Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis.

BACKGROUND AND OBJECTIVES: Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations. METHODS: Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations. RESULTS: Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day. CONCLUSION: This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

OBJECTIVES: Dietary 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/METHODS: Thirty 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. RESULTS: Sensor-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). CONCLUSIONS: Estimation 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.

Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices.

Objective: To describe best practices for manual nutritional analyses of data from passive capture wearable devices in free-living conditions. Method: 18 participants (10 female) with a mean age of 45 ± 10 years and mean BMI of 34.2 ± 4.6 kg/m2 consumed usual diet for 3 days in a free-living environment while wearing an automated passive capture device. This wearable device facilitates capture of images without manual input from the user. Data from the first nine participants were used by two trained nutritionists to identify sources contributing to inter-nutritionist variance in nutritional analyses. The nutritionists implemented best practices to mitigate these sources of variance in the next nine participants. The three best practices to reduce variance in analysis of energy intake (EI) estimation were: (1) a priori standardized food selection, (2) standardized nutrient database selection, and (3) increased number of images captured around eating episodes. Results: Inter-rater repeatability for EI, using intraclass correlation coefficient (ICC), improved by 0.39 from pre-best practices to post-best practices (0.14 vs 0.85, 95% CI, respectively), Bland-Altman analysis indicated strongly improved agreement between nutritionists for limits of agreement (LOA) post-best practices. Conclusion: Significant improvement of ICC and LOA for estimation of EI following implementation of best practices demonstrates that these practices improve the reproducibility of dietary analysis from passive capture device images in free-living environments.

Preventing ovariectomy-induced weight gain decreases tumor burden in rodent models of obesity and postmenopausal breast cancer.

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.

Bone turnover marker responses to sleep restriction and weekend recovery sleep.

BACKGROUND: Prior data demonstrated three weeks of sleep restriction and concurrent circadian disruption uncoupled bone turnover markers (BTMs), indicating decreased bone formation and no change or increased bone resorption. The effect of insufficient sleep with or without ad libitum weekend recovery sleep on BTMs is unknown. METHODS: BTMs were measured in stored serum from 20 healthy adults randomized to one of three study groups consisting of a control group (N = 3 men; 9 h/night) or one of two nocturnal sleep restriction groups in an inpatient laboratory environment. One Sleep Restriction group ("SR"; N = 9; 4 women) had 5 h sleep opportunity per night for nine nights. The other sleep restriction group had an opportunity for ad libitum Weekend Recovery sleep ("WR"; N = 8; 4 women) after four nights of 5 h sleep opportunity per night. Food intake was energy balanced at baseline and ad libitum thereafter. Fasted morning BTM levels and hourly 24 h melatonin levels were obtained on study days 3 (baseline), 5 (after 1 night of sleep restriction for WR and SR), and 11 (after a sleep restricted workweek with weekend recovery sleep in WR or 7 nights of sleep restriction in SR). Linear mixed-effects modeling was used to examine the effect of study duration (e.g., change over time), study condition, age, and sex on BTMs. Pearson correlations were used to determine associations between changes in BTMs and changes in weight and morning circadian misalignment (i.e., duration of high melatonin levels after wake time). RESULTS: There was no significant difference between the three study groups in change over time (p ≥ 0.4 for interaction between assigned group and time for all BTMs), adjusted for age and sex. There was no significant change in N-terminal propeptide of procollagen type I (P1NP), osteocalcin, or C-telopeptide of type I collagen (CTX) from baseline to day 11 (all p ≥ 0.3). In women <25 years old, there was a non-significant decline in P1NP from day 3 to day 5 (= -15.74 ± 7.80 ng/mL; p = 0.06). Change in weight and morning circadian misalignment from baseline to day 11 were correlated with statistically non-significant changes in BTMs (all p ≤ 0.05). CONCLUSION: In this small secondary analysis, we showed that nine nights of prescribed sleep restriction with or without weekend recovery sleep and ad libitum food intake did not alter BTMs. It is possible that age, sex, weight change and morning circadian misalignment modify the effects of sleep restriction on bone metabolism.

