Interventions to reduce short-wavelength ("blue") light exposure at night and their effects on sleep: A systematic review and meta-analysis

Do blue light filter applications improve sleep outcomes? A study of smartphone users' sleep quality in an observational setting

Exposure to blue light at bedtime, suppresses melatonin secretion, postponing the sleep onset and interrupting the sleep process. Some smartphone manufacturers have introduced night-mode functions, which have been claimed to aid in improving sleep quality. In this study, we evaluate the impact of blue light filter application on decreasing blue light emissions and improving sleep quality. Participants in this study recorded the pattern of using their mobile phones through a questionnaire. In order to evaluate sleep quality, we used a PSQI questionnaire. Blue light filters were used by 9.7% of respondents, 9.7% occasionally, and 80% never. The mean score of PSQI was more than 5 in 54.10% of the participants and less than 5 in 45.90%. ANOVA test was performed to assess the relationship between using blue light filter applications and sleep quality (p-value = 0.925). The findings of this study indicate a connection between the use of blue light filter apps and habitual sleep efficiency in the 31-40 age group. However, our results align only to some extent with prior research, as we did not observe sustained positive effects on all parameters of sleep quality from the long-term use of blue light filtering apps. Several studies have found that blue light exposure can suppress melatonin secretion, exacerbating sleep problems. Some studies have reported that physical blue light filters, such as lenses, can affect melatonin secretion and improve sleep quality. However, the impact of blue light filtering applications remains unclear and debatable.

Blue-light filtering spectacle lenses for visual performance, sleep, and macular health in adults

BACKGROUND

'Blue-light filtering', or 'blue-light blocking', spectacle lenses filter ultraviolet radiation and varying portions of short-wavelength visible light from reaching the eye. Various blue-light filtering lenses are commercially available. Some claims exist that they can improve visual performance with digital device use, provide retinal protection, and promote sleep quality. We investigated clinical trial evidence for these suggested effects, and considered any potential adverse effects.

OBJECTIVES

To assess the effects of blue-light filtering lenses compared with non-blue-light filtering lenses, for improving visual performance, providing macular protection, and improving sleep quality in adults.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL; containing the Cochrane Eyes and Vision Trials Register; 2022, Issue 3); Ovid MEDLINE; Ovid Embase; LILACS; the ISRCTN registry; ClinicalTrials.gov and WHO ICTRP, with no date or language restrictions. We last searched the electronic databases on 22 March 2022.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), involving adult participants, where blue-light filtering spectacle lenses were compared with non-blue-light filtering spectacle lenses.

DATA COLLECTION AND ANALYSIS

Primary outcomes were the change in visual fatigue score and critical flicker-fusion frequency (CFF), as continuous outcomes, between baseline and one month of follow-up. Secondary outcomes included best-corrected visual acuity (BCVA), contrast sensitivity, discomfort glare, proportion of eyes with a pathological macular finding, colour discrimination, proportion of participants with reduced daytime alertness, serum melatonin levels, subjective sleep quality, and patient satisfaction with their visual performance. We evaluated findings related to ocular and systemic adverse effects. We followed standard Cochrane methods for data extraction and assessed risk of bias using the Cochrane Risk of Bias 1 (RoB 1) tool. We used GRADE to assess the certainty of the evidence for each outcome.

