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We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement.NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

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Objective: To provide an overview of our whole room indirect calorimeter (WRIC), demonstrate validity and reliability of our WRIC, and explore a novel application of Bayesian hierarchical modeling to assess responses to small carbohydrate loads. Methods: Seven gas infusion studies were performed using a gas blender and profiles designed to mimic resting and postprandial metabolic events to assess WRIC validity. In a crossover design, 16 participants underwent fasting and postprandial measurements, during which they consumed a 75-kcal drink containing sucrose, dextrose, or fructose. Linear mixed effects models were used to compare resting and postprandial metabolic rate (MR) and CO (CO). Bayesian Hierarchical Modeling was also used to model postprandial CO trajectories for each participant and condition. Results: Mean total error in infusions were 1.27 ± 1.16% and 0.42 ± 1.21% for VO2 and VCO2 respectively, indicating a high level of validity. Mean resting MR was similar across conditions (x¯=1.05±0.03 kcal/min, p=0.82, ICC: 0.91). While MR increased similarly among all conditions (~13%, p=0.29), postprandial CO parameters were significantly lower for dextrose compared with sucrose or fructose. Conclusions: Our WRIC validation and novel application of statistical methods presented here provide important foundations for new research directions using WRIC.

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This randomized, double-blinded, crossover study measured the acute effect of ingesting a mixed flavonoid-caffeine (MFC) supplement compared to placebo (PL) on energy expenditure (EE) and fat oxidation (FATox) in a metabolic chamber with premenopausal women (n = 19, mean ± SD, age 30.7 ± 8.0 year, BMI 25.7 ± 3.4 kg/m2). The MFC supplement (658 mg flavonoids, split dose 8:30, 13:00) contained quercetin, green tea catechins, and anthocyanins from bilberry extract, and 214 mg caffeine. Participants were measured twice in a metabolic chamber for a day, four weeks apart, with outcomes including 22 h EE (8:30-6:30), substrate utilization from the respiratory quotient (RQ), plasma caffeine levels (16:00), and genotyping for the single-nucleotide polymorphism (SNP) rs762551. Areas under the curve (AUC) for metabolic data from the MFC and PL trials were calculated using the trapezoid rule, with a mixed linear model (GLM) used to evaluate the overall treatment effect. The 22 h oxygen consumption and EE were significantly higher with MFC than PL (1582 ± 143, 1535 ± 154 kcal/day, respectively, p = 0.003, trial difference of 46.4 ± 57.8 kcal/day). FATox trended higher for MFC when evaluated using GLM (99.2 ± 14.0, 92.4 ± 14.4 g/22 h, p = 0.054). Plasma caffeine levels were significantly higher in the MFC versus PL trial (5031 ± 289, 276 ± 323 ng/mL, respectively, p < 0.001). Trial differences for 22 h EE and plasma caffeine were unrelated after controlling for age and body mass (r = -0.249, p = 0.139), and not different for participants with the homozygous allele 1, A/A, compared to C/A and C/C (p = 0.50 and 0.56, respectively). In conclusion, EE was higher for MFC compared to PL, and similar to effects estimated from previous trials using caffeine alone. A small effect of the MFC on FATox was measured, in contrast to inconsistent findings previously reported for this caffeine dose. The trial variance for 22 h EE was not significantly related to the variance in plasma caffeine levels or CYP1A2*1F allele carriers and non-carriers.

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Background: Over-proportionally high energy requirements in some patients with anorexia nervosa (AN) have been reported, but their exact origin remains unclear. Objective: To objectively measure metabolic alterations in an AN patient with high energy requirements as judged by clinical observation. Materials and Methods: We present the case of a young woman with AN (index patient, IP; 19 years, admission BMI 13.9 kg/m2). After 3 months of treatment at BMI 17.4 kg/m2, we assessed her resting energy expenditure (REE), respiratory exchange ratio (RER), diet-induced thermogenesis (DIT), seated non-exercise physical activity (NEPA in Volt by infrared sensors), and exercise activity thermogenesis (EAT) in a metabolic chamber; body composition (bioimpedance analysis), energy intake (15d-food protocol), physical activity (accelerometry) and endocrine parameters. The IP was compared for REE, RER, DIT and seated NEPA to six AN patients (AN-C) and four healthy women (HC-1), and for EAT to another six healthy women (HC-2). Results: Our IP showed high REE (110% of predicted REE according to Harris & Benedict) and high seated NEPA (47% increase over AN-C, 40% over HC-1), whereas DIT (IP: 78 vs. HC-1: 145 ± 51 kJ/180 min) and EAT (IP: 157 vs. HC-2: 235 ± 30 kJ/30 min) were low, when compared with HC. The other AN patients showed a lower REE (AN: 87 ± 2% vs. HC: 97 ± 2% predicted) at increased DIT (AN: 187 ± 91 vs. HC: 145 ± 51 kJ/180 min) when compared with HC. RER of the IP was low (IP: 0.72 vs. 0.77 in AN-C; 0.77 in HC-1 and 0.80 in HC-2). Conclusions: Complex and variable disturbances of energy metabolism might exist in a subgroup of patients with AN during refeeding, which could lead to unexpectedly high energy requirements. Future studies need to confirm the existence, and investigate the characteristics and prevalence of this subgroup. Clinical trial Registry number: NCT02087280, https://www.clinicaltrials.gov/.

