Explore the vast range of titles available.

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.

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.

Background Long-term weight reduction remains elusive for many obese individuals. Resistant starch (RS) and exercise may be useful for weight maintenance. The effects of RS, with or without exercise, on weight regain was examined during relapse to obesity on a high carbohydrate, high fat (HC/HF) diet. Methods Obesity-prone rats were fed ad libitum for 16 weeks then weight reduced on a low fat diet to induce a 17% body weight loss (weight reduced rats). Weight reduced rats were maintained on an energy-restricted low fat diet for 18 weeks, with or without a daily bout of treadmill exercise. Rats were then allowed free access to HC/HF diet containing low (0.3%) or high (5.9%) levels of RS. Weight regain, energy balance, body composition, adipocyte cellularity, and fuel utilization were monitored as rats relapsed to obesity and surpassed their original, obese weight. Results Both RS and exercise independently attenuated weight regain by reducing the energy gap between the drive to eat and suppressed energy requirements. Exercise attenuated the deposition of lean mass during relapse, whereas its combination with RS sustained lean mass accrual as body weight returned. Early in relapse, RS lowered insulin levels and reduced the deposition of fat in subcutaneous adipose tissue. Exercise cessation at five weeks of relapse led to increased weight gain, body fat, subcutaneous adipocytes, and decreased lean mass; all detrimental consequences to overall metabolic health. Conclusions These data are the first to show the complimentary effects of dietary RS and regular exercise in countering the metabolic drive to regain weight following weight loss and suggest that exercise cessation, in the context of relapse on a HC/HF diet, may have dire metabolic consequences.

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.

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.

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. We examined the impact of obesity and weight loss on the biological drive to gain weight after the loss of ovarian function. Contrary to our hypothesis, preexisting obesity had little effect on the study's outcomes. However, the biological drive to regain weight after weight loss exacerbated the drive to gain weight from the loss of ovarian function.

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.

Background There is currently no standard clinical refeeding diet for the treatment of anorexia nervosa (AN). To provide the most efficacious AN clinical care, it is necessary to define the metabolic effects of current refeeding diets. Methods An activity-based model of anorexia nervosa (AN) was used in female rats. AN was induced over 7d by timed access to low fat (LF) diet with free access to a running wheel. Plasma hormones/metabolites and body composition were assessed at baseline, AN diagnosis (day 0), and following 28d of refeeding on LF diet. Energy balance and expenditure were measured via continuous indirect calorimetry on days −3 to +3. Results AN induction caused stress as indicated by higher levels of corticosterone versus controls ( p  < 0.0001). The rate of weight gain during refeeding was higher in AN rats than controls ( p  = 0.0188), despite lower overall energy intake ( p  < 0.0001). This was possible due to lower total energy expenditure (TEE) at the time of AN diagnosis which remained significantly lower during the entire refeeding period, driven by markedly lower resting energy expenditure (REE). AN rats exhibited lower lipid accumulation in visceral adipose tissues (VAT) but much higher liver accumulation (62 % higher in AN than control; p  < 0.05) while maintaining the same total body weight as controls. It is possible that liver lipid accumulation was caused by overfeeding of carbohydrate suggesting that a lower carbohydrate, higher fat diet may be beneficial during AN treatment. To test whether such a diet would be accepted clinically, we conducted a study in adolescent female AN patients which showed equivalent palatability and acceptability for LF and moderate fat diets. In addition, this diet was feasible to provide clinically during inpatient treatment in this population. Conclusion Refeeding a LF diet to restore body weight in female AN rats caused depressed TEE and REE which facilitated rapid regain. However, this weight gain was metabolically unhealthy as it resulted in elevated lipid accumulation in the liver. It is necessary to investigate the use of other diets, such as lower carbohydrate, moderate fat diets, in pre-clinical models to develop the optimal clinical refeeding diets for AN.

Postprandial glycaemic control is important in the prevention and therapy of type 2 diabetes and related diseases. Agents that may reduce postprandial glycaemia and/or insulinaemia, such as consumption of high-amylose foods, are considered beneficial; however, little is known about the dose–response relationship and the effects of cooking. The aim of the present study was to define the dose–response curve for postprandial glycaemic and insulinaemic excursions following meals of different amylose content and to compare the dose–response curves for meals containing cooked and uncooked starches. Following an overnight fast, rats ingested a test meal and blood was sampled over 2 h. The meal was given at 1.0 g carbohydrate/kg body weight, with an amylose content of 0, 270, 600 or 850 g/kg total starch. The area under the glucose curve did not differ under any condition investigated. For the uncooked-starch diets, area under the insulin curve was higher for the 0 g amylose/kg total starch meal than all other meals (P = 0·0001). For the cooked-starch diets, area under the insulin curve was higher in the O than the 600 and 850 g amylose/kg total starch groups (P < 0·01), but did not differ from the 270 amylose/kg total starch group. These results suggest that even a relatively small proportion of uncooked amylose (270 g/kg total starch) is sufficient to achieve a maximal attenuating effect on postprandial insulin concentrations as compared with 0 g amylose/kg total starch. Following cooking, however, a much higher proportion of amylose (≥ 600 g/kg total starch) is needed to achieve a similar effect.