Effects of ad libitum food intake, insufficient sleep and weekend recovery sleep on energy balance.

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.

Lipoprotein Lipase Overexpression in Skeletal Muscle Attenuates Weight Regain by Potentiating Energy Expenditure.

Moderate weight loss improves numerous risk factors for cardiometabolic disease; however, long-term weight loss maintenance (WLM) is often thwarted by metabolic adaptations that suppress energy expenditure and facilitate weight regain. Skeletal muscle has a prominent role in energy homeostasis; therefore, we investigated the effect of WLM and weight regain on skeletal muscle in rodents. In skeletal muscle of obesity-prone rats, WLM reduced fat oxidative capacity and downregulated genes involved in fat metabolism. Interestingly, even after weight was regained, genes involved in fat metabolism were also reduced. We then subjected mice with skeletal muscle lipoprotein lipase overexpression (mCK-hLPL), which augments fat metabolism, to WLM and weight regain and found that mCK-hLPL attenuates weight regain by potentiating energy expenditure. Irrespective of genotype, weight regain suppressed dietary fat oxidation and downregulated genes involved in fat metabolism in skeletal muscle. However, mCK-hLPL mice oxidized more fat throughout weight regain and had greater expression of genes involved in fat metabolism and lower expression of genes involved in carbohydrate metabolism during WLM and regain. In summary, these results suggest that skeletal muscle fat oxidation is reduced during WLM and regain, and therapies that improve skeletal muscle fat metabolism may attenuate rapid weight regain.

Reproducibility of Dietary Intake Measurement From Diet Diaries, Photographic Food Records, and a Novel Sensor Method.

Objective: No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake. Method: This was a retrospective analysis of data from a previous study, in which 30 participants (15 female), aged 29 ± 12 y and having a BMI of 27.9 ± 5.5, consumed three identical meals on different days. Four different methods were used to estimate total mass and energy intake on each day: (1) weighed food record; (2) photographic food record; (3) diet diary; and (4) novel mathematical model based on counts of chews and swallows (CCS models) obtained via the use of electronic sensors and video monitoring system. The study staff conducted weighed food records for all meals, took pre- and post-meal photographs, and ensured that diet diaries were completed by participants at the end of each meal. All methods were compared against the weighed food record, which was used as the reference method. Results: Reproducibility was significantly different between the diet diary and photographic food record for total energy intake (p = 0.004). The photographic record had greater reproducibility vs. the diet diary for all parameters measured. For total energy intake, the novel sensor method exhibited good reproducibility (repeatability coefficient (RC) of 59.9 (45.9, 70.4), which was better than that for the diet diary [RC = 79.6 (55.5, 103.3)] but not as repeatable as the photographic method [RC = 43.4 (32.1, 53.9)]. Conclusion: Photographic food records offer superior precision to the diet diary and, therefore, would be valuable for longitudinal studies with repeated measures of dietary intake. A novel electronic sensor also shows promise for the collection of longitudinal dietary intake data.

Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol.

Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).

The In Vivo Net Energy Content of Resistant Starch and Its Effect on Macronutrient Oxidation in Healthy Adults.

