Ocular light exposure has important influences on human health and well-being through modulation of circadian rhythms and sleep, as well as neuroendocrine and cognitive functions. Prevailing patterns of light exposure do not optimally engage these actions for many individuals, but advances in our understanding of the underpinning mechanisms and emerging lighting technologies now present opportunities to adjust lighting to promote optimal physical and mental health and performance. A newly developed, international standard provides a SI-compliant way of quantifying the influence of light on the intrinsically photosensitive, melanopsin-expressing, retinal neurons that mediate these effects. The present report provides recommendations for lighting, based on an expert scientific consensus and expressed in an easily measured quantity (melanopic equivalent daylight illuminance (melaponic EDI)) defined within this standard. The recommendations are supported by detailed analysis of the sensitivity of human circadian, neuroendocrine, and alerting responses to ocular light and provide a straightforward framework to inform lighting design and practice.

Societal and economic demands push toward an increase of employees’ 24-hour availability in health care settings as well as in service and security industries. The resulting disruption of social and biological rhythms, occurring especially during shift work, has been hypothesized to increase chronic disease risk,^1-5 and suggestive evidence supports an association between shift work and coronary heart disease (CHD), metabolic disorders, and cancer.⁶

OBJECTIVE To examine the effects of past and current night shift work and genetic type 2 diabetes vulnerability on type 2 diabetes odds. RESEARCH DESIGN AND METHODS In the UK Biobank, we examined associations of current (N = 272,214) and lifetime (N = 70,480) night shift work exposure with type 2 diabetes risk (6,770 and 1,191 prevalent cases, respectively). For 180,704 and 44,141 unrelated participants of European ancestry (4,002 and 726 cases, respectively) with genetic data, we assessed whether shift work exposure modified the relationship between a genetic risk score (comprising 110 single-nucleotide polymorphisms) for type 2 diabetes and prevalent diabetes. RESULTS Compared with day workers, all current night shift workers were at higher multivariable-adjusted odds for type 2 diabetes (none or rare night shifts: odds ratio [OR] 1.15 [95% CI 1.05–1.26]; some nights: OR 1.18 [95% CI 1.05–1.32]; and usual nights: OR 1.44 [95% CI 1.19–1.73]), except current permanent night shift workers (OR 1.09 [95% CI 0.93–1.27]). Considering a person’s lifetime work schedule and compared with never shift workers, working more night shifts per month was associated with higher type 2 diabetes odds (8/month: OR 1.36 [95% CI 1.14–1.62]; P_trend = 0.001). The association between genetic type 2 diabetes predisposition and type 2 diabetes odds was not modified by shift work exposure. CONCLUSIONS Our findings show that night shift work, especially rotating shift work including night shifts, is associated with higher type 2 diabetes odds and that the number of night shifts worked per month appears most relevant for type 2 diabetes odds. Also, shift work exposure does not modify genetic risk for type 2 diabetes, a novel finding that warrants replication.

This study explores chronotype-dependent tolerance to the demands of working morning, evening, and night shifts in terms of social jet lag, sleep duration, and sleep disturbance. A total of 238 shift-workers were chronotyped with the Munich ChronoType Questionnaire for shift-workers (MCTQ^Shift), which collects information about shift-dependent sleep duration and sleep timing. Additionally, 94 shift-workers also completed those items of the Sleep Questionnaire from the Standard Shift-Work Index (SSI) that assess sleep disturbances. Although all participants worked morning, evening, and night shifts, subsamples differed in rotation direction and speed. Sleep duration, social jet lag, and sleep disturbance were all significantly modulated by the interaction of chronotype and shift (mixed-model ANOVAs). Earlier chronotypes showed shortened sleep duration during night shifts, high social jet lag, as well as higher levels of sleep disturbance. A similar pattern was observed for later chronotypes during early shifts. Age itself only influenced sleep duration and quality per se, without showing interactions with shifts. We found that workers slept longer in fast, rotating shift schedules. Since chronotype changes with age, investigations on sleep behavior and circadian misalignment in shift-workers have to consider chronotype to fully understand interindividual and intraindividual variability, especially in view of the current demographic changes. Given the impact of sleep on health, our results stress the importance of chronotype both in understanding the effects of shift-work on sleep and in devising solutions to reduce shift-work–related health problems.