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  • Published: 17 October 2019

Daily blue-light exposure shortens lifespan and causes brain neurodegeneration in Drosophila

  • Trevor R. Nash 1   na1 ,
  • Eileen S. Chow 1   na1 ,
  • Alexander D. Law   ORCID: orcid.org/0000-0002-0954-7147 2 ,
  • Samuel D. Fu 1 ,
  • Elzbieta Fuszara   ORCID: orcid.org/0000-0003-2707-984X 3 ,
  • Aleksandra Bilska 3 ,
  • Piotr Bebas   ORCID: orcid.org/0000-0003-2956-5892 3 ,
  • Doris Kretzschmar 2 &
  • Jadwiga M. Giebultowicz 1  

npj Aging and Mechanisms of Disease volume  5 , Article number:  8 ( 2019 ) Cite this article

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Light is necessary for life, but prolonged exposure to artificial light is a matter of increasing health concern. Humans are exposed to increased amounts of light in the blue spectrum produced by light-emitting diodes (LEDs), which can interfere with normal sleep cycles. The LED technologies are relatively new; therefore, the long-term effects of exposure to blue light across the lifespan are not understood. We investigated the effects of light in the model organism, Drosophila melanogaster , and determined that flies maintained in daily cycles of 12-h blue LED and 12-h darkness had significantly reduced longevity compared with flies maintained in constant darkness or in white light with blue wavelengths blocked. Exposure of adult flies to 12 h of blue light per day accelerated aging phenotypes causing damage to retinal cells, brain neurodegeneration, and impaired locomotion. We report that brain damage and locomotor impairments do not depend on the degeneration in the retina, as these phenotypes were evident under blue light in flies with genetically ablated eyes. Blue light induces expression of stress-responsive genes in old flies but not in young, suggesting that cumulative light exposure acts as a stressor during aging. We also determined that several known blue-light-sensitive proteins are not acting in pathways mediating detrimental light effects. Our study reveals the unexpected effects of blue light on fly brain and establishes Drosophila as a model in which to investigate long-term effects of blue light at the cellular and organismal level.

Introduction

Natural light is essential for the entrainment of circadian clocks, which leads to temporal coordination of physiology and behavior. However, emerging evidence suggests that increased exposure to artificial light is a risk factor for sleep and circadian disorders. 1 , 2 With the prevalent use of LED lighting and device displays, humans are subjected to increasing amounts of light in the blue spectrum since commonly used LEDs emit a high fraction of blue light, often peaking at 460 nm (these lights appear white due to the addition of broad-spectrum yellow garnet phosphor). 3 Blue light may affect human eyes, 4 and recent data suggest that extraocular light may impact human brain physiology. 5 However, the consequences of daily exposure to blue-enriched light across the lifespan are not known. 6

Research on model organisms suggests that visible light may have a range of detrimental effects. A single acute blue-light exposure causes photoreceptor death in the retina of mice and flies. 7 , 8 , 9 There are reports that light may shorten longevity in Drosophila , 10 , 11 , 12 but the mechanisms underlying the effects of light on lifespan have not been pursued. Here, we characterized the effects of different light exposures on the mortality and aging phenotypes in Drosophila . We report that adult flies maintained in cycles of 12h light and 12h darkness show symptoms of accelerated aging, such as impaired locomotor performance, brain neurodegeneration, and reduced lifespan compared with flies reared in constant darkness. We identified blue light as responsible for these aging phenotypes and investigated the involvement of light-responsive pathways in mediating the detrimental effects of blue light on the brain.

Blue light and aging

To investigate whether light affects Drosophila longevity, we first compared the lifespan of white ( w 1118 , hereafter w ) adult flies kept in daily cycles of 12-h white fluorescent light alternating with 12 h of darkness (L:D) or in constant darkness (D:D). Survival of flies in D:D was significantly extended compared with those in L:D (Log-rank test, p  < 0.0001) and their median lifespan was extended by 42% (Fig. 1a ). The difference in mortality could be caused by delayed aging or by other factors. Aging in flies is associated with slower climbing up the vial walls, which can be measured by using the rapid iterative negative geotaxis (RING) assay. 13 To determine whether the lifespan extension of D:D flies was associated with delayed aging, we measured vertical locomotion by RING. Middle-aged (30-day-old) males kept in D:D had significantly better average climbing ability than flies kept in L:D (Fig. 1b ), suggesting that reduced lifespan of L:D flies may be due to accelerated aging. The lack of pigment granules in the retina makes w flies sensitized to light; 14 therefore, we also tested whether the longevity of wild-type Canton S (CS) flies was affected by light. Indeed, the lifespan of CS males and females was significantly reduced in L:D compared with D:D (Log-rank test, p  < 0.0001), albeit not as strongly as in w flies (Fig. 1c ). Consistent with these results, 30-day-old CS flies in L:D showed a trend toward reduced average climbing ability, which became statistically significant at a later age of 50 days (Fig. 1d ).

figure 1

White fluorescent light shortens fly lifespan and decreases mobility. a Adult white ( w ) flies aged in constant darkness (D:D) have a significantly extended lifespan compared with those aged in white fluorescent light (L:D) (Log-rank test, p  < 0.0001). b Average climbing ability was significantly lower in 30-day-old w males kept in L:D versus those kept in D:D (unpaired t test, p  = 0.0066). c Canton S (CS) adult flies aged in D:D have a significantly extended lifespan compared with those aged in L:D (Log-rank test, p  < 0.0001). d Average climbing ability was lower but not significant in 30-day-old CS males kept in L:D versus those kept in D:D, and significantly lower in 50-day-old CS males kept in L:D versus those kept in D:D (unpaired t test, p  = 0.0434). For longevity experiments in ( a , c ), N  = 100 for each genotype and light condition. Numbers above bars indicate the sample size in each light condition. Error bars show standard error of the mean (SEM)

The spectral composition of light used in the above experiments showed a substantial blue component (Supplementary Fig. 1a ); therefore, we tested the contribution of blue wavelengths commonly used in human environments (LED with peak wavelength at ~460 nm) to the lifespan reduction. Lifespan was measured in flies kept in daily cycles of 12-h blue LED light and 12 h of darkness (B:D), or white LED light with blue wavelengths blocked by a yellow filter (W–B:D) (Supplementary Fig. 1b ). To equalize the amount of exposure across light sources, all light sources hereafter were adjusted to emit similar photon flux density (PFD) as L:D, ranging from 20 to 30 µmol m −2  s −1 , at the level where flies were kept. Compared with flies aged in D:D, the median lifespan of w flies was reduced by ~50% in B:D but only by 4% in W–B:D light (Fig. 2a ). Likewise, blue light caused a more dramatic (~30%) reduction in the median lifespan of CS flies compared with W–B light, which shortened median lifespan by ~10% (Fig. 2b ). We also determined that the lifespan reduction of both w and CS flies corresponded to increased intensity of blue light (Fig. 2c, d ). Pairwise comparisons of mortality curves showed a dose-dependent effect, namely, increasing PFD from 4 to 11, from 11 to 17, and from 17 to 24 µmol each caused a significant increase in mortality (Log-rank tests with Bonferroni multiple correction, p  < 0.0001). Taken together, these results suggest that irradiation by blue wavelengths is mainly responsible for the reduced longevity of flies exposed to light.

figure 2

Light in the blue spectrum is responsible for the decrease in fly lifespan. Lifespan of w a and CS b flies is dramatically reduced in B:D compared with D:D (Log-rank test, p  < 0.0001), but minimally reduced in flies aged under white LEDs lacking blue wavelengths by means of a yellow filter (W–B:D). Median lifespan of w c and CS d flies in B : D is reduced with increasing photon flux density (PFD). Statistics shown are from pairwise comparisons of the corresponding mortality curves that showed a dose-dependent effect, increasing PFD from 4 to 11, from 11 to 17, and from 17 to 24 µmol each, causing a significant increase in mortality (Log-rank tests with Bonferroni multiple correction, p  < 0.0001). Note that the y axis does not start at 0 to highlight these differences in median lifespan. e Survival of w and CS males kept in B:D or in B:D with added orange light (B + O:D). f Mortality curves of the white-eyed ninaE 8 and red-eyed ninaE 7 mutants in B:D and D:D. In all of the above experiments, N  = 100 for each genotype and light condition

Blue light activates Rhodopsin 1, the prevalent opsin in the fly retina, which then requires exposure to orange light in order to regenerate. 15 To test whether lack of orange light may contribute to the reduced lifespan, we kept flies under B:D alone or B:D of similar intensity with the addition of orange LED light (peak at 600 nm, 1.5 µmol m −2  s −1 ) to allow for Rhodopsin regeneration. Median lifespan of both w and CS flies was not extended by the addition of orange light (Fig. 2e ), suggesting that defects in rhodopsin processing are not responsible for the reduced lifespan of flies maintained in blue light. It was reported that blue-light-induced photoreceptor death is ameliorated by mutations in the gene encoding Rhodopsin 1 ( ninaE ), which disrupt phototransduction; 9 therefore, we tested the effects of blue light on the lifespan of ninaE 7 and ninaE 8 mutants, both with reduced rhodopsin levels. 9 , 16 The lifespan of white-eyed ninaE 8 flies was shortened significantly in B:D compared with D:D (Log-rank test, p  < 0.0001) with median lifespan reduced by 21% (Fig. 2f ). The lifespan of red-eyed ninaE 7 flies was also shortened significantly in B:D compared with D:D (Log-rank test, p  < 0.0001), with median lifespan reduced by 9% (Fig. 2f ). The fact that the magnitude of lifespan reduction was smaller in mutants with impaired phototransduction than in w or CS flies suggests that phototransduction may partially contribute to the detrimental effects of blue light.

Blue light acts in the entrainment of the circadian clock even at low intensities; 17 however, we reasoned that levels of blue light that negatively affect longevity could have damaging effects on the clock. To test this, we recorded locomotor activity of flies held in L:D or B:D cycles for 5 days and then transferred to D:D for 5 days. Flies in both L:D and B:D showed prominent morning and evening activity peaks; however, B:D flies were more active throughout the entire light phase, especially at younger ages (Supplementary Fig. 2 ). Upon transfer to D:D, young flies from both regimes showed strong free-running circadian rhythms (Supplementary Fig. 2 ), suggesting that light used in this study is not damaging to the clock. Given these results, we then tested whether disruption of the circadian clock increases the susceptibility to blue light, as it is known that an intact clock confers resistance to many stresses. 18 , 19 We determined that the lifespan of flies with disrupted clocks due to a mutation in the core clock gene period ( per 01 ) was not reduced in B:D compared with w control flies with an intact clock (Supplementary Fig. 3 ), suggesting that a functional clock is not protective against the blue-light exposure used in our experiments.

