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Introduction Findings from actigraphic studies suggesting that sleep and circadian rhythms are disrupted in bipolar disorder (BD) patients have been undermined by methodological heterogeneity and the failure to adequately address potential confounders. Method Twenty-six euthymic BD cases and 29 healthy controls (HC), recruited from University Paris-Est and matched for age and gender, were compared on subjective (Pittsburgh Sleep Questionnaire Inventory; PQSI) and objective (mean scores and variability in actigraphy) measures of sleep as recorded by over 21 consecutive days. Results Multivariate generalized linear modelling (GLM) revealed significant differences between BD cases and HC for five PSQI items (total score and four subscales), four actigraphy variables (mean scores) and five actigraphy variability measures. Backward stepwise linear regression (BSLR) indicated that a combination of four variables (mean sleep duration, mean sleep latency, variability of the fragmentation index over 21 days, and mean score on PSQI daytime dysfunction sub-scale) correctly classified 89% of study participants as cases or controls (Chi-square=39.81; df=6; p=0.001). Limitations The sample size (although larger than most actigraphy studies) and incomplete matching of cases and controls may have influenced our findings. It was not possible to control for potential effects of psychotropic medication or differences in employment status between groups. Conclusions When potential confounders of sleep and circadian profiles are adequately taken into account (particularly age, gender, daytime sleepiness, mood symptoms, body mass index, and risk of sleep apnoea), a selected subset of quantitative (mean scores) and qualitative (variability) features differentiated euthymic BD cases from HC.
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Research report
Sleep in remitted bipolar disorder: A naturalistic case-control
study using actigraphy
Pierre Alexis Geoffroy
, Carole Boudebesse
, Frank Bellivier
, Mohamed Lajnef
Chantal Henry
, Marion Leboyer
, Jan Scott
, Bruno Etain
INSERM, U955, Psychiatrie génétique, Créteil 94000, France
Université Paris Est, Faculté de médecine, Créteil 94000, France
AP-HP, Hôpital H. Mondor A. Chenevier, Pôle de Psychiatrie, Créteil 94000, France
Pôle de psychiatrie, Université Lille Nord de France, CHRU de Lille, F-59000 Lille, France
Fondation FondaMental, Créteil 94000, France
AP-HP, GH Saint-Louis, Lariboisière, Fernand Widal, Pôle Neurosciences, Paris, France
Université Paris-7 Paris-Diderot, UFR de Médecine, Paris, France
Academic Psychiatry, Institute of Neuroscience, Newcastle University, UK
Centre for Affective Disorders, Institute of Psychiatry, London, UK
article info
Article history:
Received 1 November 2013
Received in revised form
15 January 2014
Accepted 16 January 2014
Available online 30 January 2014
Bipolar disorder
Circadian rhythms
Introduction: Findings from actigraphic studies suggesting that sleep and circadian rhythms are
disrupted in bipolar disorder (BD) patients have been undermined by methodological heterogeneity
and the failure to adequately address potential confounders.
Method: Twenty-six euthymic BD cases and 29 healthy controls (HC), recruited from University Paris-Est
and matched for age and gender, were compared on subjective (Pittsburgh Sleep Questionnaire
Inventory; PQSI) and objective (mean scores and variability in actigraphy) measures of sleep as recorded
by over 21 consecutive days.
Results: Multivariate generalized linear modelling (GLM) revealed signicant differences between BD
cases and HC for ve PSQI items (total score and four subscales), four actigraphy variables (mean scores)
and ve actigraphy variability measures. Backward stepwise linear regression (BSLR) indicated that a
combination of four variables (mean sleep duration, mean sleep latency, variability of the fragmentation
index over 21 days, and mean score on PSQI daytime dysfunction sub-scale) correctly classied 89% of
study participants as cases or controls (Chi-square ¼39.81; df¼6; p¼0.0 01).
Limitations: The sample size (although larger than most actigraphy studies) and incomplete matching of
cases and controls may have inuenced our ndings. It was not possible to control for potential effects of
psychotropic medication or differences in employment status between groups.
Conclusions: When potential confounders of sleep and circadian proles are adequately taken into
account (particularly age, gender, daytime sleepiness, mood symptoms, body mass index, and risk of
sleep apnoea), a selected subset of quantitative (mean scores) and qualitative (variability) features
differentiated euthymic BD cases from HC.
&2014 Elsevier B.V. All rights reserved.
1. Introduction
Bipolar disorder (BD) is a severe mental disorder and the world-
wide prevalence of the BD spectrum is 14%. Although researchers
agree that BD is multifactorial with inherited, genetic and environ-
mental risk factors, the patho-physiological determinants are
unknown (Geoffroy et al., 2013). Recent studies of circadian genes
and phenotypes indicate that sleep/activity dysregulation is a plausible
model for BD episode recurrence (McClung, 2013). Abnormalities in
sleep and circadian biomarkers during the euthymic phase have been
identied, revealing that this dysregulation is not purely an epiphe-
nomenon of acute illness episodes; indeed, increased disruption of the
sleep homoeostasis and circadian system are frequently associated
with relapse (Etainetal.,2011;Harvey,2008). Consequently, the
validation of sleep and circadian biomarkers of BD may help in the
development of both methods for earlier and more accurate detection
of unstable mental states, and of novel strategies to prevent relapse
(Frey et al., 2013).
Subjective reporting of sleep prolesisusefulinclinicalpractice,
although objective measures are increasingly utilized for research.
Contents lists available at ScienceDirect
journal homepage:
Journal of Affective Disorders
0165-0327/$- see front matter &2014 Elsevier B.V. All rights reserved.
Corresponding author at: Pôle de Psychiatrie des Hopitaux Universitaires Henri
Mondor, Centre Expert Bipolaire, Hôpital Albert Chenevier, pavillon Hartman, 40
rue de Mesly, 94000 Créteil Cedex, France. Tel.: þ33 1 49 81 32 90;
fax: þ33 1 49 81 30 99.
E-mail address: (P.A. Geoffroy).
Journal of Affective Disorders 158 (2014) 17
Actigraphy (recording the subject's sleep/wake cycles generally with
amobileportabledevice)isconsidered to be the most relevant non-
invasive technique for the measurement of sleep/wake irregularities
(Morgenthaler et al., 2007). Actigraphy has been used extensively in
the eld of chronobiology, and has more recently been applied to
research in mood disorders, and in particular the characterization of
sleep homoeostasis and circadian rhythm abnormalities in BD
(Kaplan et al., 2012). We identied nine published actigraphic studies
of BD: although most suggest that, even in remission, BD cases were
more likely than controls' to display a range of sleep abnormalities,
there was signicant heterogeneity in the methodologies employed
(Gershon et al., 2012; Harvey et al., 2005; Jones et al., 2005; Kaplan
et al., 2012; Millar et al., 2004; Mullin et al., 2011; Ritter et al., 2012;
Salvatore et al., 2008; St-Amand et al., 2012). The ve major
between-study differences were (1) the mean age of sample (e.g.
ranging from adolescents with early onset BD to older adults with
of participants including cases and controls ranging from 27 to 68);
(3) the duration of actigraphic recording (some studies recorded data
for only 3 days with a median of 6 days for the nine studies); (4)
different approaches to matching' cases and controls, with a number
of studies failing to match on demographic characteristics; and/or (5)
failure to control for confounding factors such as body mass index
(BMI) and/or sleep apnoea syndrome (SAS).
Most of these previous studies examined quantitativedifferences
(mean scores of each sleep measure for each group), and mainly
focused on four basic sleep variables (sleep duration, latency and
efciency, and waking after sleep onset). However, it is increasingly
argued that differences in terms of quality or variability (which can
be represented by the standard deviations [SD] in the mean scores
for a particular measure extracted from actigraphic recordings) may
be more sensitive markers for distinguishing BD sleep patterns from
those of controls (Scott, 2011). Only three studies have published
data relevant to this approach (Gershon et al., 2012; Millar et al.,
2004; Mullin et al., 2011), but comparing the ndings is difcult.
