The electric light is one of the most important human inventions. Sleep and other daily rhythms in physiology and behavior, however, evolved in the natural light-dark cycle 1, and electrical lighting ...is thought to have disrupted these rhythms. Yet how much the age of electrical lighting has altered the human circadian clock is unknown. Here we show that electrical lighting and the constructed environment is associated with reduced exposure to sunlight during the day, increased light exposure after sunset, and a delayed timing of the circadian clock as compared to a summer natural 14 hr 40 min:9 hr 20 min light-dark cycle camping. Furthermore, we find that after exposure to only natural light, the internal circadian clock synchronizes to solar time such that the beginning of the internal biological night occurs at sunset and the end of the internal biological night occurs before wake time just after sunrise. In addition, we find that later chronotypes show larger circadian advances when exposed to only natural light, making the timing of their internal clocks in relation to the light-dark cycle more similar to earlier chronotypes. These findings have important implications for understanding how modern light exposure patterns contribute to late sleep schedules and may disrupt sleep and circadian clocks.
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•Internal biological time tightly synchronizes to a midsummer natural light-dark cycle•Electric lighting and reduced exposure to sunlight delays circadian timing in humans•Exposure to only natural light reduces individual differences in circadian timing•Circadian clocks of evening chronotypes are later when exposed to electrical lighting
Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and ...well-being.
We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions.
We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures.
We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification.
New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.
On March 11th, 2020, the National Basketball Association (NBA) paused its season after ~ 64 games due to the Coronavirus 2019 (COVID-19) outbreak, only to resume ~ 5 months later with the top 22 ...teams isolated together (known as the "bubble") in Orlando, Florida to play eight games each as an end to the regular season. This restart, with no new travel by teams, provided a natural experiment whereby the impact of travel and home-court advantage could be systematically examined. We show here that in the pre-COVID-19 regular season, traveling across time zones reduces winning percentage, team shooting accuracy, and turnover percentage, whereas traveling in general reduces offensive rebounding and increases the number of points the opposing (home) team scores. Moreover, we demonstrate that competition in a scenario where no teams travel (restart bubble) reduces the typical effects of travel and home-court advantage on winning percentage, shooting accuracy, and rebounding. Thus, home-court advantage in professional basketball appears to be linked with the away team's impaired shooting accuracy (i.e., movement precision) and rebounding, which may be separately influenced by either circadian disruption or the general effect of travel, as these differences manifest differently when teams travel within or across multiple time zones.
Reduced exposure to daytime sunlight and increased exposure to electrical lighting at night leads to late circadian and sleep timing 1–3. We have previously shown that exposure to a natural summer ...14 hr 40 min:9 hr 20 min light-dark cycle entrains the human circadian clock to solar time, such that the internal biological night begins near sunset and ends near sunrise 1. Here we show that the beginning of the biological night and sleep occur earlier after a week’s exposure to a natural winter 9 hr 20 min:14 hr 40 min light-dark cycle as compared to the modern electrical lighting environment. Further, we find that the human circadian clock is sensitive to seasonal changes in the natural light-dark cycle, showing an expansion of the biological night in winter compared to summer, akin to that seen in non-humans 4–8. We also show that circadian and sleep timing occur earlier after spending a weekend camping in a summer 14 hr 39 min:9 hr 21 min natural light-dark cycle compared to a typical weekend in the modern environment. Weekend exposure to natural light was sufficient to achieve ∼69% of the shift in circadian timing we previously reported after a week’s exposure to natural light 1. These findings provide evidence that the human circadian clock adapts to seasonal changes in the natural light-dark cycle and is timed later in the modern environment in both winter and summer. Further, we demonstrate that earlier circadian timing can be rapidly achieved through natural light exposure during a weekend spent camping.
•Living in the modern electrical lighting environment delays the human circadian clock•The human circadian clock adapts to seasonal changes in the natural light-dark cycle•A weekend camping trip prevented the typical weekend circadian and sleep delay
Late sleep timing is associated with health problems. Stothard et al. show that the human circadian clock is timed later in modern society, especially after the weekend, compared to natural light-dark cycles. Further, the clock responds to seasonal natural light-dark cycle changes and is rapidly shifted earlier by weekend camping.
