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. Author manuscript; available in PMC: 2018 Mar 1.
Published in final edited form as: J Nerv Ment Dis. 2017 Mar;205(3):196–202. doi: 10.1097/NMD.0000000000000579

Do Sleep Disturbances Predict or Moderate the Response to Psychotherapy in Bipolar Disorder?

Louisa Sylvia 1,2, Stephanie Salcedo 3, Amy Peters 4, Pedro Vieira da Silva Magalhães 5, Ellen Frank 6, David Miklowitz 7, Michael W Otto 8, Michael Berk 9,10, Andrew A Nierenberg 1,2, Thilo Deckersbach 1,2
PMCID: PMC5325767  NIHMSID: NIHMS796973  PMID: 27660995

Abstract

This study examined whether sleep disturbance predicted or moderated responses to psychotherapy in participants who participated in STEP-BD, a national, multi-site study that examined the effectiveness of different treatment combinations for bipolar disorder. Participants received either a brief psychosocial intervention called collaborative care (CC; n=130), or intensive psychotherapy (IP; n=163), with study-based pharmacotherapy. Participants (N=243) were defined as current (past week) short sleepers (<6 hours/night), normal sleepers (6.5-8.5 hours/night), and long sleepers (≥9 hours/night), according to reported average nightly sleep duration the week before randomization. Sleep disturbances did not predict the likelihood of recovery nor time until recovery from a depressive episode. There was no difference in recovery rates between IP versus CC for normal sleepers, and medium effect sizes were observed for differences in short and long sleepers. In this study, sleep did not play a major role in predicting or moderating response to psychotherapy in bipolar disorder.

Keywords: sleep disturbance, insomnia, hypersomnia

Introduction

Bipolar disorder is a severe psychiatric illness characterized by episodes of mood elevation and depression. Individuals with this disorder have significant functional impairment, reduced quality of life, and a high risk of suicidality (Kilbourne et al., 2004; Novick, Swartz, & Frank, 2010). Pharmacotherapy is considered the foundation of treatment for this chronic disorder (Geddes & Miklowitz, 2013); however, use of medications alone often fails to bring patients to full and sustained remission (Frank, Swartz, & Kupfer, 2000). The limited efficacy of pharmacotherapy alone highlights the need for adjunctive psychosocial interventions (Lauder, Berk, Castle, Dodd, & Berk, 2010).

When psychotherapy is paired with pharmacotherapy, participants experience reduced rates of relapse, improved medication adherence, reduced residual mood symptoms, and improved overall psychosocial functioning (Miklowitz, George, Richards, Simoneau, & Suddath, 2003; Miklowitz, 2008; Otto & Miklowitz, 2004). However, there is considerable variability in response rates in clinical trials of psychotherapy, pointing to the need to identify moderators of treatment response.

Sleep disturbance is a common prodromal feature of bipolar disorder and a precipitant of mood episodes (Jackson, Cavanagh, & Scott, 2003). Sleep disturbance often precedes the onset of both manic and depression symptoms, and it worsens after episode onset (Bauer et al., 2006; Colombo, Benedetti, Barbini, Campori, & Smeraldi, 1999; Harvey, Schmidt, Scarnà, Semler, & Goodwin, 2005; Jackson et al., 2003). Moreover, during mood episodes, short sleep duration, which is indicative of insomnia, is associated with more severe symptoms, and both short and long sleep duration are associated with poorer functioning and quality of life. Sleep disturbance is also present during periods of relative remission (Harvey et al., 2005). Psychotherapy (e.g. cognitive behavioral therapy, interpersonal psychotherapy) is associated with decreased rapid eye movement (REM) density in individuals with unipolar depression (Buysse, Frank, Lowe, Cherry, & Kupfer, 1997; Nofzinger et al., 1994). During remission, instability in sleep and biological rhythms are correlated with levels of the disability in bipolar disorder (Giglio, Magalhaes, Kapczinski, Walz, & Kapczinski, 2010).

