Summary 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 14h40min:9h20min 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.

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 = 0.009). In contrast, there were no differences between lean and nonlean individuals in the clock hour of food consumption ( = 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 < 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 > 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.

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.insufficient sleep | melatonin | diet-induced thermogenesis | eating at night | appetite E merging evidence from nonhuman animal models indicates a fundamental interplay between circadian and metabolic physiology (1, 2) with implications for health and disease (3-5). Eating at inappropriate circadian times (e.g., at night) is considered a novel risk factor for weight gain and obesity, yet little research has been conducted in humans on this topic. The circadian time-keeping system in humans modulates energy metabolism so that wakefulness, activity, and food intake are promoted during the solar day and sleep, inactivity, and fasting occur during the solar night (2). With the widespread use of electrical lighting, however, work and social activities are capable of being extended further into the night (6, 7). Being awake during the biological night leads to disturbed physiology and behavior, because it creates a state of desynchrony between the circadian clock and wakefulness-sleep cycle known as circadian misalignment. Circadian misalignment is common in shiftwork. More than 20% of adults in the United States work nontraditional hours (8) and eat some of their meals during the biological night (9), which can increase blood glucose and triacylglycerol levels in response to a high-carbohydrate versus high-fat diet (10) and increase low-dens...

BackgroundWearable 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.ObjectiveWe 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.MethodsWe 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.ResultsWe 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.ConclusionsNew 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 ...

Summary 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 the beginning of the biological night and sleep occur earlier after a week 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 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 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 one week 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 earlier circadian timing can be rapidly achieved through natural light exposure during a weekend spent camping.

Weight gain, obesity and diabetes have reached alarming levels in the developed world. Traditional risk factors such as over-eating, poor nutritional choices and lack of exercise cannot fully account for the high prevalence of metabolic disease. This review paper examines the scientific evidence on two novel risk factors that contribute to dys-regulated metabolic physiology: sleep disruption and circadian misalignment. Specifically, fundamental relationships between energy metabolism and sleep and circadian rhythms and the impact of sleep and circadian disruption on metabolic physiology are examined. Millions of individuals worldwide do not obtain sufficient sleep for healthy metabolic function, and many participate in shift work and social activities at times when the internal physiological clock is promoting sleep. These behaviours predispose an individual for poor metabolic health by promoting excess caloric intake in response to reduced sleep, food intake at internal biological times when metabolic physiology is not prepared, decreased energy expenditure when wakefulness and sleep are initiated at incorrect internal biological times, and disrupted glucose metabolism during short sleep and circadian misalignment. In addition to the traditional risk factors of poor diet and exercise, disturbed sleep and circadian rhythms represent modifiable risk factors for prevention and treatment of metabolic disease and for promotion of healthy metabolism.

Caffeine’s wakefulness-promoting and sleep-disrupting effects are well established, yet whether caffeine affects human circadian timing is unknown. Here we show that evening caffeine consumption delays the human circadian melatonin rhythm in vivo, and chronic application of caffeine lengthens the circadian period of molecular oscillations in vitro primarily via an adenosine receptor/cyclic AMP-dependent mechanism. In a double-blind, placebo controlled, ~49-day long within-subject study, we found the equivalent amount of caffeine as that in a double espresso 3 hours before habitual bedtime induced a phase delay of the circadian melatonin rhythm in humans by ~40 minutes. This magnitude of delay was nearly half of the magnitude of the phase-delaying response induced by exposure to 3-hours of evening bright-light (~3000 lux; ~7 Watts/m2) that began at habitual bedtime. Furthermore, using human osteosarcoma U2OS cells expressing clock gene luciferase reporters, we found a dose-dependent lengthening of circadian period by caffeine. By pharmacological dissection and siRNA knockdown we established that perturbation of adenosine receptor signaling, but not ryanodine receptor or phosphodiesterase activity, is sufficient to account for caffeine’s effects on cellular timekeeping. We also used a cyclic AMP biosensor to show that caffeine increased cyclic AMP levels, indicating that caffeine can influence a core component of the cellular circadian clock. Taken together, our findings demonstrate that caffeine influences human circadian timing and gives new insight into how the world’s most widely consumed psychoactive drug impacts upon human physiology.

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%.