It has been reported that mammalian and fly retinal photoreceptor cells subjected to acute strong blue light become damaged; 9 , 20 therefore, we asked whether photoreceptor cells are affected by daily 12-h exposure to moderate blue light. The fly retina consists of ~800 identical units called ommatidia, containing 6 outer and 2 inner photoreceptor cells (PR), each possessing a rhabdomere consisting of tightly packed microvilli where the phototransduction occurs. We examined histologically the health of the PRs in w and CS flies kept in D:D or B:D by counting the number of identifiable rhabdomeres (arrows, Fig. 3 ) on the same area of retinal cross sections in different conditions. At the age of 35 days, w and CS flies in D:D showed the regular arrangement of PRs with the dark rhabdomeres clearly distinguishable (Fig. 3a, b ). In contrast, retinal degeneration and disorganized rhabdomeres were evident at this age in flies kept in B:D (Fig. 3a, b ). A quantification confirmed a significant reduction in the average number of distinct rhabdomeres in both w and CS flies in B:D relative to D:D (Fig. 3c, d ). By comparing rhabdomere loss between genotypes in B:D, we determined that it was more significant (unpaired t test, p  = 0.0018) in w flies than in CS flies with normal eye pigmentation. This is consistent with higher PR degeneration reported previously in w flies in unspecified light conditions. 21

figure 3

Retinal photoreceptors degenerate under blue light in flies with white or red eyes. Representative retinal cross sections of 35-day-old w a and CS b males in D:D and B:D. Red arrows point to identifiable rhabdomeres. c , d The average number of rhabdomeres is significantly reduced in 35-day-old w and CS males in B:D compared with D:D (unpaired t test, **** p  < 0.0001). Numbers above bars indicate the sample size in each light condition. Error bars show SEM

Since PR damage occurred even in wild-type flies with normal eye pigmentation, we next asked whether deeper brain tissues are affected by blue-light exposure. To examine the central brain, heads of CS flies aged in D:D or B:D for 52 days were sectioned to measure the size of vacuoles indicative of neuronal loss. A significant increase in the average area of brain vacuolization was detected in CS flies in B:D compared with age-matched flies in D:D (Fig. 4 ).

figure 4

Blue-light exposure leads to neurodegeneration in the aging fly brain. a Representative brain sections showing brain vacuoles (red arrows) in 52-day-old CS males in D:D compared with B:D. b Average area of vacuoles is significantly higher in B:D (unpaired t test, p  = 0.0165). Numbers above bars indicate the sample size in each light condition. Error bars show SEM

The observation that blue-light exposure leads to damage in both PR and the brain raised the question of whether PR degeneration is causally involved in brain neurodegeneration, or alternatively, whether blue light affects the brain independent of the retinal status. To address this, we used eyes-absent ( eya 2 ) mutants, 22 which do not develop compound eyes and thus lack PRs. The lifespan of eya 2 flies was significantly shortened in B:D compared with D:D (Log-rank test, p  < 0.0001), with median lifespan reduced by 37% for males and 42% for females (Fig. 5a ). In contrast, median lifespan was reduced by only 6% and 4%, respectively, in males and females kept in white light with blue wavelengths blocked (W–B:D) compared with flies kept in D:D (Fig. 5a ). Climbing ability was also severely compromised in eya 2 flies in B:D compared with D:D (Fig. 5b ). As in flies with normal eyes, this behavioral deficit was associated with a significant degree of brain degeneration, measured as an increased area of vacuoles in B:D eya 2 flies (Fig. 5c, d ). In an additional experiment, we measured the lifespan of another mutant lacking PRs, sine oculis ( so 1 ), and found that their lifespan was also significantly shortened by blue light; the median lifespan of so 1 in B:D was reduced by 19% compared with D:D (Supplementary Fig. 4 ). Together, these data suggest that accelerated mortality and locomotor impairments of flies maintained in B:D may occur independently of retinal damage. We hypothesize that brain neurodegeneration is a culprit in accelerating aging; however, other organs not studied here may be also involved.

figure 5

Flies lacking retina show reduced lifespan and brain neurodegeneration in blue light. a Lifespan of eyes-absent mutant ( eya 2 ) flies is significantly reduced in B:D compared with D:D (Log-rank test, p  < 0.0001 for males and females), but is similar in W–B:D conditions ( N  = 100 for each light condition). b Aged eya 2 males show a significant reduction in the average vertical climbing ability in B:D compared with D:D (unpaired t test, p  = 0.0009). c Representative brain sections showing brain vacuoles (red arrowheads) in 52-day-old eya 2 males in D:D and B:D. d The average area of brain vacuolization of 52-day-old eya 2 males was significantly increased in B:D compared with D:D (unpaired t test, p  = 0.0352). Numbers above bars indicate the sample size in each light condition. Error bars show SEM

To begin investigating molecular pathways mediating the damaging action of blue light on the brain, we first considered cryptochrome, the blue-light-sensitive photoreceptor protein encoded by the gene cry . In flies, the CRY protein is the major light sensor for the entrainment of the circadian clock, 23 , 24 and it is involved in modulation of neuronal activity and behavior by blue light. 25 , 26 To test whether CRY could mediate the phototoxicity of blue light, we measured the lifespan of flies with genetically manipulated cry expression held in B:D or D:D. We found that neither a null mutation in the cry gene nor overexpression of cry affected survival in B:D conditions (relative to D:D) compared with their respective controls (Supplementary Fig. 5a ), suggesting that CRY is not involved in the lifespan alterations caused by blue light. In addition to cry , we tested whether the recently identified Rhodopsin 7 (Rh7) plays a role in inducing the aging phenotypes. RH7 protein is sensitive to blue light and its mRNA is weakly expressed in both the brain and the retina. 27 We determined that median lifespan was similarly shortened in B:D relative to D:D, both in Rh7 1 mutants and in flies overexpressing Rh7 , compared with their respective controls (Supplementary Fig. 5b ), suggesting that this chromoprotein is not involved in mediating the effects of daily blue-light exposure on longevity. We note that it is still possible that removing all photoreceptive pathways (i.e., cry and rhodopsins together) could reduce blue-light-induced damage.

What are the proximate causes of premature aging of flies in B:D? Our recent RNA-seq study comparing the diurnal transcriptome in heads of young and old flies demonstrated that several stress-response genes are upregulated in heads of 55-day-old w flies kept in L:D 12:12 cycles, and their maximal expression over a 24-h period occurred after 12 h of light exposure. 28 These genes also become induced in young flies kept in L:D but subjected to oxidative stress by treatment with 100% oxygen. 28 Given our observation that L:D shortens the lifespan in flies (Fig. 1 ), and reports that blue light induces oxidative stress in retinal cells 9 , 20 and in the nematode Caenorhabditis elegans , 29 we tested whether blue light increased the expression of genes known to be induced by oxidative stress. The expression of selected stress-response genes was measured in heads of day 5 or day 35 w flies maintained in B:D and collected at the end of their daily 12 h of blue-light exposure. To discern the effects of light, we collected simultaneously 5- or 35-day-old w flies maintained in D:D; these flies are expected to show average expression of diurnal genes due to the absence of clock entrainment by light. Some of the known oxidative stress-response genes ( Gclc , GstO1 ) were not upregulated in B:D; however, expression of several other genes was significantly increased in 35-day-old flies in B:D compared with age-matched D:D controls (Fig. 6a ). These included cnc (the fly homolog of the transcription factor Nrf2 ), thioredoxin reductase Trxr-1 , glutathione S transferases GstD1 and GstD2 , and several heat-shock proteins: Hsp23 , Hsp68 , and Hsp70 . Most of the examined genes (with the exception of Gclc , Trxr-1 , and GstD2 ) did not increase expression in 35-day-old D:D flies compared with 5-day-old D:D flies, suggesting that blue light plays a much bigger role in upregulation of stress-response genes than aging by itself. We also observed strong upregulation of the metabolic gene, lactate dehydrogenase ( Ldh ), which is known to increase with aging and stress. 28 Importantly, none of the examined genes showed an increase in 5-day-old flies kept in B:D compared with D:D, suggesting that the cumulative action of blue light over many days is needed to induce stress-response genes, or that response to blue light is age-dependent (Fig. 6a ). To explore these possibilities further, we tested survival of flies exposed to B:D or D:D for a set number of days and then switched to the opposite conditions. We kept w flies in B:D throughout their life, or for the first 25 or 30 days of adult life followed by a transfer to D:D, and compared their lifespan. As shown in Fig. 6b , exposure for the first 25 days of adulthood caused some flies to die within a few days, but most of the remaining flies survived nearly as long as flies that were always kept in D:D. However, exposing flies to B:D for the first 30 days of adulthood (only 5 days longer than in the previous experiment) followed by a transfer to D:D resulted in the majority of flies dying shortly after the switch to D:D. These flies had a median lifespan of 34 days, similar to the 33 days of controls kept continually in B:D. In a reverse experiment, we kept flies in D:D for 30 days and then exposed them to B:D for the rest of their lives. The median survival of these 30-day-old flies was 21 days after the switch to B:D, while the median lifespan of young flies exposed to B:D was 34 days. These results suggest two conclusions. First, blue light has cumulative damaging effects, but the damage can be halted upon removal of this type of stress, provided that it does not accumulate beyond a certain irreversible threshold that causes death. Second, blue-light damage affects flies differently across their lifespan with vulnerability to this part of the visible spectrum increasing with age. In other words, blue-light-induced damage seems to accumulate faster with advancing age.

figure 6

Flies maintained in blue light show induction of stress-response genes by day 35. a Expression levels of the indicated stress-response genes in heads of 5- and 35-day-old w males maintained in B:D or D:D. For each gene measured, values from qPCR are reported as fold change relative to expression in young flies in D:D set as 1. Statistics by 2-way ANOVA (**** p  < 0.0001; ** p  < 0.01; * p  < 0.05). Bars show the average of two biorepeats; error bars show SEM. b Median lifespan of w flies exposed to B:D and/or D:D for the durations indicated by blue or black bars. Flies kept in B:D for the first 30 days of adulthood and then transferred to D:D had a similar median survival to those kept in B:D throughout their entire life, while flies kept in B:D for the first 25 days of adulthood were able to survive much longer (Log-rank test, p  < 0.0001). Flies kept in D:D for the first 30 days of adulthood experienced an increased mortality rate upon transfer to B:D. Mortality curves for each condition are shown on the right

Understanding the effects of blue light on various life processes is becoming an increasingly important health issue as humans are exposed to more blue-enriched LED illumination for most of the day, or even at night due to shift work and light pollution in large cities. 6 However, long-term consequences of increased daily blue-light exposure across the human lifespan are not known. In this study, we demonstrate that daily exposure to 12 h of visible light in the blue part of the spectrum accelerates aging in Drosophila . Light causes not only retinal damage but also neurodegeneration in the central nervous system, which may be involved in the premature decline in climbing ability and early mortality. Our data also suggest that susceptibility to light increases with age and repetitive exposure to blue light induces the expression of stress-response genes.

The detrimental effects of light on longevity have been reported recently in C. elegans ; 29 exposing these nematodes to white light or different parts of the light spectrum significantly reduced their lifespan, 29 suggesting a broad susceptibility to light in this species, albeit with stronger effects of shorter wavelengths. Our data suggest that blue light is driving the aging phenotypes in flies since it dramatically reduced the lifespan, while light in the 500–700-nm range with similar photon flux only minimally affected longevity compared with D:D.

Numerous studies reported that light in the blue spectrum causes damage to retinal cells in vitro and in vivo in mammals and Drosophila . 7 , 9 , 30 While these studies employed acute strong light, we show that photoreceptor cells of aging flies degenerate in response to 12 h of daily exposure to moderate blue light. This degeneration was more pronounced in w flies than in age-matched CS flies, presumably due to a lack of the screening red pigment in the former genotype. 21 Blue-light-induced degeneration of fly retinal photoreceptors appears to involve the phototransduction cascade, as retinal damage is mitigated by mutations that impair phototransduction. 9 We show here that this may not be the case for organismal aging, because these mutations only partially rescue the lifespan reduction caused by blue light. Likewise, addition of orange light, which is known to deactivate rhodopsin, did not rescue the lifespan. Thus, it appears that the effects of light on retinal versus organismal aging may be mediated by different mechanisms.

A surprising outcome of our study is that blue light not only damaged the retina, but also caused neurodegeneration in the brain. A significant age-specific increase in the area of vacuoles indicative of neuronal death was observed in brains of flies in B:D compared with age-matched flies in D:D. In addition, we demonstrate that blue-light-induced damage to the brain occurs whether or not the retina is present, suggesting that light can affect the brain directly and independently of the visual system degeneration. To address possible blue-light-activated pathways in the brain, we tested the involvement of the photoreceptive proteins CRY and RH7, both of which are expressed in the brain, and determined that neither loss nor overexpression of either protein significantly affected fly survival in blue light. Further studies will be required to dissect the input pathways mediating the effects of blue light on the brain. We note that the effects of light on extra-retinal tissues may not be limited to invertebrates. There are reports that the exposure of rats or mice to constant light for several months was associated with a significant reduction in the number of dopaminergic neurons. 31 , 32 In addition, transcranial blue light may impact human brain activity. 5 Taken together, these data suggested that the question of possible detrimental effects of light on brain aging deserves more attention.

We hypothesize that light-induced brain neurodegeneration may be the main cause of the decreased vertical mobility and reduced lifespan. However, at this time, we cannot exclude the possibility that other fly tissues could be affected by blue light and contribute to the accelerated aging. For example, the study on C. elegans showed that mitochondria in the muscles were damaged by constant light exposure. 29

Several studies determined that blue-light exposure results in the generation of reactive oxygen species (ROS) in the retina of mice 20 and flies 9 and even in skin cells. 33 Light exposure that shortens the lifespan of C. elegans also increases ROS levels and induced an unfolded protein response. 29 The expression of selected stress-response genes was induced by light in worms 29 and in photoreceptor cells of the fly retina. 34 Our data are consistent with these findings in that we detected increased expression of several stress-response genes in the heads of 35-day-old flies in B:D. These included cnc gene, the fly homolog of the transcription factor Nrf2 , which was also shown to be upregulated in response to blue light in murine retinal pigment epithelium cells. 35 Importantly, expression of stress-response genes was not elevated in young flies in B:D compared with D:D, which is consistent with our data showing that mortality-inducing stress requires multiple cycles of blue light and is age-dependent. In summary, our data suggest that blue light needs to be added to a range of environmental stressors that become increasingly harmful with repetitive exposure.