MAIN RESULTS

We included 17 RCTs, with sample sizes ranging from five to 156 participants, and intervention follow-up periods from less than one day to five weeks. About half of included trials used a parallel-arm design; the rest adopted a cross-over design. A variety of participant characteristics was represented across the studies, ranging from healthy adults to individuals with mental health and sleep disorders. None of the studies had a low risk of bias in all seven Cochrane RoB 1 domains. We judged 65% of studies to have a high risk of bias due to outcome assessors not being masked (detection bias) and 59% to be at high risk of bias of performance bias as participants and personnel were not masked. Thirty-five per cent of studies were pre-registered on a trial registry. We did not perform meta-analyses for any of the outcome measures, due to lack of available quantitative data, heterogenous study populations, and differences in intervention follow-up periods. There may be no difference in subjective visual fatigue scores with blue-light filtering lenses compared to non-blue-light filtering lenses, at less than one week of follow-up (low-certainty evidence). One RCT reported no difference between intervention arms (mean difference (MD) 9.76 units (indicating worse symptoms), 95% confidence interval (CI) -33.95 to 53.47; 120 participants). Further, two studies (46 participants, combined) that measured visual fatigue scores reported no significant difference between intervention arms. There may be little to no difference in CFF with blue-light filtering lenses compared to non-blue-light filtering lenses, measured at less than one day of follow-up (low-certainty evidence). One study reported no significant difference between intervention arms (MD - 1.13 Hz lower (indicating poorer performance), 95% CI - 3.00 to 0.74; 120 participants). Another study reported a less negative change in CFF (indicating less visual fatigue) with high- compared to low-blue-light filtering and no blue-light filtering lenses. Compared to non-blue-light filtering lenses, there is probably little or no effect with blue-light filtering lenses on visual performance (BCVA) (MD 0.00 logMAR units, 95% CI -0.02 to 0.02; 1 study, 156 participants; moderate-certainty evidence), and unknown effects on daytime alertness (2 RCTs, 42 participants; very low-certainty evidence); uncertainty in these effects was due to lack of available data and the small number of studies reporting these outcomes. We do not know if blue-light filtering spectacle lenses are equivalent or superior to non-blue-light filtering spectacle lenses with respect to sleep quality (very low-certainty evidence). Inconsistent findings were evident across six RCTs (148 participants); three studies reported a significant improvement in sleep scores with blue-light filtering lenses compared to non-blue-light filtering lenses, and the other three studies reported no significant difference between intervention arms. We noted differences in the populations across studies and a lack of quantitative data. Device-related adverse effects were not consistently reported (9 RCTs, 333 participants; low-certainty evidence). Nine studies reported on adverse events related to study interventions; three studies described the occurrence of such events. Reported adverse events related to blue-light filtering lenses were infrequent, but included increased depressive symptoms, headache, discomfort wearing the glasses, and lower mood. Adverse events associated with non-blue-light filtering lenses were occasional hyperthymia, and discomfort wearing the spectacles. We were unable to determine whether blue-light filtering lenses affect contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels or overall patient visual satisfaction, compared to non-blue-light filtering lenses, as none of the studies evaluated these outcomes.

AUTHORS' CONCLUSIONS

This systematic review found that blue-light filtering spectacle lenses may not attenuate symptoms of eye strain with computer use, over a short-term follow-up period, compared to non-blue-light filtering lenses. Further, this review found no clinically meaningful difference in changes to CFF with blue-light filtering lenses compared to non-blue-light filtering lenses. Based on the current best available evidence, there is probably little or no effect of blue-light filtering lenses on BCVA compared with non-blue-light filtering lenses. Potential effects on sleep quality were also indeterminate, with included trials reporting mixed outcomes among heterogeneous study populations. There was no evidence from RCT publications relating to the outcomes of contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels, or overall patient visual satisfaction. Future high-quality randomised trials are required to define more clearly the effects of blue-light filtering lenses on visual performance, macular health and sleep, in adult populations.

