There is strong evidence that short bouts of light-intensity post-meal exercise are effective at lowering post-prandial and 24-h glucose concentrations in older people with impaired glucose tolerance (IGT). It is unknown, however, whether these transient benefits result in more enduring improvements in glycemic control after training. PURPOSE: To determine the effects of a home-based, 12-week post-meal walking program on improvements in glucose metabolism, as well as on changes in body composition in overweight (BMI=30±1.8 kg/m2) older adults (N=6; 72±5.3 years) with IGT. METHODS: Participants performed three 15-minute bouts of low-intensity (3 METs) walking beginning 30 minutes after each meal on five days per week for 12 weeks. Glucose and insulin responses to an oral glucose tolerance test were determined 48 h after the last exercise bout before and after training. Changes in body composition were determined using iDXA. RESULTS: Overall adherence to the total training program (180 post-meal walking bouts) was 65%; however, participants reported completing an average of 82% of the post-dinner walks across the 12 weeks. Total areas under the curve for both glucose [29.5±9.3 vs. 29.5±8.9 (mg·dL-1)·3h·103] and insulin [9.2±5.4 vs. 9.0±4.4 (mg·dL-1)·3h·103] responses did not change between baseline and follow-up; however, HbA1c levels (6.45% vs.5.86%) and the Whole Body Insulin Sensitivity Index (4.5± 3.6 vs. 5.8 ± 8.7) showed promising improvements following training. There were no changes to body weight, body fat, or lean mass; however, visceral fat volume decreased (688.7±311.4 vs. 584.3±306.0 cm3), and four of the six participants reduced their visceral fat mass by over 37g. CONCLUSIONS: Data from this pilot study suggest that the benefits of regular, low-intensity post-meal walking on glycemic control may not last beyond 24h in older adults with IGT. On the other hand, if performed consistently over time, the transient benefits may result in more enduring improvements in HbA1c and particularly in visceral fat mass. Supported by NIH/NIA R56 AG050661

Human metabolic energy expenditure is critical to many scientific disciplines but can only be measured using expensive and/or restrictive equipment. The aim of this work is to determine whether the SCENARIO thermoregulatory model can be adapted to estimate metabolic rate (M) from core body temperature (TC). To validate this method of M estimation, data were collected from fifteen test volunteers (age = 23 ± 3yr, height = 1.73 ± 0.07m, mass = 68.6 ± 8.7kg, body fat = 16.7 ± 7.3%; mean ± SD) who wore long sleeved nylon jackets and pants (Itot,clo = 1.22, Im = 0.41) during treadmill exercise tasks (32 trials; 7.8 ± 0.5km in 1h; air temp. = 22°C, 50% RH, wind speed = 0.35ms-1). Core body temperatures were recorded by ingested thermometer pill and M data were measured via whole room indirect calorimetry. Metabolic rate was estimated for 5min epochs in a two-step process. First, for a given epoch, a range of M values were input to the SCENARIO model and a corresponding range of TC values were output. Second, the output TC range value with the lowest absolute error relative to the observed TC for the given epoch was identified and its corresponding M range input was selected as the estimated M for that epoch. This process was then repeated for each subsequent remaining epoch. Root mean square error (RMSE), mean absolute error (MAE), and bias between observed and estimated M were 186W, 130 ± 174W, and 33 ± 183W, respectively. The RMSE for total energy expenditure by exercise period was 0.30 MJ. These results indicate that the SCENARIO model is useful for estimating M from TC when measurement is otherwise impractical.