Flies are used extensively to understand the mechanisms of aging in laboratories across the world, but the specifics of light conditions in terms of intensity and spectral composition are usually not provided. Our study suggests that the light used in fly facilities may critically affect experimental outcomes and should be reported in aging studies to facilitate the consistency of the results coming from different labs. Our discovery that lifetime exposure to artificial light may cause extra-retinal damage and reduce longevity in a complex model organism provides a novel opportunity to understand the molecular mechanisms of the increasingly evident harmful side of light.

Fly maintenance and genotypes

Drosophila melanogaster was maintained on diet containing yeast (35 g/l), cornmeal (50 g/l), and molasses (5%) at 25 ± 1 °C. The genotypes used in this study are described in Supplementary Table 1 . Flies used in the experiments were mated and separated by sex when 1–2 days old. Fly colonies were reared in cycles of 12 h of fluorescent light alternating with 12 h of darkness (L:D). Experimental adult flies were maintained in constant darkness or daily cycles of 12-h light from specified light sources.

Light treatments

Light emitted from different sources was measured at the level where flies were kept by using an SQ-120: Electric Calibration Quantum Sensor (Apogee) and expressed as photon flux density (PFD). The spectrum of each light source was measured with a P100-2-VIS-NIR, optical fiber C (Ocean Optics). During the light phase of the standard L:D cycle in the fly room, flies were exposed to white fluorescent light with an average PFD of 25 µmol m −2  s −1 . In addition, all light sources used in specific experiments are described in Supplementary Table 2 . Control flies were kept in constant darkness throughout their adult lifespan and were handled under red light.

Longevity and behavioral testing

For each genotype and light condition, lifespan was measured by using at least 100 males or 100 females held in groups of 25 in narrow fly vials (Genesee Scientific) with mortality recorded and fresh diet provided every 2–3 days. Mortality curves were statistically analyzed by using the Log-rank test in GraphPad Prism 6. As a behavioral aging biomarker, we tested climbing ability by using the RING assay as described. 36 Briefly, for each group tested, three vials (without diet), each containing 25 flies, were tapped down (groups being compared were tapped simultaneously) to bring all flies to the bottom of each vial, initiating a rapid negative geotaxis response. Fly upward movement was video recorded, and images were captured 4 s after tapping. These images were analyzed by using NIH ImageJ software to calculate the flies’ average climbing height in each vial. Statistical significance between groups was determined with unpaired t tests by using GraphPad Prism 6. To assess locomotor activity, adult males were held individually in glass tubes placed in Drosophila Activity Monitors DAM2 or DAM5 (Trikinetics), and activity counts were measured every 15 min. Flies were monitored for five 24-h cycles of L:D or B:D, followed by five 24-h intervals of D:D. Activity data were analyzed by using ClockLab version 2.72 (Actimetrics). Flies were deemed rhythmic if their activity during D:D resulted in an ~24-h periodogram amplitude peak breaking the 99% confidence line, and a Fast Fourier Transform power of 0.04 or above.

Photoreceptors and brain health

To assess retinal degeneration, we quantitatively determined photoreceptor cell survival on paraffin cross sections of the eye by counting the number of rhabdomeres. The severe disorganization of the ommatidia in some of the conditions made it difficult to identify which of the rhabdomeres belonged to an ommatidia, and we therefore did not count rhabdomeres per ommatidia. Instead, we counted the number of identifiable rhabdomeres in an area of 160 × 160 pixels in images taken at the same magnification (×40) and resolution (1920 × 1440 pixels). To ensure that measurements were done at similar level of the eye, we used images where the anterior–posterior diameter was about 864 pixels and placed the area to be counted in the middle of the image. To quantify light-induced neurodegeneration in the brain, we measured the average area of all vacuoles seen on sections of the brain as described previously. 37 Analyses were done double-blind, and statistical significance determined with unpaired t tests by using GraphPad Prism 6.

RNA extraction and qRT-PCR

Frozen heads were separated from bodies by vortexing tubes in liquid nitrogen and with stainless-steel sieves with mesh- opening sizes of 710 and 425 µm. Each sample of 50 heads was homogenized in TRIzol (Thermo Fisher) with a Kontes handheld motorized pestle. RNA was extracted according to the manufacturer’s instructions, and samples were treated with rDNAse I (Takara) followed by phenol/chloroform extraction. RNA was precipitated with ethanol and sodium acetate. cDNA was synthesized from 1 µg of total RNA with the Maxima First Strand cDNA Synthesis Kit (Thermo Fisher). Quantitative real-time polymerase chain reaction (PCR) was performed with Power SYBR Green PCR Master Mix (Thermo Fisher) on a StepOnePlus Real-Time PCR System (Applied Biosystems). Relative expression of genes of interest was calculated by using DCP2 as the reference gene and 2 −ΔΔCT data analysis. All primers (Integrated DNA Technologies) were verified to have >90% efficiency; sequences can be found in Supplementary Table 3 .

Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files). Any additional material is available from the corresponding author.

Chang, A. M., Aeschbach, D., Duffy, J. F. & Czeisler, C. A. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc. Natl Acad. Sci. USA 112 , 1232–1237 (2015).

Article   CAS   Google Scholar  

Green, A., Cohen-Zion, M., Haim, A. & Dagan, Y. Evening light exposure to computer screens disrupts human sleep, biological rhythms, and attention abilities. Chronobiol. Int. 34 , 855–865 (2017).

Tsao, J. Y., Coltrin, M. E., Crawford, M. H. & Simmons, J. A. Solid-state lighting: an integrated human factors, technology, and economic perspective. IEEE 98 , 1162–1179 (2010).

Article   Google Scholar  

Behar-Cohen, F. et al. Light-emitting diodes (LED) for domestic lighting: any risks for the eye? Prog. Retinal Eye Res. 30 , 239–257 (2011).

Sun, L. et al. Human brain reacts to transcranial extraocular light. PLoS ONE 11 , e0149525 (2016).

Hatori, M. et al. Global rise of potential health hazards caused by blue light-induced circadian disruption in modern aging societies. NPJ Aging Mech. Dis. 3 , 9 (2017).

Kuse, Y., Ogawa, K., Tsuruma, K., Shimazawa, M. & Hara, H. Damage of photoreceptor-derived cells in culture induced by light emitting diode-derived blue light. Sci. Rep. 4 , 5223 (2014).

Osborne, N. N., Nunez-Alvarez, C. & del Olmo-Aguado, S. The effect of visual blue light on mitochondrial function associated with retinal ganglions cells. Exp. Eye Res. 128 , 8–14 (2014).

Chen, X. et al. Cytochrome b5 protects photoreceptors from light stress-induced lipid peroxidation and retinal degeneration. NPJ Aging Mech. Dis. 3 , 18 (2017).

Shen, J. et al. Toxic effect of visible light on drosophila life span depending on diet protein content. J. Gerontol. A Biol. 74 , 163–167 (2019).

Shibuya, K., Onodera, S. & Hori, M. Toxic wavelength of blue light changes as insects grow. Plos ONE 13 , e0199266 (2018).

Hori, M., Shibuya, K., Sato, M. & Saito, Y. Lethal effects of short-wavelength visible light on insects. Sci. Rep. 4 , 7383 (2014).

Rhodenizer, D., Martin, I., Bhandari, P., Pletcher, S. D. & Grotewiel, M. Genetic and environmental factors impact age-related impairment of negative geotaxis in Drosophila by altering age-dependent climbing speed. Exp. Gerontol. 43 , 739–748 (2008).

Stark, W. S. & Carlson, S. D. Blue and ultraviolet light induced damage to the Drosophila retina: ultrastructure. Curr. Eye Res. 3 , 1441–1454 (1984).

Wang, T. & Montell, C. Phototransduction and retinal degeneration in Drosophila. Pflug. Arch. 454 , 821–847 (2007).

Washburn, T. & Otousa, J. E. Molecular defects in Drosophila Rhodopsin Mutants. J. Biol. Chem. 264 , 15464–15466 (1989).

CAS   PubMed   Google Scholar  

Busza, A., Emery-Le, M., Rosbash, M. & Emery, P. Roles of the two Drosophila CRYPTOCHROME structural domains in circadian photoreception. Science 304 , 1503–1506 (2004).

Krishnan, N., Davis, A. J. & Giebultowicz, J. M. Circadian regulation of response to oxidative stress in Drosophila melanogaster . Biochem. Biophys. Res. Commun. 374 , 299–303 (2008).

Krishnan, N., Kretzschmar, D., Rakshit, K., Chow, E. & Giebultowicz, J. The circadian clock gene period extends healthspan in aging Drosophila melanogaster . Aging 1 , 937–948 (2009).

Organisciak, D. T. & Vaughan, D. K. Retinal light damage: mechanisms and protection. Prog. Retinal Eye Res. 29 , 113–134 (2010).

Ferreiro, M. J. et al. Drosophila melanogaster White Mutant w(1118) undergo retinal degeneration. Front. Neurosci. 11 , 732 (2018).

Bonini, N. M., Leiserson, W. M. & Benzer, S. The eyes absent gene—genetic-control of cell-survival and differentiation in the developing drosophila eye. Cell 72 , 379–395 (1993).

Stanewsky, R. et al. The cry b mutation identifies cryptochrome as a circadian photoreceptor in Drosophila . Cell 95 , 681–692 (1998).

Emery, P. et al. Drosophila CRY is a deep brain circadian photoreceptor. Neuron 26 , 493–504 (2000).

Fogle, K. J. et al. CRYPTOCHROME-mediated phototransduction by modulation of the potassium ion channel beta-subunit redox sensor. Proc. Natl. Acad. Sci. USA 112 , 2245–2250 (2015).

Baik, L. S., Recinos, Y., Chevez, J. A. & Holmes, T. C. Circadian modulation of light-evoked avoidance/attraction behavior in Drosophila. Plos ONE 13 , e0201927 (2018).

Senthilan, P. R., Grebler, R., Reinhard, N., Rieger, D. & Helfrich-Forster, C. Role of rhodopsins as circadian photoreceptors in the Drosophila melanogaster . Biology 8 , pii: E6. (2019).

Kuintzle, R. C. et al. Circadian deep sequencing reveals stress-response genes that adopt robust rhythmic expression during aging. Nat. Commun. 8 , 14529 (2017).

De Magalhaes Filho, C. D. et al. Visible light reduces C. elegans longevity. Nat. Commun. 9 , 927 (2018).

Hunter, J. J. et al. The susceptibility of the retina to photochemical damage from visible light. Prog. Retinal Eye Res. 31 , 28–42 (2012).

Romeo, S. et al. Bright light exposure reduces TH-positive dopamine neurons: implications of light pollution in Parkinson’s disease epidemiology. Sci. Rep. 3 , 1395 (2013).

Romeo, S. et al. Fluorescent light induces neurodegeneration in the rodent nigrostriatal system but near infrared LED light does not. Brain Res. 1662 , 87–101 (2017).

Nakashima, Y., Ohta, S. & Wolf, A. M. Blue light-induced oxidative stress in live skin. Free Radic. Biol. Med. 108 , 300–310 (2017).

Hall, H., Ma, J. Q., Shekhar, S., Leon-Salas, W. D. & Weake, V. M. Blue light induces a neuroprotective gene expression program in Drosophila photoreceptors. BMC Neurosci . 19 , 1 (2018).

Takayama, K. et al. Nuclear factor (erythroid-derived)-related factor 2-associated retinal pigment epithelial cell protection under blue light-induced oxidative stress. Oxid. Med. Cell. Longev. 2016 , 8694641 (2016).

Krishnan, N. et al. Loss of circadian clock accelerates aging in neurodegeneration-prone mutants. Neurobiol. Dis. 45 , 1129–1135 (2012).

Sunderhaus, E. R. & Kretzschmar, D. Mass histology to quantify neurodegeneration in Drosophila. J. Vis. Exp. 15 , 118 (2016).

Google Scholar  

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Acknowledgements

The authors thank Oksana Ostroverkhova, Eli Meyer, James Strother, Jim Pearson, and Tom Giebultowicz for help with obtaining and characterizing light sources. We thank David Hendrix, Rosalyn Fey, and Barbara Gvakharia for reading the paper. We thank Patrick Emery, Jeff Hall, Paul Hardin, and Subhash Katewa for sharing fly stocks. Other stocks used in this study were obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537). This work was supported by the National Institute of Aging of NIH under award numbers R01 AG045830 and R56 AG062621 to J.M.G.

