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Published in final edited form as: J Sleep Res. 2022 May 22;32(2):e13620. doi: 10.1111/jsr.13620

Night-to-Night Associations Between Light Exposure and Sleep Health

Michael P Mead 1,2, Kathryn J Reid 1,2, Kristen L Knutson 1,2
PMCID: PMC9679040  NIHMSID: NIHMS1799090  PMID: 35599235

Abstract

Previous research has demonstrated that exposure to light preceding and during sleep is associated with poor sleep, but most research to date has utilized either experimental or cross-sectional designs. The current study expands upon prior studies by using a microlongitudinal design that examines the night-to-night associations between light and sleep health in a diverse sample of adults (pre-registered at osf.io/k5zgv). U.S. adults aged 18 to 87 from two parent studies (N= 124) wore an actiwatch for up to 10 nights. Light variables estimated from actigraphy include both average exposure and time above light threshold of 10 (TALT10) and 40 (TALT40) lux both during sleep and for the 1-hour preceding sleep. Actigraphy-based sleep variables included sleep offset, duration, percentage, and fragmentation index. Higher average light exposure during sleep was associated with a later sleep offset time, lower sleep percentage, and higher fragmentation index (all p <.01). More minutes of TALT10 during sleep was associated with later sleep timing, lower sleep percentage, and higher fragmentation index (all p < .01) and greater TALT40 during sleep was associated with lower sleep percentage. Light exposure was not related to sleep duration. In summary, greater light exposure during sleep was related to poorer sleep continuity and later wake time. The lack of association between light and sleep duration may be the result of compensating for sleep disruption by delaying wake time. Multi-level interventions to consistently reduce light levels during sleep should be considered.

Keywords: Light, sleep health, time above light threshold, actigraphy

Introduction

There are several environmental factors that may impact sleep health, including noise, temperature, and light (Caddick, Gregory, Arsintescu, & Flynn-Evans, 2018; Y. Cho et al., 2015; Muzet, 2007). Light is particularly important due to its role in synchronizing our biological clocks, and when light exposure occurs during the biological night, this can disrupt sleep (Czeisler et al., 1986). Light exposure may disrupt sleep through increased alertness (Münch et al., 2011; Yang et al., 2018), circadian phase shifting (Czeisler et al., 1986; Minors, Waterhouse, & Wirz-Justice, 1991), and the suppression of melatonin (Reiter et al., 2007). Examining the role of light exposure, whether from light pollution from outside the home (Koo et al., 2016) or household lighting (J. R. Cho, Joo, Koo, & Hong, 2013), on sleep health can provide insight into the need to develop multilevel interventions to control light exposure and improve sleep (Hale, Troxel, & Buysse, 2020). Most research to date has used experimental or cross-sectional designs to examine the relationship between light exposure and sleep. These prior studies demonstrate that evening light exposure is related to shorter sleep duration and poor continuity (Y. Cho et al., 2015; Dautovich et al., 2019; Tahkamo, Partonen, & Pesonen, 2019), and later sleep timing (Czeisler et al., 1986). However, less is known about how nightly light exposure during or prior to sleep is related to sleep on the same night in adults.

There have been two studies in Japan, one in older adults (K. Obayashi et al., 2014) and one in patients with bipolar disorder (Esaki et al., 2021), that examined night-to-night white light exposure (lux) in the 4-hour period preceding sleep. In the study of older adults, they examined only light exposure during 4 hours preceding sleep in relation to sleep latency, and found greater evening light exposure was associated with increased sleep onset latency (K. Obayashi et al., 2014). In the study of patients with bipolar disorder, greater light exposure was related to lower sleep efficiency, increased sleep onset latency, and more time spent awake during the night (Esaki et al., 2021). These studies indicate that greater light exposure in the 4 hours preceding sleep was associated with poorer sleep health. However, these studies examined average intensity of light exposure before bed, but not how much time is spent at or above a specific light thresholds. Previous research has demonstrated that exposure above light thresholds may impact associations between light and sleep (Reid et al., 2014) and studies comparing various lux levels are needed. Moreover, the duration of light exposure may be an important factor in sleep health, in that longer durations may be more strongly associated with poorer sleep (Chang et al., 2012; Dewan, Benloucif, Reid, Wolfe, & Zee, 2011). In addition, these studies only examined light exposure preceding sleep and not during sleep.