To better understand the role of sleep in treatment outcomes for bipolar disorder, the current study investigated whether sleep disturbance (defined as shorter or longer sleepers) mediates or moderates the likelihood that patients recover from depression in response to intensive psychotherapy or collaborative care in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). STEP-BD, a National Institute of Mental Health-sponsored study of the effectiveness of treatments for bipolar disorder, found that adjunctive, intensive psychotherapy, as compared to brief psychoeducation (collaborative care), was more beneficial in achieving and reducing time to recovery from a depressive episode (Miklowitz et al., 2007). We hypothesized that 1) STEP-BD participants who are normal sleepers will have higher recovery rates from mood episodes and will recover in less time compared to short or long sleepers; and 2) individuals with sleep disturbance (i.e., short or long sleepers) would be more likely to achieve recovery with intensive psychotherapy than with collaborative care.

Method

Study Design

STEP-BD was a national, multi-site study that examined the effectiveness of different treatment combinations for symptoms of bipolar disorder, including various pharmacotherapy and psychosocial interventions. STEP-BD was the largest longitudinal treatment outcome study in bipolar disorder, enrolling 4,361 subjects across 21 sites (Sachs et al., 2003). Ethical approval was obtained by each site's respective human research committee (Sachs et al, 2003). Individuals in STEP-BD, who were currently in a depressive episode, were offered to participate a randomized control trial comparing adjunctive intensive psychotherapy to a control group in 15 clinics (Miklowitz et al., 2007). In this trial, participants, after giving additional written informed consent, were randomly assigned to either 6 weeks of treatment (up to 3 sessions) with collaborative care (CC; N=130) or 9 months of weekly treatment (up to 30 sessions) with intensive psychotherapy (cognitive behavioral therapy [CBT; N=75], family focused therapy [FFT; N=26], or interpersonal social rhythm therapy [IPSRT; N=62]) (Miklowitz et al., 2007).

Collaborative care was a brief intervention that focused on psychoeducation about bipolar disorder and employed some of the most common psychosocial strategies shown to be beneficial for bipolar disorder (Miklowitz et al., 2007). CBT emphasized challenging negative thoughts and dysfunctional beliefs, cognitive restructuring, and problem solving training (Lam, Hayward, Watkins, Wright, & Sham, 2005). FFT focused on educating the participants' family members about bipolar disorder and the family unit's impact on the illness course. It also emphasized improving communication and problem solving in the home environment (Miklowitz et al., 2000). IPSRT stressed the importance of social rhythm stability for prevention of mood disruptions by developing plans for mood and social rhythm stability, and learning strategies to manage interpersonal conflicts such as grief, relationship difficulties, or role disputes (Frank et al., 2000; Frank et al., 2005).

Participants

Participants (n = 293) were eligible for the study if they met DSM-IV criteria for bipolar I or II disorders and a current major depressive episode, and were currently being treated or willing to be treated with a mood stabilizer. If participants were currently undergoing psychotherapy, they could enroll in the study if they discontinued non-study related psychotherapy or reduce the sessions to one or fewer per month. Participants were excluded if they needed treatment for substance/alcohol abuse or dependence, were pregnant, had a history of nonresponse or intolerance to the antidepressant study drugs, or required initial use or changes to their antipsychotic medications (For a more detailed summary of inclusion/exclusion criteria, see Miklowitz et al., 2007). Depending on what arm of the main study they were in, participants were randomly assigned to double-blind pharmacotherapy with mood stabilizers (lithium, valproate, or carbamazepine), placebo plus adjunctive antidepressants (buproprion or fluoxetine), or a combination of mood stabilizer and antipsychotic medications according to patient-physician agreement and guidelines outlined in STEP-BD for best practice evidence-based pharmacotherapy (Sachs et al., 2003) Included in these analyses is a subset (n = 243; 83%) of randomized participants (n = 293), who provided information both at study entry and throughout study participation regarding their minimum and maximum sleep duration from the past week on the Clinical Monitoring Form (CMF; Sachs et al., 2003). Trained clinicians, specializing in the assessment of bipolar symptoms, would use weekly milestones, such as work, seeing friends and family, or the day of the week to help them remember the most and least amount of sleep that they had over the past week.