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These authors contributed equally: Trevor R. Nash, Eileen S. Chow

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Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA

Trevor R. Nash, Eileen S. Chow, Samuel D. Fu & Jadwiga M. Giebultowicz

Oregon Institute of Occupational Health Sciences, Oregon Health and Science University, Portland, OR, 97239, USA

Alexander D. Law & Doris Kretzschmar

Department of Animal Physiology, Faculty of Biology, University of Warsaw, 02-096, Warsaw, Poland

Elzbieta Fuszara, Aleksandra Bilska & Piotr Bebas

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J.M.G., E.S.C., and T.R.N. conceived the project; J.M.G., T.R.N., and E.S.C. designed the experiments; T.R.N., E.S.C., A.D.L., S.D.F., E.F., A.O., and D.K. performed the experiments; J.M.G., E.S.C., P.B., and D.K. wrote the paper with input from all authors.

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Correspondence to Jadwiga M. Giebultowicz .

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Nash, T.R., Chow, E.S., Law, A.D. et al. Daily blue-light exposure shortens lifespan and causes brain neurodegeneration in Drosophila . npj Aging Mech Dis 5 , 8 (2019). https://doi.org/10.1038/s41514-019-0038-6

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The influence of blue light on sleep, performance and wellbeing in young adults: A systematic review

Marcia ines silvani.

1 Faculty of Medicine, University of Lucerne, Lucerne, Switzerland

Robert Werder

2 Institute of Sports Medicine, Swiss Paraplegic Centre, Nottwil, Switzerland

Claudio Perret

Phyllis Kravet Stein , Washington University in St. Louis, United States

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Introduction: Blue light from electronic devices has a bad reputation. It has a wavelength which may influence our circadian rhythm and cause bad sleep. But there are other aspects of blue light exposure which are often overlooked, for example, it may influence performance and wellbeing. However, few resources summarize its effects systematically. Therefore, the goal of this systematic review was to distil the present evidence on blue light exposure and its influence on sleep, performance and wellbeing and discuss its significance for athletes.

Methods: The databases that were searched were Cochrane, Embase, Pubmed, Scopus, and Virtual Health Library. The studies included investigated the influence of blue light exposure on either sleep, performance, wellbeing or a combination of those parameters on healthy humans. Quality assessment was done based on the quantitative assessment tool “QualSyst.”

Results: Summarizing the influence of blue light exposure, the following results were found (expressed as proportion to the number of studies investigating the particular parameter): Fifty percent of studies found tiredness to be decreased. One fifth of studies found sleep quality to be decreased and one third found sleep duration to be decreased. Half of the studies found sleep efficacy to be decreased and slightly less than half found sleep latency to be increased. More than one half of the studies found cognitive performance to be increased. Slightly more than two thirds found alertness to be increased and reaction time to be decreased. Slightly less than half of the studies found wellbeing to be increased.

Conclusion: Blue light exposure can positively affect cognitive performance, alertness, and reaction time. This might benefit sports reliant on team-work and decision-making and may help prevent injury. Blue light might also have negative effects such as the decrease in sleep quality and sleep duration, which might worsen an athlete’s physical and cognitive performance and recovery. Further research should explore if blue light can improve sleep, performance and wellbeing to significantly benefit athletic performance.

1 Introduction

Electronic devices, such as television, computers and smartphones have become permanent features of our everyday life. In combination with the increased use of those electronic devices a decrease in sleep quality has been reported ( Hysing et al., 2015 ). This piqued researchers’ interest and it was found that blue light emitted by electronic devices suppresses the secretion of the hormone melatonin ( Tordjman et al., 2017 ). One of the main functions of melatonin is the regulation of the circadian rhythm ( Tordjman et al., 2017 ), which consequently influences sleep ( Chang et al., 2015 ). The general consensus was that f blue light from electronic media negatively affects sleep quality. However, this is not a fair representation of the whole research that has been conducted concerning blue light. In fact, numerous studies report that blue light exposure did not only have negative, but also positive effects. For instance, it was reported that blue light exposure is an effective treatment against major depression symptoms ( Strong et al., 2009 ), has a stimulating effect on cognitive brain activity ( Vandewalle et al., 2013 ) and increases physical performance ( Knaier et al., 2017b ). The positive and negative effects of blue light are of interest for athletes for three reasons. Firstly, good sleep hygiene is the foundation of a strong performance ( Samuels et al., 2016 ), it is therefore important to find out if sleep is negatively influenced by blue light. Secondly, many athletes suffer from sleep deprivation due to busy training schedules ( Romyn et al., 2016 ), it is hence of interest to investigate if blue light exposure may improve performance by increasing alertness or cognitive function. Thirdly, wellbeing has an impact on athletic performance ( Lastella et al., 2014 ) and thus it is out of interest whether this might be influenced by blue light exposure. Even though a vast amount of research has been conducted, a systematic analysis of existing findings is yet to be conducted, leaving the current standard of knowledge on blue light exposure unknown and the three questions mentioned above unanswered. This provided the rationale to conducting a systematic review on the influence of blue light on sleep, performance and wellbeing. In a first step, we decided to focus on healthy humans to ensure that enough data can be gathered for meaningful statements. At present, existing systematic reviews investigate the influence of blue light exposure on circadian rhythm ( Tähkämö et al., 2019 ), macular health ( Lawrenson et al., 2017 ), mental disorders ( Srisurapanont et al., 2021 ) or tumors ( Lai and Yew, 2016 ). To the best of our knowledge no systematic review has yet investigated the influence of blue light on sleep, performance and wellbeing either in elite athletes or in healthy humans. Therefore, the present study aimed to collect data to give clear and systematic insights on the current findings concerning these topics. The outcome of those findings will determine whether further studies are needed and if yes, what those studies might investigate.

This systematic review was conducted by following the PRSIMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Moher et al., 2015 ).

2.1 Eligibility criteria

Randomized controlled trials, cohort studies, case-control studies and cross-sectional studies in English were reviewed. These studies had to investigate the influence of blue light exposure on either sleep, performance, wellbeing or a combination of those parameters. Studies that investigated the influence of blue light on participants with health issues (e.g., eye sickness, mental disorders or sleep-wake disorders) or explored only circadian phase and melatonin levels were excluded.

2.2 Source of information and search strategies

The following databases were searched: Cochrane, Embase, Pubmed, Scopus, and Virtual Health Library. The search strategy was a compound formed by the four cluster terms blue light, sleep, performance and wellbeing, connected to each other by the term “AND.” Terms related closely to the four cluster terms were connected to the latter by the term “OR.” To gather more data the search strategy was adjusted to only include three cluster terms, either “blue light, sleep and performance” or “blue light, sleep, and wellbeing.” A detailed overview of the search strategy is shown in Table 1 . The search was conducted on 27th of September 2020.

Overview concerning the search strategy.

Number of hits on keywords and combined keywords in Title/Abstract with advanced search for Cochrane, Embase, PubMed, Scopus and VHL. For PubMed the filter “human” was added. The eight additional studies from listed references of the included studies are not represented on Table 1 . For more information see Figure 1 . N/A, Not applicable; VHL, Virtual health library.

2.3 Study selection and data collection process

All duplicates were removed and the studies were screened for the eligibility criteria. In a first step, titles and abstracts were screened and unsuitable studies were removed. The remaining studies were screened for full text. Additionally, listed references of the included studies were also screened. Figure 1 presents the detailed selection process for the research studies. The data collection included the age of the participants, their activity, the intervention type, the duration of the intervention, measurements, study design and the outcome on either sleep, performance or wellbeing.

An external file that holds a picture, illustration, etc.
Object name is fphys-13-943108-g001.jpg

Selection process for research articles included in the review. Modified version from the recommendation in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement Moher et al. (2015) . Legend: Virtual Health Library (VHL).

2.4 Quality assessment

The quantitative assessment tool “QualSyst” was used for the quality assessment ( Kmet et al., 2004 ). The studies were scored using fourteen items. Each item was marked with either a yes = 2, partial = 1, no = 0, or N/A = not applicable. Items marked with N/A were excluded from the calculation. The final score was determined by summing up the total score across the relevant items, expressed as a percentage of the theoretical maximum Studies were categorized as strong quality (>75%), moderate quality (55%–75%) and weak quality (<55%). This quality assessment was performed by two reviewers (MS, CP). Study quality scores that differed between reviewers were discussed until consensus was found.

3.1 Study selection

The initial search strategy resulted in a total of 488 hits. After title and abstract screening, 78 studies were selected for full text screening. The screening of the listed references resulted in eight additional studies. After the full text screening 36 eligible studies based on systematic use of inclusion and exclusion criteria were recorded. The quality assessment of the 36 selected studies revealed 24 studies to be of strong and twelve of moderate quality. Detailed results of the quality assessment are presented in Table 2 .

Quality assessment “QualSyst” according to Kmet et al. (2004) .

N/A not applicable, 2 indicates yes, 1 indicates partial, 0 indicates no; Quality scores: >75% strong, 55% ≥ 75% moderate, <55% weak.

3.2 Blue light and sleep

3.2.1 age, intervention, and duration.

Twenty-four studies investigated the influence of blue light exposure on sleep ( Table 3 ). The participants’ average age was 26 years, with one study not mentioning the exact age of their participants other than indicating that they were all adults ( Rångtell et al., 2016 ). Twelve studies compared blue light with a different colored light intervention such as white, red or orange light ( Table 3 ). Twelve studies used electronic devices such as smartphones, tablets and computers and compared them with blue light filter control conditions. Such as blue light blocking glasses or blue light filters for displays ( Table 3 ). The average exposure time was 2.2 h. Those studies with longer exposure times were separately calculated and averaged around 2.75 weeks. One study did not mention the exact exposure time ( Chindamo et al., 2019 ). Exact exposure times for each study are listed on Table 3 .

Effects of blue light on sleep.

Increase (↑), decrease (↓), blue light (BL), blue light blocking (BLB), cold cathode fluorescent lamp (CCFL), clear lenses (CL), Consensus Sleep Diary (CSD), dawn simulation light (DSL), electroencephalography (EEG), Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), Holland Sleep Disorder Questionnaire (HSDQ), thousand (K), Karolinska Drowsiness Test (KDT), Karolinska Sleep Diary (KSD), Karolinska Sleepiness Scale (KSS), light emitting diode (LED), orange light (OL), polysomnography (PSG), Pittsburgh Sleep Quality Index (PSQI), randomized controlled trial (RCT), red light (RL), Sleep Hygiene Index (SHI), Stanford Sleepiness Scale (SSS), Visual Analogue Scale (VAS) and white light (WL).

3.2.2 Activity and measurement

Activity during the blue light exposure included relaxed sitting, leisure, bedtime routine, reading on an electronic device, mundane tasks on a smartphone such as Facebook use, cognitive tasks, driving a car, office work, and physical training ( Table 3 ). The most commonly used methods to measure the influence of blue light on sleep were the Karolinska Sleepiness Scale (KSS), polysomnography (PSG) including electroencephalography (EEG), the Pittsburgh Sleep Quality Index (PSQI), questionnaires, actigraphy and Likert scale ( Table 3 ). The following methods of measurement were used less frequently across the included studies: Consensus Sleep Diary (CSD), Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), Holland Sleep Disorder Questionnaire (HSDQ), Karolinska Drowsiness Test (KDT), Karolinska Sleep Diary (KSD), Sleep Hygiene Index (SHI), Stanford Sleepiness Scale (SSS) and Visual Analogue Scale (VAS) ( Table 3 ). The methods of measurements were applied either before, during or after the blue light intervention.

3.2.3 Sleep responses to blue light

The term sleep included the elements tiredness, sleep quality, sleep duration, sleep efficacy, and sleep latency. More details to each element are given in the following sections.

