopts a highly streamlined and practical approach to make the tools of the trade from behavioral sleep medicine accessible to mainstream psychologists as well as sleep disorder specialists. Organized by therapeutic technique, each chapter discusses the various sleep disorders to which the therapy is relevant, an overall rationale for the intervention, step-by-step instructions for how to implement the technique, possible modifications, the supporting evidence base, and further recommended readings. Treatments for both the adult and child patient populations are covered, and each chapter is authored by an expert in the field. An extra chapter ("The use of bright light in the treatment of insomnia," by Drs. Leon Lack and Helen Wright.) which is not listed in the table of contents is availalbe for free download at'isbn=9780123815224. * Offers more coverage than any volume on the market, with discussion of virtually all sleep disorders and numerous treatment types * Addresses treatment concerns for both adult and pediatric population * Outstanding scholarship, with each chapter written by an expert in the topic area * Each chapter offers step-by-step description of procedures and covers the evidence-based data behind those procedures.
• Application of automated rapid-eye-movement (REM) analysis can characterize individual REM periods in depressed patients. Average REM count for the individual REM periods generally demonstrated considerable decreases in the second half of the REM period and differentiated patients who subsequently did not respond well to tricyclic antidepressants. These findings suggest that, even as late as six hours into a night of sleep, significant differences among depressed patients are present, based on treatment responder groups. Furthermore, a reevaluation of the previous emphasis on REM abnormalities in the first hour or two of the night may be indicated.
Cognitive behavior therapy which has been adapted to treat so many problems, has also brought data-driven and data-yielding treatment to insomnia. Focusing on this evidence-based modality, Cognitive Behavioral Treatment of Insomnia is a much-needed treatment manual that provides clinicians with the why's and how's of this approach in concise and practical terms. This book, which is written as a reader-friendly guide, is intended for clinical trainees, non-insomnia sleep specialists, and for expert CBT clinicians from outside the sleep medicine field who wish to begin the process of learning to provide empirically validated CBT-I. The Book is organized into seven parts: definition of insomnia; review of the conceptual; framework for treatment; overview of the components of therapy; session-by-session guide; dialogues; assessment and eligibility for CBT-I; and sample documentation. The organizing principles for the guide can best be expressed as two seemingly simple questions: "Who is appropriate for CBT-I?" "What does one need to know to set up a Behavioral Sleep Medicine service?" The guide provides all that one needs to confidently answer these questions. © 2005 Springer Science+Business Media, Inc. All rights reserved.
Objective: To determine whether polygraphic sleep recordings, obtained at the time of admission to an inpatient alcohol treatment program, predict abstinence and relapse 3 months following hospital discharge in nondepressed patients with primary alcoholism. Design: Two independent, consecutive cohorts of patients (group 1, n=28; group 2, n=17) underwent all-night polygraphic sleep recordings and other clinical evaluations during the first and fourth weeks of a 1-month inpatient treatment program within a Veteran Affairs Medical Center. They were reevaluated 3 months following discharge to the community. None were treated with disulfiram or other medications during or after hospitalization. Patients: All subjects were male veterans with primary alcoholism and without significant preexisting, secondary, or comorbid diagnoses such as major medical problems, depression, antisocial personality, or drug addiction. Outcome Measures: Relapse was defined as any alcohol consumption between discharge from the hospital and 3-month follow-up. Results: Ten (36%) of 28 patients in group 1 were Relapsers at 3-month follow-up. Relapsers in group 1 showed significantly shorter Rapid Eye Movement (REM) latency, increased Rapid Eye Movement percent (REM%), and increased REM Density during their admission sleep studies compared with Abstainers. To replicate these observations, group 2 was then studied as a validation sample. Six (35%) of 17 patients relapsed. As in group 1, Relapsers had significantly shorter REM latency and increased REM% compared with Abstainers; REM Density was not significantly different in the Relapsers as compared with Abstainers in group 2. Using a principal components analysis based on these three REM sleep measures to determine "REM pressure," three separate discriminant function analyses (DFAs) were calculated: one for each group and one for all patients (n=45) together. The DFA from group 1 correctly classified 22 (78.6%) of the 28 patients in group 1 and 13 (76.5%) of the 17 patients in group 2 as Relapsers or Abstainers. The DFA from group 2 correctly classified 13 (76.5%) of the 17 patients in group 2 and 23 (82.1%) of the 28 patients in group 1. The DFA formed from both groups together correctly classified 36 (80%) of the 45 patients. When the REM sleep measures at hospital admission and discharge were compared, no statistically significant effect of time was observed. Abstinence and relapse were not consistently related to other clinical measures at the time of hospital admission such as age, duration and severity of alcoholism, marital status, employment, hepatic enzyme levels, cognitive performance, or depression ratings. Conclusion: Short REM latency, increased REM%, and, possibly, increased REM Density at the time of admission to a 1-month inpatient alcohol treatment program predict relapse in nondepressed patients with primary alcoholism by 3 months following hospital discharge.
Background: Predictors of treatment response and recovery from depression in late life remain poorly understood. Previous studies have focused on a narrow range of response and recovery variables; namely, whether patients achieve or do not achieve a defined outcome or time to achieve the outcome. Whether patients vary in their pathways toward those outcomes—and the extent to which such variation can be anticipated by patient characteristics prior to treatment—has not been empirically examined. Methods: Depression symptom levels were monitored for 18 weeks in 95 persons aged 60 years or older who were experiencing a recurrence of major depression. Subjects received standardized combined nortriptyline treatment and interpersonal psychotherapy throughout the period. Cluster analysis was used to identify depression recovery patterns. Multivariate analyses considered whether recovery patterns were predicted by pretreatment psychosocial, clinical, and electroencephalographic sleep characteristics. Results: Four subgroups of elders were identified who differed in rate, stability, and direction of recovery, ie, those showing (1) rapid sustained improvement, (2) delayed but sustained improvement, (3) partial or mixed response, or (4) no response. Pretreatment characteristics reliably predicted subjects' group membership. Higher levels of acute and chronic stressors, poorer social supports, younger age at first depressive episode, endogenous depression, higher current anxiety, older current age, and poorer subjective and objective (electroencephalographic) sleep predicted poorer response profiles. Conclusions: There are multiple pathways by which individuals begin to emerge from depression; these pathways can be identified empirically. Variables from diverse psychobiologic domains can be used to predict which persons are likely to advance along which trajectories toward recovery.