Sleep Functioning Measures

Sleep duration was operationally defined as the average number of hours of sleep in the past week. This average was calculated using the minimum and maximum sleep duration values from the prior week reported on the CMF (Gruber et al., 2009). Participants were divided into three groups based on their average nightly sleep duration the week prior to baseline: short sleepers, normal sleepers, and long sleepers. Short sleepers were defined as those with an average of < 6 hours of sleep per night, normal sleepers as those with an average 6.5 - 8.5 hours per night, and long sleepers as those with ≥ 9 hours of sleep per night. These cutoffs have been validated in other studies based on their distinct clinical correlates (Edinger et al., 2000; Gruber et al., 2009; Kaneita et al., 2007).

Assessment of Treatment Outcomes

Diagnoses and Psychiatric History

Diagnoses of bipolar disorder relied on the consensus of two trained clinicians: a clinical specialist (a psychologist or social worker) who administered the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) and a psychiatrist who administered a standardized affective disorder evaluation (ADE; Sachs, 1990). Diagnosis of anxiety disorders relied on the MINI, and psychiatric history (e.g., number of previous bipolar episodes) were captured on the ADE.

Mood Symptoms

During each treatment visit, participants' mood symptoms were assessed using the CMF. Participants reporting ≤ 2 moderate mood symptoms (depression and mania/hypomania) for ≥ 8 consecutive weeks were given a clinical status designation of “recovered.” Participants were considered “not recovered” if they reported ≥ 3 depressive or manic/hypomanic symptoms (Sachs et al., 2003). Interrater reliability coefficients (according to gold standard ratings for depression and mania ratings in the CMF) ranged from 0.83 to 0.99 (intraclass correlations).

Overall Functioning

The Global Assessment of Functioning (GAF) scale is scored on a numeric scale (range: 0=inadequate information to 100=superior functioning) and is widely used to rate the social, occupational, and psychological functioning of adults (Hall, 1995). The modified Global Assessment of Functioning (GAF) scale has more detailed criteria and a more structured scoring system than the original GAF. Scores were based on the judgment of trained study research staff after completing their clinical interview.

Data Analyses

To evaluate whether sleep group (short, normal, or long sleepers) predicted recovery rates and time until recovery, we conducted logistic regression and Cox proportional hazard (survival) models. Participants were included until their final assessment point, which was a maximum of 365 days (M=166.48, SD=102.58; Sachs et al., 2003). To evaluate the ability of sleep group to predict recovery status after adjusting for treatment effects, treatment condition (intensive psychotherapy or collaborative care) was included in the model as an independent variable, and participants who were normal sleepers were compared to short and long sleepers in terms of recovery status. Demographic, mood, and medication variables that differed across sleep groups were entered as predictors into the regression models.

To examine whether sleep disturbance moderated treatment outcome, we added an interaction term with treatment condition to our models predicting recovery rates and time until recovery. We followed Kraemer and Kupfer's (2006) recommendations for examining exploratory moderators of treatment outcome in randomized controlled trials, using effect sizes. We examined the magnitude of the treatment effects at each proposed moderator level (Kraemer & Kupfer, 2006) and 95% confidence intervals, as indicated by the Newcombe-Wilson score method without continuity correction (Newcombe, 1998).

To illustrate the magnitude of effect sizes, we used the Number Needed to Treat (NNT) effect size, which is most robust for examining the clinical significance of binary outcomes (Altman & Andersen, 1999; Cook & Sackett, 1995; Cook & Sackett, 1995). NNT is the number of patients one would expect to treat with the investigational treatment, or intensive psychotherapy, in order to have one more patient respond to the treatment than if the same number was treated with the control treatment (Deckersbach et al., 2014). An NNT value of 2 is considered large, 3.5 is medium, and effect sizes greater than 9 are small (Kraemer & Kupfer, 2006). We compared short sleepers, normal sleepers, and long sleepers on the magnitude of the between group (collaborative care vs. psychotherapy) effect size. NNT for “recovered” status was examined separately for participants in each sleep group and treatment condition according to the average number of hours they reported sleeping per night at their baseline visit.