Indeed, the study among adolescents (Mullin et al., 2011), and the
study involving three days of actigraphic recording (Salvatore et al.,
2008) differed from the study in older adults over a longer period (28
days) of actigraphic monitoring (Gershon et al., 2012). Also, none of
these studies controlled for the possible effects of BMI or level of risk
for SAS on sleep proles. Thus, published ndings for the quality and
quantity of sleep in remitted BD may have been confounded by other
individual characteristics, including those known to affect sleep
directly, and by heterogeneity in sampling and/or in study design.
To overcome the limitations noted in previous studies, we report
an analysis of mean scores of subjective and objective measures, and
variability in objective measures, using methodological approaches
designed to avoid potential confounders. Previous studies examined
each reported sleep variable in independent analyses and none of
them adequately addressed potential confounders in their statistical
analytic strategy. We therefore tried to overcome some of these
previous weaknesses in design by examining which combination of
sleep measures best distinguishes cases from controls. Our hypoth-
esis was to identify and/or conrm reliable and robust circadian/
sleep markers differentiating euthymic BD cases from HC when
potential confounders of sleep and circadian differences are taken
into account (most notably, age, gender, daytime sleepiness, mood
symptoms, BMI and risk of sleep apnoea).
2. Materials and methods
2.1. Sample
With ethical approval from the Institutional review board,
written informed consent was obtained from 55 adults (BD ¼26;
HC¼29). The included cases were recruited from our university-
afliated psychiatric clinic (University Paris Est) and the HC were
recruited from individuals attending the blood donor service at the
adjacent general hospital (Henri Mondor Hospital, Creteil).
The groups (BD and HC) were matched as closely as possible for
age and gender: perfect matching was not feasible, so we
increased the size of the HC group to ensure that we had a control
of the same age and/or a control of the same gender for each case
recruited. Individuals eligible for the study were included if,
during the preceding three months, they had (1) not experienced
any periods of severe sleep disruption due to somatic conditions
(e.g. organic insomnia/hypersomnia or sleep-wake disorders) and/
or any life event that may have altered their sleep patterns (e.g.
shift work, jet-lag, child birth, trauma, or somatic disease known
to be associated with sleep disturbances); (2) not been hospita-
lised or received a treatment that may disrupt sleep (e.g. for cases:
electro-convulsive therapy); (3) not been prescribed medication or
taken drugs that may disrupt sleep (e.g. sympathomimetic stimu-
lants, corticosteroids, thyroid hormones, antiarrhythmics, beta-
blockers, clonidine, diuretics, theophylline, and medications con-
taining alcohol or caffeine) and not changed either the dose or
type of psychotropic treatment.
Bipolar cases also had to full the following inclusion criteria:
the DSM-IV criteria for BD according to the French version of the
Diagnostic Interview for Genetic Studies (DIGS) (Nurnberger et al.,
198 8); currently euthymic i.e. they scored o8 on both the Mon-
tgomeryAsberg Depression Rating Scale (MADRS; Montgomery
and Asberg, 1979) and the Young Manic Rating Scale (YMRS;
Young et al., 1978); and the International Society of Bipolar Disorder
task force criteria (Tohe n et a l., 20 09 ) for remission (i.e. they had not
experienced a BD episode in the prior three months). Patients
experiencing a BD relapse whilst participating in the study were
Controls were assessed with the DIGS and the Family Interview
for Genetic Studies (Maxwell, 1992); individuals were excluded if
they had any personal history of DSM-IV disorders and family
history of schizophrenia, affective disorders and/or suicide attempts.
2.2. Assessment procedures
Cases and controls were rst assessed for affective symptoms
using the MADRS and YMRS, and their BMI was calculated.
Participants then completed the Berlin Questionnaire (a measure
of the risk of SAS) (Netzer et al., 1999), and the Epworth Sleepiness
Scale (a measure daytime sleepiness) (Johns, 1991), to allow these
potential confounding factors to be taken into account.
2.2.1. Self-ratings of sleep prole
All participants recorded their subjective sleep quality using
the French version of the 19-item Pittsburgh Sleep Quality Index
(PSQI) (Blais et al., 1997). Here, we report the mean total score over
21 consecutive days and seven other specic measures of sleep
(with higher scores indicating lower quality or worse outcome):
sleep quality, latency, duration and efciency, disturbances, use of
sleeping medication and daytime dysfunction (e.g. sleepiness and
2.2.2. Actigraphy
All participants were asked to wear an actigraph (AW-7
CamNtech) on the wrist of their non-dominant hand for the 21
consecutive days, the days for which they completed the PSQI self-
ratings. The AW-7 actigraph is an accelerometer that detects the
intensity and the amount of movement as a function of time. For
this study, data were sampled in one-minute epochs and partici-
pants were instructed to press the event-marker when they went
P.A. Geoffroy et al. / Journal of Affective Disorders 158 (2014) 172
to bed to go to sleep and when they got out of bed at the start of
the next day. They also completed a sleep diary for these 21 days.
Other requirements were kept to a minimum to try to minimise
intrusion into sleep/activity routines. Recording was made during
periods when the participants were not on vacation and not
involved in unusual activities.
Two experienced psychiatrists (PAG and CB) analysed the
actigraphic records of cases and controls using the sleep detection
algorithm provided by Actiwatch software (Actiwatch Activity &
Sleep Analysis Ltd CamNtech
7.28) and any incongruences
between the sleep diary and actigraphy rest periods were claried
prior to statistical analyses (see Boudebesse et al., 2012 for details).
We selected the ten clinically interpretable actigraphic mea-
sures that have been most frequently reported in previous studies
(Gershon et al., 2012; Harvey et al., 2005; Jones et al., 2005; Kaplan
et al., 2012; Millar et al., 2004; Mullin et al., 2011; Ritter et al.,
2012; Salvatore et al., 2008; St-Amand et al., 2012). There were six
measures of basic sleep prole: time in bed, sleep duration, sleep
latency, wake after sleep onset (WASO), sleep efciency, fragmen-
tation index (a measure of sleep continuity); two measures of
activity during sleep: mean activity in active periods of sleep,
mean sleep activity; and two measures of variability: inter-daily
stability and intra-daily variability. To facilitate comparison with
other studies that used different measures of variability, we also
estimated the variability of the basic sleep measures over 21 days
(using standard deviations (SD) of the mean scores) (see Millar
et al., 2004; Mullin et al., 2011 for the rationale and details of this
2.3. Statistical analysis
All analyses were performed using SPSS 19.0. We included
several covariates in the analysis: age and/or gender because
matching was incomplete, and also BMI, SAS, daytime sleepiness
(Epworth) and the presence of residual or low-grade mood
symptoms (as measured using the MADRS or YMRS) that can all
be associated with differences in sleep prole independently of
case-control group status (i.e. BD or HC). We undertook a power
calculation to assess the sample size required to detect a between-
group Effect Size (ES) of Z0.35 (classied as a medium ES
according to Cohen (1977) for self-rated and objective measures
of sleep whilst controlling for these potential confounders. We
thereby estimated that the sample size of 55 individuals had
480% power to detect differences at a signicance level of 0.05
(and an ESZ0.35) in a multivariate analysis.
The analysis involved three stages. First, we checked the
normality of the distributions of variables and undertook log
transformation if required (none were needed). Second, we used
multivariate Generalized Linear Modelling (GLM) for the analysis
of sleep variables (self-ratings; actigraphy measures). GLM is a
exible statistical model that incorporates normally distributed
dependent variables and categorical or continuous independent
variables (we also report Wilk's Lambda, to indicate the proportion
of generalized variance in the dependent variables explained by
the model and also to give the best estimate of overall power of
the analysis to differentiate between groups). We used bootstrap-
ping (i.e. resampling) to reduce biases introduced by any outliers
and to obtain the best estimates of summary statistics. Any
variables that were signicantly different between groups (even
allowing for interactions between group and other categorical or
continuous variables) were then entered into a Backward Stepwise
Logistic Regression (BSLR) to assess the combination of these
selected variables that best classied individuals as cases or
controls. The proportion of cases correctly classied is reported
along with the odds ratios (OR) and 95% condence intervals (95%
CI) for each variable included in the nal optimal model.
