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Light exposure at night and sleep quality in bipolar disorder: The APPLE cohort study
Journal of Affective Disorders 257 (2019) 314–320
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Research paper
Light exposure at night and sleep quality in bipolar disorder: The APPLE cohort study
T
Yuichi Esakia,b, , Tsuyoshi Kitajimab, Kenji Obayashic, Keigo Saekic, Kiyoshi Fujitaa,d, Nakao Iwatab ⁎
a
Department of Psychiatry, Okehazama Hospital, Aichi, Japan Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan c Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan d The Neuroscience Research Center, Aichi, Japan b
ARTICLE INFO
ABSTRACT
Keywords: Bipolar disorder Sleep Light at night Actigraphy Circadian rhythm
Background: Sleep disturbance in bipolar disorder (BD) is common and is associated with a risk for mood episode recurrence. Thus, it is important to identify factors that are related to sleep disturbance in BD. This crosssectional study investigated the association between exposure to light at night (LAN) and sleep parameters in patients with BD. Methods: The sleep parameters of 175 outpatients with BD were recorded using actigraphy at their homes for seven consecutive nights and were evaluated using the Insomnia Severity Index (ISI). The average LAN intensity in the bedroom during bedtime and rising time was measured using a portable photometer, and the participants were divided into two groups: “Light” (≥5 lx) and “Dark” (<5 lx). The association between LAN and sleep parameters was tested with multivariable analysis by adjusting for potential confounder such as age, gender, current smoker, mood state, day length, daytime light exposure, and sedative medications. Results: After adjusting for potential confounder, the actigraphy sleep parameters showed significantly lower sleep efficiency (mean, 80.1%vs. 83.4%; p = 0.01), longer log-transformed sleep onset latency (2.9 vs. 2.6 min; p = 0.01), and greater wake after sleep onset (51.4 vs. 41.6 min; p = 0.02) in the Light group than in the Dark group. Whereas, there were no significant differences in the ISI scores between the groups. Limitations: This was a cross-sectional study; therefore, the results do not necessarily imply that LAN causes sleep disturbance. Conclusions: Reducing LAN exposure may contribute to improved sleep quality in patients with BD.
1. Introduction Bipolar disorder (BD) is a severe, recurrent mental illness that affects 1%–4% of the population (Merikangas et al., 2007) and is characterized by mood episodes that can involve mania, hypomania, and often depression. BD also presents with sleep disturbance (Dallaspezia and Benedetti, 2015; Ng et al., 2015; Plante and Winkelman, 2008). A reduced need for sleep is commonly observed during manic or hypomanic episodes, whereas insomnia or hypersomnia frequently occur during depressive episodes (Harvey, 2008). Even in the euthymic period, patients with BD experience more sleep problems than healthy controls (Geoffroy et al., 2015; Ng et al., 2015). Residual sleep disturbance is associated with a risk for mood episode recurrence (Cretu et al., 2016; Gershon et al., 2017; Sylvia et al., 2012). Furthermore,
⁎
sleep loss has been shown to be associated with suicidal ideation in patients with BD with a suicide attempt history (Stange et al., 2016). Therefore, it is important to identify factors that are related to sleep disturbance in patients with BD. Light exposure is closely related to BD pathophysiology. It has been reported that an increase in the duration of sunshine in spring may have a significant effect on the onset of BD (Bauer et al., 2017). Bright light therapy can be effective in patients with BD who are experiencing current depressive episodes (Sit et al., 2018; Tseng et al., 2016; Zhou et al., 2018), but injudicious light therapy can induce mixed states (Sit et al., 2007). In contrast, dark therapy, which restricts light exposure at night, improves acute manic symptoms (Barbini et al., 2005; Henriksen et al., 2016). Midnight light exposure strongly suppressed endogenous melatonin concentration in patients with BD (Hallam et al.,
Corresponding author at: Department of Psychiatry, Fujita Health University School of Medicine, Aichi, 4701192, Japan. E-mail address: [email protected] (Y. Esaki).
https://doi.org/10.1016/j.jad.2019.07.031 Received 17 January 2019; Received in revised form 9 June 2019; Accepted 4 July 2019 Available online 05 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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2006), and blocking of blue light in the evening with the use of amber lenses improved sleep initiation in patients with BD (Phelps, 2008). Therefore, light is an important factor that affects the condition of BD. Given the prevalence of artificial lighting, exposure to light at night (LAN) has become a part of modern daily life (Navara and Nelson, 2007). LAN exposure is linked to negative health effects, including cancer, metabolic syndrome, mood disorders, and sleep disorders (Cho et al., 2015). One of the most obvious negative effects of LAN is sleep disturbance. An epidemiological study demonstrated that a significant association exists between LAN exposure and sleep disorder in the elderly (Obayashi et al., 2014). Another experimental study reported that exposure to artificial LAN during sleep even at a very low intensity (up to 10 lx) caused an increase in awakening and shallow sleep in healthy young men (Cho et al., 2016). Therefore, it is likely that LAN exposure may cause sleep disturbance in patients with BD. However, to the best of our knowledge, no study has investigated the association between LAN and sleep quality in patients with BD. The aim of this study was to investigate the associations between LAN exposure and sleep parameters in patients with BD. We hypothesized that LAN exposure is associated with poor sleep quality.
photometry measurement. Consequently, data for 175 patients were analyzed. The study was approved by the Ethics Committee of Okehazama Hospital. Written informed consent was obtained from all the participating patients, and the study was registered at UMIN-CTR (identifier: UMIN000028239).
2. Materials and methods
2.3. LAN exposure assessment
2.1. Clinical sample
The photometer recorded LAN exposure in the bedroom at 1-min intervals. The participants were asked to position the light sensor of the photometer near their head and at eye level and to start and end the recording at bedtime and rising time, respectively. We calculated the average LAN as the average night light intensity over the entire period from bedtime to rising time. A previous experimental study reported that a LAN exposure of ≥5 lx affected the sleep quality of healthy young men (Cho et al., 2016). Thus, we used the value of 5 lx as a cutoff and divided the participants into two groups: the Light group (average LAN of ≥5 lx) and the Dark group (average LAN of ≤5 lx). The procedure for measuring LAN exposure was based on the procedure of the HEIJO-KYO study (Obayashi et al., 2012).
2.2. Procedure The participants were assessed for demographic and clinical characteristics at the clinic and were then asked perform the following for seven consecutive days: (1) wear an actigraph (Actiwatch Spectrum Plus; Respironics Inc., PA, USA) on the wrist of their nondominant arm throughout the day and night, except when bathing or performing aquatic activities; (2) record bedtime and rising time in a sleep diary; and (3) record bedroom light levels from bedtime to rising time using a portable photometer (LX-28SD; Sato Shouji Inc., Kanagawa, Japan). Bedtime was defined as the time that the participant went to bed with the intention to sleep, excluding the time spent in bed reading books or watching TV. Rising time was defined as the time when the participant finally got out of bed.