Additionally, for each sleep group, a 3 × 2 × 2 mixed model analysis of variance (MANOVA) was used to examine the change in sleep duration within subjects and across sleep groups and treatment arms, with sleep group (short sleepers, normal sleepers, and long sleepers) and treatment group (intensive psychotherapy, collaborative care) as the between subjects factors and study visit (pre-, post- intervention) as the within subjects factor.

Results

Study Sample

Table 1 shows the demographic and clinical characteristics of the 243 depressed bipolar participants. The average age was 40.32 (SD = 11.47), 60% (n = 146) were female, and 61% (n=136) had bipolar I disorder. There were no significant differences in demographic and clinical characteristics between our sample and the 50 participants with no CMF sleep data (data not shown).

Table 1. Demographic and Illness Characteristics of 243 Depressed Bipolar Patients By Sleep Group.

Short Sleepers (SS) Normal Sleepers (NS) Long Sleepers (LS) Overall
M ± SD M ± SD M ± SD M ± SD
Age 41.06 ± 9.98 40.43 ± 12.98 39.71 ± 11.27 40.32 ± 11.47
Depressive Severity 6.68 ± 2.20 6.31 ± 2.36 6.28 ± 1.85 6.40 ± 2.12
Mania Severitya 1.35 ± 1.04 1.21 ± 1.14 0.96 ± 0.96 1.15 ± 1.05
Number of Therapy Sessions 8.61± 9.79 9.23 ± 11.06 8.47± 9.91 8.75 ± 10.22
Baseline GAF 57.84 ± 8.70 54.93 ± 9.51 57.95 ± 9.20 56.96 ± 9.23
N (%) N (%) N (%) N (%)
Female Sex 44 (67) 42 (55) 60 (61) 146 (60)
Education >1 year college 52 (80) 57 (78) 75 (82) 184 (80)
Married 23 (34) 24 (32) 29 (30) 76 (32)
Diagnosis
 Bipolar I 33 (52) 42 (58) 61 (67) 136 (61)
 Bipolar II 30 (48) 30 (42) 30 (33) 90 (40)
Lifetime Anxiety Disorder 40 (63) 45 (63) 62 (68) 147 (65)
Age at illness onset
 < 15 17 (29) 17 (25) 25 (30) 59 (28)
 > 15 42 (71) 52 (75) 58 (70) 152 (72)
Number Lifetime Manic Episodes
 1-9 17 (30) 25 (38) 30 (37) 72 (36)
 10-20 15 (27) 6 (9) 11 (14) 32 (16)
 20+ 24 (43) 34 (52) 40 (49) 98 (49)
Number Lifetime Depressive Episodes
 1-9 23 (41) 23 (35) 21 (26) 67 (33)
 10-20 6 (11) 8 (12) 12 (15) 26 (13)
 20+ 27 (48) 34 (52) 48 (59) 109 (54)
# Anxiety Disorders
 0 23 (36) 28 (38) 29 (32) 80 (35)
 1 20 (32) 18 (25) 26 (29) 64 (29)
 2 11 (17) 16 (22) 16 (18) 43 (19)
 3 6 (10) 7 (10) 10 (11) 23 (10)
 4 1 (2) 3 (4) 9 (10) 13 (6)
 5 2 (3) 1 (1) 1 (1) 4 (2)
# Comorbid Conditions
 0 14 (21) 18 (23) 21 (21) 53 (22)
 1 19 (28) 11 (14) 18 (18) 48 (20)
 2 14 (21) 23 (30) 19 (19) 56 (23)
 3 20 (30) 25 (33) 41 (41) 86 (35)
Baseline Medications
 Antidepressants 31 (46) 30 (41) 40 (41) 101 (42)
 Atypical Antipsychoticsa,b 12 (18) 25 (34) 31 (32) 68 (29)
 Anxiolytics 19 (28) 19 (26) 27 (28) 65 (27)
 Anticonvulsantsc 37 (55) 30 (41) 56 (58) 123 (52)
 Lithium 21 (31) 27 (37) 30 (40) 78 (33)
 Other Mood Stabilizersb,c 25 (37) 13 (18) 32 (33) 70 (30)
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Abbreviations: GAF (Global Assessment of Functioning), Depressive Severity (refers to summary score of depression symptoms [excluding sleep variables] from the Clinical Monitoring Form recorded within 1 week of the date of randomization to treatment), Mania Severity (refers to summary score of mania symptoms [excluding sleep variables] from the Clinical Monitoring Form recorded within 1 week of the date of randomization to treatment)