Berries and other anthocyanin-rich treatments have prevented weight gain and adiposity in rodent models of diet-induced obesity. Their efficacy may be explained by modulation of energy substrate utilization. However, this effect has never been translated to humans. The objective of this study was to evaluate the effects of berry intake on energy substrate use and glucoregulation in volunteers consuming a high-fat diet. Twenty-seven overweight or obese men were enrolled in a randomized, placebo-controlled crossover study with two treatment periods. Subjects were fed an investigator controlled, high-fat (40% of energy from fat) diet which contained either 600 g/day blackberries (BB, 1500 mg/day flavonoids) or a calorie and carbohydrate matched amount of gelatin (GEL, flavonoid-free control) for seven days prior to a meal-based glucose tolerance test (MTT) in combination with a 24 h stay in a room-sized indirect calorimeter. The washout period that separated the treatment periods was also seven days. The BB treatment resulted in a significant reduction in average 24 h respiratory quotient (RQ) (0.810 vs. 0.817, BB vs. GEL, p = 0.040), indicating increased fat oxidation. RQ during the MTT was significantly lower with the BB treatment (0.84) compared to GEL control (0.85), p = 0.004. A 4 h time isolation during dinner showed similar treatment effects, where RQ was reduced and fat oxidation increased with BB (0.818 vs. 0.836, 28 vs. 25 g, respectively; BB vs. GEL treatments). The glucose AUC was not different between the BB and GEL treatments during the MTT (3488 vs. 4070 mg·min/dL, respectively, p = 0.12). However, the insulin AUC was significantly lower with the BB compared to the GEL control (6485 vs. 8245 µU·min/mL, p = 0.0002), and HOMA-IR improved with BB (p = 0.0318). Blackberry consumption may promote increased fat oxidation and improved insulin sensitivity in overweight or obese males fed a high fat diet.

ECTemp™ is a heart rate (HR)-based core temperature (CT) estimation algorithm mainly used as a real-time thermal-work strain indicator in military populations. ECTemp™ may also be valuable for resting CT estimation, which is critical for circadian rhythm research. This investigation developed and incorporated a sigmoid equation into ECTemp™ to better estimate resting CT. HR and CT data were collected over two calorimeter test trials from 16 volunteers (age, 23 ± 3 yrs; height, 1.72 ± 0.07 m; body mass, 68.5 ± 8.1 kg) during periods of sleep and inactivity. Half of the test trials were combined with ECTemp™’s original development dataset to train the new sigmoid model while the other was used for model validation. Models were compared by their estimation accuracy and precision. While both models produced accurate CT estimates, the sigmoid model had a smaller bias (−0.04 ± 0.26°C vs. −0.19 ± 0.29°C) and root mean square error (RMSE; 0.26°C vs. 0.35°C). ECTemp™ is a validated HR-based resting CT estimation algorithm. The new sigmoid equation corrects lower CT estimates while producing nearly identical estimates to the original quadratic equation at higher CT. The demonstrated accuracy of ECTemp™ encourages future research to explore the algorithm’s potential as a non-invasive means of tracking CT circadian rhythms.

Introduction: Pacing in competitive sports is an attempt to optimize an individual's energetic resources given the demands of the event and the environment. An athlete may approach an event with a pacing strategy based upon an understanding of the event demands and their own learned experience. Their predefined pacing strategy may be modified during the event based upon the environmental conditions, the athlete's volition and the onset of fatigue. Recent work indicates that the onset of fatigue is driven by a complex integration of the state of the peripheral muscles/peripheral sensory system and the central nervous system. Work rate is adapted in order to optimize performance and prevent potentially harmful changes to homeostasis (Sports Med 2013;43:301–311). The purpose of this study was to examine whether control-theory based computational optimization techniques could be used to optimize the pace of humans over a novel task given physiological and task performance feedback.

Metabolic energy expenditure is a physiological measure of importance to multiple scientific fields including nutrition, athletic performance, and thermoregulatory modeling. However, measuring metabolic rate in non-laboratory settings is difficult due to the restrictions imposed by laboratory grade measurement methods. The use of probabilistic graphical models, a type of machine learning model, may provide a means to estimate hidden variables such as metabolic rate from more easily observed variables such as heart rate and core body temperature. Using a probabilistic graphical model approach, a particle filter was applied to estimate metabolic rate from continuous heart rate and core body temperature observations. This paper examines which set of observations allows the particle filter to make more accurate estimations of metabolic rate and whether or not the addition of change in metabolic rate as a state variable improves accuracy. Observation and state parameters were learned by linear regression from continuous heart rate, core temperature, and metabolic rate collected from 15 volunteers (age: 23 ± 3 yr, ± SD) over N = 24, 3-hour periods during which 1 hour was spent running up to 8 km distance. State segmentations were learned using k-means clustering with up to 10 states. Observations of heart rate alone and with core temperature were used to predict metabolic rate with a root mean square error ± standard deviation of 166 ± 27 W and 133 ± 26 W.