Fig. 1 presents a flowchart profile of enrollment to this study. In total, 365 outpatients with BD from Okehazama Hospital, Fujita Mental Care Satellite Zengo, Fujita Mental Care Satellite Tokushige, and Fujita Health University were recruited to the APPLE (Association between the Pathology of Bipolar Disorder and Light Exposure in Daily Life) cohort study between August 2017 and November 2018. The inclusion criteria were being of an age of 18–75 years and being diagnosed with BD I or II according to the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). Night shift workers and those judged by a clinician to be at serious suicidal risk were excluded. Among the 365 patients, 49 patients did not meet the inclusion criteria, and 131 patients refused to participate in the study. Therefore, in total, 185 patients were enrolled in this study. Five patients discontinued the study because of discomfort from wearing the actigraph, and five patients were excluded from the analysis because of difficulty with the
2.4. Sleep assessment Sleep quality was evaluated subjectively and objectively using the
Fig. 1. Flowchart of patient enrollment from the initial screening to the final analysis. 315
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Insomnia Severity Index (ISI) questionnaire and actigraphy, respectively. ISI is a self-rated seven-item questionnaire designed to assess the severity of the nighttime and daytime components of insomnia (Bastien et al., 2001). In the current study, ISI was administered at the start of the experimental period to evaluate the severity of the participants’ insomnia over the previous week. Actigraphy objectively monitors sleep and activity patterns over days to weeks and has been validated to be as accurate as polysomnography (PSG) in measuring sleep in patients with BD (Kaplan et al., 2012). The actigraph used in this study sampled data for 1 min epochs, and we used a moderate threshold of 40 counts per min indicative of the participant being awake. Time in bed, regardless of activity, was defined by sleep diary entries, not by actigraphy data. The actigraphy sleep data were automatically analyzed with the sleep detection algorithm in the software for the actigraph, Actiware version 6.0.9 (Respironics Inc.). This actigraphy analysis method has been used in previous studies of the general population and patients with BD (Boudebesse et al., 2013; Obayashi et al., 2014). Five actigraphy sleep parameters were used in this study: (1) total sleep time (TST), which is the total time spent asleep from sleep start to end during the main sleep phase, excluding wake after sleep onset; (2) sleep efficiency (SE), which is the percentage of TST between bedtime and rising time for the main sleep phase (i.e., TST divided by time in bed); (3) wake after sleep onset (WASO), which is the total time spent awake from sleep start to end; (4) sleep onset latency (SOL), which is the time from bedtime to the start of sleep; and (5) midpoint of sleep, which is the mid-time from sleep start to end.
the light parameters (average LAN and average daytime light) and sleep parameters (ISI score, bedtime, rising time, TST, SE, WASO, SOL, and midpoint of sleep) between the Light and Dark groups. The associations between LAN exposure and sleep parameters were evaluated using analysis of covariance (ANCOVA). Considering that the average daytime light and SOL data were not normally distributed, they were naturally log transformed for the analyses. ANCOVA was used to adjust the mean values of the sleep parameters for variables associated with sleep, including age (per year), gender (male or female), current smoker (yes or no), depressive status (yes or no), manic status (yes or no), day length (long or short: category based on the median value), log-transformed average daytime light (per lux), and sedative medications (sedative atypical antipsychotic, sedative antidepressant, and hypnotics; yes or no). The statistical tests were performed using SPSS version 25.0 for Windows (IBM Inc., Armonk, NY), and the significance level was set at P < 0.05. 3. Results The mean (SD) age of the 175 participants included in the analysis was 45.5 (13.1) years, and 95 patients (53.7%) were female. The median (IQR) of the MADRS and YMRS scores were 8.0 (3.0–14.0) and 2.0 (0–5.0) points, respectively. The mean (SD) or median (IQR) of the sleep parameters were as follows: ISI score, 10.5 (5.6) points; SE, 82.1 (8.8)%; SOL, 16.2 (7.8–28.4) min; WASO, 45.2 (27.4) min; TST, 390.2 (96.6) min; and midpoint of sleep, 03:17 (1:38). The median (IQR) of the average LAN intensity was 1.9 (0.3–9.4) lx. The night-to-night correlations of the LAN exposure in 7 days were moderate (all P < 0.001; Supplemental Table 1). Table 1 and Supplemental Table 2 present the demographic characteristics, clinical characteristics, and medications for the two groups. The Light group included 65 (37.1%) participants. The proportion of female participants was higher (58.2% vs. 47.7%), that of participants
2.5. Other assessments Each participant's current depressive or manic status was assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS) (Montgomery and Asberg, 1979; Young et al., 1978). We defined cutoff points on MADRS (eight points) and YMRS (eight points) as indicators of depressive and manic states, respectively, according to the recommendations of the International Society for Bipolar Disorders Task Force (Tohen et al., 2009). Daytime light exposure was evaluated objectively using actigraphy that could measure ambient light at 1-min intervals throughout the period between the participant's rising time and bedtime. Periods during which the actigraph was not worn were automatically excluded by the detection algorithm in the Actiware version 6.0.9 software (Respironics Inc.); if this extended to more than half of the daytime period, the data for that day were treated as missing. Any data recorded as <1 lx during the daytime period were considered to be artifacts caused by clothing covering the actigraph and were excluded from the analysis (Scheuermaier et al., 2010). We calculated the average daytime light as the average daytime light intensity from rising time to bedtime. We collected information on the participant's medications from their clinical records, including mood stabilizers (lithium, lamotrigine, valproate, and carbamazepine), sedative antipsychotics (olanzapine, quetiapine, and risperidone), sedative antidepressants (trazodone and mirtazapine), and hypnotics. We also obtained the day length from sunrise to sunset in Aichi, Japan (latitude, 35 °N), for each participant's first measurement day from the web page of the National Astronomical Observatory of Japan.
Table 1 Demographic and clinical characteristics for the Light and Dark groups. Characteristics Demographic characteristics Age, years, mean (SD) Gender, female, n (%) Married, n (%) Education (≥13 years), n (%) Employed, n (%) Current smoker, n (%) Clinical characteristics BD type I, n (%) Onset age of BD, years, mean (SD) Duration of illness, years, mean (SD) MADRS score, points, median (IQR) Depressive state, n (%) YMRS score, points, median (IQR) Manic state, n (%) Family history, n (%) Day length, min, median (IQR) Medication Lithium, n (%) Lamotrigine, n (%) Valproate, n (%) Carbamazepine, n (%) Sedative atypical antipsychotic, n (%) Sedative antidepressant, n (%) Hypnotics, n (%)
2.6. Statistical analyses Normally distributed variables are presented as mean and standard deviation (SD), those with an asymmetrical distribution as median and interquartile range (IQR), and categorical variables as number and percentage. The night-to-night correlations of LAN exposure in 7 days were evaluated using the Spearman's rank correlation coefficient (rs). Unpaired t-tests and the Mann–Whitney U test were used to compare
Light (n = 65)
Dark (n = 110)
45.8 (13.4) 31 (58.2) 27 (41.5) 39 (60.0) 27 (43.5) 20 (30.8)
45.2 (13.0) 64 (47.7) 64 (58.2) 62 (56.4) 35 (39.8) 34 (30.9)
28 (43.1) 33.5 (13.8) 12.5 (7.8) 8.5 (4.0–13.0) 35 (54.7) 3.0 (1.2–6.0) 11 (17.2) 12 (19.4) 777 (690–835)
35 (31.8) 32.7 (11.1) 12.4 (9.2) 8.0 (2.7–15.2) 58 (52.7) 2.0 (0–4.0) 10 (9.1) 17 (19.1) 756 (673–831)
26 (40.0) 22.(33.8) 18 (27.7) 3 (4.6) 17 (26.2) 10 (15.4) 40 (61.5)
42 (61.8) 37.(33.6) 26 (23.6) 0 (0) 33 (30.0) 26 (23.6) 72 (65.5)
Data are expressed as mean (standard deviation), median (interquartile range), or number (percentage). In the Light group, the average night light exposure was ≥5 lx, and in the Dark group, it was < 5 lx. MADRS, Montgomery–Åsberg Depression Rating Scale; YMRS, Young Mania Rating Scale.