Notes: Where data points were missing, percentages are calculated out of total number of available cases. Diagnoses were determined using the Affective Disorders Evaluation.

a

Difference between short sleepers and long sleepers (p < .05)

b

Difference between short sleepers and normal sleepers (p < .05)

c

Difference between normal sleepers and long sleepers (p < .05)

Psychosocial Treatment Outcome

Participants demonstrated significantly higher year end recovery rates if they were randomly assigned to intensive psychotherapy than collaborative care, χ2(1, n = 243) = 4.00, p < .05. These findings are consistent with the results found in the full sample (n = 293; Miklowitz et al., 2007).

Clinical and Demographic Variables by Sleep Group

Out of the 243 participants with baseline sleep data available, 67 were identified as short sleepers, 99 as long sleepers, and 77 as normal sleepers. The subgroups did not differ in sex, education, marital status, bipolar subtype, depressive symptom severity, or having a lifetime anxiety disorder (all p's > .18; see Table 1). There were differences among the three groups in baseline manic symptom severity, F(2, 242) = 2.95, p = .05, and mood stabilizer usage, χ2(2, n = 238) = 7.61, p < .05. There were statistical trends towards differences in global functioning (GAF) scores, F(2, 235) = 2.69, p = .07, atypical antipsychotic usage, χ2(2, n = 238) = 4.00, p = .07, and anticonvulsant usage, χ2(2, n = 238) = 5.43, p = .06 (Table 1).

Pairwise comparisons showed that short sleepers had greater mania severity than long sleepers, t(240, n = 236) = 2.35, p < .05. Compared to normal sleepers, short sleepers were significantly more likely to be taking mood stabilizers χ2 (1, n = 141) = 6.96, p < .05, but less likely to be taking atypical antipsychotics χ2(1, n = 141) = 4.58, p < .05. Normal sleepers were significantly less likely than long sleepers to be taking mood stabilizers χ2 (1, n = 171) = 5.15, p < .05, and anticonvulsants χ2 (1, n = 171) = 4.96, p < .05. Short sleepers were also significantly less likely to take atypical antipsychotics than long sleepers χ2 (1, n = 164) = 4.04, p < .05. Note the varying degrees of freedom are due to missing values. Binary logistical regressions showed that baseline mania severity did not significantly predict recovery rates in any of the sleep groups (all p's > .12).

Does Sleep Type Predict Recovery or Time to Recovery?

Logistic and Cox regressions were used to examine predictors of recovery and time to recovery. Treatment group, sleep group, baseline GAF, and medication use (anticonvulsants, atypical antipsychotics, other mood stabilizers) were entered as additional predictors in the models. Results of the modeling sequence are shown in Table 2. Sleep group (short, long, normal) did not predict likelihood of recovery (p's > .41; see Table 2) nor time until recovery (p's > .57). Higher baseline GAF scores and increased mood stabilizer use (not Lithium) significantly predicted recovery (p's < .05) but not time until recovery (p's > .16; see Table 2).

Table 2. Logistic Regression and Cox Regression Analyses Evaluating sleep group as predictors of Likelihood of Recovery and Time until Recovery.