3. Results
3.1. Socio-demographic and clinical characteristics
The sample is composed of 55 individuals (26 BD cases; 29 HC),
the mean age was about 54 years and more than half (30/55) were
female (Table 1). Cases fullled the criteria for BD subtype I
(n¼16) or BD subtype II (n¼10). One patient did not receive
medication, and nine patients received only one psychotropic
medication and 16 received Z2 medications. The psychotropic
treatments used were lithium (n¼12), anti-convulsants (n¼15),
atypical antipsychotics (n¼7), typical antipsychotics (n¼4), ben-
zodiazepines (n¼4), antidepressants (n¼5).
There were no statistically signicant differences between the
groups for age, gender, BMI, level of risk of SAS, or daytime
sleepiness. Levels of mood symptoms were very low in both
groups, but the median score on the MADRS was slightly higher
for the BD group than controls (0.50 vs. 0.00); there were no
between-group differences on the YMRS.
3.2. Subjective and objective measures of sleep
Mean scores for a number of subjective and objective ratings of
sleep differed between cases and controls. There were statistically
signicant differences between the groups for several PSQI and
actigraphy measures, even when taking into account gender, risk of
SAS, age, BMI, and scores on the Epworth, MADRS, and YMRS. When
considering the multivariate values for the model as a whole, there
was a signicant difference between the patients with BD and HC
(Wilks' lambda, F¼90.89; df 26,16; p¼0.0001; statistical power, 0.93).
Several interactions between group and other variables were
identied; in particular, ve PSQI items (Total score and scores for
PSQI subscales 1, 4, 5 and 7), four mean actigraphy measures (sleep
latency, duration, efcacy, and fragmentation index, inter-daily stabi-
lity) and ve actigraphy variability measures (variability in sleep
duration, sleep efciency, fragmentation index and time in bed)
differed between the groups (Tabl e 2). As measured by the PSQI,
patients with BD were more likely to report poorer sleep quality
(p¼0.001), poorer sleep efciency (p¼0.001), more frequent sleep
disturbances (p¼0.001), and more daytime dysfunction (p¼0.001);
consequently they had poorer global sleep quality as measured by the
PSQI total score (p¼0.0001). As determined from mean actigraphy
scores, patients with BD were more likely to have longer sleep latency
Table 1
Baseline characteristics of bipolar disorder (BD) cases and healthy controls (HC).
Baseline BD (n¼26) HC (n¼29) p
characteristics Mean (7SD) Mean (7SD)
Age (years) 53.50 (711.49) 54.10 (79.11) 0.78
MontgomeryAsberg 1.85 (72.80) 0.48 (71.35) 0.02
Depression rating scale
Median score 0.50 0.00
Young mania rating Scale 0.65 (71.38) 0.14 (70.44) 0.16
Median score 0.00 0.00
Body mass index 26.74 (75.72) 26.64 (74.17) 0.68
Daytime sleepiness
8.85 (73.90) 6.86 (73.72) 0.12
n(%) n(%)
Sleep apnoea syndrome
High risk category 6 (23%) 2 (7%)
Low risk category 20 (77%) 27 (93%)
Gender distribution 0.13
Female 17 (65%) 13 (45%)
Male 9 (35%) 16 (55%)
Risk of sleep apnoea syndrome assessed using the Berlin Questionnaire.
Daytime sleepiness assessed using the Epworth Sleepiness Scale.
P.A. Geoffroy et al. / Journal of Affective Disorders 158 (2014) 173
(p¼0.007), longer sleep duration (p¼0.02), poorer sleep efciency
(p¼0.04), higher fragmentation index (p¼0.05) and poorer inter-daily
stability (p¼0.03). For actigraphy variability over 21 days, patients
with BD were more likely to show more variable time in bed
(p¼0.005), sleep duration (p¼0.04), sleep efciency (p¼0.03) and
fragmentation index (p¼0.03).
Table 2
Differences between sleep measures for cases (BD) and controls (HC).
Sleep measures BD (n¼26) HC (n¼29) Multivariate generalized linear model
Mean (7SD) Mean (7SD) Fp Observed power
PSQI measures
Total score 7.38 (73.49) 4.11 ( 72.13) 4.45 0.0 001 0.99
Sub-scale 1 1.19 0.71 3.64 0.001 0.99
Subjective sleep quality (70.69) ( 70.66)
Sub-scale 2 1.19 0.82 0.70 0.74 0.33
Sleep latency (70.80) ( 70.91)
Sub-scale 3 0.62 0.50 1.53 0.15 0.70
Sleep duration (70.94) ( 70.75)
Sub-scale 4 0.88 0.43 3.86 0.001 0.99
Habitual sleep efciency (70.95) ( 70.69)
Sub-scale 5 1.58 1.18 3.67 0.001 0.99
Sleep disturbances (70.58) ( 70.48)
Sub-scale 6 0.69 0.14 1.08 0.18 0.68
Use of night sedation (71.09) (70.59)
Sub-scale 7 1.23 0.32 3.53 0.001 0.98
Daytime dysfunction (70.99) ( 70.48)
Mean actigraphy scores
Time in bed (min) 511.81 ( 750.28) 482.31 (749.36) 1.79 0.08 0.79
Sleep duration (min) 475.42 (764.5) 455.83 (753.98) 2.38 0.019 0.91
Sleep latency (min) 25.23 (733.65) 11.59 ( 77.98) 2.81 0.007 0.95
Wake after sleep onset (min) 57.88 (723.17) 52.62 ( 727.33) 0.57 0.85 0.27
Sleep efciency (%) 81.54 (79.87) 84.90 (76.32) 2.13 0.036 0.86
Fragmentation index 32.04 (710.51) 28.69 (710.88) 2.01 0.05 0.84
Mean activity in active sleep periods
115.54 ( 737.67) 91.10 (724.39) 1.21 0.31 0.58
Mean overall sleep activity 19.35 ( 713.10) 12.69 ( 77.07) 1.69 0.11 0.76
Inter-daily stability 0.503 (70.14) 0.512 (70.12) 2.17 0.033 0.87
Intra-daily variability 0.82 ( 70.17) 0.80 ( 70.18) 0.90 0.56 0.43
Actigraphy measure variability over 21 days
Time in bed (min) 1.44 (70.7) 1.33 (70.5) 2.97 0.005 0.96
Sleep duration (min) 1.3 (70.6) 1.13 (70.4) 2.08 0.04 0.86
Sleep latency (min) 0.67 (71.2) 0.43 (70.7) 1.40 0.21 0.66
Wake after sleep onset (min) 0.49 (70.3) 0.36 ( 70.3) 1.80 0.08 0.79
Sleep efciency (%) 8.77 (77.5) 6.13 ( 73.8) 2.26 0.026 0.89
Fragmentation index 12.52 (77.3) 9.28 (72.7) 2.16 0.033 0.96
Model controlled for age, gender, daytime sleepiness (Epworth), current mood symptoms (MADRS and YMRS), BMI and risk of sleep apnoea (Berlin).
Intensity of movement during periods of sleep when activity occurs.
Table 3
Backward Stepwise Logistic Regression (BSLR) showing (a) the best combination of variables for correctly classifying study participants as
cases or controls and (b) the overall classication rate by group.