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Exercise plays an important role in the prevention and treatment of chronic liver disease and associated metabolic disorders. A single bout of exercise induces tissue blood flow redistribution, which decreases splanchnic circulation and leads to physiologic hypoxia in the gastrointestinal system and liver. The transcription factor, hypoxia inducible factor-1α (HIF-1α), and its regulator, prolylhydroxylase 2 (PHD2), play pivotal roles in the response to oxygen flux by regulating downstream gene expression levels in the liver. We hypothesized that exercise increases the HIF-1α levels in the liver, and that the hepatic PHD2/HIF-1α axis is involved in postexercise restoration of systemic energy homeostasis. Through constant O2 consumption, CO2 production, food and water intake, and physical activity detection with metabolic chambers, we observed that one 30-min session of swimming exercise enhances systemic energy metabolism in mice. By using the noninvasive bioluminescence imaging ROSA26 oxygen-dependent domain Luc mouse model, we reveal that exercise increases in vivo HIFα levels in the liver. Intraperitoneal injections of the PHD inhibitor, dimethyloxalylglycine, mimicked exercise-induced HIFα increase, whereas the HIF-1α inhibitor, PX-478, blocked this effect. We next constructed liver-specific knockout (LKO) mouse models with albumin- Cre-mediated, hepatocyte-specific Hif1a and Phd2 deletion. Compared with their controls, Hif1a-LKO and Phd2-LKO mice exhibited distinct patterns of hepatic metabolism-related gene expression profiles. Moreover, Hif1a-LKO mice failed to restore systemic energy homeostasis after exercise. In conclusion, the current study demonstrates that a single bout of exercise disrupts systemic energy homeostasis, increasing the HIF-1α levels in the liver. These findings also provide evidence that the hepatic PHD2/HIF-1α axis is involved in postexercise systemic metabolic homeostasis.-Luo, B., Xiang, D., Wu, D., Liu, C., Fang, Y., Chen, P., Hu, Y.-P. Hepatic PHD2/HIF-1α axis is involved in postexercise systemic energy homeostasis.

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This study was undertaken to quantify the additional increase in diet-induced oxygen uptake after exhaustive high-intensity intermittent exercise (HIIE), consisting of 6-7 bouts of 20-sec bicycle exercise (intensity: 170% V˙O2max) with a 10-sec rest between bouts. Using a metabolic chamber, the oxygen uptake of ten men was measured from 10:30 am to 07:00 am the next day on two separate days with or without HIIE, with lunch (12:00) and supper (18:00) (Diet experiment). On two other days, the oxygen uptake of six different subjects was measured from 10:30 to 16:00 with or without HIIE, but without meals (Fasting experiment). Ten minutes of exercise at 50% V˙O2maxpreceded the HIIE in both experiments; EPOC (excess postexercise oxygen consumption) after HIIE was found to wear off before 12:00 in both experiments. In the Diet experiment, oxygen uptake during HIIE and EPOC were 123.4 ± 12.0 and 115.3 ± 32.3 mL·kg-1, respectively. Meals elevated resting oxygen uptake on both days, but those on the HIIE day were significantly higher than on the control day. This enhanced diet-induced oxygen uptake (difference in resting oxygen uptake from 12:00-23:00 between HIIE and control day: ΔDIT) was 146.1 ± 90.9 mL·kg-1, comparable to the oxygen uptake during the HIIE and EPOC The ΔDIT was correlated with subjects' V˙O2max(52.1 ± 6.6 mL·kg-1·min-1) (r = 0.76, n = 10, P < 0.05). We concluded that HIIE enhances diet-induced oxygen uptake significantly, and that it is related to the cardiorespiratory fitness.