We examine the case of post-exercise excess CO2 production and instantaneous substrate oxidation in two older women, one with impaired glucose tolerance and the other one is euglycemic. Both subjects stayed in the room-size calorimeter for 48 hours and performed three bouts of postprandial exercise on the second day. The instantaneous gas exchange rates have been estimated along with the instantaneous respiratory exchange ratio (RER) for the whole 48- hour experiment. The relative dynamics of O2 consumption and RER showed a greater reliance on the carbohydrate as energy source in dysglycemic woman than in euglycemic woman. Also, the rate of post-exercise excessive CO2 output, quantified as the time lag between peaks in O2 consumption and peaks in RER was found to be higher in dysglycemic woman suggesting heavier reliance on anaerobic metabolism during exercise. For the first time, results relating the excess post-exercise CO2 production and impaired glucose tolerance are presented.

ECTemp is a heart rate (HR) based core temperature (CT) estimation model is being used to monitor and manage heat strain in warfighters and athletes during exercise in the heat. ECTemp may also be valuable for sedentary CT research on circadian rhythm disturbances. A recent modification to better reflect physiology may also improve ECTemp prediction of CT and circadian rhythm indicators (Midline Estimator of Rhythm MESOR, amplitude, and acrophase). PURPOSE: To compare the accuracy of the original ECTemp model (Quadratic model) and a modified ECTemp model (Sigmoid model) in estimating CT during exercise and rest periods as well as circadian rhythm indicators. METHODS: 12 subjects (Age, 23±3 yr; HT, 173.8±7.7 cm; BM, 70.12±8.94 kg) were monitored continuously for CT and HR while enclosed in a calorimeter chamber over two 22.5-hr trials. Except for a one hour treadmill protocol, participants were required to restrict physical activity to sedentary tasks. Circadian rhythm indicators were extracted from rest periods using mixed effects models. Pearson’s correlation coefficients and mean absolute errors (MAE) were determined to evaluate each model’s performance during exercise and rest. RESULTS: Sigmoid model estimates had slightly stronger correlations with CT during exercise (0.90 vs. 0.89) and rest (0.74 vs. 0.67). Similarly, MAE for the Sigmoid model were lower for the Sigmoid model during exercise (0.27±0.23°C vs. 0.28±0.23°C, p < 0.001) and rest (0.22±0.18°C vs. 0.26±0.22°C, p < 0.001). MAE were significantly lower for Sigmoid model estimates of the MESOR (0.07±0.06°C vs. 0.16±0.07°C, p < 0.001) and acrophase (1.19±0.97 hr vs. 1.57±0.97 hr, p < 0.001) but similar for amplitude (0.08±0.07°C vs. 0.09±0.06°C, p = 0.74). CONCLUSIONS: While both models performed well (overall MAE < 0.28°C), the Sigmoid model had more accurate estimates of exercise and rest CT as well as closer estimates of circadian rhythm indicators. Consequently, the modified ECTemp model appears to have potential as a CT estimator in conditions unsuitable for direct CT measurement regardless of activity level. Disclaimer: The views expressed are those of the authors and do not reflect the official policy of the Department of Defense, or the U.S. Government.

: While circadian rhythm analysis provides information critical to physiological status monitoring, there are considerable challenges to core body temperature (CT) measurement outside of stringent laboratory environments. This study evaluated ECTemp, a heart rate-based CT estimation algorithm, in the assessment of circadian rhythm indicators. Eleven participants (age, 23 +/- 3 years; height, 173.8 +/- 7.7 cm; body mass, 70.12 +/- 8.94 kg) were assessed on two occasions in which they were confined to a calorimeter chamber for a 22.5-hr period. Circadian rhythm indicators (MESOR, amplitude, and acrophase) were determined using a mixed effects cosinor regression models. ECTemp provided reasonable estimates of MESOR (RMSE, 0.17) and amplitude (RMSE, 0.10). While ECTemp estimates of circadian rhythm indicators were lower than observed CT, the differences were smaller than heart-rate based models analyzed in previous studies. As such, ECTemp demonstrates strong potential for estimating circadian CT rhythm indicators, particularly if the algorithm is updated to fit additional data from periods of low CT and heart rate.