Light exposure behaviors predict mood, memory and sleep quality

Ample research has shown that light influences our emotions, cognition, and sleep quality. However, little work has examined whether different light exposure-related behaviors, such as daytime exposure to electric light and nighttime usage of gadgets, especially before sleep, influence sleep quality and cognition. Three-hundred-and-one Malaysian adults (Mean_Age±SD = 28 ± 9) completed the Light Exposure Behavior Assessment tool that measured five light exposure behaviors. They also completed the Morningness-Eveningness Questionnaire, Positive and Negative Affect Schedule, Pittsburgh Sleep Quality Index, and single items assessing trouble in memory and concentration. A partial least square structural equation model, showing 72.72% predictive power, revealed that less use of wearable blue filters outdoors during the day and more within one hour before sleep predicted early peak time (direct effect = -0.25). Increased time spent outdoors predicted a positive affect (direct effect = 0.33) and a circadian phase advancement (direct effect: rising time = 0.14, peak time = 0.20, retiring time = 0.17). Increased use of mobile phone before sleep predicted a circadian phase delay (direct effect: retiring time = -0.25; rising time = -0.23; peak time = -0.22; morning affect = -0.12), reduced sleep quality (direct effect = 0.13), and increased trouble in memory and concentration (total effect = 0.20 and 0.23, respectively). Increased use of tunable, LED, or dawn-simulating electric light in the morning and daytime predicted a circadian phase advancement (direct effect: peak time = 0.15, morning affect = 0.14, retiring time = 0.15) and good sleep quality (direct effect = -0.16). The results provide valuable insights into developing a healthy light diet to promote health and wellness.

Blue LED light induces cytotoxicity via ROS production and mitochondrial damage in bovine subcutaneous preadipocytes

The purpose of this study was to investigate the effect and mechanism of blue light irradiation on bovine subcutaneous preadipocytes. In this study, preadipocytes were divided into dark group (control) and blue light group. Results show that blue light exposure time-dependently reduced the viability of preadipocytes and induced mitochondrial damage, in accompaniment with the accumulation of intracellular reactive oxygen species (ROS). Meanwhile, blue light caused oxidative stress, as evidenced by the increased MDA level, the reduced T-AOC contents, as well as the decreased activities of antioxidant enzymes. Additionally, blue light treatment induced apoptosis and G2/M phase arrest via Bcl-2/Bax/cleaved caspase-3 pathway and P53/GADD45 pathway, respectively. Protein expressions of LC3-II/LC3-I and P62 were up-regulated under blue light exposure, indicating blue light initiated autophagy but impeded autophagic degradation. Moreover, blue light caused an increase in the secretion of pro-inflammatory factors (TNF-α, IL-1β, and IL-6). Pretreatment with N-acetylcysteine (NAC), a potent ROS scavenger, restored the loss of mitochondrial membrane potential (Δψ) and reduced excess ROS. Additionally, the above negative effects of blue light on cells were alleviated after NAC administration. In conclusion, this study demonstrates blue light induces cellular ROS overproduction and Δψ depolarization, resulting in the decrease of cell viability and the activation of apoptosis, autophagy, and inflammation, providing a reference for the application of blue light in the regulation of fat cells in the future.

Sleep Disorders during Adolescence

Sleep disorders during adolescence period increase each year and adversely affect the physical and mental health of adolescents. After-school social activities and various work outside the school may cause delays in bedtime. In addition, there can be shifts in the circadian rhythm due to a number of biological changes seen in the transition to adolescence, which can result in a wide range of sleep problems, such as not being able to fall asleep at night, difficulty waking up in the morning, daytime sleepiness, sleep deprivation and deterioration in sleep quality. It is important to know the causes of sleep disorders, possible effects on physical health and mental health, and protective and risk-forming factors seen in adolescent period; to intervene in these disorders and to develop preventive measures. Preventive measures, such as increasing awareness about sleep disorders in adolescents, informing families and adolescents about the issue, and organizing school start-up times for this age group, may contribute significantly to solving this important issue, which has increased year-to-year.

Associations of meeting 24-h movement behavior guidelines with cognitive difficulty and social relationships in children and adolescents with attention deficit/hyperactive disorder

BACKGROUND

Evidence-based 24-h movement behavior (24-HMB) guidelines have been developed to integrate recommendations for the time spent on physical activity, sedentary behavior, and sleep. For children and adolescents, these 24-HMB guidelines recommend a maximum of two hours of recreational screen time (as part of sedentary behavior), a minimum of 60 min per day of moderate to vigorous physical activity (MVPA), and an age-appropriate sleep duration (9-11 h for 5 to 13-year-olds; 8-10 h for 14 to 17-year-olds). Although adherence to the guidelines has been associated with positive health outcomes, the effects of adhering to the 24-HMB recommendations have not been fully examined in children and adolescents with attention eficit/hyperactive disorder (ADHD). Therefore, this study examined potential associations between meeting the 24-HMB guidelines and indicators of cognitive and social difficulties in children and adolescents with ADHD.