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4. Discussion
Table 2 Comparisons of light and sleep parameters between the Light and Dark groups. Characteristics Light parameters Average LAN, lux, median (IQR) Average daytime light, lux, median (IQR) Sleep parameters Subjective parameters, mean (SD) ISI score, points Bedtime, clock time Rising time, clock time Actigraphy parameters SE, %, mean (SD) SOL, min, median (IQR) WASO, min, mean (SD) TST, min, mean (SD) Midpoint of sleep, clock time, mean (SD)
Light
Dark
P
13.4 (8.3–24.9)
0.4 (0.1–1.4)
<0.01
254.5 (171.7–326.1)
215.7 (149.7–297.4)
0.08
10.4 (5.5) 23:30 (1:43) 7:20 (1:30)
10.7 (5.8) 23:04 (1:45) 7:10 (2:05)
0.7 0.11 0.56
79.7 (9.7) 19.0 (11.4–35.9) 51.3 (30.4) 373.7 (85.1) 03:29 (1:28)
83.6 (7.8) 13.7 (7.7–25.1) 41.6 (25.0) 399.9 (101.8) 03:09 (1:43)
<0.01 0.01 0.02 0.08 0.19
To the best of our knowledge, this is the first study that explores the associations between LAN exposure and sleep quality in BD. The results showed that LAN exposure was significantly associated with sleep parameters, including decreased SE, prolonged SOL, and increased WASO, that were objectively measured using actigraphy in patients with BD. Results of multivariable analyses suggested that these associations were independent of several confounding factors. However, LAN exposure was not significantly associated with sleep quality that was measured subjectively by ISI. Our results are consistent with those of previous studies that reported an association between LAN exposure and sleep quality in the general population. In a previous epidemiological study that involved the use of actigraphy in a general elderly population, increased LAN intensity was significantly associated with poor sleep quality, including parameters such as decreased SE, prolonged SOL and WASO, and shortened TST (Obayashi et al., 2014). A laboratory study reported that for healthy young men, exposure to dim artificial LAN during sleep was significantly associated with increased WASO, increased stage N1 sleep, and decreased stage N2 sleep, which were measured using PSG (Cho et al., 2016). Experimental studies reported that patients with BD were supersensitive to nocturnal melatonin suppression by light compared with healthy subjects (Hallam et al., 2006; Kennedy et al., 1996; McIntyre et al., 1989; Nurnberger et al., 1988). Thus, we speculate that the influence of LAN exposure on sleep is stronger in patients with BD than in healthy people. However, further investigations are needed to confirm the relationship between LAN and sleep, by comparing patients with BD and controls. The study of a general elderly population described earlier (Obayashi et al., 2014) reported the associations between LAN exposure and sleep quality when measured subjectively using the Pittsburgh Sleep Quality Index (PSQI) and objectively using actigraphy. In contrast, our study showed an association only with the actigraphy results and not with the subjective assessments of sleep using ISI. A possible explanation is that patients with BD may have sleep misperception. A recent study reported that symptomatic patients with BD had significantly higher PSQI scores than healthy control subjects, even though there was no difference between the two groups in terms of sleep quality measured using actigraphy (Krishnamurthy et al., 2018). Another study reported that euthymic patients with BD demonstrated a greater discrepancy between subjective and objective measures of sleep than groups of people with insomnia as well as healthy controls (Harvey et al., 2005). Our results support the findings of previous studies, and it is possible that LAN exposure worsens sleep quality without the patient being aware of this. Another possibility is that factors other than LAN affected the ISI scores. Significant correlations between ISI scores and depression, anxiety, and fatigue have been reported (Morin et al., 2011). Although we adjusted the sleep parameters
Data are expressed as mean (standard deviation) or median (interquartile range). The Light group included the participants (n = 65) for whom average night light exposure was ≥5 lx; the Dark group were those (n = 110) for whom it was <5 lx. ISI, Insomnia Severity Index; SE, sleep efficiency; SOL, sleep onset latency; WASO, wake after sleep onset; TST, total sleep time.
who were married was lower (41.5% vs. 58.2%), that of BD type I participants was higher (43.1% vs. 31.8%), and that of manic state participants was higher (17.2% vs. 9.1%) in the Light group than in the Dark group. Three participants in the Light group were prescribed carbamazepine, whereas no participant was prescribed carbamazepine in the Dark group. Table 2 presents the LAN and sleep parameter data for the two groups. The median LAN exposure was approximately 30 times higher for the Light group than for the Dark group. The Light group showed significantly lower SE (79.7%vs. 83.6%), longer SOL (19.0 vs. 13.7 min), and more WASO (51.3 vs. 41.6 min) than the Dark group. There was no significant difference between the groups in terms of the ISI score, TST, or midpoint of sleep. Table 3 summarizes the ANCOVA analysis comparing the adjusted sleep parameters between the two groups. The Light group showed significantly lower SE (P = 0.01), longer log-transformed SOL (P = 0.01), increased WASO (P = 0.02), and delayed midpoint of sleep (P = 0.048) than the Dark group. These associations between LAN and sleep parameters were independent of age; gender; current smoker status; depressive and manic status; day length; log-transformed average daytime light; and use of sedative atypical antipsychotics, sedative antidepressants, and hypnotics. Conversely, the adjusted ISI and TST values did not show significant differences between the groups (ISI, P = 0.45; TST, P = 0.40).