The current study sought to expand upon these prior studies by examining the night-to-night associations between light and sleep health in a sample of U.S. adults. We expected that: 1) Nights with greater average light exposure, and more time spent above threshold, during the sleep period would be related to shorter sleep duration, poorer sleep continuity, and earlier wake time. 2) Nights with greater average light exposure, and more time spent above threshold, in the hour preceding sleep onset would be related to shorter sleep duration, poorer sleep continuity, and later wake time. Hypotheses were pre-registered at osf.io/k5zgv.

Methods

Participants

Participants (N= 124) were recruited from two separate studies conducted by the same research team. Participants in study 1 (N= 50) were from a study that sought to examine racial differences in sleep health and metabolic function, and exclusion criteria included diabetes, sleep disorders, history of cardiovascular event(s), medication use, drug abuse, color blindness, Lasik eye surgery, women who were post-menopausal, and shift work. Participants in study 2 (N= 74) were from a study designed to assess sleep in the home environment and were eligible to participate if they were over the age of 18 years and had polysomnography previously recorded in the clinic or research laboratory. For the current analyses, participants were included if their apnea-hypopnea index (AHI) was less than 15 events/hour. To harmonize exclusion criteria across study samples only non-Hispanic white and African American participants from study 2 were included in analyses (study 1 recruited only non-Hispanic white and African American participants). Participants who were missing more than 50% of days of sleep or light data (participants were included if they had at least 5 nights for study 1, 4 nights for study 2) were removed from analyses. Six participants were removed for incomplete data, and two were removed for technical issues. In study 1, 76% of participants had at least 10 nights of data, 16% had 9 nights, 2% had 8 nights, and 6% had 7 nights. In study 2, 77% of participants had 7 nights of data, 15% had 6, 4% had 5, and 4% had 4.

Procedure

In study 1 (Rangaraj, Siddula, Burgess, Pannain, & Knutson, 2020), individuals were recruited via flyers, newspapers, and online ads and tools. Participants completed a ten-day naturalistic assessment in which they wore an Actiwatch-2 (Respironics/Phillips) for 10 consecutive days and nights and completed a series of sociodemographic questionnaires. In study 2, participants were recruited from ongoing sleep research studies and a local sleep disorders clinic. Participants wore an Actiwatch-2 (Respironics/Phillips) for 7 consecutive days and night and completed a daily sleep diary. Participants in each study completed the same demographic questionnaires and had one night of in-home polysomnography. We compared the demographics, light levels, and sleep characteristics in the two samples using independent t-tests and only the following differed significantly between study 1 and study 2: mean ± SD age (41.2 ±15.7 vs 30.7 ± 8.6 years), mean sleep percentage (89.0 ± 5.4 vs. 90.8 ± 3.6 %) and TALT 10 before bed (10.6 ± 8.8 vs. 6.8 ± 6.2 minutes). All participants provided written informed consent and were compensated for their participation, and the studies were approved by both institutions’ IRB.

Measures

Sleep.

Nightly sleep characteristics were estimated from wrist actigraphy. Actigraphy is a wrist worn accelerometer that uses movement to estimate sleep vs. wake and is an effective measurement tool for objective estimation of sleep (Ancoli-Israel et al., 2003). Rest intervals were identified using event markers, a sleep diary and activity data and all records were scored by the same individual (KK). For the two parent studies, 30-second epochs with 10 immobile minutes and a medium wake threshold were used to identify wake vs. sleep states (Actiware version 6.0.9). The following variables were generated: sleep offset (the time of day an individual’s sleep period ends), sleep duration (the number of minutes spent asleep during the nocturnal sleep period), sleep percentage (amount of sleep divided by sleep period), and sleep fragmentation index (calculated by summing the number of minutes moving percentage, the % of assumed sleep time spent moving, with the immobility phases of 1 minute percentage, the % of immobility phases that are only 1 min or less). Rest period midpoint (time of night that is halfway between rest period start and rest period end) was generated as a covariate to adjust for sleep timing. Nights in which participants completed an in-home polysomnography were excluded from all analyses.

Light.