Variable 95% C.I.
pOdds Ratio Lower Upper
(a) Variables included in the nal BSLR model
Sleep duration 0.015 1.03 1.01 1.05
Sleep latency 0.012 1.24 1.05 1.47
Fragmentation index: mean variability 0.01 1.63 1.12 2.31
Daytime dysfunction (PSQI subscale-7) 0.005 8.35 2.50 18.11
Observed Predicted
Controls Cases % Correctly classied
(b) Classication table and summary statistics
25 3 89.3
Cases 3 23 88.5
% of all participants correctly classied 89%.
Chi-square 39.81 (df 6) p¼0.001.
One control participant was excluded from the analysis due to inadequate data.
P.A. Geoffroy et al. / Journal of Affective Disorders 158 (2014) 174
3.3. Regression analysis
We next used BSLR to determine the optimal combination of
variables that best classied participants as cases or controls.
When the variables identied by the GLM were entered into
a BSLR; a combination of four variables (mean sleep duration,
mean sleep latency, variability in fragmentation index over 21
days, and mean score on PSQI sub-scale 7: daytime dysfunction)
correctly classied 89% of the study participants as cases or
controls (Chi-square¼39.81; df ¼6; p¼0.001). The OR for these
variables ranged from 1.03 (sleep duration) to 8.35 (PSQI daytime
dysfunctionsub-scale) (Table 3). However, whilst daytime dys-
function is the variable with the highest OR, on its own it classied
about 75% cases, so the other measures signicantly contributed to
the improved classication obtained by the nal model.
4. Discussion
We report a study of sleep abnormalities in remitted bipolar
(BD) patients compared with HC that also controlled for major
potential confounders, and replicate and extend previous ndings.
In particular, objective and subjective assessments of sleep indi-
cate that, compared to controls, BD patients show longer sleep
duration, longer sleep latency, poorer sleep efciency, higher
fragmentation index (with actigraphy) (Gershon et al., 2012;
Harvey et al., 2005; Jones et al., 2005; Kaplan et al., 2012; Millar
et al., 2004; Mullin et al., 2011; Ritter et al., 2012; Salvatore et al.,
2008; St-Amand et al., 2012), lower sleep quality, weaker sleep
efciency, more sleep disturbances, and more daytime dysfunction
(with PSQI) (Rocha et al., 2013). The partial discrepancy between
PSQI (subjective measure) and actigraphy (objective measure)
ndings has also been observed previously: patients with BD
underestimated their sleep latency and duration on questionnaire
assessments compared to actigraphy examination. This may arise
as a consequence of misperceptions in patients with BD regarding
their sleep quality (Gershon et al., 2012; Harvey et al., 2005; Millar
et al., 2004). Nevertheless, actigraphy demonstrated a high corre-
lation with polysomnography (PSG) measures in patients with BD
regarding sleep latency, sleep duration, fragmentation index and
sleep efciency, which further validate our ndings (Kaplan et al.,
We report several new ndings for issues that have been
under-explored: patients with BD presented with more variability
for time in bed, sleep duration, sleep efciency and fragmentation
index. Indeed, sleep duration, sleep efciency and fragmentation
index are altered not only in quantity (mean) but also in variability
(SD) in BD patients. This indicates that analyses of actigraphic
markers should not be restricted to classical sleep markers
(latency, duration, efciency and WASO), but should also
be extended to the variability of markers. Indeed, Millar et al.
(2004) were the rst to explore the variability of markers in
different groups and showed that the means of none of the four
actigraphic measures differed between cases and controls, but
variability of two of the four measures did (sleep duration and
night waking time) with an effect size around 0.35. Similarly,
Gershon et al. (2012) conrmed more variability of both time in
bed and sleep duration for BD patients than controls (although the
latter did not survive their Bonferroni's correction) Millar et al.
(2004) also found more variability of night wake time among
remitted cases. Both Gershon et al. (2012) and our study
(0.4070.3 vs. 0.3670.3) replicate this nding. Further, we
observed signicant variability of sleep efciency and fragmenta-
tion index, which conrmed the greater variability of sleep
patterns and circadian rhythms in patients with remitted BD than
controls. Mullin et al. (2011), despite also assessing the variability
of actigraphic measures, did not nd any signicant differences;
however, that study involved recordings for only four nights,
which may have been too short to detect variability. Studies
involving sufcient duration of recording would be useful to
explore further the variability of actigraphic measures in BD
patients, and to examine trait-markers of BD.
The results of our regression analysis deserve comment. The
fact that 89.3% of BD and 88.5% of HC could be correctly classied
indicates that a combination of different methods of assessment
(i.e. PSQI, mean and SD actigraphy measures) may serve as a useful
marker of BD. Daytime dysfunction, sleep duration, sleep latency
and fragmentation index variability appeared, when combined, to
identify clear circadian biomarkers of remitted BD. Indeed, it may
be possible to dene a biosignature of BD using these four
features. We partially replicate the ndings of Millar et al. (2004)
whose best multivariate model involved a combination of one
actigraphic (variability of sleep duration) and two subjective sleep
variables (average sleep duration, and average onset latency).
These two studies indicate that individuals are best classied by
a combination of subjective and objective measures (quantity and
variability). The other comment is that it is noteworthy that
although daytime dysfunction was a sub-scale score of the PSQI,
it is debatable whether this is a specic measure of sleep pattern/
dysfunction. Indeed in BD cases it may be a measure of overall
effects of illness or possible effects of medication, not only of
daytime consequences of disturbed sleep.
Our study has some limitations. Controlling for psychotropic
medications is always difcult in bipolar disorder research. In this
study, the size of the case sub-group did not allow us to include
treatment data as a covariate in any within group' sub-analyses.
However, the type and dose of psychotropic medications have
been found to be unrelated to actigraphic measures in patients
with remitted BD (Salvatore et al., 2008). Moreover, we believe
that on balance, it can be hypothesized that medication would be
as likely or more likely to improve the patients' quality of sleep as
to have a detrimental effect. We also attempted to control for
daytime sedation by using scores on the Epworth scale as a
covariate in the preliminary between group comparisons. As such,
the observed differences between cases and controls may have
been attenuated rather than exaggerated by such treatments.
Indeed, antipsychotics, valproate and lithium have been shown
to regulate sleep and circadian rhythms in patients with bipolar
disorder (Geddes and Miklowitz, 2013). Therefore, we believe that
it is unlikely that psychotropic drugs had a major inuence. Our
sample size is small; however, it is of reasonable size for an
actigraphic study and larger than the median for the previous
studies cited (median¼40) (Gershon et al., 2012; Harvey et al.,
2005; Jones et al., 2005; Kaplan et al., 2012; Millar et al., 2004;
Mullin et al., 2011; Ritter et al., 2012; Salvatore et al., 2008; St-
Amand et al., 2012). We studied several markers of subjective and
objective sleep. Although GLM is an appropriate method for
assessing multiple, potentially inter-dependent measures, and
the power calculation (and estimated effect sizes) suggest we
have reduced the risk of type II error, it is clear that it would be
benecial to use a larger sample for further studies. Nevertheless,
our study allowed controlling for confounding factors that were
insufciently taken into account in previous studies; our greater
attention to potential confounding factors was an important
methodological strength relative to most previous studies.
We were thus able to conrm that patients with BD in
remission experienced substantial sleep disturbances that can be
measured both objectively and subjectively. This provides further
evidence of the involvement of sleep and circadian rhythm
abnormalities in the pathophysiology of BD, as proposed by, e.g.,
Harvey et al. (Harvey, 2008). Indeed, genetic variants of candidate
genes (mainly circadian genes) may predispose individuals to be
P.A. Geoffroy et al. / Journal of Affective Disorders 158 (2014) 175
relatively less able to adapt their circadian rhythms appropriately
to their environment, and therefore to greater vulnerability to
sleep disturbances. Since circadian and neurotransmission systems
are highly connected, circadian and/or sleep-related abnormalities
may affect the functioning of the dopamine and serotonin circui-
try, which in turn may affect mood regulation. These chronic sleep
and circadian rhythm disturbances (observable in acute episodes
and in remission) may thereby contribute to the more general issues
persistently encountered bipolar patients such as poor quality of
cognitive functioning, residual emotional hyper-reactivity or vulner-
ability to metabolic disturbances (Boland et al., 2012; Boudebesse
and Henry, 2012; McClung, 2013; Soreca et al., 2012).