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PURPOSE: Human sleep is generally consolidated into a single prolonged period, and its metabolic consequence is to impose an extended period of fasting. Changes in sleep stage and homeostatic sleep drive following sleep onset may affect sleeping metabolic rate through cross talk between the mechanisms controlling energy metabolism and sleep. The purpose of this study was to isolate the effects of sleep stage and time after sleep onset on sleeping metabolic rate. METHODS: The sleeping metabolic rate of 29 healthy adults was measured using whole room indirect calorimetry, during which polysomnographic recording of sleep was performed. The effects of sleep stage and time after sleep onset on sleeping metabolic rate were evaluated using a semi-parametric regression analysis. A parametric analysis was used for the effect of sleep stage and a non-parametric analysis was used for the effect of time. RESULTS: Energy expenditure differed significantly between sleep stages: wake after sleep onset (WASO)>stage 2, slow wave sleep (SWS), and REM; stage 1>stage 2 and SWS; and REM>SWS. Similarly, carbohydrate oxidation differed significantly between sleep stages: WASO > stage 2 and SWS; and stage 1>SWS. Energy expenditure and carbohydrate oxidation decreased during the first half of sleep followed by an increase during the second half of sleep. CONCLUSIONS: This study identified characteristic phenotypes in energy expenditure and carbohydrate oxidation indicating that sleeping metabolic rate differs between sleep stages.

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The current experiment tested the hypothesis that consumption of a high-fat diet (HFD) would differentially affect metabolic parameters in obesity-prone Osborne-Mendel (OM) and obesity-resistant S5B/Pl (S5B) rats. In OM rats consuming a HFD, an increase in HFD intake, body mass, and percent fat mass, and a HFD-induced decrease in metabolic rate and energy expenditure were demonstrated. In S5B rats consuming a HFD, no change in percent body fat or HFD intake was demonstrated and HFD increased metabolic rate and energy expenditure. To assess whether HFD differentially altered skeletal muscle markers of metabolism in OM and S5B rats, the expression of the transporters, CD36 and GLUT4, and the energy sensors, AMPK and PPARγ, in the gastrocnemius muscle was measured. Oxidation and lipid accumulation in the gastrocnemius muscle was histologically determined. Consumption of a HFD decreased phosphorylated AMPK and PPARγ expression in the skeletal muscle of obesity-prone OM rats. Lipid accumulation in skeletal muscle was significantly higher in OM rats fed a HFD. Overall, these data suggest that the differential response to HFD on metabolic rate, energy expenditure, and phosphorylated AMPK and PPARγ in OM and S5B rats, may partially account for differences in the susceptibility to develop obesity.

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Whole body fat oxidation increases during exercise. However, 24-h fat oxidation on a day with exercise often remains similar to that of sedentary day, when energy intake is increased to achieve an energy-balanced condition. The present study aimed to examine a possibility that time of the day when exercise is performed makes differences in 24-h fat oxidation. As a potential mechanism of exercise affecting 24-h fat oxidation, its relation to exercise-induced transient energy deficit was examined. Nine young male endurance athletes underwent three trials of indirect calorimetry using a metabolic chamber, in which they performed a session of 100 min of exercise before breakfast (AM), after lunch (PM), or two sessions of 50 min of exercise before breakfast and after lunch (AM/PM) at 65% of maximal oxygen uptake. Experimental meals were designed to achieve individual energy balance. Twenty-four-hour energy expenditure was similar among the trials, but 24-h fat oxidation was 1,142 ± 97, 809 ± 88, and 608 ± 46 kcal/24 h in descending order of its magnitude for AM, AM/PM, and PM, respectively (P < 0.05). Twenty-four-hour carbohydrate oxidation was 2,558 ± 110, 2,374 ± 114, and 2,062 ± 96 kcal/24 h for PM, AM/PM, and AM, respectively. In spite of energy-balanced condition over 24 h, exercise induced a transient energy deficit, the magnitude of which was negatively correlated with 24-h fat oxidation (r = -0.72, P < 0.01). Similarly, transient carbohydrate deficit after exercise was negatively correlated with 24-h fat oxidation (r = -0.40, P < 0.05). The time of the day when exercise is performed affects 24-h fat oxidation, and the transient energy/carbohydrate deficit after exercise is implied as a factor affecting 24-h fat oxidation.