Nightly light exposure was estimated with the Actiwatch for both the 60 minutes preceding sleep onset and during the sleep period. We selected 60 minutes because it is a more acute exposure closest to bedtime. Three light variables were calculated for each period for each night of recording: average light exposure (lux), time above light threshold (TALT) of 10 lux and TALT of 40 lux (minutes). While 10 lux is commonly used (C.-H. Cho et al., 2016; Kubota et al., 2002; Wams et al., 2017), there is no standard lux level to test associations between light and sleep. Previous research has also used a 40 lux threshold (J. R. Cho et al., 2013), and sensitivity models were run replacing 10 lux with 40 lux. Previous research has used the light estimates from wrist actigraphy (Reid et al., 2014; Woelders, Beersma, Gordijn, Hut, & Wams, 2017).

Covariates.

Demographic variables were included as covariates in all models. Gender, race, age, and socioeconomic status (highest degree obtained) were self-reported by participants via questionnaire. AHI was assessed via full polysomnography, which included electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) and electrocardiography (ECG) signals, oronasal airflow by thermocouples and nasal pressure transducer, respiratory effort from thoracic and abdominal respiratory inductance plethysmography, and pulse oximetry. Apneas and hypopneas were scored following AASM criteria (Iber C, 2007). To adjust for sleep timing and seasonal variations in light exposure and sleep, rest period midpoint and month of the year, each derived from actigraphy, were included as covariates.

Data Analysis

SPSS version 27 was used for all analyses. Before running models, assumptions of normality were tested. Several variables were not normally distributed: sleep percentage, sleep midpoint, sleep fragmentation, average light before sleep, average light during sleep, TALT10 before sleep, TALT10 during sleep, TALT40 before sleep, and TALT40 during sleep. To approximate a normal distribution, we transformed these variables as follows: sleep percentage and sleep midpoint were reverse log10 transformed due to negative skew, sleep fragmentation was log10 transformed due to positive skew, and all light variables were natural log transformed. Transformed variables were used in all analyses.

Mixed linear modeling was used to test the associations between night-to-night light exposure and sleep. Autoregressive heterogenous covariance structures were used to allow variances at each time point to covary, and random intercepts were included to allow mean differences in sleep between participants. For each sleep outcome variable, 3 steps were taken. First, an empty model was run to calculate the intraclass correlations for level 1 (within-person) and 2 (between-person). The intraclass correlations identify how much of the variance in each sleep outcome is at level 1 (within-person, between-day) or level 2 (between-person). For example, an intraclass correlation of .60 at level 1 would indicate that 60% of the outcome variable is attributable to within-person, between-day factors, while 40% is attributable to between-person differences. These empty models indicated that 47.92% of the variance in sleep offset, 72.62% in sleep duration, 60% in sleep percent, and 66.67% in fragmentation index existed within-persons, and 52.08% of the variance in sleep offset, 27.38% in sleep duration, 40% in sleep percent, and 33.33% in fragmentation index existed between persons. In the second step, covariates were added to the model. Third, light variables were then added into the model. Each light variable was independently tested, with 24 models in total. Given multiple comparisons, p values of < .01 were used to determine significant associations. Analytic decisions were pre-registered at osf.io/k5zgv.

Results

Participants (see Table 1) were on average 37 (SD= 14.2) years old, majority female (53.2%) and African American (53.2%). Participants slept an average of 6 hours per night, woke up at 7:34 A.M., had a sleep percentage of 89.7%, and sleep fragmentation index of 19.6%. Participants were exposed to an average of 9 lux in the hour before sleep and 2.5 lux during sleep. Lastly, participants spent an average of 9 minutes above TALT10 before sleep and 11 minutes above TALT10 during sleep per night.

Table 1.