The ndings we report have several important clinical implica-
tions. Sleep and circadian rhythm disturbances in remitted BD
might be treatable by manipulating the circadian system using
chronobiological medication (e.g. melatonin or melatonin analo-
gues) or sleep focused psychological interventions (Kaplan and
Harvey, 2013; Livianos et al., 2012; Pacchierotti et al., 2001;
St-Amand et al., 2012). Attention to rhythm abnormalities is of
particular interest because biological rhythm dysfunctions have
been found to be associated to a wide range of sources of poor
functioning and are the earliest markers of impending mood
relapses (Giglio et al., 2010). Thus, better identication of these
abnormalities in clinical practice might facilitate the prevention of
the evolution of prodromes of mood relapses, and also help
improve functioning in inter-episodic BD. It may allow a more
complete approach including combinations of treatments acting
on several dimensions including sleep or circadian disturbances
(Geoffroy et al., 2012). These ndings more generally indicate
a new area for research both into possible therapeutic targets
and to improve our understanding of the circadian pathophysiol-
ogy of BD.
5. Conclusion
This 21-day case-control study demonstrates the presence of
subjective and objective disturbances of sleep and activity markers
in patients with BD during remission. Disturbances identied by
actigraphy are both quantitative (mean scores) and qualitative
(variability). We further conrm the greater variability in sleep
markers in remitted patients with BD. These ndings indicate that
future studies should examine both the mean scores and the
variability over extended periods of time. In addition, we show
that a combination of subjective and objective measures (quantity
and variability) may be a better circadian biosignature of BD than
any single measure on its own.
Role of funding source
This work was supported by AP-HP (Assistance Publique des Hôpitaux de Paris)
(Promotor of the study and PHRC AOR11096 PI : BE); the Fondation FondaMental (RTRS
Santé Mentale) (for funding the data management of database) and the Investissements
d'Avenir program managed by the ANR under reference ANR-11-IDEX-000402. This
work was also supported by Laboratoires Servier (funding for the purchase actiwatches
and for sequence analyses). Laboratoires Servier had no responsibility for the design of
the protocol, or for the acquisition, analysis or interpretation of data.
Conict of interest
PA Geoffroy has received a prize from Bayer for being Laureate of the Medical
University of Lille.
C. Boudebesse has received a research bursary from Laboratoires Servier; a
prize for her thesis in medicine from Sano-Aventis; and honoraria from Ostuka as
an independent symposium speaker.
B. Etain and F. Bellivier have received honoraria and nancial compensation as
independent symposium speakers from Sano-Aventis, Lundbeck, AstraZeneca, Eli
Lilly, Bristol-Myers Squibb and Servier.
C. Henry has received honoraria and nancial compensation as independent
symposium speakers from Sano-Aventis, Lundbeck, AstraZeneca, Eli Lilly, Bristol-
Myers Squibb.
J. Scott has received funding to attend national and international conferences,
nancial compensation as an independent symposium speaker for talks on early
onset in BD and psychosocial aspects of BD, and advisory board fees from
AstraZeneca, BMS-Otsuka, Eli Lilly, GSK, Jansen-Cilag, Lundbeck, Sano-Aventis
and Servier.
M. Leboyer has received honoraria and nancial compensation as an indepen-
dent symposium speaker from AstraZeneca and Servier.
We thank the patients with BD and controls who agreed to participate in this
study. We thank the staff at the inclusion sites in Paris-Créteil (especially E. Abadie
for her participation in the clinical assessment). We are grateful to the Clinical
Investigation Centre (O. Montagne and P. Le Corvoisier). We thank J.R. Richard,
L. Ouvrard and N. Bord for their assistance.
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... Charging time of the device was up to 2 h, and patients were instructed to charge it during moments they would not wear it. To minimize data noise, the device was to be worn on the nondominant wrist, a common practice in studies using wearable devices (e.g., actigraphy) 46 . This criterion ensures that reliable features can be derived from raw physiological measurements 47 . ...
... To extract physiological features from sensors raw measurements, we proceeded as follows. Cardiorespiratory (such as heart rate, breathing rate, heart rate variability) [18][19][20][21][22][23] , actigraphy (e.g., L5, M10, etc.) 46,47,51 and sleep-based physiological features (e.g., sleep stages such as REM/NREM/WASO) 52 were extracted respectively from PPG, 3-axis accelerometer and both sensors' data using a combination of standard algorithms from the literature [53][54][55] . These features were further grouped into physical activity (12 features), heart rate (25 features), heart rate variability (39 features), breathing rate (12 features) and sleep (13 features) and were smoothed with a mean filter to remove potential outliers. ...
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Major Depressive Disorder (MDD) has heterogeneous manifestations, leading to difficulties in predicting the evolution of the disease and in patient's follow-up. We aimed to develop a machine learning algorithm that identifies a biosignature to provide a clinical score of depressive symptoms using individual physiological data. We performed a prospective, multicenter clinical trial where outpatients diagnosed with MDD were enrolled and wore a passive monitoring device constantly for 6 months. A total of 101 physiological measures related to physical activity, heart rate, heart rate variability, breathing rate, and sleep were acquired. For each patient, the algorithm was trained on daily physiological features over the first 3 months as well as corresponding standardized clinical evaluations performed at baseline and months 1, 2 and 3. The ability of the algorithm to predict the patient's clinical state was tested using the data from the remaining 3 months. The algorithm was composed of 3 interconnected steps: label detrending, feature selection, and a regression predicting the detrended labels from the selected features. Across our cohort, the algorithm predicted the daily mood status with 86% accuracy, outperforming the baseline prediction using MADRS alone. These findings suggest the existence of a predictive biosignature of depressive symptoms with at least 62 physiological features involved for each patient. Predicting clinical states through an objective biosignature could lead to a new categorization of MDD phenotypes.
... In BD, compared to HCs, longer sleep latency and longer sleep duration were most often reported (58)(59)(60)(61)(62)(63) and were associated with symptoms of depression (64). Although reported less often, later sleep midpoint was also noted (65). ...
... Results from this systematic review provide evidence that BD is associated with disruptions in rest-activity patterns including longer sleep duration, longer sleep onset latency (58)(59)(60)(61)(62)(63), and lower average daily activity (16,75) that may be especially associated with depression. BD mania is instead linked to more complex and variable patterns over a shorter temporal scale that can also be predictive of future relapse (54). ...
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Background Disruptions in rest and activity patterns are core features of bipolar disorder (BD). However, previous methods have been limited in fully characterizing the patterns. There is still a need to capture dysfunction in daily activity as well as rest patterns in order to more holistically understand the nature of 24-h rhythms in BD. Recent developments in the standardization, processing, and analyses of wearable digital actigraphy devices are advancing longitudinal investigation of rest-activity patterns in real time. The current systematic review aimed to summarize the literature on actigraphy measures of rest-activity patterns in BD to inform the future use of this technology. Methods A comprehensive systematic review using PRISMA guidelines was conducted through PubMed, MEDLINE, PsycINFO, and EMBASE databases, for papers published up to February 2021. Relevant articles utilizing actigraphy measures were extracted and summarized. These papers contributed to three research areas addressed, pertaining to the nature of rest-activity patterns in BD, and the effects of therapeutic interventions on these patterns. Results Seventy articles were included. BD was associated with longer sleep onset latency and duration, particularly during depressive episodes and with predictive value for worsening of future manic symptoms. Lower overall daily activity was also associated with BD, especially during depressive episodes, while more variable activity patterns within a day were seen in mania. A small number of studies linked these disruptions with differential patterns of brain functioning and cognitive impairments, as well as more adverse outcomes including increased suicide risk. The stabilizing effect of therapeutic options, including pharmacotherapies and chronotherapies, on activity patterns was supported. Conclusion The use of actigraphy provides valuable information about rest-activity patterns in BD. Although results suggest that variability in rhythms over time may be a specific feature of BD, definitive conclusions are limited by the small number of studies assessing longitudinal changes over days. Thus, there is an urgent need to extend this work to examine patterns of rhythmicity and regularity in BD. Actigraphy research holds great promise to identify a much-needed specific phenotypic marker for BD that will aid in the development of improved detection, treatment, and prevention options.
