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Insufficient sleep is associated with obesity, yet little is known about how repeated nights of insufficient sleep influence energy expenditure and balance. We studied 16 adults in a 14- to 15-d-long inpatient study and quantified effects of 5 d of insufficient sleep, equivalent to a work week, on energy expenditure and energy intake compared with adequate sleep. We found that insufficient sleep increased total daily energy expenditure by ∼5%; however, energy intake—especially at night after dinner—was in excess of energy needed to maintain energy balance. Insufficient sleep led to 0.82 ± 0.47 kg (±SD) weight gain despite changes in hunger and satiety hormones ghrelin and leptin, and peptide YY, which signaled excess energy stores. Insufficient sleep delayed circadian melatonin phase and also led to an earlier circadian phase of wake time. Sex differences showed women, not men, maintained weight during adequate sleep, whereas insufficient sleep reduced dietary restraint and led to weight gain in women. Our findings suggest that increased food intake during insufficient sleep is a physiological adaptation to provide energy needed to sustain additional wakefulness; yet when food is easily accessible, intake surpasses that needed. We also found that transitioning from an insufficient to adequate/recovery sleep schedule decreased energy intake, especially of fats and carbohydrates, and led to −0.03 ± 0.50 kg weight loss. These findings provide evidence that sleep plays a key role in energy metabolism. Importantly, they demonstrate physiological and behavioral mechanisms by which insufficient sleep may contribute to overweight and obesity.

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.

Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants’ food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light’s kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters’ annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The PRISMA 2020 guidelines were followed to review the full texts of 161 scientific manuscripts. The contributions of this review article are twofold: (i) A taxonomy of sensors for quantifying various aspects of eating behavior is established, classifying the types of sensors used (such as acoustic, motion, strain, distance, physiological, cameras, and others). (ii) The accuracy of measurement devices and methods is assessed. The review highlights the advantages and limitations of methods that measure and monitor different eating metrics using a combination of sensor modalities and machine learning algorithms. Furthermore, it emphasizes the importance of testing these methods outside of restricted laboratory conditions, and it highlights the necessity of further research to develop privacy-preserving approaches, such as filtering out non-food-related sounds or images, to ensure user confidentiality and comfort. The review concludes with a discussion of challenges and future trends in the use of sensors for monitoring eating behavior.

The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.

Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of −17.7 ± 226.9 g and −6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.

Background Obesity and adult weight gain are linked to increased breast cancer risk and poorer clinical outcomes in postmenopausal women, particularly for hormone-dependent tumors. Menopause is a time when significant weight gain occurs in many women, and clinical and preclinical studies have identified menopause (or ovariectomy) as a period of vulnerability for breast cancer development and promotion. Methods We hypothesized that preventing weight gain after ovariectomy (OVX) may be sufficient to prevent the formation of new tumors and decrease growth of existing mammary tumors. We tested this hypothesis in a rat model of obesity and carcinogen-induced postmenopausal mammary cancer and validated our findings in a murine xenograft model with implanted human tumors. Results In both models, preventing weight gain after OVX significantly decreased obesity-associated tumor development and growth. Importantly, we did not induce weight loss in these animals, but simply prevented weight gain. In both lean and obese rats, preventing weight gain reduced visceral fat accumulation and associated insulin resistance. Similarly, the intervention decreased circulating tumor-promoting growth factors and inflammatory cytokines (i.e., BDNF, TNFα, FGF-2), with greater effects in obese compared to lean rats. In obese rats, preventing weight gain decreased adipocyte size, adipose tissue macrophage infiltration, reduced expression of the tumor-promoting growth factor FGF-1 in mammary adipose, and reduced phosphorylated FGFR indicating reduced FGF signaling in tumors. Conclusions Together, these findings suggest that the underlying mechanisms associated with the anti-tumor effects of weight maintenance are multi-factorial, and that weight maintenance during the peri-/postmenopausal period may be a viable strategy for reducing obesity-associated breast cancer risk and progression in women.

ObjectivesDietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard.Subjects/MethodsThirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences.ResultsSensor-derived EI from regression modeling (R2 = 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland–Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively).ConclusionsEstimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Abstract Study Objectives Insufficient sleep is believed to promote positive energy balance (EB) and weight gain. Increasing weekend sleep duration to “recover” from weekday sleep loss is common, yet little is known regarding how weekend recovery sleep influences EB. We conducted a randomized controlled trial to assess how: (1) 2 days and 8 days of insufficient sleep and (2) ad libitum weekend recovery sleep impact EB (energy intake [EI] – energy expenditure [EE]). Methods Following ten baseline days with 9 h per night sleep opportunities, participants completed one of three 10-day experimental protocols with ad libitum EI: control (9 h sleep opportunities; n = 8; 23 ± 5 years [mean ± SD]); sleep restriction (SR; 5 h sleep opportunities; n = 14; 25 ± 5 years); sleep restriction with weekend recovery sleep (SR + WR; 5 days insufficient sleep, 2 days ad libitum weekend recovery sleep, 3 days recurrent insufficient sleep; n = 14; 27 ± 4 years). Results Twenty-four hour EB increased (p < 0.001; main effect) by an average of 797.7 ± 96.7 (±SEM) kcal during the 10-day experimental protocol versus baseline with no significant differences between groups. Percent change from baseline in 24 h-EE was higher (p < 0.05) on day 2 of insufficient sleep (SR and SR + WR groups; 10 ± 1%) versus adequate sleep (control group; 4 ± 3%). Conclusions In this between-group study, the effects of adequate sleep and insufficient sleep, with or without or weekend recovery sleep, on 24 h-EB were similar. Examining EB and body weight changes using within-subject cross-over designs and “free-living” conditions outside the laboratory (e.g. sleep extension) are needed to advance our understanding of the links between insufficient sleep, weekend recovery sleep and weight-gain.

Background People living with HIV (PLWH) suffer from chronic inflammation even with effective antiretroviral therapy (ART). A high-fat, low-fiber western-type diet has been linked with inflammation, in part through gut microbiome changes. In sub-Saharan Africa (SSA), a region with high HIV burden, urbanization has been linked with a shift from traditional agrarian towards westernized diets, and with changes in food security. To explore the relationship between diet, inflammation, and the gut microbiome in PLWH, we enrolled 1) ART Naïve PLWH who provided samples before and after 24 weeks of ART, 2) PLWH on ART at both timepoints and 3) HIV-seronegative controls. Individuals were evenly recruited from rural and urban Zimbabwe. Using a food frequency survey designed to measure intake of agrarian versus western-type food items in Zimbabwe, we determined how diet differs with urbanization, HIV-infection and treatment, and is related to inflammation and the gut microbiome. Results Individuals residing in a rural area of Zimbabwe less frequently consumed high-fat, low-fiber western type food items and had lower consumption of diverse food items overall, except for sadza, a subsistence staple, processed from home-grown grains. Consumption of a more western-type diet correlated with lower CD4 + T cell percentage in untreated and treated PLWH and increased T cell exhaustion in PLWH on ART. PLWH on ART at time of enrollment also consumed diverse food items at a lower frequency and more often were underweight. Low food consumption correlated with muted improvements in T cell exhaustion after 24 weeks of ART. Individuals residing in the rural area had more Prevotella -rich/ Bacteroides -poor microbiomes, but this was not significantly mediated by diet. Carbohydrate substrate degradation capabilities in the microbiome, based on predictions made using metagenomic polysaccharide utilization loci, correlated with dietary intake patterns. Conclusions Taken together, this work supports that consumption of more high-fat/low-fiber type food items has the potential to exacerbate HIV pathogenesis in a sub-Saharan setting where HIV burden is high and reinforces the importance of nutritional support for promoting immunologic response to ART in PLWH in SSA. 35TT7j443dinAMBYPs5hTa Video Abstract