3.2.3.1 Tiredness

Tiredness can be assessed in the categories sleepiness, fatigue, wakefulness, alertness, and arousal. For sleepiness the following data was found: eight studies reported a decrease in subjective sleepiness when exposed to blue light conditions ( Lockley et al., 2006 ; Viola et al., 2008 ; Cajochen et al., 2011 ; Chang et al., 2015 ; Grønli et al., 2016 ; Münch et al., 2016 ; Heo et al., 2017 ; Motamedzadeh et al., 2017 ). However, one study reported a higher subjective sleepiness while wearing blue light blocking glasses when compared to the blue light condition, where no blue light blocking glasses were worn ( Van Der Lely et al., 2015 ). Five studies did not find any significant change in subjective sleepiness between blue light and non-blue light conditions ( Phipps-Nelson et al., 2009 ; Sahin and Figueiro, 2013 ; Heath et al., 2014 ; Ayaki et al., 2016 ; Rångtell et al., 2016 ) and two studies reported an increase in subjective sleepiness when exposed to the blue light condition ( Iskra-Golec et al., 2012 ; Gabel et al., 2013 ). One study found fatigue to be decreased following blue light exposure ( Viola et al., 2008 ). One study found wakefulness not to be significantly decreased when exposed to the blue light condition ( Chellappa et al., 2013 ), however one study found that morning alertness was delayed when exposed to the blue light condition, adding to the evidence that blue light may increase tiredness ( Chang et al., 2015 ). Another study showed no significant change in arousal levels ( Rångtell et al., 2016 ). Summarizing all these results under the term tiredness, seventeen studies reported measures related to tiredness. Out of those seventeen studies, nine studies reported blue light exposure to be decreasing tiredness. But because the term tiredness categorizes sleepiness, fatigue, wakefulness, morning alertness, and arousal, the studies which included more than one of these characteristics should be counted for the number of times mentioned. This means Chang et al. (2015) , Rångtell et al. (2016) , Viola et al. (2008) should be counted twice, creating a new total of 20 occurrences. Out of those 20 occurrences, ten occurrences, counting the reference Viola et al. (2008) twice, reported of decreasing tiredness ( Lockley et al., 2006 ; Viola et al., 2008 ; Cajochen et al., 2011 ; Chang et al., 2015 ; Van Der Lely et al., 2015 ; Grønli et al., 2016 ; Münch et al., 2016 ; Heo et al., 2017 ; Motamedzadeh et al., 2017 ). Seven occurrences, with reference Rångtell et al. (2016) counting twice, reported no significant change between conditions ( Phipps-Nelson et al., 2009 ; Chellappa et al., 2013 ; Sahin and Figueiro, 2013 ; Heath et al., 2014 ; Ayaki et al., 2016 ; Rångtell et al., 2016 ). And three studies reported an increase in tiredness in the blue light condition ( Iskra-Golec et al., 2012 ; Gabel et al., 2013 ; Chang et al., 2015 ). Summarized, there were thirteen out of 20 occurrences which reported blue light to be influencing tiredness, out of which ten occurrences reported a decrease and three studies reported an increase in tiredness.

3.2.3.2 Sleep quality

One study found that sleep quality was higher in the non-blue light condition compared to the blue light condition, adding to the evidence that blue light can decrease sleep quality ( Burkhart and Phelps, 2009 ). Three studies reported nonsignificant changes in sleep quality by blue light exposure ( Sander et al., 2015 ; Bowler and Bourke, 2019 ; Driller and Uiga, 2019 ). Additionally, one study reported increased sleep quality following the blue light condition ( Viola et al., 2008 ). There was one ( Burkhart and Phelps, 2009 ) out of five studies that suggested blue light to be decreasing sleep quality and one ( Viola et al., 2008 ) out of five studies suggesting an increase in sleep quality through blue light exposure.

3.2.3.3 Sleep duration

One study reported an increase in sleep duration when exposed to the blue light condition ( Viola et al., 2008 ), however three studies reported a decrease in sleep duration when exposed to the blue light condition ( Münch et al., 2016 ; Yang et al., 2018 ; Chindamo et al., 2019 ). Five studies showed no significant change in sleep duration ( Chang et al., 2015 ; Sander et al., 2015 ; Grønli et al., 2016 ; Rångtell et al., 2016 ; Driller and Uiga, 2019 ). Summarizing these findings, three out of nine studies reported of decreasing sleep duration through blue light exposure and only one study reported an increase in sleep duration through blue light exposure.

3.2.3.4 Sleep efficacy and sleep latency

Two studies showed a higher sleep efficacy in the non-blue light condition compared to the blue light condition, adding to the evidence that blue light exposure can decrease sleep efficacy ( Ayaki et al., 2016 ; Yang et al., 2018 ) Two other studies showed no significant change in sleep efficacy following the blue light condition ( Chang et al., 2015 ; Driller and Uiga, 2019 ). This means two out of four studies found sleep efficacy to be decreased following blue light exposure. Sleep latency was found to be decreased in the non-blue light condition compared to the blue light condition in one study, ( Ayaki et al., 2016 ). Two other studies found an increase in sleep latency in the blue light condition ( Chang et al., 2015 ; Chindamo et al., 2019 ). Five studies found no significant change in sleep latency between the conditions ( Heath et al., 2014 ; Grønli et al., 2016 ; Rångtell et al., 2016 ; Knufinke et al., 2018 ; Yang et al., 2018 ). Summarizing this, three out of eight studies suggested that blue light exposure increased sleep latency.

3.3 Blue light and performance

3.3.1 age, intervention, and duration.

Twenty-three studies (see Table 4 ) were included to investigate the influence of blue light on performance. The participants were aged on average 29.2 years. Nineteen studies compared blue light with a different colored light exposure such as white, red, yellow, amber or green light. Four studies used electronic devices such as smartphones, tablets and computers and compared them with situations where the participants used blue light filters such as blue light blocking glasses or blue light filters for displays or hard-copy books ( Heath et al., 2014 ; Slama et al., 2015 ; Heo et al., 2017 ; Driller and Uiga, 2019 ). Two studies compared blue light exposure to caffeine use ( Taillard et al., 2012 ; Beaven and Ekström, 2013 ). The average exposure time was 1.7 h. Those studies with longer exposure times were separately calculated and they averaged around 2.5 weeks. The exact exposure times for each study were listed in Table 4 .

Influence of blue light on performance.

Increase (↑), decrease (↓), blue light (BL), caffeine (CAF), cold cathode fluorescent lamp (CCFL), Continuous Performance Test (CPT), control lighting condition (CTRL), dawn simulation light (DSL), 90-s Digit-Symbol Substitution Test (DSST), electroencephalography (EEG), event related potential (ERP), green light (GL), inappropriate line crossings (ILC), thousand (K), light emitting diode (LED), working memory task (n-back task), Psychomotor Speed Test (PST), Paced Visual Serial Addition Task (PVSAT), Psychomotor Vigilance Task (PVT), randomized controlled trial (RCT), red light (RL), Sustained Attention to Response Task (SART) and Systems Technology Incorporated driving simulator (STI), watt (W), white light (WL) and yellow light (YL).

3.3.2 Activity and measurement

Activities during the blue light exposure included relaxed sitting, bedtime routine, reading and texting on an electronic device, mundane tasks on a smartphone such as Facebook use and playing games, cognitive tasks, oddball tasks, driving a car, office work, and physical exercise. Methods most commonly used to measure the influence of blue light on sleep included: Psychomotor Vigilance Task (PVT), electroencephalography (EEG), working memory task (n-back task) and Go/NoGo task. The following methods of measurement were used less frequently across the included studies: Continuous Performance Test (CPT), 90-s Digit-Symbol Substitution Test (DSST), event related potential P300 (ERP), handgrip strength, inappropriate line crossings (ILC) during a driving task, Likert scale, Paced Visual Serial Addition Task (PVSAT), Psychomotor Speed Test (PST), Actiwatch, Sustained Attention to Response Rask (SART) and Systems Technology Incorporated driving simulator (STI) ( Table 4 ). These methods of measurements were applied either before, during or after the blue light intervention.

3.3.3 Performance responses to blue light

The term performance includes the elements cognitive performance, alertness, reaction times, accuracy, daytime dysfunction, heart rate response, and handgrip strength. More details to each element are given in the following sections.

3.3.3.1 Cognitive performance

Two studies reported an increase in cognitive performance when exposed to the blue light condition ( An et al., 2009 ; Motamedzadeh et al., 2017 ). In alignment with these finding, one study reported an increase in office work performance when exposed to the blue light condition ( Viola et al., 2008 ) and another showed an increase in driving performance when exposed to the blue light condition ( Taillard et al., 2012 ). However, no significant difference between light conditions was found during simulated driving ( Phipps-Nelson et al., 2009 ). Two studies showed no difference in cognitive performance following blue light exposure on the previous evening ( Gabel et al., 2013 ; Scheuermaier et al., 2018 ). Summarizing these findings as “cognitive performance,” four out of seven studies reported blue light to increase cognitive performance.

3.3.3.2 Alertness

Three studies reported of increase in alertness when exposed to the blue light condition ( Lehrl et al., 2007 ; Viola et al., 2008 ; Yang et al., 2018 ). Two studies reported an increase in sustained attention when exposed to the blue light condition ( Cajochen et al., 2011 ; Motamedzadeh et al., 2017 ). One study reported an increase in concentration in the blue light condition ( Viola et al., 2008 ). One study reported a decrease in omission errors ( Motamedzadeh et al., 2017 ) after blue light exposure. Two studies found no significant change in cognitive alertness when exposed to the blue light condition ( Sahin and Figueiro, 2013 ; Heath et al., 2014 ). One study reported of making more commission errors in a CPT the next morning after exposure to the blue light condition ( Heo et al., 2017 ). In summary, there were seven occurrences out of ten occurrences, counting the references Motamedzadeh et al. (2017) , Viola et al. (2008) twice for multiple results, which reported blue light to be increasing alertness.

3.3.3.3 Reaction times and accuracy

Seven studies reported a decrease in reaction times when exposed to the blue light condition ( Lockley et al., 2006 ; Phipps-Nelson et al., 2009 ; Tulppo et al., 2014 ; Alkozei et al., 2016 ; Münch et al., 2016 ; Motamedzadeh et al., 2017 ; Yang et al., 2018 ). One study reported a decrease in reaction times in blue-eyed participants during blue light exposure ( Beaven and Ekström, 2013 ). Additionally, one study reported improved performance in the Go/NoGo task when exposed to the blue light condition ( Cajochen et al., 2011 ). Four studies showed no significant change in reaction times when exposed to the blue light condition ( An et al., 2009 ; Tulppo et al., 2014 ; Baek and Min, 2015 ; Knaier et al., 2017a ). In summary, nine out of thirteen studies showed blue light to decrease reaction time. Blue light exposure only increased accuracy in one study ( Heath et al., 2014 ), whilst three studies found no significant change in accuracy ( Beaven and Ekström, 2013 ; Slama et al., 2015 ; Alkozei et al., 2016 ).

3.3.3.4 Daytime dysfunction

One study reported daytime dysfunction to be increased when exposed to the blue light condition ( Viola et al., 2008 ).

3.3.3.5 Heart rate and handgrip strength

One study found that heart rate was increased when exposed to the blue light condition, but also the red light condition compared to the dark condition ( Figueiro et al., 2009 ). Another study found no significant effect on heart rate the next morning after evening exposure to the blue light ( Driller and Uiga, 2019 ). Handgrip strength was not influenced by exposure to the blue light condition ( Knaier et al., 2017a ).

3.4 Blue light and wellbeing

3.4.1 age, intervention, and duration.

Eight studies were included to investigate the influence of blue light on wellbeing ( Table 5 ). The participants were aged on average 29.5 years, one study did not mention the exact age of their participants but stated that they were all adults ( Rångtell et al., 2016 ). Four studies compared blue light with a different colored light such as white, red, green or orange light ( Table 5 ). Four studies used electronic devices such as smartphones, tablets and computers and compared them with situations where the participants used blue light filters such as blue light blocking glasses or blue light filters for displays ( Table 5 ). Additionally, one study compared blue light exposure to caffeine use ( Beaven and Ekström, 2013 ). The average exposure time was 1.6 h. Those studies with longer exposure times were separately calculated and they averaged around 3.5 weeks. The exact exposure times for each study were listed on Table 5 .

Influence of blue light on wellbeing.

Increase (↑), decrease (↓), blue light (BL), blue light blocking (BLB), caffeine (CAF), dawn simulation light (DSL), Headache and Eye Strain scale (H&ES), thousand (K), Karolinska Sleepiness Scale (KSS), Positive And Negative Affect Schedule (PANAS), Profile Of Mood States (POMS), randomized control trials (RCT), Swedish Core Affect Scales (SCAS), University of Wales Institute of Science and Technology (UWIST), Visual Analogue Scale (VAS), and white light (WL).

3.4.2 Activity and measurement

Activity during the blue light exposure included relaxed sitting, leisure, bedtime routine, reading on an electronic device, mundane tasks on a smartphone such as Facebook use and playing games, cognitive tasks, and office work. Methods to measure the influence of blue light on sleep were: Positive And Negative Affect Schedule (PANAS), Visual Analogue Scale (VAS) and Karolinska Sleepiness Scale (KSS). The following methods of measurement were used less frequently across the included studies: Headache and Eye Strain scale (H&ES), Likert scale, Profile of Mood States (POMS) and Polish adaptation of the University of Wales Institute of Science and Technology (UWIST) mood adjective check list ( Table 5 ). Those methods were used to measure the influence of blue light either before, during or after the intervention.