... Studies show that a large proportion of patients diagnosed with BD who are in clinical remission experience significant functional disorders (30-60%) [18]. Various studies have shown that depressive symptoms, cognitive disorders, and sleep disorders are permanent even in euthymic periods of patients [4,9,24]. ...
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The progression of bipolar disorder (BD) is characterized by recurrent episodes of depression, mania, and hypomania, thus affecting the daily functionality of individuals. Previous studies have shown that a large proportion of patients diagnosed with BD who are in clinical remission experience significant functional disorders. The present study aimed to investigate the relationships between cognitive impairment and serum progesterone, allopregnanolone and BDNF levels in male bipolar disorder patients who are in the euthymic period. Our study included 41 euthymic male patients with bipolar disorder and 40 age, sex, body mass index (BMI) and smoking-matched male healthy control subjects. Neuropsychiatric tests such as the Stroop Test TBAG Form, Auditory Verbal Digit Span Test- Form B (VADS-B) and Cancellation Test were administered to all participants, and 5–7 ml of peripheral venous blood sample was taken from all participants. Serum allopregnanolone, progesterone and BDNF levels were also measured in all participants. Serum allopregnanolone and progesterone levels were found to be lower in bipolar patients, and it was observed that the serum level of allopregnanolone decreased as the disease duration increased. The serum BDNF levels were similar between groups. The cognitive functions assessed using the Stroop, VADS-B and cancellation tests were found to be better in healthy subjects. The neurocognitive test performances of all participants were strongly positively correlated with allopregnanolone levels. The present study supports the hypothesis that allopregnanolone acts as an endogenous mood stabilizer.
... Other residual symptoms include sleep disorders, emotional dysregulation, and sexual dysfunction. The presence of residual symptoms has been linked to an overall negative impact on functional outcomes of patients with bipolar disorder [83,84]. Residual symptoms appear to impact the natural course of bipolar disorder and represent potential predictors of long-term outcome and recovery. ...
Patients with bipolar disorders may experience serious impairments in psychosocial functioning and quality of life, despite adequate treatment. Numerous treatments are available for acute affective episodes, in most cases promoting symptomatic remission from affective episodes. Symptomatic remission does not necessarily lead to an acceptable level of functioning, defined as full recovery. Full recovery in bipolar disorder is mostly hampered by cognitive impairment and subclinical depressive symptoms, defined as residual symptoms. The aim of this chapter is to give an insight into the concept of clinical recovery in bipolar disorders, going from symptomatic remission and the available treatment strategies to a wider spectrum of new opportunities in the management of bipolar disorders.KeywordsBipolar disordersRecoveryRemissionFunctional recoveryFunctional remediation
Bipolar disorder (BD) is a common mental condition with a seasonal pattern (SP) of onset. In the spring, there is a higher incidence rate of mania or mixed onset and suicide. However, the underlying mechanism of this SP remains unclear. In this study, targeted metabolomics was used to understand metabolic changes in patients with BD before and after the spring equinox. Nine patients with BD and matched healthy controls were tested for serum metabolomics at the spring equinox and 15 days before and after the spring equinox. The results showed that 27 metabolite levels changed significantly, three of which interacted between three time points and groups involving triglyceride (TG, 20:4_34:2), TG (20:4_34:3) and TG (16:0_36:6). The identified metabolic pathways mainly involved arginine biosynthesis, D-glutamine and D-glutamate metabolism, and nitrogen metabolism. Changes in solar radiation and lunar cycle during spring may be the external causes of metabolic changes. These findings help to further explore seasonal metabolic changes in patients with BD and provide insights into the mechanisms of patients' emotional changes in spring.
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Objective: Bipolar disorder (BD) is a mood swing illness characterized by episodes ranging from depressive lows to manic highs. Although the specific origin of BD is unknown, genetics, environment, and changes in brain structure and chemistry may all have a role. Through magnetic resonance imaging (MRI) evaluations, this study looked into functional abnormalities involving the striatum between BD group and healthy controls (HC), compared the whole-brain gray matter (GM) morphological patterns between the groups and see whether functional connectivity has its underlying structural basis. Materials and methods: We applied sliding windows to functional magnetic resonance imaging (fMRI) data from 49 BD patients and 44 HCs to generate temporal correlations maps to determine strength and variability of the striatum-to-whole-brain-network functional connectivity (FC) in each window whilst also employing voxel-based morphometry (VBM) to high-resolution structural MRI data to uncover structural differences between the groups. Results: Our analyses revealed increased striatal connectivity in three consecutive windows 69, 70, and 71 (180, 182, and 184 s) in individuals with BD (p < 0.05; Bonferroni corrected) in fMRI images. Moreover, the VBM findings of structural images showed gray matter (GM) deficits in the left precentral gyrus and middle frontal gyrus of the BD patients (p = 0.001, uncorrected) when compared to HCs. Variability of striatal connectivity did not reveal significant differences between the groups. Conclusion: These findings revealed that BD was associated with a weakening of the precentral gyrus and middle frontal gyrus, also implying that bipolar illness may be linked to striatal functional brain alterations.
Objectives: Despite international efforts to identify biomarkers of depression, none has been transferred to clinical practice, neither for diagnosis, evolution, nor therapeutic response. This led us to build a French national cohort (through the clinical and research network named SoPsy within the French biological psychiatry society (AFPBN) and sleep society (SFRMS)), to better identify markers of sleep and biological rhythms and validate more homogeneous subgroups of patients, but also to specify the manifestations and pathogeneses of depressive disorders. Before inclusions, we sought to provide a predefined, standardized, and robust set of data to be collected in all centers. Methods: A Delphi process was performed to achieve consensus through the independent rating of invited experts, the SoPsy-depression co-investigators (n=34). The initial set open for vote included 94 questionnaires targeting adult and child psychiatry, sleep and addiction. Results: Two questionnaire rounds were completed with 94% participation in the first round and 100% participation in the second round. The results of the Delphi survey incorporated the consensus opinion of the 32 members who completed both rounds. Nineteen of the 94 questionnaires achieved consensus at the first round and seventy of 75 at the second round. The five remaining questionnaires were submitted to three experts involved in the steering committee during a dedicated meeting. At the end, 24 questionnaires were retained in the mandatory and 26 in the optional questionnaire set. Conclusions: A validated data collection set of questionnaires is now available to assess psychiatry, addiction, sleep and chronobiology dimensions of depressive disorders.
Background The underlying neurobiological mechanisms on suicidal behavior in bipolar disorder remain unclear. We aim to explore the mechanisms of suicide by detecting dynamic functional connectivity (dFC) of corticostriatal circuitry and cognition in depressed bipolar II disorder (BD II) with recent suicide attempt (SA). Methods We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 68 depressed patients with BD-II (30 with SA and 38 without SA) and 35 healthy controls (HCs). The whole-brain dFC variability of corticostriatal circuitry was calculated using a sliding-window analysis. Their correlations with cognitive dysfunction were further detected. Support vector machine (SVM) classification tested the potential of dFC to differentiate BD-II with SA from HCs. Results Increased dFC variability between the right vCa and the right insula was found in SA compared to non-SA and HCs, and negatively correlated with speed of processing. Decreased dFC variability between the left dlPu and the right postcentral gyrus was found in non-SA compared to SA and HCs, and positively correlated with reasoning problem-solving. Both SA and non-SA exhibited decreased dFC variability between the right dCa and the left MTG, and between the right dlPu and the right calcarine when compared to HCs. SVM classification achieved an accuracy of 75.24 % and AUC of 0.835 to differentiate SA from non-SA, while combining the abnormal dFC features between SA and non-SA. Conclusions Aberrant dFC variability of corticostriatal circuitry may serve as potential neuromarker for SA in BD-II, which might help to discriminate suicidal BD-II patients from non-suicidal patients and HCs.