Table 3 Comparisons of adjusted sleep parameters between the Light and Dark groups using ANCOVA. Adjusted sleep parameters Subjective sleep parameters ISI score, points Actigraphy sleep parameters SE, % log-transformed SOL, min WASO, min TST, min Midpoint of sleep, clock time
Light
Dark
Difference
P
10.9 (9.7–12.3)
10.3 (9.4–11.3)
0.6 (−0.9–2.1)
0.45
80.1 (78.0–82.1) 2.9 (2.8–3.1) 51.4 (44.7–58.1) 383.3 (360.6–405.9) 03:33 (03:12–03:54)
83.4 (81.9–85.0) 2.6 (2.5–2.8) 41.6 (36.5–46.7) 395.3 (378.2–412.5) 03:06 (02:51–03:22)
3.3 (0.7–5.9) 0.3 (0.0–0.5) 9.7 (1.2–18.3) 13.4 (−15.3–42.3) 0:26 (0:00–0:53)
0.01 0.01 0.02 0.40 0.048
Data are expressed as mean (95% confidence interval) after adjusting for age, gender, current smoker, depressive and manic status, day length, log-transformed average daytime light, and use of sedative atypical antipsychotics, sedative antidepressants, and hypnotics. The Light group included the participants (n = 65) for whom average night light exposure was ≥5 lx; the Dark group included those (n = 110) for whom it was <5 lx. ISI, Insomnia Severity Index; SE, sleep efficiency; SOL, sleep onset latency; WASO, wake after sleep onset; TST, total sleep time. 317
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for depressive symptoms in this study as potential confounding factors, we did not adjust for anxiety or fatigue. Furthermore, our study did not exclude other sleep disturbances, such as obstructive sleep apnea. The mechanism underlying the association between LAN and sleep disturbance in BD remains unknown, but possible mechanisms are indicated by circadian rhythm dysfunction and the alerting effect by light exposure. Light exposure is the most important environmental cue for the regulation of circadian rhythms in humans (Dumont and Beaulieu, 2007). The effects of LAN exposure may include disruption of the biological clock and suppression of melatonin secretion (Reiter et al., 2007). These effects may be associated with increases in SOL and alertness (Cho et al., 2015). Therefore, exposure to LAN causes sleep disturbance. Furthermore, various studies have suggested associations between circadian rhythm disruption and BD (Abreu and Braganca, 2015; Melo et al., 2017; Takaesu, 2018). For example, patients with BD are prone to exhibit the evening chronotype regardless of their mood state (Seleem et al., 2015); in addition, BD is frequently associated with circadian sleep–wake rhythm disorders, particularly delayed sleep–wake rhythm disorder (Talih et al., 2018). Experimental studies have demonstrated that patients with BD are supersensitive to the suppression of nocturnal melatonin by light (Hallam et al., 2006; Kennedy et al., 1996; McIntyre et al., 1989; Nurnberger et al., 1988). Similarly, we observed that LAN exposure was significantly associated with a delayed midpoint of sleep. Thus, circadian rhythm dysfunction may be a mediator of the association between LAN exposure and sleep disturbance in BD. Another possible mechanism is the alerting effect by light exposure. Light exposure, especially in the blue wavelength, causes direct alerting effects (Lockley et al., 2006). In addition, a previous study reported that wearing blue blocking glasses in the evening significantly decreased subjective alertness before bedtime (van der Lely et al., 2015). Therefore, it is likely that circadian rhythm disruption and the alerting effect may be involved in the association between LAN exposure and poorer sleep quality in BD. The clinical implications of our study can be interpreted by comparing our results with the effect of sedative medications in patients with BD. Among the actigraphy sleep parameters, SE includes TST, SOL, and WASO in its measurement. Thus, SE is useful for confirming a patient's sleep quality. A previous study reported that adjunctive quetiapine therapy in patients with bipolar or unipolar depression improved SE by 4.7% (baseline, 69.8%; after 21–28 days,74.5%), as measured using PSG (Gedge et al., 2010). Another study reported that olanzapine augmentation treatment in patients with bipolar or unipolar depression improved SE by 12% (baseline, 71%; after 28–31 days, 83%), which was measured using PSG (Lazowski et al., 2014). However, in our study, SE was 3.3% lower in the Light group than in the Dark group (80.1% vs. 83.4%, respectively). The effect of this difference on SE is smaller than that of the sedative medications used in previous studies. However, the sedative antipsychotic drugs are associated with potential adverse effects such as weight gain, diabetes, increased lipids, and anticholinergic effects (Stroup and Gray, 2018). Therefore, it may be important to convey the need to darken patient's bedroom and limit her/his exposure to blue light, particularly to electronic devices, before administering sedation medication to patients experiencing sleep disturbance. This study had several limitations. First, this was a cross-sectional study and did not demonstrate causalities. Second, this study involved non-random sampling because the participants were recruited from our hospitals or clinics. Therefore, it may be difficult to generalize the findings to all patients with BD. Third, although we instructed the participants to position the photometer light sensor near their head and at eye level at night, we were unable to confirm its actual position. In addition, we had no data on light exposure if the participant left the bedroom at night, such as to visit the bathroom; our analysis included
only the LAN intensity in the bedroom from bedtime to rising time. Fourth, we only measured the light intensity (in lux) of LAN exposure. A previous study reported that measuring photopic illuminance (in lux) is inadequate for quantifying light intended to regulate non-visual physiology and behavior (Lucas et al., 2014). A recent study demonstrated that melatonin suppression is better predicted by melanopic illuminance (melanopic lux) (Prayag et al., 2019). Therefore, future studies measuring melanopic lux would reveal more appropriate associations between LAN and sleep disturbance. Finally, the LAN exposure was recorded only for 7 days; therefore, the average of LAN intensity of 7 days may not be the accurate representative value for that period. However, the night-to-night correlations of the LAN exposure in 7 days were moderately high in our results (Supplemental Table). Therefore, the average LAN intensity may be acceptable as the representative value for 7 days. In conclusion, the findings of this study showed a significant association between LAN exposure and sleep quality measured using actigraphy in patients with BD. Sleep disorders are associated with an increased risk for mood episode recurrence in patients with BD (Cretu et al., 2016; Gershon et al., 2017; Sylvia et al., 2012). Reducing LAN exposure may improve sleep disorder in patients with BD, thus helping to prevent recurrence. However, further investigations are needed to confirm if LAN exposure causes sleep disturbance in BD. Conflicts of interest The authors report no conflicts of interest related to this research. Dr. Kitajima has received speaker's honoraria from Eisai, Mitsubishi Tanabe, Otsuka, Takeda, Eli Lilly, MSD, Meiji, Yoshitomi, Fukuda, Dainippon Sumitomo, Shionogi, and Novo Nordisk, and has received a research grant from Eisai, MSD and Takeda. Dr. Obayashi and Dr. Saeki has received a research grant from YKK AP Inc.; Ushio Inc.; Tokyo Electric Power Company; EnviroLife Research Institute Co., Ltd.; Sekisui Chemical Co., Ltd; LIXIL Corp.; and KYOCERA Corp. Dr. Fujita has received speaker's honoraria from Dainippon Sumitomo, Eli Lilly, GlaxoSmithKline, Janssen, Yoshitomi, Otsuka, Meiji, Shionogi, Novartis, and Kracie. Dr Iwata has received speaker's honoraria from Astellas, Dainippon Sumitomo, Eli Lilly, GlaxoSmithKline, Janssen, Yoshitomi, Otsuka, Meiji, Shionogi, Novartis, and Pfizer and has had research grants from GlaxoSmithKline, Meiji, Otsuka, Mitsubishi Tanabe, Dainippon Sumitomo, Daiichisankyo, and Eisai. Role of the funding source This study was supported by Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology (18K15529), Japan Foundation for Neuroscience and Mental Health, and The Neuroscience Research Center. Acknowledgments We are grateful to the patients who participated in this study. We also thank Soji Tsuboi and Miyuki Yamamoto for their valuable support during this research. CRediT authorship contribution statement Yuichi Esaki: Conceptualization, Investigation, Data curation, Writing - original draft. Tsuyoshi Kitajima: Conceptualization, Writing - review & editing. Kenji Obayashi: Conceptualization, Writing - review & editing. Keigo Saeki: Writing - review & editing. Kiyoshi Fujita: Investigation, Writing - review & editing. Nakao Iwata: Writing - review & editing.