Demographic, sleep, and light characteristics (N=124)

Gender, n (%)
 Male 58 (46.8%)
 Female 66 (53.2%)
Age, mean (SD), years 36.9 (14.23)
Race, n (%)
 White 58 (46.8%)
 African American 66 (53.2%)
Apnea hypopnea index, mean (SD) 4.2(4.26)
Degree
 High school diploma or lower 26 (21%)
 Associate degree or higher 94 (76%)
 Missing 4 (3.2%)
Average light before sleep, mean (SD), lux 9.1 (13.1)
Average light during sleep, mean (SD), lux 2.5 (8.8)
TALT10 before sleep, mean (SD), minutes 9.1 (8.1)
TALT10 during sleep, mean (SD), minutes 11.2 (17.6)
TALT40 before sleep, mean (SD), minutes 2.8 (4.0)
TALT40 during sleep, mean (SD), minutes 4.1 (10.2)
Sleep midpoint, mean (SD, minutes) 3:31 A.M. (193)
Sleep offset, mean (SD, minutes) 7:34 A.M. (90)
Sleep percent, mean (SD), % 89.7 (5.0)
Sleep fragmentation, mean (SD) 19.6 (7.2)
Sleep duration, mean (SD), minutes 392.4 (62.8)
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Results of mixed linear models revealed significant associations between night-to-night light exposure and sleep (see Table 2). Average light exposure before sleep was not related to sleep, but greater light exposure during sleep was associated with later sleep offset (b= .12), lower sleep percentage (b= −.05), and greater sleep fragmentation index (b= .03). Greater TALT10 before sleep was not associated with any sleep outcome (ps > .01). Greater TALT10 during sleep was significantly associated with a later sleep offset (b= .08), lower sleep percentage (b= −.02), and higher sleep fragmentation index (b= .02) but was not related to sleep duration (p > .01). Lastly, greater TALT40 during sleep was associated with lower sleep percentage (b= −.02).

Table 2.

Night-to-night associations between light and nocturnal sleep

Sleep Offset Sleep Duration Sleep Percentage Sleep Fragmentation Index
b p b p b p b p
Average light
 Before sleep .02 .46 5.42 .04 .01 .21 −.01 .21
 During sleep . 12 .005 2.57 .56 −.05 <.001 .03 .002
TALT10
 Before Sleep .02 .48 4.70 .06 .01 .10 −.01 .03
 During Sleep .08 .001 1.06 .66 −.02 <.001 .02 .006
TALT40
 Before Sleep −.02 .50 3.62 .28 .01 .24 −.01 .17
 During Sleep .08 .01 1.30 .69 −.02 .001 .01 .10
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*

All models adjusted for gender, race, age, apnea-hypopnea index, month of year, education, rest period midpoint.

**

adjusted significance level of p< .01 due to multiple comparisons

Discussion

The current study examined whether nightly light exposure, both in the hour preceding sleep and during the sleep period, was related to sleep health. Results demonstrated that greater average light exposure and TALT10 during sleep was related to a later sleep offset and poorer sleep continuity. These results are consistent with previous experimental research demonstrating that light exposure during sleep disrupts sleep (Y. Cho et al., 2015; Czeisler et al., 1986). However, the null findings related to sleep duration was contrary to expectations. It is possible that in a natural environment, participants compensated for disrupted sleep by sleeping in longer, and thus duration was not impaired. The relationships between light exposure and sleep offset may depend upon the timing of light exposure during the sleep period. Light exposure early in the sleep period may delay sleep offset, while light exposure later in the sleep period may advance sleep offset. If light exposure occurs throughout the night, the delaying effect of light exposure early in the sleep period would be counteracted by the advancing effect of sleep during the latter part of the sleep period. Future research could identify the timing of light exposure during the sleep period and test how it impacts sleep offset. Contradicting previous research (Esaki et al., 2021; K. Obayashi et al., 2014), average light exposure in the 1 hour before sleep was not related to any sleep outcome. It may be that light exposure at night does not immediately affect sleep the same night but could affect sleep on subsequent nights, particularly if it occurs regularly, and future work could utilize longer assessment periods to observe how patterns of light exposure over time impact sleep health.