Berries and/or berry extracts have been shown to reduce adiposity in animal models consuming a high fat diet, possibly due to alterations in energy substrate utilization. The objective of this research was to test the effect of berry intake on energy substrate use in humans consuming a high fat diet. In a placebo controlled crossover study design with 2 treatment periods, overweight/obese men (n=27) were fed a high fat diet (40% en fat) containing either 600 g/d blackberries (BB, 1200 mg/d flavonoids) or 600 g/d sweetened gelatin (calorie matched, flavonoid‐free control) for 1 week prior to a meal‐based glucose tolerance test (MTT) (75 g of sugar from waffle and syrup) and 24h stay in a room‐sized indirect calorimeter. Across the 24 hr period, the average RQ was significantly lower with the BB treatment (0.810 vs. 0.817, BB vs. gelatin, p=0.040) while energy expenditure (EE) was not different (2485 vs. 2439 kcal, p=0.11, BB vs. gelatin treatments). During the MTT, AUC of glucose was not different between BB and gelatin treatment (3488 vs. 4070 mg*min/dL, respectively, p=0.12). However, AUC of insulin was lower after consumption of BB compared to gelatin (6485 vs. 8245 uU*min/ml, p=0.0002). During this 4 hr MTT period, EE was not statistically different between the 2 treatments (BB, 452 kcal; gelatin, 440 kcal, p=0.20). However, there was a significant reduction in the RQ with BBs (BB, 0.84; gelatin, 0.85, p=0.004) which was independent of age or BMI. During the 4 hr period that started with dinner, the comparisons were similar, where a reduction in RQ and an increase in fat oxidation were observed with the BB treatment (0.818 vs. 0.836, 28 vs. 24 g, respectively; BB vs. gelatin treatments) with no difference in EE (493 vs. 483, p=0.26, BB vs. gelatin). The 24 hr stay in the calorimeter included 30 min of treadmill walking in the afternoon following lunch. During this time period, again there was a significant reduction in RQ with BB treatment (0.856 vs 0.871; BB vs. gelatin). Blackberry consumption may contribute to increased fat oxidation and improved glucose sensitivity in overweight males on a high fat diet. This is the first study to demonstrate the potential for blackberries to combat the metabolic effects of a high fat diet in human subjects. Mechanistic studies aimed at describing biochemical changes leading to these observations are underway. Support or Funding Information Supported by the U.S. Department of Agriculture.