3.4.3 Wellbeing responses to blue light

The term wellbeing includes the elements mood, irritability, arousal, tension, anxiety, and motivation. More details to each element are given in the following sections.

3.4.3.1 Mood

Two studies found an increase in positive mood when exposed to blue light ( Viola et al., 2008 ; Ekström and Beaven, 2014 ). One study showed higher results for mood in the non-blue light condition, adding to the evidence that blue light can decrease mood ( Burkhart and Phelps, 2009 ). However, one study showed no change in subjective wellbeing in the blue light condition ( Gabel et al., 2013 ).

3.4.3.2 Irritability

One study reported irritability to be decreased when exposed to the blue light condition ( Viola et al., 2008 ).

3.4.3.3 Arousal

One study showed an increase in energetic arousal in the blue light condition ( Iskra-Golec et al., 2012 ). For tense arousal, hedonic tone, no significant changes were found ( Iskra-Golec et al., 2012 ). Likewise no significant change was found for subjective arousal and other feelings ( Rångtell et al., 2016 ).

3.4.3.4 Tension and anxiety

One study reported tension and anxiety following the blue light condition not to be significantly affected ( Heo et al., 2017 ).

3.4.3.5 Motivation

The motivation to exercise and the perceived exertion during exercise on the following day was reported not to be influenced by blue light exposure ( Driller and Uiga, 2019 ).

4 Discussion

This systematic review summarized the current data of blue light exposure and its influence on sleep, performance and wellbeing. One half of the study results found tiredness and sleep efficacy to be decreased by blue light exposure. Sleep quality, sleep duration, and sleep latency did not seem to be systematically affected by blue light exposure. Most studies found cognitive performance and alertness to be increased and reaction time decreased by blue light exposure. The wellbeing markers mood, irritability, arousal, tension, and anxiety were shown to be increased by blue light exposure by slightly less than half of the included studies.

It is the norm to measure restful and good sleep based on sleep quality. However, sleep can also be perceived as restful if the next day’s tiredness was low, sleep efficacy was high or sleep latency short. Sleep is a very wide-ranging term. In addition to sleep quality, other elements of sleep, such as tiredness, sleep duration, sleep efficacy and sleep latency can help to interprete sleep health. Recently, a growing number of studies research the influence of sleep on athletic performance. One reason for this might be the belief that a good night’s sleep is the foundation for good performance ( Samuels et al., 2016 ). Thus, it is of interest to athletes to build an evidence base investigating the influence of blue light exposure on sleep, especially as the use of electronic devices has become a permanent feature of our everyday life.

4.1.1 Tiredness

Tiredness due to sleep deprivation or physical strain might decrease performance. Since tiredness is a broad term summarizing sleepiness, fatigue, wakefulness, arousal, and morning alertness as one, other terms, which are related to the terms listed, can be used to draw parallels to the term tiredness. For example, mental fatigue is not explicitly named among these terms, but its meaning is similar to the other terms describing tiredness. It was found that mental fatigue has an influence on physical performance as well as on cognitive performance ( Van Cutsem et al., 2017 ). This means that if mental fatigue influences physical performance, tiredness as a whole might also influence physical performance. Thus, tiredness might be a relevant factor for athletes to consider if they want to improve their performance. The included studies showed that out of the 20 occurrences reporting tiredness to be influenced by blue light, 10 occurrences found a positive effect of blue light exposure as it decreases tiredness ( Table 3 ). The significance of this finding is that athletes might be able to use blue light exposure to reduce their tiredness before a competition. In addition, given the connection between physical performance and tiredness ( Van Cutsem et al., 2017 ), blue light exposure may indeed improve an athlete’s physical performance, reduce the risk of injury and help in staying focused. Regarding the influence of tiredness on decision making, there is one study that found decision making not to be influenced by fatigue and therefore not by tiredness ( Almonroeder et al., 2020 ). In case an athlete shows signs of tiredness on competition days, about 2 h of blue light exposure might help to reduce tiredness, which was the average time of the included studies ( Table 3 ). A more practical choice of blue light exposure might be the use of smartphones. However, this does not mean that electronic devices are better than blue light bulbs because this systematic review cannot show relevant differences in the results regarding the two intervention methods. Since almost exclusively, questionnaires were used, and rating tiredness is often subjective ( Enoka and Duchateau, 2016 ), this raises the question of reliability of these results. Among the questionnaires, the KSS was most often used, which appears to be a valid tool to assess tiredness as it correlates with EEG measurements ( Kaida et al., 2006 ). For further research using questionnaires with unknown reliability and validity, adding a PVT, which measures reaction time, might increase the objectivity of test results. The assumption here is that when tiredness is increased, performance consequentially will be decreased ( Brown et al., 2013 ) and the result of the PVT will worsen. Additionally, athletic self-report tests based on parameters such as heart-rate or jump test data could help to quantify tiredness as stated ( Thorpe et al., 2017 ). Further research should explore the interaction of blue light and tiredness with the focus on the relationship between tiredness and risk of injury, staying focused and motivated.

4.1.2 Sleep quality

Even though blue light can have a positive effect by reducing tiredness, the evidence is mixed. This systematic review found that three out of five studies showed no significant change of sleep quality by blue light exposure ( Table 3 ), whilst two studies found sleep quality to be influenced by blue light. One study found blue light to be increasing sleep quality, the other found it to be decreasing. However, it is clear that the consequence of reduced sleep quality is bad sleep. Additionally it was recently found that bad sleep decreases performance and recovery ( Hamlin et al., 2021 ). Both are very important factors for athletes, because performance and recovery are the foundation of their success. Caution is advised when making suggestion to use blue light exposure to improve sleep quality, as it may actually negatively affect an athlete’s sleep and recovery. Since Burkhart and Phelps (2009) found a decrease in sleep quality after 3 h of blue light exposure, it might be recommended to restrict the usage of blue light emitting devices 3 h before bedtime. With that, sleep quality should not be decreased and the athlete might rest better. Additionally, it was found that an improvement in sleep quality can cause an increase in reaction time, accuracy, endurance performance and a decrease in injury and illness ( Krystal and Edinger, 2008 ). Restricting the usage of blue light emitting devices before bedtime might be a harsh interference with the daily habits of the athlete. Therefore, coach and athlete should talk about the advantages of a sensible usage of blue light and decide together how they want to restrict the blue light exposure in the athlete’s daily life. It might be enough to only restrict the use of blue light emitting devices some hours before bedtime during periods of great physical exertion, for example during intense training camps or before competitions. Sleep quality might have an influence on tiredness and how fatigue is perceived ( Lavidor et al., 2003 ). It was found that fatigue was appearing alongside bad sleep quality ( Fortier-Brochu et al., 2010 ). Combining these two findings, this might mean that reduced tiredness after a night’s rest, is a sign of better sleep quality. Summarized, these findings might suggest that by improving sleep quality, tiredness is reduced on a subjective level and physical performance might improve. Further research should focus on the influence of blue light exposure on sleep quality during intense training programs and competition, because those are the times when good performance and good recovery, which might be dependent on good sleep quality, matter the most. As for the reliability of the studies included it was found that exclusively questionnaires were used. Whilst the PSQI used in some studies is reported of having a high reliability and a good validity ( Backhaus et al., 2002 ). Future studies should evaluate if more objective forms of measurements such as PSG, NREM sleep EEG, and actigraphy ( Backhaus et al., 2002 ), are needed and worth the time to increase objectivity and reliability.

4.1.3 Sleep duration, sleep efficacy, sleep latency

Three out of nine studies found sleep duration to be decreased by blue light exposure and one study found it to be increased ( Table 3 ). There are arguments for and against sleep duration being relevant for athletes. On one hand athletes can suffer from sleep deprivation due to harsh training schedules ( Romyn et al., 2016 ) and if blue light would also increase the needed duration of sleep, then sleep deprivation might only get worse if blue light exposure was not moderated. On the other hand, an argument can be made against sleep duration being relevant because compared to sleep quality, sleep duration might have less influence on perceived fatigue ( Fortier-Brochu et al., 2010 ). This might mean that sleep duration is less relevant than sleep quality when assessing an athlete’s sleeping habits. Other quantitative measurements of sleep are sleep efficacy and sleep latency. With two showing sleep efficacy to be increased by blue light exposure, two others found no effect ( Table 3 ). These results are similar to sleep duration as it describes the ratio of sleep time to bedtime ( Reed and Sacco, 2016 ). Sleep latency can be interpreted alongside sleep efficacy as it describes the time spent in bed until the participant falls asleep. Since sleep latency is related to sleep efficacy and sleep duration, similar interpretations and consequences are expected. Three out of eight studies found an increase in sleep latency through blue light exposure ( Table 3 ). Further research is needed to evaluate if sleep duration, sleep efficacy, and sleep latency are influenced by blue light exposure.

4.2 Performance

Sleep, caffeine and even small rituals, which put the athlete in the right headspace can improve performance ( Broch and Kristiansen, 2014 ; Spriet, 2014 ). But blue light is not something that is usually considered when preparing for a competition. Blue light is not expected to directly improve the physical aspect of performance, but rather the mental aspects such as recognizing opportunities, planning and decision-making. However, further studies will be needed to assess the exact influence of blue light exposure on these mental aspects.

4.2.1 Cognitive performance and alertness

Cognitive performance describes the abilities of paying attention, memorizing, decision-making, planning, and reasoning ( Dhakal and Bobrin, 2022 ). Improving cognitive performance with blue light exposure might be useful for sports which include teamwork, decision-making and quickly changing situations. Additional effects of improving cognitive performance include the prevention of injury because the athlete might be more aware of their surroundings. With four out of seven studies, the slight majority found cognitive performance to be increased by blue light exposure ( Table 4 ). The validity and reliability of the measurement of cognitive performance is not clear. EEG, n-back tasks, driving, H&ES and PVT were used, but there were no studies which investigated the validity and reliability of these tests for cognitive performance. For further research other valid methods should be explored to measure cognitive performance. For example, the Stroop Color and Word Test (SCWT) is a color/word test measuring cognitive function ( Stroop, 1935 ) and the Test of Attentional Performance (TAP) is a test including 13 different subtests, like Go/Nogo tests, working memory and alertness tests ( Zimmermann and Fimm, 2002 ). Since the definition of alertness and attention are close, and the latter is part of cognitive performance, alertness might also be interpreted alongside cognitive performance. With seven out of ten occurrences, a slight majority regarding alertness showed an increased alertness through blue light exposure ( Table 4 ). Since cognitive performance was also increased, the increase for alertness is what was expected. The studies used reliable methods such as CPT ( Raz et al., 2014 ) and n-back task ( Jaeggi et al., 2010 ) to assess alertness. PVT is also a good method, but research has shown that there is need to calibrate PVT in specific margins to give reliable answers ( Basner et al., 2021 ). The Likert scale was described as invalid and not reliable ( Louangrath, 2018 ), so the results achieved for two mentioned studies with it ( Lehrl et al., 2007 ; Viola et al., 2008 ), should be carefully assessed.

4.2.2 Reaction times and accuracy

Sports which include teamwork, decision-making and quick changing situations often put athletes in situations where performance is determined by who recognizes the situation faster and is quicker to act accordingly. This process is what the term reaction time describes, which is influenced by recognizing the situation, decision-making and taking action. With nine out of thirteen studies, the majority found reaction time to be improved by blue light exposure ( Table 4 ). Since assessing situations and planning are part of cognitive performance, this raises the question on how reaction time and cognitive performance are related. Reaction time has mental and motor aspects, whereas cognitive performance has only mental aspects. Since both cognitive performance and reaction time are influenced by blue light, this might suggest that reaction time and cognitive performance are connected. There might be evidence for that to be the case, since reaction time is measured with PVT and PVT is used in multiple studies to measure cognitive performance. As for the measurement of CPT, n-back tasks and PVTs were used by the included studies and were described as reliable methods ( Jaeggi et al., 2010 ; Raz et al., 2014 ; Basner et al., 2021 ). Further research should test these findings in an athletic setting to find out if blue light exposure can make a difference in an athlete’s mental performance. Research should further investigate howblue light influences accuracy since there were only a total of four studies addressing this parameter, out of which one study ( Heath et al., 2014 ) showed accuracy to be increased by blue light exposure.