Average light, TALT10, and TALT40 preceding sleep was not related to any sleep outcome. Given previous research showing pre-sleep light disrupts sleep (Esaki et al., 2021; K. Obayashi et al., 2014), these findings were contrary to hypotheses. It is possible that light exposure in the hour preceding sleep may not be harmful for sleep. In a within-person experimental study (Heath et al., 2014), adolescents used technology 1 hour before bed on three separate nights, and each night participated in one of the three conditions: 80 lux, 50 lux, and 1 lux of screen light from an iPad device held 40cm from their face. There were no significant differences between the screen light conditions in subjective sleepiness, subjective and polysomnography assessed sleep onset latency, slow-rolling eye movements, slow wave sleep, and rapid eye movement sleep. As authors note, these unexpected findings could be attributed to the light exposure conditions being one hour before bed, whereas previous studies have used longer durations. Similar to Heath and colleagues (Heath et al., 2014), our study estimated light exposure in the hour preceding sleep, while previous studies have used 4 hours. Perhaps light exposure needs to occur over a longer period of time to impact sleep, or that light levels were simply too low to have an effect. Future research could compare the effects of light exposure over different periods of time, which could help define sleep health recommendations regarding pre-sleep light exposure.

Results from this study provide further evidence that multilevel sleep health interventions that reduce light exposure during the sleep period are needed. At the individual level, interventions that aim to reduce light exposure during the sleep period could improve sleep health. For example, sleep interventions could motivate participants to reduce light exposure by blocking out light with window curtains or using eye masks (Babaii, Adib-Hajbaghery, & Hajibagheri, 2015; Jun, Kapella, & Hershberger, 2021). However, the majority of the work regarding eye mask interventions has been conducted in hospital settings (Brito et al., 2020), and is unclear whether this intervention would be effective in the home environment. At the structural and community level. urban planning that reduces light exposure may be an effective way to improve sleep health, but more research on this is needed as the effectiveness of these interventions remains unclear (Billings, Hale, & Johnson, 2020; Hale et al., 2020).

The current study had several strengths. This is the first study to examine night-to-night associations between light, both preceding and during sleep, and sleep health in a naturalistic environment. Experimental studies inducing light exposure do not reflect the night-to-night natural environment, and identifying associations between light and sleep in a natural setting can inform interventions targeting light exposure at both the community (e.g., street lighting) and individual (e.g., bedroom lighting) level. Second, this study included a sample of adults with naturalistic sleep observed over a period of 7–10 consecutive nights. Also, the two studies that we merged did differ on some measures, including age, sleep percentage and TALT 10 before bed, however these differences may have enhanced generalizability by increasing range of ages, sleep continuity and light exposure. Lastly, multiple aspects of light exposure were included. Thus, this study tested the effect of both the duration and intensity of light exposure. These strengths should be interpreted in light of its limitations. It is possible that light exposure was underestimated due to the actiwatch being covered by either clothing or bedding before or during the sleep period. Participants were instructed to keep the actiwatch uncovered when possible, but future research should consider additional methods for naturalistic light exposure, such as a bedside light meter (Kenji Obayashi, Yamagami, Kurumatani, & Saeki, 2019; Stremler et al., 2021). Our results demonstrated that greater light exposure during sleep was associated with poorer sleep, but temporal precedence cannot be established within the sleep period. For example, we cannot determine whether sleep was fragmented due to light exposure, or whether lights were turned on following fragmented sleep. Future research could determine the temporality of associations by examining epoch-to-epoch patterns of light and sleep. Moreover, future research could examine 24-hour patterns of light exposure across a week to examine how both daytime and nocturnal light exposure relate to sleep. For example, optimal light exposure during the daytime may advance the onset of melatonin even on nights with evening light exposure (Park, Choi, Jo, Cho, & Joo, 2020), but it is not yet clear how this would impact sleep health in a naturalistic setting. Lastly, actigraphy estimated sleep cannot distinguish the source of light exposure (e.g., light pollution vs. bedside lamp).

Results from the current study suggest that increased levels of light exposure during the sleep period is related to poorer sleep health. Future work could continue to examine night-to-night associations between evening light exposure and sleep health, which can help refine multilevel sleep health interventions targeting light exposure at both the structural and individual levels.

Financial Disclosure:

Research reported in this publication was supported by the National Institutes of Health under award numbers NHBLI T32HL007909 (Mead). NHLBI P30 HL101859 (Knutson), NIDDK R01DK095207 (Knutson), NHLBI R01HL152442 (Knutson, Reid).

Footnotes

Non-financial Disclosure: Michael Mead has no non-financial disclosures. Kathryn Reid has no non-financial disclosures. Kristen Knutson has no non-financial disclosures.

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