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Supplementary materials
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Research paper
Light exposure at night and sleep quality in bipolar disorder: The APPLE cohort study
T
Yuichi Esakia,b, , Tsuyoshi Kitajimab, Kenji Obayashic, Keigo Saekic, Kiyoshi Fujitaa,d, Nakao Iwatab ⁎
a
Department of Psychiatry, Okehazama Hospital, Aichi, Japan Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan c Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan d The Neuroscience Research Center, Aichi, Japan b
ARTICLE INFO
ABSTRACT
Keywords: Bipolar disorder Sleep Light at night Actigraphy Circadian rhythm
Background: Sleep disturbance in bipolar disorder (BD) is common and is associated with a risk for mood episode recurrence. Thus, it is important to identify factors that are related to sleep disturbance in BD. This crosssectional study investigated the association between exposure to light at night (LAN) and sleep parameters in patients with BD. Methods: The sleep parameters of 175 outpatients with BD were recorded using actigraphy at their homes for seven consecutive nights and were evaluated using the Insomnia Severity Index (ISI). The average LAN intensity in the bedroom during bedtime and rising time was measured using a portable photometer, and the participants were divided into two groups: “Light” (≥5 lx) and “Dark” (<5 lx). The association between LAN and sleep parameters was tested with multivariable analysis by adjusting for potential confounder such as age, gender, current smoker, mood state, day length, daytime light exposure, and sedative medications. Results: After adjusting for potential confounder, the actigraphy sleep parameters showed significantly lower sleep efficiency (mean, 80.1%vs. 83.4%; p = 0.01), longer log-transformed sleep onset latency (2.9 vs. 2.6 min; p = 0.01), and greater wake after sleep onset (51.4 vs. 41.6 min; p = 0.02) in the Light group than in the Dark group. Whereas, there were no significant differences in the ISI scores between the groups. Limitations: This was a cross-sectional study; therefore, the results do not necessarily imply that LAN causes sleep disturbance. Conclusions: Reducing LAN exposure may contribute to improved sleep quality in patients with BD.
1. Introduction Bipolar disorder (BD) is a severe, recurrent mental illness that affects 1%–4% of the population (Merikangas et al., 2007) and is characterized by mood episodes that can involve mania, hypomania, and often depression. BD also presents with sleep disturbance (Dallaspezia and Benedetti, 2015; Ng et al., 2015; Plante and Winkelman, 2008). A reduced need for sleep is commonly observed during manic or hypomanic episodes, whereas insomnia or hypersomnia frequently occur during depressive episodes (Harvey, 2008). Even in the euthymic period, patients with BD experience more sleep problems than healthy controls (Geoffroy et al., 2015; Ng et al., 2015). Residual sleep disturbance is associated with a risk for mood episode recurrence (Cretu et al., 2016; Gershon et al., 2017; Sylvia et al., 2012). Furthermore,
⁎
sleep loss has been shown to be associated with suicidal ideation in patients with BD with a suicide attempt history (Stange et al., 2016). Therefore, it is important to identify factors that are related to sleep disturbance in patients with BD. Light exposure is closely related to BD pathophysiology. It has been reported that an increase in the duration of sunshine in spring may have a significant effect on the onset of BD (Bauer et al., 2017). Bright light therapy can be effective in patients with BD who are experiencing current depressive episodes (Sit et al., 2018; Tseng et al., 2016; Zhou et al., 2018), but injudicious light therapy can induce mixed states (Sit et al., 2007). In contrast, dark therapy, which restricts light exposure at night, improves acute manic symptoms (Barbini et al., 2005; Henriksen et al., 2016). Midnight light exposure strongly suppressed endogenous melatonin concentration in patients with BD (Hallam et al.,
Corresponding author at: Department of Psychiatry, Fujita Health University School of Medicine, Aichi, 4701192, Japan. E-mail address: [email protected] (Y. Esaki).
https://doi.org/10.1016/j.jad.2019.07.031 Received 17 January 2019; Received in revised form 9 June 2019; Accepted 4 July 2019 Available online 05 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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2006), and blocking of blue light in the evening with the use of amber lenses improved sleep initiation in patients with BD (Phelps, 2008). Therefore, light is an important factor that affects the condition of BD. Given the prevalence of artificial lighting, exposure to light at night (LAN) has become a part of modern daily life (Navara and Nelson, 2007). LAN exposure is linked to negative health effects, including cancer, metabolic syndrome, mood disorders, and sleep disorders (Cho et al., 2015). One of the most obvious negative effects of LAN is sleep disturbance. An epidemiological study demonstrated that a significant association exists between LAN exposure and sleep disorder in the elderly (Obayashi et al., 2014). Another experimental study reported that exposure to artificial LAN during sleep even at a very low intensity (up to 10 lx) caused an increase in awakening and shallow sleep in healthy young men (Cho et al., 2016). Therefore, it is likely that LAN exposure may cause sleep disturbance in patients with BD. However, to the best of our knowledge, no study has investigated the association between LAN and sleep quality in patients with BD. The aim of this study was to investigate the associations between LAN exposure and sleep parameters in patients with BD. We hypothesized that LAN exposure is associated with poor sleep quality.
photometry measurement. Consequently, data for 175 patients were analyzed. The study was approved by the Ethics Committee of Okehazama Hospital. Written informed consent was obtained from all the participating patients, and the study was registered at UMIN-CTR (identifier: UMIN000028239).
2. Materials and methods
2.3. LAN exposure assessment
2.1. Clinical sample
The photometer recorded LAN exposure in the bedroom at 1-min intervals. The participants were asked to position the light sensor of the photometer near their head and at eye level and to start and end the recording at bedtime and rising time, respectively. We calculated the average LAN as the average night light intensity over the entire period from bedtime to rising time. A previous experimental study reported that a LAN exposure of ≥5 lx affected the sleep quality of healthy young men (Cho et al., 2016). Thus, we used the value of 5 lx as a cutoff and divided the participants into two groups: the Light group (average LAN of ≥5 lx) and the Dark group (average LAN of ≤5 lx). The procedure for measuring LAN exposure was based on the procedure of the HEIJO-KYO study (Obayashi et al., 2012).
2.2. Procedure The participants were assessed for demographic and clinical characteristics at the clinic and were then asked perform the following for seven consecutive days: (1) wear an actigraph (Actiwatch Spectrum Plus; Respironics Inc., PA, USA) on the wrist of their nondominant arm throughout the day and night, except when bathing or performing aquatic activities; (2) record bedtime and rising time in a sleep diary; and (3) record bedroom light levels from bedtime to rising time using a portable photometer (LX-28SD; Sato Shouji Inc., Kanagawa, Japan). Bedtime was defined as the time that the participant went to bed with the intention to sleep, excluding the time spent in bed reading books or watching TV. Rising time was defined as the time when the participant finally got out of bed.