Résumé L’utilisation de la technologie connectée en médecine augmente considérablement. L’adoption de ces outils dans la pratique clinique de la psychiatrie semble inévitable. Comment les utiliser de manière efficace ? Pour y répondre, cette courte revue focalise sa réflexion sur trois des apports majeurs de la psychiatrie connectée : 1/l’assistance et l’amélioration des soins actuels ; 2/le développement de nouveaux traitements ; 3/la production de connaissances scientifiques et médicales.
Sleep disturbances are a key feature of bipolar disorder (BD), and poor sleep has been linked to mood symptoms. Recent use of ecological momentary assessment (EMA) has allowed for nuanced exploration of the sleep-mood link; though, the scale and directionality of this relationship is still unclear. Using EMA, actigraphy, and self-reported sleep measures, this study examines the concurrent and predictive relationships between sleep and mood. Participants with BD (n = 56) wore actigraphy devices for up to 14 days and completed validated scales and daily EMA surveys about mood and sleep quality. Linear mixed models were used to examine overall and time-lagged relationships between sleep and mood variables. EMA mood ratings were correlated with validated rating scales for depression, mania, anxiety, and impulsivity. Poor self-reported sleep quality was associated with worse overall ratings of sadness and anger. Worse self-reported sleep quality was associated with greater sadness the following day. Higher daytime impulsivity was associated with worse sleep quality the following night. Exploratory analyses found relationships between worse and more variable mood (sadness, anger, and impulsivity) with worse and more variable sleep that evening (efficiency, WASO, and sleep onset time). The sample size was modest, fairly homogenous, and included mainly euthymic persons with BD. EMA-based assessments of mood and sleep are correlated with validated scale scores and provide novel insight into intra-individual variability. Further work on the complex two-way interactions between sleep and mood is needed to better understand how to improve outcomes in BD.
Background Actigraphy is increasingly used in sleep research and the clinical care of patients with sleep and circadian rhythm abnormalities. The following practice parameters update the previous practice parameters published in 2003 for the use of actigraphy in the study of sleep and circadian rhythms. Methods Based upon a systematic grading of evidence, members of the Standards of Practice Committee, including those with expertise in the use of actigraphy, developed these practice parameters as a guide to the appropriate use of actigraphy, both as a diagnostic tool in the evaluation of sleep disorders and as an outcome measure of treatment efficacy in clinical settings with appropriate patient populations. Recommendations Actigraphy provides an acceptably accurate estimate of sleep patterns in normal, healthy adult populations and inpatients suspected of certain sleep disorders. More specifically, actigraphy is indicated to assist in the evaluation of patients with advanced sleep phase syndrome (ASPS), delayed sleep phase syndrome (DSPS), and shift work disorder. Additionally, there is some evidence to support the use of actigraphy in the evaluation of patients suspected of jet lag disorder and non-24hr sleep/wake syndrome (including that associated with blindness). When polysomnography is not available, actigraphy is indicated to estimate total sleep time in patients with obstructive sleep apnea. In patients with insomnia and hypersomnia, there is evidence to support the use of actigraphy in the characterization of circadian rhythms and sleep patterns/disturbances. In assessing response to therapy, actigraphy has proven useful as an outcome measure in patients with circadian rhythm disorders and insomnia. In older adults (including older nursing home residents), in whom traditional sleep monitoring can be difficult, actigraphy is indicated for characterizing sleep and circadian patterns and to document treatment responses. Similarly, in normal infants and children, as well as special pediatric populations, actigraphy has proven useful for delineating sleep patterns and documenting treatment responses. Conclusions Recent research utilizing actigraphy in the assessment and management of sleep disorders has allowed the development of evidence-based recommendations for the use of actigraphy in the clinical setting. Additional research is warranted to further refine and broaden its clinical value.
Background: Although sleep apnea is common, it often goes undiagnosed in primary care encounters. Objective: To test the Berlin Questionnaire as a means of identifying patients with sleep apnea. Design: Survey followed by portable, unattended sleep studies in a subset of patients. Setting: Five primary care sites in Cleveland, Ohio. Patients: 744 adults (of 1008 surveyed [74%]), of whom 100 underwent sleep studies. Measurements: Survey items addressed the presence and frequency of snoring behavior, waketime sleepiness or fatigue, and history of obesity or hypertension. Patients with persistent and frequent symptoms in any two of these three domains were considered to be at high risk for sleep apnea. Portable sleep monitoring was conducted to measure the number of respiratory events per hour in bed (respiratory disturbance index [RDI]). Results: Questions about symptoms demonstrated internal consistency (Cronbach correlations, 0.86 to 0.92). Of the 744 respondents, 279 (37.5%) were in a high-risk group that was defined a priori. For the 100 patients who underwent sleep studies, risk grouping was useful in prediction of the RDI. For example, being in the high-risk group predicted an RDI greater than 5 with a sensitivity of 0.86, a specificity of 0.77, a positive predictive value of 0.89, and a likelihood ratio of 3.79. Conclusion: The Berlin Questionnaire provides a means of identifying patients who are likely to have sleep apnea.
Introduction: Emotional reactivity and sleep constitute key dimensions of bipolar disorder. Emotional reactivity referred to emotion response intensity and emotion response threshold. Higher emotion reactivity is described during both mood episodes and periods of remission in bipolar disorder. As well, sleep disturbances are described during both acute episodes and euthymic periods in bipolar disorder. Links between sleep and emotion regulation start to be studied in general population. Interactions between sleep and emotion systems can rely on shared neuronal structures, which involve limbic system. Future research on sleep and emotion regulation relationships is required in bipolar disorder. Methods: A systematic review of the scientific literature was conducted. Studies on emotional reactivity in bipolar disorder during periods of remission were presented. Sleep studies of bipolar disorder during inter-critical periods were discussed too. Researches on interactions between sleep and emotion regulation in general population were presented. Finally, therapeutic applications focusing on sleep and emotional regulation in bipolar disorder were described. Results: Patients with bipolar disorder display disturbances of sleep and emotion reactivity even during periods of remission. Indeed, bipolar patients display more intense and more labile emotions assessed by self-questionnaires, increase positive attribution to neutral stimuli corroborated by startle reflex, functional changing on imagery studies. Sleep disturbances during inter-critical periods refer to clinical poor sleep quality and to increase time in bed, more frequent nocturnal awaking, more variable sleep-wake patterns assessed by actigraphy and polysomnography. In general population some studies have shown the impact of sleep restriction on emotion dysregulation. F-MRI studies show that healthy participants present increase activation of some structures such as amygdala involved in emotion processing. Deregulation of these areas has already been noticed in previous studies of euthymic bipolar patients without sleep restriction procedure. Considering sleep and emotion processes enhance our understanding of medication action mechanisms. Lithium and sodium valproate reduce melatonin light sensitivity and increased the activity period. It can be postulated that these effects on circadian system can impact sleep regulation. Furthermore, an f-MRI study shows that mood stabilizers can reduce amygdala activation of bipolar patients compared to untreated bipolar patients during an emotional task. Emotion and sleep regulation can be targeted by specific psychotherapy. Interpersonal and social rhythm therapy, cognitive behavioral therapy of insomnia and mindfulness applied to bipolar disorder are presented and discussed. Discussion: Disturbances of sleep and emotional reactivity remain during periods of remission in bipolar disorder. Further research is required to better understand relationships between these two processes in bipolar disorder. Sleep and emotion dysgulations can be targeted by specific psychotherapy. More systematic assessment of sleep an emotional reactivity in remitted bipolar patients can lead to consider and treat this residual symptomatology that could reduce recurrences.