4.2.3 Driving

Accident-free driving is an important health and safety issue, which might potentially be influenced by blue light exposure. In fact, two studies ( Phipps-Nelson et al., 2009 ; Taillard et al., 2012 ) investigated this topic. Whereas one study showed a moderate but significant increase in driving performance ( Taillard et al., 2012 ) the other did not find any difference between the blue light and the red light condition ( Phipps-Nelson et al., 2009 ), although the blue light exposure significantly decreased PVT reaction time. However, further studies are warranted to elucidate this important health and safety issue.

4.3 Wellbeing

The wellbeing of athletes is strained because they are under a lot of pressure to perform well. The desire to perform well might lead to overtraining and mental stress. High stress levels might distract athletes from their optimal performance in competitions. A study stated that stress might lead to injury and issues with their mental health ( Rice et al., 2016 ). Improving wellbeing might hence allow athletes to improve their physical performance. Interestingly, blue light exposure may help relieve athletes of stress and confer relaxation. This systematic review summarized mood, irritability, arousal, tension, and anxiety under the term wellbeing: five out of ten occurrences found blue light exposure to be influencing wellbeing. Out of these five occurrences four found wellbeing to be increased by blue light exposure ( Table 5 ). This suggests that blue light may help athletes to improve their performance. However, there is need for more research regarding the influence of wellbeing on performance. A connection between performance and wellbeing was observed but its underlying mechanism is not understood ( Daniels and Harris, 2000 ). Interestingly, there is evidence for a correlation between injury and stress ( Lavallée and Flint, 1996 ). This might mean, that if an improvement in wellbeing equals lower stress levels, frequency of injury might be decreased. Further research could investigate the influence of blue light on stress levels and survey the occurrence of injury and illness. The review showed that electronic devices and light bulbs emission can improve wellbeing. Athletes could use their smartphones immediately before competitions to distract them from stress and use the blue light from the smartphone as an additional effect to improving wellbeing. As for the question if the research methodology is valid: PANAS, UWIST, and VAS are all valid and reliable methods to measure wellbeing ( Matthews et al., 1990 ; Kreindler et al., 2003 ; Crawford and Henry, 2004 ), and POMS was described as a consistent form of measurement ( Norcross et al., 1984 ).

4.4 Limitations

The participants were healthy humans and on average between 20 and 30 years old. The discussed results were applied to athletes, because the average age of the participants lies in the expected age range of athletes. Only three studies ( Tulppo et al., 2014 ; Knaier et al., 2017a ; Knufinke et al., 2018 ) included athletes as study participants. The included studies focused on slightly different research questions, for this reason multiple terms, e.g., sleepiness and fatigue, were summarized under one umbrella term, e.g., tiredness. Since participants were exposed to blue light during mostly non-strenuous and non-athletic tasks, the influence of physical stress and hormones was left unexplored. The influence of mental health on wellbeing and performance and any influence of blue light was not assessed. There is evidence for interactions between sleep, performance and wellbeing, but exploring these further goes beyond the scope of the discussion. To be able to give definitive answers to the question if blue light influences athletes and athletic performance, further studies are needed which specifically discuss the influence of blue light on athletes in different situations. Further, the question if blue light exposure during sleep time (e.g., checking the smart phone at 2 a.m. in the morning) or during actual sleep might be harmful remains unanswered by our review. Thus, further investigations seem warranted so clarify this important topic.

5 Conclusion

This systematic review has shown that blue light exposure might influence sleep, performance and wellbeing. A majority of studies found positive effects such as an increase in cognitive performance, alertness and reduced reaction times. Improving cognitive performance is useful in sports which require team-work and decision-making. Additionally, injury might be prevented by increased levels of alertness. Increased wellbeing might reduce stress and therefore lessen the risk of injury. An important negative effect of blue light exposure might be a decrease in sleep quality and sleep duration, because it might negatively influence performance and recovery. However, in general, the specific effects of blue light exposure seem still to be a murky field and more investigations are needed before final firm and evidence-based conclusions can be drawn. Based on our present findings, further research is recommended to determine if blue light exposure can improve athletic performance in specific aspects by influencing sleep, performance and wellbeing.

Data availability statement

Author contributions.

The authors confirm contribution as follows: conception and idea: CP and RW; literature research: MS; quality assessment: CP and MS; drafting of the manuscript: MS with inputs from all authors; all authors reviewed the results and approved the final version of the manuscript; supervising: CP and RW.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