ViewpointAging and ExerciseLast Word on Viewpoint: A time for exercise: the exercise windowElsamma ChackoElsamma ChackoConnecticut Valley Hospital, Middletown, ConnecticutPublished Online:18 Jan 2017https://doi.org/10.1152/japplphysiol.00951.2016MoreSectionsPDF (29 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat to the editor: Every participant in this Viewpoint (2) conversation about exercise timing as it applies to people with diabetes has contributed a unique perspective honed by years of dedicated work. Thank you.Although all forms of exercise come with health benefits, I saw the postmeal glucose surge as the critical challenge and taming it as the primary goal of diabetes management. With this focus I elected not to address high-intensity exercise, meal composition, or lipids. In free-living circumstances I could also see problems of acceptance and adherence among people with diabetes, if the exercise was any more complex than, say, a brisk walk.Specifically, I agree that high-intensity/fasted exercise (Boulé, Francois, McDonald in Ref. 1) and resistance exercise (Paoli in Ref. 1) offer significant health benefits. For example, as Boulé points out, a solid glycogen-depleting interval exercise, done premeal, offers excellent insulin sensitivity improvement (see Boulé in Ref. 1, Ref. 3 therein); the same exercise done postmeal does not help glycemia. In the mid-postprandial period, a high-intensity interval exercise of short enough duration to keep hepatic glucose at bay is what helps (4). Adding such an activity to the front of the daily aerobic routine three times a week may be beneficial.The issue of meal composition/glycemic load is raised by Kruse, McDonald, and Sacchetti (see Ref. 1). Indeed, eating a breakfast itself lowers the glucose spike of lunch—the second meal phenomenon. Balancing the meal with protein, healthy fat, vegetables, and fiber lowers the glycemic load of the meal, moderates the glucose spike, and improves satiety. A timely mid-postprandial exercise further lowers glucose surges of breakfast, lunch, and supper (van Dijk, Ref. 5, Fig. 1 therein).McDonald and Yardley (see Ref. 1) raise the serious issue of hypoglycemia in type 1 diabetes. We addressed this in a recent note (3). Nelson's subjects could do moderate exercise at 30 min postmeal for 35 min to normalize glucose levels—without causing hypoglycemia—in both healthy individuals and people with type 1 diabetes (7). It could be that when exercise stops within the mid-postprandial interval, the glucose still coming in from the gut offers some insurance against hypoglycemia. Additionally, exercise may obviate the need for preprandial insulin in some cases.Volume of exercise is significant (van Dijk, Ref. 4). Thirty minutes of brisk walk every day exceed the current recommendations for moderate-intensity exercise.McDonald mentions lipids. Premeal (Sacchetti, Ref. 3) and postmeal (5) exercises improve lipids. When Heden and colleagues (5) compared premeal vs postdinner in type 2 diabetes, postdinner resistance exercise was better for triglycerides and glucose.The issue of practicality (Kruse and McDonald, see Ref. 1) is challenging. Personalized meals and exercise programs can be designed around work schedules, to some extent, for better adherence. Some early risers and retired people may not mind postbreakfast exercise. Among others, some with 1-h lunch breaks can walk after lunch. Perhaps those with half-hour lunch breaks may drink a healthy shake of 2–3 carbs at 11:30 AM and walk at 12 PM if that is feasible. Walking after dinner is an option, but high-intensity exercise later in the day may increase the risk for nocturnal hypoglycemia (6).The effects on hypoglycemia and cardiometabolic markers need to be established by focusing research efforts during the mid-postprandial period and fasting hours.AUTHOR CONTRIBUTIONSE.C. drafted manuscript; E.C. edited and revised manuscript; E.C. approved final version of manuscript.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the author(s).References1. Boulé NG, Terada T, Francois ME, Hawley JA, Cotter JD, Kruse NT, McDonald MW, Olver TD, Paoli A, Sacchetti M, Di Luigi L, van Dijk J-W, van Loon LJC, Yardley J, DiPietro L, Gribok A, Rumpler W. Commentaries on Viewpoint: A time for exercise: the exercise window. J Appl Physiol, 2016. doi:10.1152/japplphysiol.00938.2016.Link | Google Scholar2. Chacko E. Viewpoint:A time for exercise: the exercise window. J Appl Physiol, 2016. 10.1152/japplphysiol.00685.2016.Link | ISI | Google Scholar3. Chacko E. Preventing exercise-induced hypoglycaemia in insulin-dependent diabetes. Diabetologia 59: 2487–2488, 2016. doi:10.1007/s00125-016-4093-2. Crossref | PubMed | ISI | Google Scholar4. Gillen JB, Little JP, Punthakee Z, Tarnopolsky MA, Riddell MC, Gibala MJ. Acute high-intensity interval exercise reduces the postprandial glucose response and prevalence of hyperglycaemia in patients with type 2 diabetes. Diabetes Obes Metab 14: 575–577, 2012. doi:10.1111/j.1463-1326.2012.01564.x. Crossref | PubMed | ISI | Google Scholar5. Heden TD, Winn NC, Mari A, Booth FW, Rector RS, Thyfault JP, Kanaley JA. Postdinner resistance exercise improves postprandial risk factors more effectively than predinner resistance exercise in patients with type 2 diabetes. J Appl Physiol (1985) 118: 624–634, 2015. doi:10.1152/japplphysiol.00917.2014. Link | ISI | Google Scholar6. Maran A, Pavan P, Bonsembiante B, Brugin E, Ermolao A, Avogaro A, Zaccaria M. Continuous glucose monitoring reveals delayed nocturnal hypoglycemia after intermittent high-intensity exercise in nontrained patients with type 1 diabetes. Diabetes Technol Ther 12: 763–768, 2010. doi:10.1089/dia.2010.0038. Crossref | PubMed | ISI | Google Scholar7. Nelson JD, Poussier P, Marliss EB, Albisser AM, Zinman B. Metabolic response of normal man and insulin-infused diabetics to postprandial exercise. Am J Physiol Endocrinol Metab 242: E309–E316, 1982. Link | ISI | Google ScholarAUTHOR NOTESAddress for reprint requests and other correspondence: E. Chacko, Connecticut Valley Hospital, 1000 Silver St., Middletown, CT 06457 (e-mail: [email protected]com). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation More from this issue > Volume 122Issue 1January 2017Pages 214-214 Copyright & PermissionsCopyright © 2017 the American Physiological Societyhttps://doi.org/10.1152/japplphysiol.00951.2016PubMed28100447History Received 26 October 2016 Accepted 27 October 2016 Published online 18 January 2017 Published in print 1 January 2017 Metrics