Fig. 1 presents a flowchart profile of enrollment to this study. In total, 365 outpatients with BD from Okehazama Hospital, Fujita Mental Care Satellite Zengo, Fujita Mental Care Satellite Tokushige, and Fujita Health University were recruited to the APPLE (Association between the Pathology of Bipolar Disorder and Light Exposure in Daily Life) cohort study between August 2017 and November 2018. The inclusion criteria were being of an age of 18–75 years and being diagnosed with BD I or II according to the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). Night shift workers and those judged by a clinician to be at serious suicidal risk were excluded. Among the 365 patients, 49 patients did not meet the inclusion criteria, and 131 patients refused to participate in the study. Therefore, in total, 185 patients were enrolled in this study. Five patients discontinued the study because of discomfort from wearing the actigraph, and five patients were excluded from the analysis because of difficulty with the
2.4. Sleep assessment Sleep quality was evaluated subjectively and objectively using the
Fig. 1. Flowchart of patient enrollment from the initial screening to the final analysis. 315
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Insomnia Severity Index (ISI) questionnaire and actigraphy, respectively. ISI is a self-rated seven-item questionnaire designed to assess the severity of the nighttime and daytime components of insomnia (Bastien et al., 2001). In the current study, ISI was administered at the start of the experimental period to evaluate the severity of the participants’ insomnia over the previous week. Actigraphy objectively monitors sleep and activity patterns over days to weeks and has been validated to be as accurate as polysomnography (PSG) in measuring sleep in patients with BD (Kaplan et al., 2012). The actigraph used in this study sampled data for 1 min epochs, and we used a moderate threshold of 40 counts per min indicative of the participant being awake. Time in bed, regardless of activity, was defined by sleep diary entries, not by actigraphy data. The actigraphy sleep data were automatically analyzed with the sleep detection algorithm in the software for the actigraph, Actiware version 6.0.9 (Respironics Inc.). This actigraphy analysis method has been used in previous studies of the general population and patients with BD (Boudebesse et al., 2013; Obayashi et al., 2014). Five actigraphy sleep parameters were used in this study: (1) total sleep time (TST), which is the total time spent asleep from sleep start to end during the main sleep phase, excluding wake after sleep onset; (2) sleep efficiency (SE), which is the percentage of TST between bedtime and rising time for the main sleep phase (i.e., TST divided by time in bed); (3) wake after sleep onset (WASO), which is the total time spent awake from sleep start to end; (4) sleep onset latency (SOL), which is the time from bedtime to the start of sleep; and (5) midpoint of sleep, which is the mid-time from sleep start to end.
the light parameters (average LAN and average daytime light) and sleep parameters (ISI score, bedtime, rising time, TST, SE, WASO, SOL, and midpoint of sleep) between the Light and Dark groups. The associations between LAN exposure and sleep parameters were evaluated using analysis of covariance (ANCOVA). Considering that the average daytime light and SOL data were not normally distributed, they were naturally log transformed for the analyses. ANCOVA was used to adjust the mean values of the sleep parameters for variables associated with sleep, including age (per year), gender (male or female), current smoker (yes or no), depressive status (yes or no), manic status (yes or no), day length (long or short: category based on the median value), log-transformed average daytime light (per lux), and sedative medications (sedative atypical antipsychotic, sedative antidepressant, and hypnotics; yes or no). The statistical tests were performed using SPSS version 25.0 for Windows (IBM Inc., Armonk, NY), and the significance level was set at P < 0.05. 3. Results The mean (SD) age of the 175 participants included in the analysis was 45.5 (13.1) years, and 95 patients (53.7%) were female. The median (IQR) of the MADRS and YMRS scores were 8.0 (3.0–14.0) and 2.0 (0–5.0) points, respectively. The mean (SD) or median (IQR) of the sleep parameters were as follows: ISI score, 10.5 (5.6) points; SE, 82.1 (8.8)%; SOL, 16.2 (7.8–28.4) min; WASO, 45.2 (27.4) min; TST, 390.2 (96.6) min; and midpoint of sleep, 03:17 (1:38). The median (IQR) of the average LAN intensity was 1.9 (0.3–9.4) lx. The night-to-night correlations of the LAN exposure in 7 days were moderate (all P < 0.001; Supplemental Table 1). Table 1 and Supplemental Table 2 present the demographic characteristics, clinical characteristics, and medications for the two groups. The Light group included 65 (37.1%) participants. The proportion of female participants was higher (58.2% vs. 47.7%), that of participants
2.5. Other assessments Each participant's current depressive or manic status was assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS) (Montgomery and Asberg, 1979; Young et al., 1978). We defined cutoff points on MADRS (eight points) and YMRS (eight points) as indicators of depressive and manic states, respectively, according to the recommendations of the International Society for Bipolar Disorders Task Force (Tohen et al., 2009). Daytime light exposure was evaluated objectively using actigraphy that could measure ambient light at 1-min intervals throughout the period between the participant's rising time and bedtime. Periods during which the actigraph was not worn were automatically excluded by the detection algorithm in the Actiware version 6.0.9 software (Respironics Inc.); if this extended to more than half of the daytime period, the data for that day were treated as missing. Any data recorded as <1 lx during the daytime period were considered to be artifacts caused by clothing covering the actigraph and were excluded from the analysis (Scheuermaier et al., 2010). We calculated the average daytime light as the average daytime light intensity from rising time to bedtime. We collected information on the participant's medications from their clinical records, including mood stabilizers (lithium, lamotrigine, valproate, and carbamazepine), sedative antipsychotics (olanzapine, quetiapine, and risperidone), sedative antidepressants (trazodone and mirtazapine), and hypnotics. We also obtained the day length from sunrise to sunset in Aichi, Japan (latitude, 35 °N), for each participant's first measurement day from the web page of the National Astronomical Observatory of Japan.
Table 1 Demographic and clinical characteristics for the Light and Dark groups. Characteristics Demographic characteristics Age, years, mean (SD) Gender, female, n (%) Married, n (%) Education (≥13 years), n (%) Employed, n (%) Current smoker, n (%) Clinical characteristics BD type I, n (%) Onset age of BD, years, mean (SD) Duration of illness, years, mean (SD) MADRS score, points, median (IQR) Depressive state, n (%) YMRS score, points, median (IQR) Manic state, n (%) Family history, n (%) Day length, min, median (IQR) Medication Lithium, n (%) Lamotrigine, n (%) Valproate, n (%) Carbamazepine, n (%) Sedative atypical antipsychotic, n (%) Sedative antidepressant, n (%) Hypnotics, n (%)
2.6. Statistical analyses Normally distributed variables are presented as mean and standard deviation (SD), those with an asymmetrical distribution as median and interquartile range (IQR), and categorical variables as number and percentage. The night-to-night correlations of LAN exposure in 7 days were evaluated using the Spearman's rank correlation coefficient (rs). Unpaired t-tests and the Mann–Whitney U test were used to compare
Light (n = 65)
Dark (n = 110)
45.8 (13.4) 31 (58.2) 27 (41.5) 39 (60.0) 27 (43.5) 20 (30.8)
45.2 (13.0) 64 (47.7) 64 (58.2) 62 (56.4) 35 (39.8) 34 (30.9)
28 (43.1) 33.5 (13.8) 12.5 (7.8) 8.5 (4.0–13.0) 35 (54.7) 3.0 (1.2–6.0) 11 (17.2) 12 (19.4) 777 (690–835)
35 (31.8) 32.7 (11.1) 12.4 (9.2) 8.0 (2.7–15.2) 58 (52.7) 2.0 (0–4.0) 10 (9.1) 17 (19.1) 756 (673–831)
26 (40.0) 22.(33.8) 18 (27.7) 3 (4.6) 17 (26.2) 10 (15.4) 40 (61.5)
42 (61.8) 37.(33.6) 26 (23.6) 0 (0) 33 (30.0) 26 (23.6) 72 (65.5)
Data are expressed as mean (standard deviation), median (interquartile range), or number (percentage). In the Light group, the average night light exposure was ≥5 lx, and in the Dark group, it was < 5 lx. MADRS, Montgomery–Åsberg Depression Rating Scale; YMRS, Young Mania Rating Scale.
