Stability of the timing of food intake at daily and monthly timescales in young adults

Cross-sectional observations have shown that the timing of eating may be important for health-related outcomes. Here we examined the stability of eating timing, using both clock hour and relative circadian time, across one semester (n = 14) at daily and monthly time-scales. At three time points ~ 1 month apart, circadian phase was determined during an overnight in-laboratory visit and eating was photographically recorded for one week to assess timing and composition. Day-to-day stability was measured using the Composite Phase Deviation (deviation from a perfectly regular pattern) and intraclass correlation coefficients (ICC) were used to determine individual stability across months (weekly average compared across months). Day-to-day clock timing of caloric events had poor stability within individuals (~ 3-h variation; ICC = 0.12–0.34). The timing of eating was stable across months (~ 1-h variation, ICCs ranging from 0.54–0.63), but less stable across months when measured relative to circadian timing (ICC = 0.33–0.41). Our findings suggest that though day-to-day variability in the timing of eating has poor stability, the timing of eating measured for a week is stable across months within individuals. This indicates two relevant timescales: a monthly timescale with more stability in eating timing than a daily timescale. Thus, a single day’s food documentation may not represent habitual (longer timescale) patterns

Early Morning Food Intake as a Risk Factor for Metabolic Dysregulation

Increased risk of obesity and diabetes in shift workers may be related to food intake at adverse circadian times. Early morning shiftwork represents the largest proportion of shift workers in the United States, yet little is known about the impact of food intake in the early morning on metabolism. Eighteen participants (9 female) completed a counterbalanced 16 day design with two conditions separated by ~1 week: 8 h sleep opportunity at habitual time and simulated early morning shiftwork with 6.5 h sleep opportunity starting ~1 h earlier than habitual time. After wake time, resting energy expenditure (REE) was measured and blood was sampled for melatonin and fasting glucose and insulin. Following breakfast, post-prandial blood samples were collected every 40 min for 2 h and the thermic effect of food (TEF) was assessed for 3.25 h. Total sleep time was decreased by ~85 min (p &lt; 0.0001), melatonin levels were higher (p &lt; 0.0001) and post-prandial glucose levels were higher (p &lt; 0.05) after one day of simulated early morning shiftwork compared with habitual wake time. REE was lower after simulated early morning shiftwork; however, TEF after breakfast was similar to habitual wake time. Insufficient sleep and caloric intake during a circadian phase of high melatonin levels may contribute to metabolic dysregulation in early morning shift workers. </div

Chronic Insufficient Sleep Has a Limited Impact on Circadian Rhythmicity of Subjective Hunger and Awakening Fasted Metabolic Hormones

Weight gain and obesity have reached epidemic proportions in modern society. Insufficient sleep—which is also prevalent in modern society—and eating at inappropriate circadian times have been identified as risk factors for weight gain, yet the impact of chronic insufficient sleep on the circadian timing of subjective hunger and physiologic metabolic outcomes are not well understood. We investigated how chronic insufficient sleep impacts the circadian timing of subjective hunger and fasting metabolic hormones in a 32-day in-laboratory randomized single-blind control study, with healthy younger participants (range, 20–34 years) randomized to either Control (1:2 sleep:wake ratio, 6.67 h sleep:13.33 h wake, n = 7, equivalent to 8 h of sleep per 24 h) or chronic sleep restriction (CSR, 1:3.3 sleep:wake ratio, 4.67 h sleep:15.33 h wake, n = 8, equivalent to 5.6 h of sleep per 24 h) conditions. Participants lived on a “20 h day” designed to distribute all behaviors and food intake equally across all phases of the circadian cycle over every six consecutive 20 h protocol days. During each 20 h day, participants were provided a nutritionist-designed, isocaloric diet consisting of 45–50% carbohydrate, 30–35% fat, and 15–20% protein adjusted for sex, weight, and age. Subjective non-numeric ratings of hunger were recorded before and after meals and fasting blood samples were taken within 5 min of awakening. Subjective levels of hunger and fasting concentrations of leptin, ghrelin, insulin, glucose, adiponectin, and cortisol all demonstrated circadian patterns; there were no differences, however, between CSR and Control conditions in subjective hunger ratings or any fasting hormone concentrations. These findings suggest that chronic insufficient sleep may have a limited role in altering the robust circadian profile of subjective hunger and fasted metabolic hormones.Clinical Trial RegistrationThe study was registered as clinical trial #NCT01581125

Chronic Circadian Disruption and Sleep Restriction Influence Subjective Hunger, Appetite, and Food Preference

Chronic circadian disruption (CCD), such as occurs during rotating shiftwork, and insufficient sleep are each independently associated with poor health outcomes, including obesity and glucose intolerance. A potential mechanism for poor health is increased energy intake (i.e., eating), particularly during the circadian night, when the physiological response to energy intake is altered. However, the contributions of CCD and insufficient sleep to subjective hunger, appetite, food preference, and appetitive hormones are not clear. To disentangle the influences of these factors, we studied seventeen healthy young adults in a 32-day in-laboratory study designed to distribute sleep, wakefulness, and energy intake equally across all phases of the circadian cycle, thereby imposing CCD. Participants were randomized to the Control (1:2 sleep:wake ratio, n = 8) or chronic sleep restriction (CSR, 1:3.3 sleep:wake ratio, n = 9) conditions. Throughout each waking episode the participants completed visual analog scales pertaining to hunger, appetite, and food preference. A fasting blood sample was collected to assess appetitive hormones. CCD was associated with a significant decrease in hunger and appetite in a multitude of domains in both the Control and CSR groups. This change in hunger was significantly correlated with changes in the ghrelin/leptin ratio. These findings further our understanding of the contributions of CCD and insufficient sleep on subjective hunger and appetite as well as of their possible contributions to adverse health behaviors