  • Alkozei A., Smith R., Pisner D. A., Vanuk J. R., Berryhill S. M., Fridman A., et al. (2016). Exposure to blue light increases subsequent functional activation of the prefrontal cortex during performance of a working memory task . Sleep 39 , 1671–1680. 10.5665/sleep.6090 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Almonroeder T. G., Tighe S. M., Miller T. M., Lanning C. R. (2020). The influence of fatigue on decision-making in athletes: A systematic review . Sports Biomech. 19 , 76–89. 10.1080/14763141.2018.1472798 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • An M., Huang J., Shimomura Y., Katsuura T. (2009). Time-of-day-dependent effects of monochromatic light exposure on human cognitive function . J. Physiol. Anthropol. 28 , 217–223. 10.2114/jpa2.28.217 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ayaki M., Hattori A., Maruyama Y., Nakano M., Yoshimura M., Kitazawa M., et al. (2016). Protective effect of blue light shield eyewear for adults against light pollution from self-luminous devices used at night . Chronobiol. Int. 33 , 134–139. 10.3109/07420528.2015.1119158 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Backhaus J., Junghanns K., Broocks A., Riemann D., Hohagen F. (2002). Test-retest reliability and validity of the Pittsburgh sleep quality Index in primary insomnia . J. Psychosom. Res. 53 , 737–740. 10.1016/s0022-3999(02)00330-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baek H., Min B.-K. (2015). Blue light aids in coping with the post-lunch dip: An EEG study . Ergonomics 58 , 803–810. 10.1080/00140139.2014.983300 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Basner M., Moore T. M., Nasrini J., Gur R. C., Dinges D. F. (2021). Response speed measurements on the Psychomotor Vigilance Test: How precise is precise enough? Sleep 44 , zsaa121. 10.1093/sleep/zsaa121 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Beaven C. M., Ekström J. (2013). A comparison of blue light and caffeine effects on cognitive function and alertness in humans . PLoS ONE 8 , e76707. 10.1371/journal.pone.0076707 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bowler J., Bourke P. (20191953). Facebook use and sleep quality: Light interacts with socially induced alertness . Br. J. Psychol. 110 , 519–529. 10.1111/bjop.12351 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Broch T. B., Kristiansen E. (2014). The margin for error”: Ritual coping with cultural pressures . Scand. J. Med. Sci. Sports 24 , 837–845. 10.1111/sms.12077 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Brown T., Johnson R., Milavetz G. (2013). Identifying periods of drowsy driving using EEG . Ann. Adv. Automot. Med. 57 , 99–108. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Burkhart K., Phelps J. (2009). Amber lenses to block blue light and improve sleep: A randomized trial . Chronobiol. Int. 26 , 1602–1612. 10.3109/07420520903523719 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cajochen C., Frey S., Anders D., Späti J., Bues M., Pross A., et al. (20111985). Evening exposure to a light emitting diodes (LED)-backlit computer screen affects circadian physiology and cognitive performance . J. Appl. Physiol. 110 , 1432–1438. 10.1152/japplphysiol.00165.2011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chang A.-M., Aeschbach D., Duffy J. F., Czeisler C. A. (2015). Evening use of light emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness . Proc. Natl. Acad. Sci. U. S. A. 112 , 1232–1237. 10.1073/pnas.1418490112 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chellappa S. L., Steiner R., Oelhafen P., Lang D., Götz T., Krebs J., et al. (2013). Acute exposure to evening blue-enriched light impacts on human sleep . J. Sleep. Res. 22 , 573–580. 10.1111/jsr.12050 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chindamo S., Buja A., DeBattisti E., Terraneo A., Marini E., Gomez Perez L. J., et al. (2019). Sleep and new media usage in toddlers . Eur. J. Pediatr. 178 , 483–490. 10.1007/s00431-019-03318-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Crawford J. R., Henry J. D. (2004). The Positive and Negative Affect Schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample . Br. J. Clin. Psychol. 43 , 245–265. 10.1348/0144665031752934 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Daniels K., Harris C. (2000). Work, psychological wellbeing and performance . Occup. Med. 50 , 304–309. 10.1093/occmed/50.5.304 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dhakal A., Bobrin B. D. (2022). “ Cognitive deficits ,” in StatPearls (Treasure Island (FL): StatPearls Publishing; ). Available at: http://www.ncbi.nlm.nih.gov/books/NBK559052/ (Accessed March 10, 2022). [ PubMed ] [ Google Scholar ]
  • Driller M., Uiga L. (2019). The influence of night-time electronic device use on subsequent sleep and propensity to be physically active the following day . Chronobiol. Int. 36 , 717–724. 10.1080/07420528.2019.1588287 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ekström J. G., Beaven C. M. (2014). Effects of blue light and caffeine on mood . Psychopharmacol. (Berl.) 231 , 3677–3683. 10.1007/s00213-014-3503-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Enoka R. M., Duchateau J. (2016). Translating fatigue to human performance . Med. Sci. Sports Exerc. 48 , 2228–2238. 10.1249/MSS.0000000000000929 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Figueiro M. G., Bierman A., Plitnick B., Rea M. S. (2009). Preliminary evidence that both blue and red light can induce alertness at night . BMC Neurosci. 10 , 105. 10.1186/1471-2202-10-105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Fortier-Brochu E., Beaulieu-Bonneau S., Ivers H., Morin C. M. (2010). Relations between sleep, fatigue, and health-related quality of life in individuals with insomnia . J. Psychosom. Res. 69 , 475–483. 10.1016/j.jpsychores.2010.05.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gabel V., Maire M., Reichert C. F., Chellappa S. L., Schmidt C., Hommes V., et al. (2013). Effects of artificial dawn and morning blue light on daytime cognitive performance, wellbeing, cortisol and melatonin levels . Chronobiol. Int. 30 , 988–997. 10.3109/07420528.2013.793196 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Grønli J., Byrkjedal I. K., Bjorvatn B., Nødtvedt Ø., Hamre B., Pallesen S., et al. (2016). Reading from an iPad or from a book in bed: The impact on human sleep. A randomized controlled crossover trial . Sleep. Med. 21 , 86–92. 10.1016/j.sleep.2016.02.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hamlin M. J., Deuchrass R. W., Olsen P. D., Choukri M. A., Marshall H. C., Lizamore C. A., et al. (2021). The effect of sleep quality and quantity on athlete’s health and perceived training quality . Front. Sports Act. Living 3 , 705650. 10.3389/fspor.2021.705650 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Heath M., Sutherland C., Bartel K., Gradisar M., Williamson P., Lovato N., et al. (2014). Does one hour of bright or short-wavelength filtered tablet screenlight have a meaningful effect on adolescents’ pre-bedtime alertness, sleep, and daytime functioning? Chronobiol. Int. 31 , 496–505. 10.3109/07420528.2013.872121 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Heo J.-Y., Kim K., Fava M., Mischoulon D., Papakostas G. I., Kim M.-J., et al. (2017). Effects of smartphone use with and without blue light at night in healthy adults: A randomized, double-blind, crossover, placebo-controlled comparison . J. Psychiatr. Res. 87 , 61–70. 10.1016/j.jpsychires.2016.12.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Hysing M., Pallesen S., Stormark K. M., Jakobsen R., Lundervold A. J., Sivertsen B., et al. (2015). Sleep and use of electronic devices in adolescence: Results from a large population-based study . BMJ Open 5 , e006748. 10.1136/bmjopen-2014-006748 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Iskra-Golec I., Wazna A., Smith L. (2012). Effects of blue-enriched light on the daily course of mood, sleepiness and light perception: A field experiment . Light. Res. Technol. 44 , 506–513. 10.1177/1477153512447528 [ CrossRef ] [ Google Scholar ]
  • Jaeggi S. M., Buschkuehl M., Perrig W. J., Meier B. (2010). The concurrent validity of the N-back task as a working memory measure . Memory 18 , 394–412. 10.1080/09658211003702171 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kaida K., Takahashi M., Akerstedt T., Nakata A., Otsuka Y., Haratani T., et al. (2006). Validation of the Karolinska sleepiness scale against performance and EEG variables . Clin. Neurophysiol. 117 , 1574–1581. 10.1016/j.clinph.2006.03.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kmet L. M., Lee R. C., Cook L. S. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields . Edmonton: Alberta Heritage Foundation for Medical Research. Alberta Heritage Foundation for Medical Research, A., Health Technology Assessment Unit, U. of C., and Faculty of Medicine, C. H. R. [ Google Scholar ]
  • Knaier R., Schäfer J., Rossmeissl A., Klenk C., Hanssen H., Höchsmann C., et al. (2017a). Effects of bright and blue light on acoustic reaction time and maximum handgrip strength in male athletes: A randomized controlled trial . Eur. J. Appl. Physiol. 117 , 1689–1696. 10.1007/s00421-017-3659-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Knaier R., Schäfer J., Rossmeissl A., Klenk C., Hanssen H., Höchsmann C., et al. (2017b). Prime time light exposures do not seem to improve maximal physical performance in male elite athletes, but enhance end-spurt performance . Front. Physiol. 8 , 264. 10.3389/fphys.2017.00264 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Knufinke M., Nieuwenhuys A., Geurts S. A. E., Coenen A. M. L., Kompier M. A. J. (2018). Self-reported sleep quantity, quality and sleep hygiene in elite athletes . J. Sleep. Res. 27 , 78–85. 10.1111/jsr.12509 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kreindler D., Levitt A., Woolridge N., Lumsden C. J. (2003). Portable mood mapping: The validity and reliability of analog scale displays for mood assessment via hand-held computer . Psychiatry Res. 120 , 165–177. 10.1016/s0165-1781(03)00196-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Krystal A. D., Edinger J. D. (2008). Measuring sleep quality . Sleep. Med. 9 ( Suppl. 1 ), S10–S17. 10.1016/S1389-9457(08)70011-X [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lai Y., Yew Y. (2016). Neonatal blue light phototherapy and melanocytic nevus count in children: A systematic review and meta-analysis of observational studies . Pediatr. Dermatol. 33 , 62–68. 10.1111/pde.12730 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lastella M., Lovell G. P., Sargent C. (2014). Athletes’ precompetitive sleep behaviour and its relationship with subsequent precompetitive mood and performance . Eur. J. Sport Sci. 14 ( Suppl. 1 ), S123–S130. 10.1080/17461391.2012.660505 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lavallée L., Flint F. (1996). The relationship of stress, competitive anxiety, mood state, and social support to athletic injury . J. Athl. Train. 31 , 296–299. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Lavidor M., Weller A., Babkoff H. (2003). How sleep is related to fatigue . Br. J. Health Psychol. 8 , 95–105. 10.1348/135910703762879237 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lawrenson J. G., Hull C. C., Downie L. E. (2017). The effect of blue light blocking spectacle lenses on visual performance, macular health and the sleep-wake cycle: A systematic review of the literature . Ophthalmic Physiol. Opt. 37 , 644–654. 10.1111/opo.12406 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lehrl S., Gerstmeyer K., Jacob J. H., Frieling H., Henkel A. W., Meyrer R., et al. (2007). Blue light improves cognitive performance . J. Neural Transm. 114 , 457–460. 10.1007/s00702-006-0621-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lockley S. W., Evans E. E., Scheer F. A. J. L., Brainard G. C., Czeisler C. A., Aeschbach D., et al. (2006). Short-wavelength sensitivity for the direct effects of light on alertness, vigilance, and the waking electroencephalogram in humans . Sleep 29 , 161–168. [ PubMed ] [ Google Scholar ]
  • Louangrath P. (2018). Reliability and validity of survey scales . 10.5281/zenodo.1322695 [ CrossRef ] [ Google Scholar ]
  • Matthews G., Jones D. M., Chamberlain A. G. (1990). Refining the measurement of mood: The UWIST mood adjective checklist . Br. J. Psychol. 81 , 17–42. 10.1111/j.2044-8295.1990.tb02343.x [ CrossRef ] [ Google Scholar ]
  • Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement . Syst. Rev. 4 , 1. 10.1186/2046-4053-4-1 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Motamedzadeh M., Golmohammadi R., Kazemi R., Heidarimoghadam R. (2017). The effect of blue-enriched white light on cognitive performances and sleepiness of night-shift workers: A field study . Physiol. Behav. 177 , 208–214. 10.1016/j.physbeh.2017.05.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Münch M., Nowozin C., Regente J., Bes F., De Zeeuw J., Hädel S., et al. (2016). Blue-enriched morning light as a countermeasure to light at the wrong time: Effects on cognition, sleepiness, sleep, and circadian phase . Neuropsychobiology 74 , 207–218. 10.1159/000477093 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Norcross J. C., Guadagnoli E., Prochaska J. O. (1984). Factor structure of the profile of mood States (POMS): Two partial replications . J. Clin. Psychol. 40 , 1270–1277. 10.1002/1097-4679(198409)40:5<1270::aid-jclp2270400526>3.0.co;2-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Phipps-Nelson J., Redman J. R., Schlangen L. J. M., Rajaratnam S. M. W. (2009). Blue light exposure reduces objective measures of sleepiness during prolonged nighttime performance testing . Chronobiol. Int. 26 , 891–912. 10.1080/07420520903044364 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Rångtell F. H., Ekstrand E., Rapp L., Lagermalm A., Liethof L., Búcaro M. O., et al. (2016). Two hours of evening reading on a self-luminous tablet vs. reading a physical book does not alter sleep after daytime bright light exposure . Sleep. Med. 23 , 111–118. 10.1016/j.sleep.2016.06.016 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Raz S., Bar-Haim Y., Sadeh A., Dan O. (2014). Reliability and validity of the online continuous performance test among young adults . Assessment 21 , 108–118. 10.1177/1073191112443409 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Reed D. L., Sacco W. P. (2016). Measuring sleep efficiency: What should the denominator be? J. Clin. Sleep. Med. 12 , 263–266. 10.5664/jcsm.5498 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Rice S. M., Purcell R., De Silva S., Mawren D., McGorry P. D., Parker A. G., et al. (2016). The mental health of elite athletes: A narrative systematic review . Sports Med. 46 , 1333–1353. 10.1007/s40279-016-0492-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Romyn G., Robey E., Dimmock J. A., Halson S. L., Peeling P. (2016). Sleep, anxiety and electronic device use by athletes in the training and competition environments . Eur. J. Sport Sci. 16 , 301–308. 10.1080/17461391.2015.1023221 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sahin L., Figueiro M. G. (2013). Alerting effects of short-wavelength (blue) and long-wavelength (red) lights in the afternoon . Physiol. Behav. 116–117 , 1–7. 10.1016/j.physbeh.2013.03.014 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Samuels C., James L., Lawson D., Meeuwisse W. (2016). The athlete sleep screening questionnaire: A new tool for assessing and managing sleep in elite athletes . Br. J. Sports Med. 50 , 418–422. 10.1136/bjsports-2014-094332 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sander B., Markvart J., Kessel L., Argyraki A., Johnsen K. (2015). Can sleep quality and wellbeing be improved by changing the indoor lighting in the homes of healthy, elderly citizens? Chronobiol. Int. 32 , 1049–1060. 10.3109/07420528.2015.1056304 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Scheuermaier K., Münch M., Ronda J. M., Duffy J. F. (2018). Improved cognitive morning performance in healthy older adults following blue-enriched light exposure on the previous evening . Behav. Brain Res. 348 , 267–275. 10.1016/j.bbr.2018.04.021 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Slama H., Deliens G., Schmitz R., Peigneux P., Leproult R. (2015). Afternoon nap and bright light exposure improve cognitive flexibility post lunch . PloS One 10 , e0125359. 10.1371/journal.pone.0125359 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Spriet L. L. (2014). Exercise and sport performance with low doses of caffeine . Sports Med. 44 ( Suppl. 2 ), S175–S184. 10.1007/s40279-014-0257-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Srisurapanont K., Samakarn Y., Kamklong B., Siratrairat P., Bumiputra A., Jaikwang M., et al. (2021). Blue-wavelength light therapy for post-traumatic brain injury sleepiness, sleep disturbance, depression, and fatigue: A systematic review and network meta-analysis . PloS One 16 , e0246172. 10.1371/journal.pone.0246172 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Strong R. E., Marchant B. K., Reimherr F. W., Williams E., Soni P., Mestas R., et al. (2009). Narrow-band blue-light treatment of seasonal affective disorder in adults and the influence of additional nonseasonal symptoms . Depress. Anxiety 26 , 273–278. 10.1002/da.20538 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Stroop J. R. (1935). Studies of interference in serial verbal reactions . J. Exp. Psychol. 18 , 643–662. 10.1037/h0054651 [ CrossRef ] [ Google Scholar ]
  • Tähkämö L., Partonen T., Pesonen A.-K. (2019). Systematic review of light exposure impact on human circadian rhythm . Chronobiol. Int. 36 , 151–170. 10.1080/07420528.2018.1527773 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Taillard J., Capelli A., Sagaspe P., Anund A., Akerstedt T., Philip P., et al. (2012). In-car nocturnal blue light exposure improves motorway driving: A randomized controlled trial . PloS One 7 , e46750. 10.1371/journal.pone.0046750 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Thorpe R. T., Atkinson G., Drust B., Gregson W. (2017). Monitoring fatigue status in elite team-sport athletes: Implications for practice . Int. J. Sports Physiol. Perform. 12 , S227–S234. 10.1123/ijspp.2016-0434 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tordjman S., Chokron S., Delorme R., Charrier A., Bellissant E., Jaafari N., et al. (2017). Melatonin: Pharmacology, functions and therapeutic benefits . Curr. Neuropharmacol. 15 , 434–443. 10.2174/1570159X14666161228122115 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tulppo M. P., Jurvelin H., Roivainen E., Nissilä J., Hautala A. J., Kiviniemi A. M., et al. (2014). Effects of bright light treatment on psychomotor speed in athletes . Front. Physiol. 5 , 184. 10.3389/fphys.2014.00184 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Van Cutsem J., Marcora S., De Pauw K., Bailey S., Meeusen R., Roelands B., et al. (2017). The effects of mental fatigue on physical performance: A systematic review . Sports Med. 47 , 1569–1588. 10.1007/s40279-016-0672-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Van Der Lely S., Frey S., Garbazza C., Wirz-Justice A., Jenni O. G., Steiner R., et al. (2015). Blue blocker glasses as a countermeasure for alerting effects of evening light-emitting diode screen exposure in male teenagers . J. Adolesc. Health 56 , 113–119. 10.1016/j.jadohealth.2014.08.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vandewalle G., Collignon O., Hull J. T., Daneault V., Albouy G., Lepore F., et al. (2013). Blue light stimulates cognitive brain activity in visually blind individuals . J. Cogn. Neurosci. 25 , 2072–2085. 10.1162/jocn_a_00450 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Viola A. U., James L. M., Schlangen L. J. M., Dijk D.-J. (2008). Blue-enriched white light in the workplace improves self-reported alertness, performance and sleep quality . Scand. J. Work Environ. Health 34 , 297–306. 10.5271/sjweh.1268 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yang M., Ma N., Zhu Y., Su Y.-C., Chen Q., Hsiao F.-C., et al. (2018). The acute effects of intermittent light exposure in the evening on alertness and subsequent sleep architecture . Int. J. Environ. Res. Public Health 15 , 524. 10.3390/ijerph15030524 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zimmermann P., Fimm B. (2002). “ A test battery for attentional performance ,” in Applied neuropsychology of attention (Herzogenrath: Psychology Press; ). [ Google Scholar ]

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COMMENTS

  1. Download .nbib

    Introduction Exposure to blue light has seriously increased in our environment since the arrival of light emitting diodes (LEDs) and, in recent years, the proliferation of digital devices rich in blue light. This raises some questions about its potential deleterious effects on eye health.

  2. Research progress about the effect and prevention of blue

    High energy short wave blue light between 415 and 455 nm is the most harmful. Direct penetration of crystals into the retina causes irreversible photochemical retinal damage [1]. As the harmful effects of blue light are gradually realized by the public, eye discomfort related to blue light is becoming a more prevalent concern.

  3. Effects of blue light on the circadian system and eye

    The accumulating experimental evidence has indicated that exposure to blue light can affect many physiologic functions, and it can be used to treat circadian and sleep dysfunctions. However, blue light can also induce photoreceptor damage.

  4. Daily blue-light exposure shortens lifespan and causes brain

    Blue light and aging To investigate whether light affects Drosophila longevity, we first compared the lifespan of white ( w1118, hereafter w) adult flies kept in daily cycles of 12-h white...

  5. The influence of blue light on sleep, performance and

    Further research should explore if blue light can improve sleep, performance and wellbeing to significantly benefit athletic performance. Keywords: sleep quality, exercise, recovery, cognitive performance, physical activity Go to: 1 Introduction