The in vivo net energy content of resistant starch (RS) has not been measured in humans so it has not been possible to account for the contribution of RS to dietary energy intake. We aimed to determine the in vivo net energy content of RS and examine its effect on macronutrient oxidation. This was a randomized, double-blind cross-over study. Eighteen healthy adults spent 24 h in a whole room indirect calorimeter to measure total energy expenditure (TEE), substrate oxidation, and postprandial metabolites in response to three diets: 1) digestible starch (DS), 2) RS (33% dietary fiber; RS), or 3) RS with high fiber (RSF, 56% fiber). The in vivo net energy content of RS and RSF are 2.74 ± 0.41 and 3.16 ± 0.27 kcal/g, respectively. There was no difference in TEE or protein oxidation between DS, RS, and RSF. However, RS and RSF consumption caused a 32% increase in fat oxidation (p = 0.04) with a concomitant 18% decrease in carbohydrate oxidation (p = 0.03) versus DS. Insulin responses were unaltered after breakfast but lower in RS and RSF after lunch, at equivalent glucose concentrations, indicating improved insulin sensitivity. The average in vivo net energy content of RS is 2.95 kcal/g, regardless of dietary fiber content. RS and RSF consumption increase fat and decrease carbohydrate oxidation with postprandial insulin responses lowered after lunch, suggesting improved insulin sensitivity at subsequent meals.

Regular exercise potentiates energetically expensive hepatic de novo lipogenesis during early weight regain.

Exercise is a potent facilitator of long-term weight loss maintenance (WLM), whereby it decreases appetite and increases energy expenditure beyond the cost of the exercise bout. We have previously shown that exercise may amplify energy expenditure through energetically expensive nutrient deposition. Therefore, we investigated the effect of exercise on hepatic de novo lipogenesis (DNL) during WLM and relapse to obesity. Obese rats were calorically restricted with (EX) or without (SED) treadmill exercise (1 h/day, 6 days/wk, 15 m/min) to induce and maintain weight loss. After 6 wk of WLM, subsets of WLM-SED and WLM-EX rats were allowed ad libitum access to food for 1 day to promote relapse (REL). An energy gap-matched group of sedentary, relapsing rats (REL-GM) were provided a diet matched to the positive energy imbalance of the REL-EX rats. During relapse, exercise increased enrichment of hepatic DN-derived lipids and induced hepatic molecular adaptations favoring DNL compared with the gap-matched controls. In the liver, compared with both REL-SED and REL-GM rats, REL-EX rats had lower hepatic expression of genes required for cholesterol biosynthesis; greater hepatic expression of genes that mediate very low-density lipoprotein synthesis and secretion; and greater mRNA expression of Cyp27a1, which encodes an enzyme involved in the biosynthesis of bile acids. Altogether, these data provide compelling evidence that the liver has an active role in exercise-mediated potentiation of energy expenditure during early relapse.

Impact of Exercise and Activity on Weight Regain and Musculoskeletal Health Post-Ovariectomy.

The purpose of this study was to determine whether obesity and/or exercise training alters weight regain and musculoskeletal health after ovariectomy (OVX). Female rats were fed high-fat diet (HFD) to reveal obesity-prone (OP) and obesity-resistant (OR) phenotypes. The OP and OR exercising (EX) and sedentary (SED) rats were calorically restricted to lose 15% of body weight using medium-fat diet. Rats were then maintained in energy balance for 8 wk before OVX. After OVX and a brief calorically limited phase, rats were allowed to eat ad libitum until body weight plateaued. Starting at weight loss, EX ran 1 h·d, 6 d·wk, 15 m·min. Energy intake, spontaneous physical activity (SPA), and total energy expenditure were evaluated at the end of weight maintenance pre-OVX, and at three time points post-OVX: before weight regain, during early regain, and after regain. Data are presented as mean ± SE. Exercise attenuated weight regain after OVX in OP only (OP-EX, 123 ± 10 g; OP-SED, 165 ± 12 g; OR-EX, 121 ± 6 g; OR-SED, 116 ± 6 g), which was primarily an attenuation of fat gain. The early post-OVX increase in energy intake explained much of the weight regain, and was similar across groups. Exercising improved bone strength, as did maintaining SPA. Group differences in muscle mitochondrial respiration were not significant. The large decrease in SPA due to OVX was persistent, but early weight regain was dependent on decreased SPA. In conclusion, leanness and exercise do not necessarily protect from OVX-induced weight gain. Exercise prevented weight gain in obese rats, but loss of SPA was the greatest contributor to post-OVX weight gain. Thus, understanding the mechanisms resulting in reduction in SPA after ovarian hormone loss is critical in the prevention of menopause-associated metabolic dysfunction.