Predictor b Wald OR (95% CI) p ΔR2*
Logistic regression: predicting recovery .097
 Treatment group** -0.49 3.06 0.62 (0.36-1.06) .08
 Short sleepers*** -0.24 0.50 0.79 (0.41-1.52) .48
 Long sleepers*** -0.28 0.68 0.76 (0.39-1.47) .41
 Baseline GAF 0.04 0.02 1.04 (1.01-1.07) .01
 Anticonvulsants 0.05 0.02 1.05 (0.52-2.11) .89
 Atypical Antipsychotics -0.27 0.74 0.76 (0.41-1.41) .39
 Other Mood Stabilizers 0.77 3.92 2.16 (1.01-4.63) .05
Cox regression: predicting time until recovery -.076
 Treatment group** -0.22 1.59 0.80 (0.57-1.13) .21
 Short sleepers*** -0.10 0.25 0.90 (0.60-1.36) .62
 Long sleepers*** -0.12 0.33 0.88 (0.58-1.35) .57
 Baseline GAF 0.01 1.18 1.01 (0.99-1.03) .28
 Anticonvulsants 0.12 0.33 1.13 (0.74-1.73) .57
 Atypical Antipsychotics 0.04 0.04 1.04 (0.71-1.52) .85
 Other Mood Stabilizers 0.36 2.02 1.43 (0.87-2.36) .16
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Abbreviations: OR (Odds ratio); CI (Confidence interval)

Notes:

*

For logistic regressions, R2 represents Nagelkerke R2, an estimate of the increment in variance in the probability of recovery accounted for by the predictors tested. For Cox regressions, R2 represents Cox–Snell R2, an estimate of the relative association between survival and the predictors tested.

**

Treatment group: intensive psychotherapy (1) versus collaborative care (0).

***

Sleep group: dummy coded with the normal sleepers group coded as the reference group, so only coefficients relative to the reference group are shown.

Moderator Analyses

The treatment interaction terms for sleep group did not reach significance for either model, so sleep group did not moderate recovery or time to recovery (p's > .20). Sixty-three percent (n = 25) of short sleepers recovered with intensive psychotherapy, whereas only 41% (n = 11) recovered with collaborative care. This treatment response rate difference resulted in a medium effect size (NNT = 4.55; see Table 3). Thus, we would need to treat 4.55 short sleepers with IP rather than collaborative care to have an additional short sleeper recovering with IP. A similar pattern of effects was observed for long sleepers. Seventy percent (n = 35) of long sleepers recovered with psychotherapy, whereas only 51% (n = 25) recovered with collaborative care. This treatment difference resulted in a medium effect size (NNT = 5.26; see Table 2). That is, we would need to treat 5.26 long sleepers with IP compared to collaborative care to have an additional long sleeper recover from IP. Finally, 57% of normal sleepers (n = 26) recovered with psychotherapy, and 58% (n = 18) recovered with collaborative care. These recovery rates corresponded to a very small effect size (NNT = 100; see Table 3).

Table 3. Moderator Effects of Sleep Group on Recovery Rates with Collaborative Care and Psychotherapy for Bipolar Depression.

Psychotherapy Collaborative Care 95% Confidence Intervals
Sleep Group N Number Recovered % Recovered N Number Recovered % Recovered NNT Lower Higher
Short Sleepers 40 25 63% 27 11 41% 4.55 -42 2
Normal Sleepers 46 26 57% 31 18 58% 100 -4 5
Long Sleepers 50 35 70% 49 25 51% 5.26 -448 3
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Abbreviations: NNT, Number needed to treat; CI, Confidence interval.

Changes in Sleep with Treatment

Table 4 shows the changes in average sleep duration post-treatment for each sleep group. The 3 × 2 × 2 MANOVA assessing sleep change as function of baseline sleep group and treatment indicated a significant main effect of sleep group, F(2, 237) = 192.7, p < .001, and study visit, F(1, 237) = 8.10, p < .01. There was no main effect of treatment group, F(1, 237) = 0.69, p = .41, indicating that sleep duration did not differ for intensive psychotherapy and collaborative care. There was a significant study visit by sleep group interaction, F(2, 237) = 87.92, p < .001, indicating that change in sleep duration varied according to sleep group over the treatment phase. Sleep duration pre- to post-intervention increased for short sleepers (MPre- = 4.94, MPost- = 6.72), decreased for long sleepers (MPre- = 10.84, MPost- = 8.24), and did not change for normal sleepers. There was no study visit by treatment group interaction, indicating that change in sleep duration over treatment did not vary according to treatment condition. There was also no study visit by treatment group by sleep group interaction, or sleep group by treatment group interaction.

Table 4. Average Sleep Duration for Treatment Groups.