Sleep disturbance is common in bipolar disorder. Stimulus control and sleep restriction are powerful, clinically useful behavioral interventions for insomnia, typically delivered as part of cognitive-behavioral therapy for insomnia (CBT-I). Both involve short-term sleep deprivation. The potential for manic or hypomanic symptoms to emerge after sleep deprivation in bipolar disorder raises questions about the appropriateness of these methods for treating insomnia. In a series of patients with bipolar disorder who underwent behavioral treatment for insomnia, the authors found that regularizing bedtimes and rise times was often sufficient to bring about improvements in sleep. Two patients in a total group of 15 patients reported mild increases in hypomanic symptoms the week following instruction on stimulus control. Total sleep time did not change for these individuals. Two of five patients who underwent sleep restriction reported mild hypomania that was unrelated to weekly sleep duration. Sleep restriction and stimulus control appear to be safe and efficacious procedures for treating insomnia in patients with bipolar disorder. Practitioners should encourage regularity in bedtimes and rise times as a first step in treatment, and carefully monitor changes in mood and daytime sleepiness throughout the intervention.
Objective: A growing amount of data suggests that sleep dysfunction is frequently observed in bipolar disorder (BD) patients even when they do not fulfill the criteria for major mood episodes. Thus, we performed a case-control study assessing sleep status in a group of euthymic BD patients and a group of health controls. Methods: A total of 209 subjects (104 health controls and 105 BD patients) were enrolled in the study. The Pittsburgh Sleep Quality Index (PSQI) was used for sleep assessment. Inclusion criteria for the BD group were a diagnosis of BD, following DSM-IV-TR criteria, according to the MINI-plus structured clinical interview. Euthymia was established as a score lower than 7 both in the Hamilton Depression Rating Scale (HDRS) and in the Young Mania Rating Scale (YMRS). Health controls were also interviewed using the MINI-plus and included in this study if they were free of any current or past DSM-IV-TR axis I psychiatric disorder as well the actual use of psychopharmacological medications. Results: While 21.2 % of the control group displayed poor sleep quality according to the global PSQI-BR score, 82.9 % of the euthymic BD patients had poor sleep quality (p=0.000). PSQI sleep duration subcomponent showed comparable results in the two groups (p=0.535), even though BD patients had significant disruptions in sleep latency (p=0.000) and sleep efficiency (p=0.000) subcomponents. Conclusion: We were able to show that BD patients, even in euthymic phase, exhibit a significantly worse sleep quality as compared with health controls as assessed by PSQI total score and five of its seven subcomponents.
We review recent developments in the acute and long-term treatment of bipolar disorder and identify promising future routes to therapeutic innovation. Overall, advances in drug treatment remain quite modest. Antipsychotic drugs are effective in the acute treatment of mania; their efficacy in the treatment of depression is variable with the clearest evidence for quetiapine. Despite their widespread use, considerable uncertainty and controversy remains about the use of antidepressant drugs in the management of depressive episodes. Lithium has the strongest evidence for long-term relapse prevention; the evidence for anticonvulsants such as divalproex and lamotrigine is less robust and there is much uncertainty about the longer term benefits of antipsychotics. Substantial progress has been made in the development and assessment of adjunctive psychosocial interventions. Long-term maintenance and possibly acute stabilisation of depression can be enhanced by the combination of psychosocial treatments with drugs. The development of future treatments should consider both the neurobiological and psychosocial mechanisms underlying the disorder. We should continue to repurpose treatments and to recognise the role of serendipity. We should also investigate optimum combinations of pharmacological and psychotherapeutic treatments at different stages of the illness. Clarification of the mechanisms by which different treatments affect sleep and circadian rhythms and their relation with daily mood fluctuations is likely to help with the treatment selection for individual patients. To be economically viable, existing psychotherapy protocols need to be made briefer and more efficient for improved scalability and sustainability in widespread implementation.
Mood disorders are serious diseases that affect a large portion of the population. There have been many hypotheses put forth over the years to explain the development of major depression, bipolar disorder, and other mood disorders. These hypotheses include disruptions in monoamine transmission, hypothalamus-pituitary-adrenal axis function, immune function, neurogenesis, mitochondrial dysfunction, and neuropeptide signaling (to name a few). Nearly all people suffering from mood disorders have significant disruptions in circadian rhythms and the sleep/wake cycle. In fact, altered sleep patterns are one of the major diagnostic criteria for these disorders. Moreover, environmental disruptions to circadian rhythms, including shift work, travel across time zones, and irregular social schedules, tend to precipitate or exacerbate mood-related episodes. Recent studies have found that molecular clocks are found throughout the brain and body where they participate in the regulation of most physiological processes, including those thought to be involved in mood regulation. This review will summarize recent data that implicate the circadian system as a vital regulator of a variety of systems that are thought to play a role in the development of mood disorders.
Bipolar disorder is a multifactorial psychiatric disorder with developmental and progressive neurophysiological alterations. This disorder is typically characterized by cyclical and recurrent episodes of mania and depression but is heterogeneous in its clinical presentation and outcome. Although the DSM-IV-TR criteria identify several features that are of phenomenological relevance, these are of less utility for defining homogeneous subgroups, for analyses of correlations with biomarkers or for directing focused medication strategies. We provide a comprehensive review of existing evidence regarding to age at onset in bipolar disorder. Eight admixture studies demonstrate three homogeneous subgroups of patients with bipolar disorder identified according to age at onset (early, intermediate and late age at onset), with two cutoff points, at 21 and 34 years. It is suggested that the early-onset subgroup has specific clinical features and outcomes different from those of the other subgroups. Early-onset subgroup may be considered a more suitable clinical phenotype for the identification of susceptibility genes with recent data demonstrating associations with genetic variants specifically in this subgroup. The use of age at onset as a specifier may also facilitate the identification of other biological markers for use in brain imaging, circadian, inflammatory and cognitive research. A key challenge is posed by the use of age at onset in treatment decision algorithms, although further research is required to increase the evidence-base. We discuss three potential benefits of specifying age at onset, namely: focused medication strategies, the targeted prevention of specific comorbid conditions and decreasing the duration of untreated illness. We argue that age at onset should be included as a specifier for bipolar disorders.
Although the etiology of bipolar disorder remains uncertain, multiple studies examining neuroimaging, peripheral markers and genetics have provided important insights into the pathophysiologic processes underlying bipolar disorder. Neuroimaging studies have consistently demonstrated loss of gray matter, as well as altered activation of subcortical, anterior temporal and ventral prefrontal regions in response to emotional stimuli in bipolar disorder. Genetics studies have identified several potential candidate genes associated with increased risk for developing bipolar disorder that involve circadian rhythm, neuronal development and calcium metabolism. Notably, several groups have found decreased levels of neurotrophic factors and increased pro-inflammatory cytokines and oxidative stress markers. Together these findings provide the background for the identification of potential biomarkers for vulnerability, disease expression and to help understand the course of illness and treatment response. In other areas of medicine, validated biomarkers now inform clinical decision-making. Although the findings reviewed herein hold promise, further research involving large collaborative studies is needed to validate these potential biomarkers prior to employing them for clinical purposes. Therefore, in this positional paper from the ISBD-BIONET (biomarkers network from the International Society for Bipolar Disorders), we will discuss our view of biomarkers for these three areas: neuroimaging, peripheral measurements and genetics; and conclude the paper with our position for the next steps in the search for biomarkers for bipolar disorder.