Ad libitum Weekend Recovery Sleep Fails to Prevent Metabolic Dysregulation during a Repeating Pattern of Insufficient Sleep and Weekend Recovery Sleep.

People commonly increase sleep duration on the weekend to recover from sleep loss incurred during the workweek. Whether ad libitum weekend recovery sleep prevents metabolic dysregulation caused by recurrent insufficient sleep is unknown. Here, we assessed sleep, circadian timing, energy intake, weight gain, and insulin sensitivity during sustained insufficient sleep (9 nights) and during recurrent insufficient sleep following ad libitum weekend recovery sleep. Healthy, young adults were randomly assigned to one of three groups: (1) control (CON; 9-h sleep opportunities, n = 8), (2) sleep restriction without weekend recovery sleep (SR; 5-h sleep opportunities, n = 14), and (3) sleep restriction with weekend recovery sleep (WR; insufficient sleep for 5-day workweek, then 2 days of weekend recovery, then 2 nights of insufficient sleep, n = 14). For SR and WR groups, insufficient sleep increased after-dinner energy intake and body weight versus baseline. During ad libitum weekend recovery sleep, participants cumulatively slept ∼1.1 h more than baseline, and after-dinner energy intake decreased versus insufficient sleep. However, during recurrent insufficient sleep following the weekend, the circadian phase was delayed, and after-dinner energy intake and body weight increased versus baseline. In SR, whole-body insulin sensitivity decreased ∼13% during insufficient sleep versus baseline, and in WR, whole-body, hepatic, and muscle insulin sensitivity decreased ∼9%-27% during recurrent insufficient sleep versus baseline. Furthermore, during the weekend, total sleep duration was lower in women versus men, and energy intake decreased to baseline levels in women but not in men. Our findings suggest that weekend recovery sleep is not an effective strategy to prevent metabolic dysregulation associated with recurrent insufficient sleep.

Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.

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.

Compensation for cold-induced thermogenesis during weight loss maintenance and regain.

Prevalence of obesity is exacerbated by low rates of successful long-term weight loss maintenance (WLM). In part, relapse from WLM to obesity is due to a reduction in energy expenditure (EE) that persists throughout WLM and relapse. Thus, interventions that increase EE might facilitate WLM. In obese mice that were calorically restricted to reduce body weight by ~20%, we manipulated EE throughout WLM and early relapse using intermittent cold exposure (ICE; 4°C, 90 min/day, 5 days/wk, within the last 3 h of the light cycle). EE, energy intake, and spontaneous physical activity were measured during the obese, WLM, and relapse phases. During WLM and relapse, the ICE group expended more energy during the light cycle because of cold exposure but expended less energy in the dark cycle, which led to no overall difference in total daily EE. The compensation in EE appeared to be mediated by activity, whereby the ICE group was more active during the light cycle because of cold exposure but less active during the dark cycle, which led to no overall effect on total daily activity during WLM and relapse. In brown adipose tissue of relapsing mice, the ICE group had greater mRNA expression of Dio2 and protein expression of UCP1 but lower mRNA expression of Prdm16. In summary, these findings indicate that despite robust increases in EE during cold exposures, ICE is unable to alter total daily EE during WLM or early relapse, likely due to compensatory behaviors in activity.

Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor.

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.

A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake.