METHODS

Cross-sectional data on 3470 children and adolescents with ADHD aged between 6 and 17 years was extracted from the National Survey for Children's Health (NSCH 2020). Adherence to 24-HMB guidelines comprised screen time, physical activity, and sleep. ADHD-related outcomes included four indicators; one relating to cognitive difficulties (i.e., serious difficulties in concentrating, remembering, or making decisions) and three indicators of social difficulties (i.e., difficulties in making or keeping friends, bullying others, being bullied). Logistic regression was performed to determine the associations between adherence to 24-HMB guidelines and the cognitive and social outcomes described above, while adjusting for confounders.

RESULTS

In total, 44.8% of participants met at least one movement behavior guideline, while only 5.7% met all three. Adjusted logistic regressions further showed that meeting all three guidelines was associated with lower odds of cognitive difficulties in relation to none of the guidelines, but the strongest model included only screen time and physical activity as predictors (OR = 0.26, 95% CI 0.12-0.53, p < .001). For social relationships, meeting all three guidelines was associated with lower odds of difficulty keeping friends (OR = 0.46, 95% CI 0.21-0.97, p = .04) in relation to none of the guidelines. Meeting the guideline for screen time was associated with lower odds of being bullied (OR = 0.61, 95% CI 0.39-0.97, p = .04) in relation to none of the guidelines. While screen time only, sleep only and the combination of both were associated with lower odds of bullying others, sleep alone was the strongest predictor (OR = 0.44, 95% CI 0.26-0.76, p = .003) in relation to none of the guidelines.

CONCLUSION

Meeting 24-HMB guidelines was associated with reduced likelihood of cognitive and social difficulties in children and adolescents with ADHD. These findings highlight the importance of adhering to healthy lifestyle behaviors as outlined in the 24-HMB recommendations with regard to cognitive and social difficulties in children and adolescents with ADHD. These results need to be confirmed by longitudinal and interventional studies with a large sample size.

Circadian Interventions as Adjunctive Therapies to Cognitive-Behavioral Therapy for Insomnia

The circadian system plays a key role in the sleep-wake cycle. A mismatch between the behavioral timing of sleep and the circadian timing of sleepiness/alertness can contribute to insomnia. Patients who report primarily difficulty falling asleep or early morning awakenings may benefit from circadian interventions administered adjunctively to cognitive-behavioral therapy for insomnia. Specific circadian interventions that clinicians may consider include bright light therapy, scheduled dim light, blue-blocking glasses, and melatonin. Implementation of these interventions differs depending on the patient's insomnia subtype. Further, careful attention must be paid to the timing of these interventions to ensure they are administered correctly.

Does knowledge of sleep hygiene recommendations match behaviour in Australian shift workers? A cross-sectional study

OBJECTIVES

Shiftworkers routinely obtain inadequate sleep, which has major health consequences. Sleep hygiene describes a range of behaviours, lifestyle and environmental factors that can improve sleep. To date, limited research has examined sleep hygiene in shiftworkers. This study aimed to assess the sociodemographic and behavioural correlates of sleep hygiene knowledge and engagement with sleep hygiene practices in Australian shiftworkers.

STUDY DESIGN

An online, cross-sectional survey.

SETTING AND PARTICIPANTS

Australian adults from across multiple industries (n=588) who work shift work.

MEASURES

The online survey included questions regarding sleep hygiene knowledge and questions from modified versions of the Pittsburgh Sleep Quality Index and Sleep Hygiene Index.