Psychotherapy Collaborative Care All
Sleep Group Pre-M (SD) Post-M (SD) Pre-M (SD) Post-M (SD) Pre-M (SD) Post-M (SD)
Short Sleepers 4.81a (1.03) 6.91b (2.03) 5.14a (0.68) 6.43b (2.13) 4.94a (0.92) 6.72b (2.07)
Normal Sleepers 7.54a (0.66) 7.17a (1.60) 7.71a (0.67) 7.57a (1.40) 7.61a (0.66) 7.33a (1.53)
Long Sleepers 10.91a (1.85) 7.99b (1.55) 10.77a (1.58) 8.5b (2.22) 10.84a (1.72) 8.24b (1.92)
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Abbreviations: Pre- (Baseline visit), Post- (Post-intervention)

Note: Values with differing subscripts are significantly different at the p < .05 level.

Discussion

The present study investigated whether sleep disturbance serves as a predictor and/or a moderator of psychotherapy response in depressed individuals with bipolar disorder. Sleep neither predicted the likelihood of recovery nor the time to recovery in our analyses. Contrary to our hypothesis, receiving intensive psychotherapy as opposed to collaborative care did not offer a major advantage in terms of recovery rates. This is somewhat surprising, as individuals with bipolar disorder who are poor sleepers may experience more stressors in their lives or have less ability to manage the stressors, which would be addressed in intensive psychotherapy, but not collaborative care. This hypothesis is consistent with prior studies indicating that stressful life events can have a negative impact on sleep quality (Bernert, Merrill, Braithwaite, Van Orden, & Joiner, 2007; Frank et al., 2000; Haynes, McQuaid, Ancoli-Israel, & Martin, 2006). Noteworthy shortcomings of the present study included that sleep duration was assessed by self-report which is vulnerable to recall bias, and we the study did not include objective measures of sleep quantity or quality such as polysomnography or actigraphy (Fernandez-Mendoza et al., 2011; Mercer, Bootzin, & Lack, 2002). Therefore, it is not possible to formally diagnose individuals with insomnia or hypersomnia. Future research should utilize objective measures or daily sleep diaries to provide more data on weekly sleep patterns. Randomization to treatment group was not stratified according to sleep-type, possibly confounding our finding of sleep on recovery. Of note, this study also does not examine the role of sleep on mania or hypomania on treatment response as participants were only enrolled if depressed at baseline. Further, due to sample size restrictions, we did not investigate the differential effects of the type of psychotherapy, which consisted of three treatments. Therefore, it is possible that the extent to which sleep was emphasized differed depending on the treatment modality received. Future research should more closely examine whether sleep patterns influence response to psychotherapy differentially depending on type of services. Lastly, the measure of sleep only covered the prior past week, and it would be useful to know if habitual long-term sleep patterns had a similar relationship. Furthermore, it is possible that sleep variability, rather than sleep duration alone may have a greater impact on treatment response. With these caveats in mind, in summary, our findings indicate that sleep during the past week does not seem to play a major role in predicting or moderating response to psychotherapy in bipolar disorder, suggesting that examining current sleep may not be a necessary factor that clinicians need to consider when determining the most appropriate type of psychosocial intervention for their patients.

Acknowledgments

Grant support: STEP-BD was funded in part by contract N01MH80001 from the National Institute of Mental Health (Gary Sachs). Support for the development of the psychosocial treatments was provided by grants MH29618 (Ellen Frank), MH43931 (David Miklowitz), and MH55101 (David Miklowitz) from the National Institute of Mental Health and by the National Alliance for Research on Schizophrenia and Depression (David Miklowitz).