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4. Discussion
Table 2 Comparisons of light and sleep parameters between the Light and Dark groups. Characteristics Light parameters Average LAN, lux, median (IQR) Average daytime light, lux, median (IQR) Sleep parameters Subjective parameters, mean (SD) ISI score, points Bedtime, clock time Rising time, clock time Actigraphy parameters SE, %, mean (SD) SOL, min, median (IQR) WASO, min, mean (SD) TST, min, mean (SD) Midpoint of sleep, clock time, mean (SD)
Light
Dark
P
13.4 (8.3–24.9)
0.4 (0.1–1.4)
<0.01
254.5 (171.7–326.1)
215.7 (149.7–297.4)
0.08
10.4 (5.5) 23:30 (1:43) 7:20 (1:30)
10.7 (5.8) 23:04 (1:45) 7:10 (2:05)
0.7 0.11 0.56
79.7 (9.7) 19.0 (11.4–35.9) 51.3 (30.4) 373.7 (85.1) 03:29 (1:28)
83.6 (7.8) 13.7 (7.7–25.1) 41.6 (25.0) 399.9 (101.8) 03:09 (1:43)
<0.01 0.01 0.02 0.08 0.19
To the best of our knowledge, this is the first study that explores the associations between LAN exposure and sleep quality in BD. The results showed that LAN exposure was significantly associated with sleep parameters, including decreased SE, prolonged SOL, and increased WASO, that were objectively measured using actigraphy in patients with BD. Results of multivariable analyses suggested that these associations were independent of several confounding factors. However, LAN exposure was not significantly associated with sleep quality that was measured subjectively by ISI. Our results are consistent with those of previous studies that reported an association between LAN exposure and sleep quality in the general population. In a previous epidemiological study that involved the use of actigraphy in a general elderly population, increased LAN intensity was significantly associated with poor sleep quality, including parameters such as decreased SE, prolonged SOL and WASO, and shortened TST (Obayashi et al., 2014). A laboratory study reported that for healthy young men, exposure to dim artificial LAN during sleep was significantly associated with increased WASO, increased stage N1 sleep, and decreased stage N2 sleep, which were measured using PSG (Cho et al., 2016). Experimental studies reported that patients with BD were supersensitive to nocturnal melatonin suppression by light compared with healthy subjects (Hallam et al., 2006; Kennedy et al., 1996; McIntyre et al., 1989; Nurnberger et al., 1988). Thus, we speculate that the influence of LAN exposure on sleep is stronger in patients with BD than in healthy people. However, further investigations are needed to confirm the relationship between LAN and sleep, by comparing patients with BD and controls. The study of a general elderly population described earlier (Obayashi et al., 2014) reported the associations between LAN exposure and sleep quality when measured subjectively using the Pittsburgh Sleep Quality Index (PSQI) and objectively using actigraphy. In contrast, our study showed an association only with the actigraphy results and not with the subjective assessments of sleep using ISI. A possible explanation is that patients with BD may have sleep misperception. A recent study reported that symptomatic patients with BD had significantly higher PSQI scores than healthy control subjects, even though there was no difference between the two groups in terms of sleep quality measured using actigraphy (Krishnamurthy et al., 2018). Another study reported that euthymic patients with BD demonstrated a greater discrepancy between subjective and objective measures of sleep than groups of people with insomnia as well as healthy controls (Harvey et al., 2005). Our results support the findings of previous studies, and it is possible that LAN exposure worsens sleep quality without the patient being aware of this. Another possibility is that factors other than LAN affected the ISI scores. Significant correlations between ISI scores and depression, anxiety, and fatigue have been reported (Morin et al., 2011). Although we adjusted the sleep parameters
Data are expressed as mean (standard deviation) or median (interquartile range). The Light group included the participants (n = 65) for whom average night light exposure was ≥5 lx; the Dark group were those (n = 110) for whom it was <5 lx. ISI, Insomnia Severity Index; SE, sleep efficiency; SOL, sleep onset latency; WASO, wake after sleep onset; TST, total sleep time.
who were married was lower (41.5% vs. 58.2%), that of BD type I participants was higher (43.1% vs. 31.8%), and that of manic state participants was higher (17.2% vs. 9.1%) in the Light group than in the Dark group. Three participants in the Light group were prescribed carbamazepine, whereas no participant was prescribed carbamazepine in the Dark group. Table 2 presents the LAN and sleep parameter data for the two groups. The median LAN exposure was approximately 30 times higher for the Light group than for the Dark group. The Light group showed significantly lower SE (79.7%vs. 83.6%), longer SOL (19.0 vs. 13.7 min), and more WASO (51.3 vs. 41.6 min) than the Dark group. There was no significant difference between the groups in terms of the ISI score, TST, or midpoint of sleep. Table 3 summarizes the ANCOVA analysis comparing the adjusted sleep parameters between the two groups. The Light group showed significantly lower SE (P = 0.01), longer log-transformed SOL (P = 0.01), increased WASO (P = 0.02), and delayed midpoint of sleep (P = 0.048) than the Dark group. These associations between LAN and sleep parameters were independent of age; gender; current smoker status; depressive and manic status; day length; log-transformed average daytime light; and use of sedative atypical antipsychotics, sedative antidepressants, and hypnotics. Conversely, the adjusted ISI and TST values did not show significant differences between the groups (ISI, P = 0.45; TST, P = 0.40).