RESULTS

Of the 588 participants, 52.9% reported having heard of 'sleep hygiene'. Of these participants, 77.5% reported understanding the term moderately, extremely or very well. Engagement with each sleep hygiene practice was varied. Common sleep hygiene practices were controlling the bedroom environment (eg, a cool, dark and quiet bedroom). Less common practices were avoiding light as bedtime approaches. Logistic regressions revealed that shiftworkers who had heard of sleep hygiene were more likely to engage in sleep hygiene practices and had better sleep quality compared with those who had not heard of sleep hygiene. Increased engagement in sleep hygiene practices did not predict the likelihood of individuals reporting better sleep quality.

CONCLUSIONS

Shiftworkers demonstrated varied knowledge, understanding and engagement with individual sleep hygiene practices. Future research should focus on the development of sleep hygiene interventions that accommodate the unique challenges of shift work to optimise sleep.

Sleep and circadian rhythms: pillars of health-a Keystone Symposia report

The human circadian system consists of the master clock in the suprachiasmatic nuclei of the hypothalamus as well as in peripheral molecular clocks located in organs throughout the body. This system plays a major role in the temporal organization of biological and physiological processes, such as body temperature, blood pressure, hormone secretion, gene expression, and immune functions, which all manifest consistent diurnal patterns. Many facets of modern life, such as work schedules, travel, and social activities, can lead to sleep/wake and eating schedules that are misaligned relative to the biological clock. This misalignment can disrupt and impair physiological and psychological parameters that may ultimately put people at higher risk for chronic diseases like cancer, cardiovascular disease, and other metabolic disorders. Understanding the mechanisms that regulate sleep circadian rhythms may ultimately lead to insights on behavioral interventions that can lower the risk of these diseases. On February 25, 2021, experts in sleep, circadian rhythms, and chronobiology met virtually for the Keystone eSymposium "Sleep & Circadian Rhythms: Pillars of Health" to discuss the latest research for understanding the bidirectional relationships between sleep, circadian rhythms, and health and disease.

Guiding principles for determining work shift duration and addressing the effects of work shift duration on performance, safety, and health: guidance from the American Academy of Sleep Medicine and the Sleep Research Society

CITATION

Risks associated with fatigue that accumulates during work shifts have historically been managed through working time arrangements that specify fixed maximum durations of work shifts and minimum durations of time off. By themselves, such arrangements are not sufficient to curb risks to performance, safety, and health caused by misalignment between work schedules and the biological regulation of waking alertness and sleep. Science-based approaches for determining shift duration and mitigating associated risks, while addressing operational needs, require: (1) a recognition of the factors contributing to fatigue and fatigue-related risks; (2) an understanding of evidence-based countermeasures that may reduce fatigue and/or fatigue-related risks; and (3) an informed approach to selecting workplace-specific strategies for managing work hours. We propose a series of guiding principles to assist stakeholders with designing a shift duration decision-making process that effectively balances the need to meet operational demands with the need to manage fatigue-related risks.

Guiding principles for determining work shift duration and addressing the effects of work shift duration on performance, safety, and health: guidance from the American Academy of Sleep Medicine and the Sleep Research Society

Risks associated with fatigue that accumulates during work shifts have historically been managed through working time arrangements that specify fixed maximum durations of work shifts and minimum durations of time off. By themselves, such arrangements are not sufficient to curb risks to performance, safety, and health caused by misalignment between work schedules and the biological regulation of waking alertness and sleep. Science-based approaches for determining shift duration and mitigating associated risks, while addressing operational needs, require: (1) a recognition of the factors contributing to fatigue and fatigue-related risks; (2) an understanding of evidence-based countermeasures that may reduce fatigue and/or fatigue-related risks; and (3) an informed approach to selecting workplace-specific strategies for managing work hours. We propose a series of guiding principles to assist stakeholders with designing a shift duration decision-making process that effectively balances the need to meet operational demands with the need to manage fatigue-related risks.