The paper demonstrates that minute-to-minute metabolic response to meals with different macronutrient content can be measured and discerned in the whole-body indirect calorimeter. The ability to discriminate between high-carbohydrate and high-fat meals is achieved by applying a modified regularization technique with additional constraints imposed on oxygen consumption rate. These additional constraints reduce the differences in accuracy between the oxygen and carbon dioxide analyzers. The modified technique was applied to 63 calorimeter sessions that were each 24 h long. The data were collected from 16 healthy volunteers (eight males, eight females, aged 22-35 years). Each volunteer performed four 24-h long calorimeter sessions. At each session, they received one of four treatment combinations involving exercise (high or low intensity) and diet (a high-fat or high-carbohydrate shake for lunch). One volunteer did not complete all four assignments, which brought the total number of sessions to 63 instead of 64. During the 24-h stay in the calorimeter, subjects wore a continuous glucose monitoring system, which was used as a benchmark for subject's postprandial glycemic response. The minute-by-minute respiratory exchange ratio (RER) data showed excellent agreement with concurrent subcutaneous glucose concentrations in postprandial state. The averaged minute-to-minute RER response to the high-carbohydrate shake was significantly different from the response to high-fat shake. Also, postprandial RER slopes were significantly different for two dietary treatments. The results show that whole-body respiration calorimeters can be utilized as tools to study short-term kinetics of substrate oxidation in humans.

In the obesogenic environment, bioactive food components show promise in ameliorating metabolic dysfunction related to type 2 diabetes and metabolic syndrome. Preliminary animal studies on berries indicate that body composition, weight, and clinical markers of glucose regulation may be positively impacted by consumption of anthocyanin rich blue and black raspberries. The aim of our pilot study was to determine the influence of blackberry feeding on clinical markers related to glucose regulation as well as substrate utilization measured by indirect calorimetry in humans. This randomized, controlled, cross-over study was conducted at the USDA Beltsville Human Nutrition Research Center. Fifteen overweight or obese male subjects (age range 31–69) were fed a controlled, high fat diet (40% en from fat) for 7 days that included either 600 g/d of fresh blackberries (BB) or an isocaloric amount of gelatin (control). There was a 7 day washout period between treatments. For the last 24 hours of each treatment period, subjects remained inside of a room-sized indirect calorimeter, where a breakfast meal tolerance test (MTT, 75 g of sugar from waffle and syrup) was administered with their treatment, and timed blood draws (−15, 0, 30, 60, 90, 120, 180, 240 min) were collected and analyzed for glucose, insulin, and nonesterified fatty acids. Energy expenditure (EE), substrate oxidation, and respiratory quotient (RQ) were measured from gas exchange for the total 24 hr period as well as specific isolated time periods. Data were analyzed with mixed models using BMI, age, and treatment order, and their interactions with treatment as covariates. AUC of glucose was not different between BB and gelatin treatment (4286 vs 5027 mg·dL−1·min, respectively, p=0.17). However, AUC of insulin was lower after consumption of BB compared to gelatin (5322 vs 7325 uU·ml−1·min, p=0.008). During this 4 hr MTT period, EE was not different between the 2 treatments (BB, 405 kcal; gelatin, 399 kcal, p=0.79). However, preliminary data suggest that an interaction between BMI and treatment may be affecting RQ such that RQ is lower in higher BMI subjects fed BB compared to gelatin. Across the 24 hr period, RQ and EE were not different between the two treatments (.82 vs .83, 2184 vs 2124 kcal, respectively; BB vs gelatin treatments). However, during the 4 hr period that started with dinner, preliminary data suggest that a significant interaction of treatment and BMI for EE and RQ were observed (p=0.0385 and p=0.0059, respectively) where the BMI effect is the opposite of what was observed for the MTT time period; subjects with lower BMIs had a greater EE and lower RQ when fed BBs and the effect was attenuated with higher BMI subjects. The 24 hr stay in the calorimeter included 30 min of treadmill walking in the afternoon following the MTT. During this time period, a significant interaction of BMI with treatment demonstrated a decrease in RQ in higher BMI subjects fed BBs compared to gelatin (p for interaction = 0.041). In addition to improved insulin sensitivity, our data suggest a potential for anthocyanin rich BBs to change adiposity as RQ was indicative of preferential oxidation of fat for energy in higher BMI subjects during the MTT as well as during a bout of non-strenuous activity. Further, beneficial effects of BBs may not be limited to overweight/obese subjects in terms of switching substrates to increase fat oxidation. More subjects are being enrolled to confirm these preliminary findings. Support or Funding Information This study is funded by the USDA. Serum insulin by treatment across the 4 hour meal tolerance test. Serum glucose by treatment across the 4 hour meal tolerance test.