Source of Funding: Louisa Sylvia was employed by Massachusetts General Hospital, served as a Consultant for Bracket Global and Clintara, received research support from NIMH, is a former stockholder in Concordant Rater Systems, and has received support from New Harbinger Publishers. Amy Peters has received research support from the National Institute of Mental Health (NIMH). Ellen Frank has served as a consultant for Servier International, and has received other financial or material support from Guilford Press and the American Psychological Association Press. David J. Miklowitz has received research support or honoraria from NIMH, Brain and Behavior Research Foundation, Danny Alberts Foundation, and Attias Family Foundation; He has received other financial or material support from Guilford Press and John Wiley and Sons. Michael Otto has served as a consultant for MicroTransponder, Inc., receives research support from NIMH, and royalties from Oxford University Press and Routledge. Michael Berk is an employee of Barwon Health and Deakin University; he has received research support from NIH, NHMRC, CRC, Rotary, Beyond Blue, Stanley Medical Research Institute and the Simons Foundation; he has received honoraria from Lundbeck, Astrazeneca, Servier, Lilly, Janssen, Pfizer, and Merck; he has served as a speaker or on the advisory board for Astrazeneca, Lundbeck, Lilly, and Janssen; and he has received financial or material support from Allen & Unwin and Cambridge University Press. Andrew A. Nierenberg has served as a consultant to Appliance Computing Inc. (Mindsite), Brain Cells, Inc., Brandeis University, Bristol Myers Squibb, Clintara, Dianippon Sumitomo (Now Sunovion), Eli Lilly and Company, EpiQ, Forest, Novartis, PamLabs, PGx Health, Shire, Schering-Plough, Sunovion, Takeda Pharmaceuticals, Teva, and Targacept. He has consulted through the MGH Clinical Trials Network and Institute (CTNI) to Astra Zeneca, Brain Cells, Inc, Dianippon Sumitomo/Sepracor, Johnson and Johnson, Labopharm, Merck, Methylation Science, Novartis, PGx Health, Shire, Schering-Plough, Targacept, and Takeda/Lundbeck Pharmaceuticals. Andrew Nierenberg received honoraria or travel expenses including CME activities from APSARD, Belvoir Publishing, Boston Center for the Arts, University of Texas Southwestern Dallas, Hillside Hospital, American Drug Utilization Review, American Society for Clinical Psychopharmacology, Bayamon Region Psychiatric Society, San Juan, PR, Baystate Medical Center, Canadian Psychiatric Association, Columbia University, Douglas Hospital/McGill University, IMEDEX, International Society for Bipolar Disorders, Israel Society for Biological Psychiatry, John Hopkins University, MJ Consulting, New York State, Massachusetts Association of College Counselors, Medscape, MBL Publishing, Physicians Postgraduate Press, Ryan Licht Sang Foundation, Slack Publishing, SUNY Buffalo, University of Florida, University of Miami, University of Wisconsin, University of Pisa, and SciMed. Andrew Nierenberg is a presenter for the Massachusetts General Hospital Psychiatry Academy (MGHPA). The education programs conducted by the MGHPA were supported through Independent Medical Education (IME) grants from the following pharmaceutical companies in 2008: Astra Zeneca, Eli Lilly, and Janssen Pharmaceuticals; in 2009 Astra Zeneca, Eli Lilly, and Bristol-Myers Squibb. No speaker bureaus or boards since 2003. Andrew Nierenberg owns stock options in Appliance Computing, Inc. and Brain Cells, Inc. Additional income is possible from Infomedic.com depending on overall revenues of the company but no revenue has been received to date. Through MGH, Andrew Nierenberg is named for copyrights to the Clinical Positive Affect Scale and the MGH Structured Clinical Interview for the Montgomery Asberg Depression Scale exclusively licensed to the MGH Clinical Trials Network and Institute (CTNI). He has received grant/research support from AHRQ, Cephalon, Forest, Mylin, NIMH, PamLabs, Pfizer Pharmaceuticals, Takeda, and Shire. In the next 2 years, it is possible that he will receive grants from Dey Pharmaceuticals, Sunovion, and Targacept. Thilo Deckersbach has been funded by NIMH, NARSAD, TSA, OCF and Tufts University. He has received honoraria, consultation fees and/or royalties from the MGH Psychiatry Academy, BrainCells Inc., Systems Research and Applications Corporation, Boston University, the Catalan.

Footnotes

Conflicts of Interest: Stephanie Salcedo reports no relevant conflicts of interest. Pedro Vieira da Silva Magalhães reports no relevant conflicts of interest.

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