Sleep and physical activity in university students: A systematic review and meta-analysis

University students have low levels of physical activity and report disturbances to sleep, which are independently associated with poor health outcomes. Some research suggests that there is a bi-directional relationship between sleep and physical activity in adults. However, the relationship between sleep and physical activity in university students has not yet been evaluated. Therefore, the aim of this systematic review and meta-analysis was to qualitatively synthesise and quantitatively evaluate the evidence for the association between sleep and physical activity in university students. Twenty-nine eligible studies were included, with a total of 141,035 participants (43% men and 57% women). Only four studies used device-based measures of sleep and/or physical activity, with the remainder including self-report measures. Qualitative synthesis found that the majority of studies did not find any association between sleep and physical activity in university students. However, random-effects meta-analysis showed that moderate-to-high intensity physical activity was associated with lower PSQI scores (e.g., better sleep quality) [r = -0.18, 95% CI (-0.37, 0.03), p = 0.100]. Further, a weak negative association between moderate-to-vigorous physical activity level and sleep duration was also found [r = -0.02, 95% CI (-0.16, 0.12), p = 0.760]. As the findings of this review are predominantly derived from cross-sectional investigations, with limited use of device-based measurement tools, further research is needed to investigate the relationship between sleep and physical activity in university students. Future studies should employ longitudinal designs, with self-report and device-based measures, and consider the intensity and time of physical activity as well as records of napping behaviour.

Prospective associations between pre-sleep electronics use and same-night sleep in healthy school-aged children

Electronic devices are routinely associated with adverse effects on sleep; however, prospective studies among healthy children are unavailable. This study examined relationships among specific and total electronic device use within the hour before bed and same-night sleep patterns among 55 pre-pubertal children (7-11 years) without medical, psychiatric or sleep disorders. Sleep was assessed via subjective reports and actigraphy for 5 weeknights and pre-bed device use was assessed via daily diary. Neither total devices use nor any single type predicted sleep parameters the same night. The extent to which pre-bed electronics use impacts sleep in healthy children requires further investigation.

Non-Pharmacological Interventions to Improve Chronic Disease Risk Factors and Sleep in Shift Workers: A Systematic Review and Meta-Analysis

Shift work is associated with adverse chronic health outcomes. Addressing chronic disease risk factors including biomedical risk factors, behavioural risk factors, as well as sleep and perceived health status, affords an opportunity to improve health outcomes in shift workers. The present study aimed to conduct a systematic review, qualitative synthesis, and meta-analysis of non-pharmacological interventions targeting chronic disease risk factors, including sleep, in shift workers. A total of 8465 records were retrieved; 65 publications were eligible for inclusion in qualitative analysis. Random-effects meta-analysis were conducted for eight eligible health outcomes, including a total of thirty-nine studies. Interventions resulted in increased objective sleep duration (Hedges' g = 0.73; CI: 0.36, 1.10, k = 16), improved objective sleep efficiency (Hedges' g = 0.48; CI: 0.20, 0.76, k = 10) and a small increase in both subjective sleep duration (Hedges' g = 0.11; CI: -0.04, 0.27, k = 19) and sleep quality (Hedges' g = 0.11; CI: -0.11, 0.33, k = 21). Interventions also improved perceived health status (Hedges' g = 0.20; CI: -0.05, 0.46, k = 8), decreased systolic (Hedges' g = 0.26; CI: -0.54, 0.02, k = 7) and diastolic (Hedges' g = 0.06; CI: -0.23, 0.36, k = 7) blood pressure, and reduced body mass index (Hedges' g = -0.04; CI: -0.37, 0.29, k = 9). The current study suggests interventions may improve chronic disease risk factors and sleep in shift workers; however, this could only be objectively assessed for a limited number of risk factor endpoints. Future interventions could explore the impact of non-pharmacological interventions on a broader range of chronic disease risk factors to better characterise targets for improved health outcomes in shift workers.