BACKGROUND:The number of days of data and number of subjects necessary to estimate total physical activity (TPA) and moderate-to-vigorous physical activity (MVPA) requires an understanding of within- and between-subject variances, and the influence of sex, body composition, and age.METHODS:Seventy-one adults wore accelerometers for 7-day intervals over 6 consecutive months.RESULTS:Body fat and sex influenced TPA and MVPA. The sources of subject-related variation for TPA and MVPA were within-subject (48.4% and 54.3%), between-subject (34.3% and 31.8%), and calendar effects (17.3% and 13.9%). Based on within-subject variances, the error associated with estimating TPA and MVPA by collecting 1 to 7 days of data ranged from 28.2% to 13.3% for TPA and 62.0% to 28.6% for MVPA. Based on between-subject variances, detecting a 10% difference between 2 groups at a power of 90% requires approximately 200 and 725 subjects per group for TPA and MVPA, respectively.CONCLUSIONS:Estimates of MVPA are more variable than TPA in overweight adults, therefore more days of data are required to estimate MVPA and larger sample sizes to detect treatment differences for MVPA. Log-transforming data reduces the need for additional days of data collection, thereby improving chances of detecting treatment effects.

Background:People with a family history of type 2 diabetes have lower energy expenditure (EE) and more obesity than those having no such family history. Resistance exercise (RE) may induce excess postexercise energy expenditure (EPEE) and reduce long-term risk for obesity in this susceptible group.Purpose:To determine the effect of RE on EPEE for 15 hr after a single exercise bout in healthy, untrained young men having a family history of type 2 diabetes.Design:Seven untrained men (23 ± 1.2 years, BMI 24 ± 1.1) completed a 48-hr protocol in a whole room calorimeter. The first day served as a control day, with a moderate 40-min RE bout occurring on the second day. Differences in postexercise EE were compared with matched periods from the control day for cumulative 15-min intervals (up to 150 min) and 15 hr after the RE bout was completed.Results:The most robust difference in EPEE between the experimental and control days was observed in the first 15-min postexercise period (M = 1.4Kcal/min; SD = 0.7; p < .05). No statistically significant differences in EPEE were noted beyond 90-min of continuous measurement.Conclusions:Young people with a family history of type 2 diabetes may not show EPEE after a single RE bout when observed for 15 hr after RE and long-term resistance training may be required to promote EPEE.