Chronic Insufficient Sleep Has a Limited Impact on Circadian Rhythmicity of Subjective Hunger and Awakening Fasted Metabolic Hormones

Weight gain and obesity have reached epidemic proportions in modern society. Insufficient sleep—which is also prevalent in modern society—and eating at inappropriate circadian times have been identified as risk factors for weight gain, yet the impact of chronic insufficient sleep on the circadian timing of subjective hunger and physiologic metabolic outcomes are not well understood. We investigated how chronic insufficient sleep impacts the circadian timing of subjective hunger and fasting metabolic hormones in a 32-day in-laboratory randomized single-blind control study, with healthy younger participants (range, 20–34 years) randomized to either Control (1:2 sleep:wake ratio, 6.67 h sleep:13.33 h wake, n = 7, equivalent to 8 h of sleep per 24 h) or chronic sleep restriction (CSR, 1:3.3 sleep:wake ratio, 4.67 h sleep:15.33 h wake, n = 8, equivalent to 5.6 h of sleep per 24 h) conditions. Participants lived on a “20 h day” designed to distribute all behaviors and food intake equally across all phases of the circadian cycle over every six consecutive 20 h protocol days. During each 20 h day, participants were provided a nutritionist-designed, isocaloric diet consisting of 45–50% carbohydrate, 30–35% fat, and 15–20% protein adjusted for sex, weight, and age. Subjective non-numeric ratings of hunger were recorded before and after meals and fasting blood samples were taken within 5 min of awakening. Subjective levels of hunger and fasting concentrations of leptin, ghrelin, insulin, glucose, adiponectin, and cortisol all demonstrated circadian patterns; there were no differences, however, between CSR and Control conditions in subjective hunger ratings or any fasting hormone concentrations. These findings suggest that chronic insufficient sleep may have a limited role in altering the robust circadian profile of subjective hunger and fasted metabolic hormones. Clinical Trial Registration The study was registered as clinical trial #NCT01581125

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

© Akane Sano, Sara Taylor, Andrew W McHill, Andrew JK Phillips, Laura K Barger, Elizabeth Klerman, Rosalind Picard. Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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

Prediction of Happy-Sad mood from daily behaviors and previous sleep history

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.MIT Media Lab ConsortiumSamsung (Firm)National Institutes of Health (U.S.) (NIH grant R01GM105018)National Institutes of Health (U.S.) (NIH grant T32HL007901)National Institutes of Health (U.S.) (NIH grant K99HL119618)National Institutes of Health (U.S.) (NIH grant K24HL105664

Effect of slow wave sleep disruption on metabolic parameters in adolescents

STUDY OBJECTIVES: Cross-sectional studies report a correlation between slow wave sleep (SWS) duration and insulin sensitivity (SI) in children and adults. Suppression of SWS causes insulin resistance in adults but effects in children are unknown. This study was designed to determine the effect of SWS fragmentation on SI in children. METHODS: Fourteen pubertal children (11.3–14.1 y, body mass index 29(th) to 97(th) percentile) were randomized to sleep studies and mixed meal (MM) tolerance tests with and without SWS disruption. Beta-cell responsiveness (Φ) and SI were determined using oral minimal modeling. RESULTS: During the disruption night, auditory stimuli (68.1 ± 10.7/night; mean ± standard error) decreased SWS by 40.0 ± 8.0%. SWS fragmentation did not affect fasting glucose (non-disrupted 76.9 ± 2.3 versus disrupted 80.6 ± 2.1 mg/dL), insulin (9.2 ± 1.6 versus 10.4 ± 2.0 μIU/mL), or C-peptide (1.9 ± 0.2 versus 1.9 ± 0.1 ng/mL) levels and did not impair SI (12.9 ± 2.3 versus 10.1 ± 1.6 10(−4) dL/kg/min per μIU/mL) or Φ (73.4 ± 7.8 versus 74.4 ± 8.4 10(−9) min(−1)) to a MM challenge. Only the subjects in the most insulin-sensitive tertile demonstrated a consistent decrease in SI after SWS disruption. CONCLUSION: Pubertal children across a range of body mass indices may be resistant to the adverse metabolic effects of acute SWS disruption. Only those subjects with high SI (i.e., having the greatest “metabolic reserve”) demonstrated a consistent decrease in SI. These results suggest that adolescents may have a unique ability to adapt to metabolic stressors, such as acute SWS disruption, to maintain euglycemia. Additional studies are necessary to confirm that this resiliency is maintained in settings of chronic SWS disruption. CITATION: Shaw ND, McHill AW, Schiavon M, Kangarloo T, Mankowski PW, Cobelli C, Klerman EB, Hall JE. Effect of slow wave sleep disruption on metabolic parameters in adolescents. SLEEP 2016;39(8):1591–1599