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BACKGROUND: As part of the growing lifestyle diversity in modern society, there is wide variation in the time of day individuals choose to exercise. Recent surveys in the US and Japan have reported that on weekdays, more people exercise in the evening, with fewer individuals exercising in the morning or afternoon. Exercise performed in the post-prandial state has little effect on accumulated fat oxidation over 24 h (24-h fat oxidation) when energy intake is matched to energy expenditure (energy-balanced condition). The present study explored the possibility that exercise increases 24-h fat oxidation only when performed in a post-absorptive state, i.e. before breakfast. METHODS: Indirect calorimetry using a metabolic chamber was performed in 10 young, non-obese men over 24 h. Subjects remained sedentary (control) or performed 60-min exercise before breakfast (morning), after lunch (afternoon), or after dinner (evening) at 50% of VO2max. All trials were designed to be energy balanced over 24 h. Time course of energy and substrate balance relative to the start of calorimetry were estimated from the differences between input (meal consumption) and output (oxidation). FINDINGS: Fat oxidation over 24 h was increased only when exercise was performed before breakfast (control, 456 ± 61; morning, 717 ± 64; afternoon, 446 ± 57; and evening, 432 ± 44 kcal/day). Fat oxidation over 24 h was negatively correlated with the magnitude of the transient deficit in energy and carbohydrate. INTERPRETATION: Under energy-balanced conditions, 24-h fat oxidation was increased by exercise only when performed before breakfast. Transient carbohydrate deficits, i.e., glycogen depletion, observed after morning exercise may have contributed to increased 24-h fat oxidation.

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It is unclear whether physical activity changes following long-term overfeeding and in response to different dietary protein intakes. Twenty-five (16 males, 9 females) healthy adults (18-35 yr) with BMI ranging from 19 to 30 kg/m(2) enrolled in this inpatient study. In a parallel group design, participants were fed 140% of energy needs, with 5, 15, or 25% of energy from protein, for 56 days. Participants wore an RT3 accelerometer for at least 59 days throughout baseline and during overfeeding and completed 24-h whole room metabolic chamber assessments at baseline and on days 1, 14, and 56 of overfeeding and on day 57, when the baseline energy intake was consumed, to measure percent of time active and spontaneous physical activity (SPA; kcal/day). Changes in activity were also assessed by doubly labeled water (DLW). From accelerometry, vector magnitude (VM), a weight-independent measure of activity, and activity energy expenditure (AEE) increased with weight gain during overfeeding. AEE remained increased after adjusting for changes in body composition. Activity-related energy expenditure (AREE) from DLW and percent activity and SPA in the metabolic chamber increased with overfeeding, but SPA was no longer significant after adjusting for change in body composition. Change in VM and AEE were positively correlated with weight gain; however, change in activity was not affected by protein intake. Overfeeding produces an increase in physical activity and in energy expended in physical activity after adjusting for changes in body composition, suggesting that increased activity in response to weight gain might be one mechanism to support adaptive thermogenesis.

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BACKGROUND: Several active video game (AVG) intervention studies failed in showing an increase in physical activity by using accelerometry measurements. OBJECTIVE: To test the validity of accelerometry for monitoring AVG playing intensity. METHOD: Twenty-two adults performed 80 activities included in the Wii Sports and Wii Fit Plus series. The energy expenditure (EE) and subsequent MET values were measured by indirect calorimetry using metabolic chambers. Subjects wore an accelerometer-based monitor displaying MET values. For each activity, METs values obtained from indirect calorimetry and accelerometry were compared. Each activity was classified as light or moderate to vigorous physical activity (LPA: < 3METs or MVPA: ⩾ 3METs) for the two methods. RESULTS: AVG intensities have been slightly but significantly underestimated by the acceleromater-based monitor compared to the indirect calorimetry (2.5 ± 1.0 instead of 2.7 ± 0.9 METs). Fourty percent of activities have been significantly misestimated, and 20% have been misclassified. CONCLUSION: Those results point out the potential bias of accelerometry measurements for evaluating AVG intensities. Because average AVG intensity lays at the boundary between LPA and MVPA classes, misclassifications can frequently occur. Accelerometry data should be interpreted with caution in intervention studies using AVG.

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