Proteomics holds great promise for understanding human physiology, developing health biomarkers, and precision medicine. However, how much the plasma proteome varies with time of day and is regulated ...by the master circadian suprachiasmatic nucleus brain clock, assessed here by the melatonin rhythm, is largely unknown. Here, we assessed 24-h time-of-day patterns of human plasma proteins in six healthy men during daytime food intake and nighttime sleep in phase with the endogenous circadian clock (i.e., circadian alignment) versus daytime sleep and nighttime food intake out of phase with the endogenous circadian clock (i.e., circadian misalignment induced by simulated nightshift work). We identified 24-h time-of-day patterns in 573 of 1,129 proteins analyzed, with 30 proteins showing strong regulation by the circadian cycle. Relative to circadian alignment, the average abundance and/or 24-h time-of-day patterns of 127 proteins were altered during circadian misalignment. Altered proteins were associated with biological pathways involved in immune function, metabolism, and cancer. Of the 30 circadian-regulated proteins, the majority peaked between 1400 hours and 2100 hours, and these 30 proteins were associated with basic pathways involved in extracellular matrix organization, tyrosine kinase signaling, and signaling by receptor tyrosine-protein kinase erbB-2. Furthermore, circadian misalignment altered multiple proteins known to regulate glucose homeostasis and/or energy metabolism, with implications for altered metabolic physiology. Our findings demonstrate the circadian clock, the behavioral wake–sleep/food intake–fasting cycle, and interactions between these processes regulate 24-h time-of-day patterns of human plasma proteins and help identify mechanisms of circadian misalignment that may contribute to metabolic dysregulation.
Significance Demands of modern society force many work operations into the night, when the intrinsic circadian timing system promotes sleep. Overnight shiftwork is associated with increased risk for ...adverse metabolic health and sleep disruption. Uncovering potential physiological mechanisms that contribute to metabolic dysregulation when work and eating occur at inappropriate circadian times is vital to the development of effective treatment strategies. In this study, healthy volunteers underwent a commonly used simulated shiftwork protocol to quantify changes in metabolic, sleep, and circadian physiology when working and eating during the night as compared with a traditional day work schedule. We demonstrate that nightshift work reduces total daily energy expenditure, representing a contributing mechanism for unwanted weight gain and obesity.
Eating at a time when the internal circadian clock promotes sleep is a novel risk factor for weight gain and obesity, yet little is known about mechanisms by which circadian misalignment leads to metabolic dysregulation in humans. We studied 14 adults in a 6-d inpatient simulated shiftwork protocol and quantified changes in energy expenditure, macronutrient utilization, appetitive hormones, sleep, and circadian phase during day versus nightshift work. We found that total daily energy expenditure increased by ∼4% on the transition day to the first nightshift, which consisted of an afternoon nap and extended wakefulness, whereas total daily energy expenditure decreased by ∼3% on each of the second and third nightshift days, which consisted of daytime sleep followed by afternoon and nighttime wakefulness. Contrary to expectations, energy expenditure decreased by ∼12–16% during scheduled daytime sleep opportunities despite disturbed sleep. The thermic effect of feeding also decreased in response to a late dinner on the first nightshift. Total daily fat utilization increased on the first and second nightshift days, contrary to expectations, and carbohydrate and protein utilization were reduced on the second nightshift day. Ratings of hunger were decreased during nightshift days despite decreases in 24-h levels of the satiety hormones leptin and peptide-YY. Findings suggest that reduced total daily energy expenditure during nightshift schedules and reduced energy expenditure in response to dinner represent contributing mechanisms by which humans working and eating during the biological night, when the circadian clock is promoting sleep, may increase the risk of weight gain and obesity.