Table 3 Comparisons of adjusted sleep parameters between the Light and Dark groups using ANCOVA. Adjusted sleep parameters Subjective sleep parameters ISI score, points Actigraphy sleep parameters SE, % log-transformed SOL, min WASO, min TST, min Midpoint of sleep, clock time
Light
Dark
Difference
P
10.9 (9.7–12.3)
10.3 (9.4–11.3)
0.6 (−0.9–2.1)
0.45
80.1 (78.0–82.1) 2.9 (2.8–3.1) 51.4 (44.7–58.1) 383.3 (360.6–405.9) 03:33 (03:12–03:54)
83.4 (81.9–85.0) 2.6 (2.5–2.8) 41.6 (36.5–46.7) 395.3 (378.2–412.5) 03:06 (02:51–03:22)
3.3 (0.7–5.9) 0.3 (0.0–0.5) 9.7 (1.2–18.3) 13.4 (−15.3–42.3) 0:26 (0:00–0:53)
0.01 0.01 0.02 0.40 0.048
Data are expressed as mean (95% confidence interval) after adjusting for age, gender, current smoker, depressive and manic status, day length, log-transformed average daytime light, and use of sedative atypical antipsychotics, sedative antidepressants, and hypnotics. The Light group included the participants (n = 65) for whom average night light exposure was ≥5 lx; the Dark group included those (n = 110) for whom it was <5 lx. ISI, Insomnia Severity Index; SE, sleep efficiency; SOL, sleep onset latency; WASO, wake after sleep onset; TST, total sleep time. 317
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for depressive symptoms in this study as potential confounding factors, we did not adjust for anxiety or fatigue. Furthermore, our study did not exclude other sleep disturbances, such as obstructive sleep apnea. The mechanism underlying the association between LAN and sleep disturbance in BD remains unknown, but possible mechanisms are indicated by circadian rhythm dysfunction and the alerting effect by light exposure. Light exposure is the most important environmental cue for the regulation of circadian rhythms in humans (Dumont and Beaulieu, 2007). The effects of LAN exposure may include disruption of the biological clock and suppression of melatonin secretion (Reiter et al., 2007). These effects may be associated with increases in SOL and alertness (Cho et al., 2015). Therefore, exposure to LAN causes sleep disturbance. Furthermore, various studies have suggested associations between circadian rhythm disruption and BD (Abreu and Braganca, 2015; Melo et al., 2017; Takaesu, 2018). For example, patients with BD are prone to exhibit the evening chronotype regardless of their mood state (Seleem et al., 2015); in addition, BD is frequently associated with circadian sleep–wake rhythm disorders, particularly delayed sleep–wake rhythm disorder (Talih et al., 2018). Experimental studies have demonstrated that patients with BD are supersensitive to the suppression of nocturnal melatonin by light (Hallam et al., 2006; Kennedy et al., 1996; McIntyre et al., 1989; Nurnberger et al., 1988). Similarly, we observed that LAN exposure was significantly associated with a delayed midpoint of sleep. Thus, circadian rhythm dysfunction may be a mediator of the association between LAN exposure and sleep disturbance in BD. Another possible mechanism is the alerting effect by light exposure. Light exposure, especially in the blue wavelength, causes direct alerting effects (Lockley et al., 2006). In addition, a previous study reported that wearing blue blocking glasses in the evening significantly decreased subjective alertness before bedtime (van der Lely et al., 2015). Therefore, it is likely that circadian rhythm disruption and the alerting effect may be involved in the association between LAN exposure and poorer sleep quality in BD. The clinical implications of our study can be interpreted by comparing our results with the effect of sedative medications in patients with BD. Among the actigraphy sleep parameters, SE includes TST, SOL, and WASO in its measurement. Thus, SE is useful for confirming a patient's sleep quality. A previous study reported that adjunctive quetiapine therapy in patients with bipolar or unipolar depression improved SE by 4.7% (baseline, 69.8%; after 21–28 days,74.5%), as measured using PSG (Gedge et al., 2010). Another study reported that olanzapine augmentation treatment in patients with bipolar or unipolar depression improved SE by 12% (baseline, 71%; after 28–31 days, 83%), which was measured using PSG (Lazowski et al., 2014). However, in our study, SE was 3.3% lower in the Light group than in the Dark group (80.1% vs. 83.4%, respectively). The effect of this difference on SE is smaller than that of the sedative medications used in previous studies. However, the sedative antipsychotic drugs are associated with potential adverse effects such as weight gain, diabetes, increased lipids, and anticholinergic effects (Stroup and Gray, 2018). Therefore, it may be important to convey the need to darken patient's bedroom and limit her/his exposure to blue light, particularly to electronic devices, before administering sedation medication to patients experiencing sleep disturbance. This study had several limitations. First, this was a cross-sectional study and did not demonstrate causalities. Second, this study involved non-random sampling because the participants were recruited from our hospitals or clinics. Therefore, it may be difficult to generalize the findings to all patients with BD. Third, although we instructed the participants to position the photometer light sensor near their head and at eye level at night, we were unable to confirm its actual position. In addition, we had no data on light exposure if the participant left the bedroom at night, such as to visit the bathroom; our analysis included
only the LAN intensity in the bedroom from bedtime to rising time. Fourth, we only measured the light intensity (in lux) of LAN exposure. A previous study reported that measuring photopic illuminance (in lux) is inadequate for quantifying light intended to regulate non-visual physiology and behavior (Lucas et al., 2014). A recent study demonstrated that melatonin suppression is better predicted by melanopic illuminance (melanopic lux) (Prayag et al., 2019). Therefore, future studies measuring melanopic lux would reveal more appropriate associations between LAN and sleep disturbance. Finally, the LAN exposure was recorded only for 7 days; therefore, the average of LAN intensity of 7 days may not be the accurate representative value for that period. However, the night-to-night correlations of the LAN exposure in 7 days were moderately high in our results (Supplemental Table). Therefore, the average LAN intensity may be acceptable as the representative value for 7 days. In conclusion, the findings of this study showed a significant association between LAN exposure and sleep quality measured using actigraphy in patients with BD. Sleep disorders are associated with an increased risk for mood episode recurrence in patients with BD (Cretu et al., 2016; Gershon et al., 2017; Sylvia et al., 2012). Reducing LAN exposure may improve sleep disorder in patients with BD, thus helping to prevent recurrence. However, further investigations are needed to confirm if LAN exposure causes sleep disturbance in BD. Conflicts of interest The authors report no conflicts of interest related to this research. Dr. Kitajima has received speaker's honoraria from Eisai, Mitsubishi Tanabe, Otsuka, Takeda, Eli Lilly, MSD, Meiji, Yoshitomi, Fukuda, Dainippon Sumitomo, Shionogi, and Novo Nordisk, and has received a research grant from Eisai, MSD and Takeda. Dr. Obayashi and Dr. Saeki has received a research grant from YKK AP Inc.; Ushio Inc.; Tokyo Electric Power Company; EnviroLife Research Institute Co., Ltd.; Sekisui Chemical Co., Ltd; LIXIL Corp.; and KYOCERA Corp. Dr. Fujita has received speaker's honoraria from Dainippon Sumitomo, Eli Lilly, GlaxoSmithKline, Janssen, Yoshitomi, Otsuka, Meiji, Shionogi, Novartis, and Kracie. Dr Iwata has received speaker's honoraria from Astellas, Dainippon Sumitomo, Eli Lilly, GlaxoSmithKline, Janssen, Yoshitomi, Otsuka, Meiji, Shionogi, Novartis, and Pfizer and has had research grants from GlaxoSmithKline, Meiji, Otsuka, Mitsubishi Tanabe, Dainippon Sumitomo, Daiichisankyo, and Eisai. Role of the funding source This study was supported by Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology (18K15529), Japan Foundation for Neuroscience and Mental Health, and The Neuroscience Research Center. Acknowledgments We are grateful to the patients who participated in this study. We also thank Soji Tsuboi and Miyuki Yamamoto for their valuable support during this research. CRediT authorship contribution statement Yuichi Esaki: Conceptualization, Investigation, Data curation, Writing - original draft. Tsuyoshi Kitajima: Conceptualization, Writing - review & editing. Kenji Obayashi: Conceptualization, Writing - review & editing. Keigo Saeki: Writing - review & editing. Kiyoshi Fujita: Investigation, Writing - review & editing. Nakao Iwata: Writing - review & editing.
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Supplementary materials
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