Accurate measurement of energy intake (EI) is important for estimation of energy balance, and, correspondingly, body weight dynamics. Traditional measurements of EI rely on self-report, which may be inaccurate and underestimate EI. The imperfections in traditional methodologies such as 24-hour dietary recall, dietary record, and food frequency questionnaire stipulate development of technology-driven methods that rely on wearable sensors and imaging devices to achieve an objective and accurate assessment of EI. The aim of this research was to systematically review and examine peer-reviewed papers that cover the estimation of EI in humans, with the focus on emerging technology-driven methodologies. Five major electronic databases were searched for articles published from January 2005 to August 2017: Pubmed, Science Direct, IEEE Xplore, ACM library, and Google Scholar. Twenty-six eligible studies were retrieved that met the inclusion criteria. The review identified that while the current methods of estimating EI show promise, accurate estimation of EI in free-living individuals presents many challenges and opportunities. The most accurate result identified for EI (kcal) estimation had an average accuracy of 94%. However, collectively, the results were obtained from a limited number of food items (i.e., 19), small sample sizes (i.e., 45 meal images), and primarily controlled conditions. Therefore, new methods that accurately estimate EI over long time periods in free-living conditions are needed.

The importance of field experiments in testing of sensors for dietary assessment and eating behavior monitoring.

The field of sensor-based dietary assessment and behavioral monitoring is rapidly expanding. New devices and methods for detection for food intake and characterization of ingestive behavior, energy intake and nutrition have been introduced. Quite often the testing of new devices is limited to restricted meals in laboratory setting, which has the advantage of being controlled, but may not be representative of real life conditions. To illustrate the importance of field testing, we performed a statistical comparison of meal microstructure metrics acquired in laboratory versus a field-like study. In the laboratory study, individual participants ate a self-selected meal in isolation. In the field-like study, participants consumed selfselected meals in a social setting. In both studies, the participants were monitored by both video observation and wearable food intake sensors. Statistically significant differences were observed in the duration of the meals, duration of ingestion, number of bouts of ingestion, duration of pauses between ingestive bouts, number of bites and other metrics. These results suggest that field testing presents a far different picture of ingestion process and therefore is needed for any realistic assessment of the monitoring devices.

Meal Microstructure Characterization from Sensor-Based Food Intake Detection.

To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (p-value <0.001). There were no significant differences in the number of eating events for push button resolutions of 0.1, 1, and 5 s, but there were significant differences in resolutions of 10-30s (p-value <0.05). The results suggest that the desired time resolution of sensor-based food intake detection should be ≤5 s to accurately detect meal microstructure. Furthermore, the AIM provides more accurate measurement of the eating episode duration than the diet diary.

Prior weight loss exacerbates the biological drive to gain weight after the loss of ovarian function.

Both the history of obesity and weight loss may change how menopause affects metabolic health. The purpose was to determine whether obesity and/or weight loss status alters energy balance (EB) and subsequent weight gain after the loss of ovarian function. Female lean and obese Wistar rats were randomized to 15% weight loss (WL) or ad libitum fed controls (CON). After the weight loss period, WL rats were kept in EB at the reduced weight for 8 weeks prior to ovariectomy (OVX). After OVX, all rats were allowed to eat ad libitum until weight plateaued. Energy intake (EI), spontaneous physical activity, and total energy expenditure (TEE) were measured with indirect calorimetry before OVX, immediately after OVX, and after weight plateau. Changes in energy intake (EI), TEE, and weight gain immediately after OVX were similar between lean and obese rats. However, obese rats gained more total weight and fat mass than lean rats over the full regain period. Post-OVX, EI increased more (P ≤ 0.03) in WL rats (58.9 ± 3.5 kcal/d) than CON rats (8.5 ± 5.2 kcal/d), and EI partially normalized (change from preOVX: 20.5 ± 4.2 vs. 1.5 ± 4.9 kcal/day) by the end of the study. As a result, WL rats gained weight (week 1:44 ± 20 vs. 7 ± 25 g) more rapidly (mean = 44 ± 20 vs. 7 ± 25 g/week; P < 0.001) than CON Prior obesity did not affect changes in EB or weight regain following OVX, whereas a history of weight loss prior to OVX augmented disruptions in EB after OVX, resulting in more rapid weight regain.