Millions of individuals routinely remain awake for more than 18 h daily, which causes performance decrements. It is unknown if these functional impairments are the result of that extended wakefulness ...or from the associated shortened sleep durations. We therefore examined changes in objective reaction time performance and subjective alertness in a 32-d inpatient protocol in which participants were scheduled to wakefulness durations below 16 h while on a 20-h “day,” with randomization into standard sleep:wake ratio (1:2) or chronic sleep restriction (CSR) ratio (1:3.3) conditions. This protocol allowed determination of the contribution of sleep deficiency independent of extended wakefulness, since individual episodes of wakefulness in the CSR condition were only 15.33 h in duration (less than the usual 16 h of wakefulness in a 24-h day) and sleep episodes were 4.67 h in duration each cycle. We found that chronic short sleep duration, even without extended wakefulness, doubled neurobehavioral reaction time performance and increased lapses of attention fivefold, yet did not uniformly decrease self-reported alertness. Further, these impairments in neurobehavioral performance were worsened during the circadian night and were not recovered during the circadian day, indicating that the deleterious effect from the homeostatic buildup of CSR is expressed even during the circadian promotion of daytime arousal. These findings reveal a fundamental aspect of human biology: Chronic insufficient sleep duration equivalent to 5.6 h of sleep opportunity per 24 h impairs neurobehavioral performance and self-assessment of alertness, even without extended wakefulness.
Weight gain and obesity have reached alarming levels. Eating at a later clock hour is a newly described risk factor for adverse metabolic health; yet, how eating at a later circadian time influences ...body composition is unknown. Using clock hour to document eating times may be misleading owing to individual differences in circadian timing relative to clock hour.
This study examined the relations between the timing of food consumption relative to clock hour and endogenous circadian time, content of food intake, and body composition.
We enrolled 110 participants, aged 18–22 y, in a 30-d cross-sectional study to document sleep and circadian behaviors within their regular daily routines. We used a time-stamped-picture mobile phone application to record all food intake across 7 consecutive days during a participant's regular daily routines and assessed their body composition and timing of melatonin release during an in-laboratory assessment.
Nonlean individuals (high body fat) consumed most of their calories 1.1 h closer to melatonin onset, which heralds the beginning of the biological night, than did lean individuals (low body fat) (log-rank P = 0.009). In contrast, there were no differences between lean and nonlean individuals in the clock hour of food consumption (P = 0.72). Multiple regression analysis showed that the timing of food intake relative to melatonin onset was significantly associated with the percentage of body fat and body mass index (both P < 0.05) while controlling for sex, whereas no relations were found between the clock hour of food intake, caloric amount, meal macronutrient composition, activity or exercise level, or sleep duration and either of these body composition measures (all P > 0.72).
These results provide evidence that the consumption of food during the circadian evening and/or night, independent of more traditional risk factors such as amount or content of food intake and activity level, plays an important role in body composition. This trial was registered at clinicaltrials.gov as NCT02846077.
Sleep inertia: current insights Hilditch, Cassie J; McHill, Andrew W
Nature and science of sleep,
08/2019, Volume:
11
Journal Article
Peer reviewed
Open access
Sleep inertia, or the grogginess felt upon awakening, is associated with significant cognitive performance decrements that dissipate as time awake increases. This impairment in cognitive performance ...has been observed in both tightly controlled in-laboratory studies and in real-world scenarios. Further, these decrements in performance are exaggerated by prior sleep loss and the time of day in which a person awakens. This review will examine current insights into the causes of sleep inertia, factors that may positively or negatively influence the degree of sleep inertia, the consequences of sleep inertia both in the laboratory and in real-world settings, and lastly discuss potential countermeasures to lessen the impact of sleep inertia.
Traditional risk factors for obesity and the metabolic syndrome, such as excess energy intake and lack of physical activity, cannot fully explain the high prevalence of these conditions. Insufficient ...sleep and circadian misalignment predispose individuals to poor metabolic health and promote weight gain and have received increased research attention in the past 10 years. Insufficient sleep is defined as sleeping less than recommended for health benefits, whereas circadian misalignment is defined as wakefulness and food intake occurring when the internal circadian system is promoting sleep. This Review discusses the impact of insufficient sleep and circadian misalignment in humans on appetite hormones (focusing on ghrelin, leptin and peptide-YY), energy expenditure, food intake and choice, and risk of obesity. Some potential strategies to reduce the adverse effects of sleep disruption on metabolic health are provided and future research priorities are highlighted. Millions of individuals worldwide do not obtain sufficient sleep for healthy metabolic functions. Furthermore, modern working patterns, lifestyles and technologies are often not conducive to adequate sleep at times when the internal physiological clock is promoting it (for example, late-night screen time, shift work and nocturnal social activities). Efforts are needed to highlight the importance of optimal sleep and circadian health in the maintenance of metabolic health and body weight regulation.
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