This experiment demonstrated that automated pace guidance generated from real-time physiological monitoring allowed less stressful completion of a timed (60 minute limit) 5 mile treadmill exercise. An optimal pacing policy was estimated from a Markov decision process that balanced the goals of the movement task and the thermal-work strain safety constraints. The machine guided pace was based on current physiological strain index (PSI), the time, and the distance already completed. Fourteen healthy and fit young subjects participated in the study (9 men, 5 women). Each participated in an unguided exercise session followed by a guided one. In the unguided session, they were instructed to complete 5 miles in 60 minutes and to try to finish at the lowest body temperature possible; in the guided sessions, participants were instructed to match machine-provided pacing guidance provided every 2 minutes. Continuous real-time measures of heart rate and core body temperature were obtained from a wearable Hidalgo Equivital TM EQ-02 and the MiniMitter Jonah thermometer pill. Of the fourteen subjects, 13 completed the 5 miles in one hour for the unguided session; at least three different self-pacing strategies were observed, with an alternating speed proving to be most effective. In the guided sessions, 6 subjects were stopped by the machine guidance for exceeding the algorithms PSI "safety" limit. Eight subjects were guided to complete the task with significantly lower PSIs. The results indicate that machine guided advice shows promise for preventing hyperthermia and improving outcomes for performers of an unfamiliar task.

Resistant maltodextrin (RM) is a novel soluble, nonviscous dietary fiber. Its metabolizable energy (ME) and net energy (NE) values derived from nutrient balance studies are unknown, as is the effect of RM on fecal microbiota. A randomized, placebo-controlled, double-blind crossover study was conducted (n = 14 men) to determine the ME and NE of RM and its influence on fecal excretion of macronutrients and microbiota. Participants were assigned to a sequence consisting of 3 treatment periods [24 d each: 0 g/d RM + 50 g/d maltodextrin and 2 amounts of dietary RM (25 g/d RM + 25 g of maltodextrin/d and 50 g/d RM + 0 g/d maltodextrin)] and were provided all the foods they were to consume to maintain their body weight. After an adaptation period, excreta were collected during a 7-d period. After the collection period, 24-h energy expenditure was measured. Fluorescence in situ hybridization, quantitative polymerase chain reaction, and 454 titanium technology–based 16S rRNA sequencing were used to analyze fecal microbiota composition. Fecal amounts of energy (544, 662, 737 kJ/d), nitrogen (1.5, 1.8, 2.1 g/d), RM (0.3, 0.6, 1.2 g/d), and total carbohydrate (11.1, 14.2, 16.2 g/d) increased with increasing dose (0, 25, 50 g) of RM (P < 0.0001). Fat excretion did not differ among treatments. The ME value of RM was 8.2 and 10.4 kJ/g, and the NE value of RM was −8.2 and 2.0 kJ/g for the 25 and 50 g/d RM doses, respectively. Both doses of RM increased fecal wet weight (118, 148, 161 g/d; P < 0.0001) and fecal dry weight (26.5, 32.0, 35.8 g/d; P < 0.0001) compared with the maltodextrin placebo. Total counts of fecal bacteria increased by 12% for the 25 g/d RM dose (P = 0.17) and 18% for the 50 g/d RM dose (P = 0.019). RM intake was associated with statistically significant increases (P < 0.001) in various operational taxonomic units matching closest to ruminococcus, eubacterium, lachnospiraceae, bacteroides, holdemania, and faecalibacterium, implicating RM in their growth in the gut. Our findings provide empirical data important for food labeling regulations related to the energy value of RM and suggest that RM increases fecal bulk by enhancing the excretion of nitrogen and carbohydrate and the growth of specific microbial populations.

The paper describes concurrent, minute-by-minute dynamics of subcutaneous glucose concentration and energy expenditure in young male subjects performing 40-min resistance exercise in a whole room calorimeter. The observed negative correlation between subcutaneous glucose concentration, as measured by continuous glucose monitoring (CGM) sensor and energy expenditure is exploited to propose and validate a simple linear model, which is used to estimate minute-by-minute energy expenditure from CGM sensor readings. The data were collected from seven young adult male subjects during their 48-hour stay in calorimeter room. Each subject had two 48-hour calorimeter sessions, except one subject who only performed one session. The minute-by-minute CGM data were regressed on energy expenditure (EE) data thus obtaining a linear model connecting these two quantities. This model was subsequently used to estimate EE from CGM readings for the data that were not used in the training dataset. The performance of the linear regression models was analyzed using Bland-Altman plots and it is demonstrated that the CGM sensor can provide a valid predictor variable which can be combined with other physiological parameters to estimate energy expenditure in field conditions.

OBJECTIVE The purpose of this study was to compare the effectiveness of three 15-min bouts of postmeal walking with 45 min of sustained walking on 24-h glycemic control in older persons at risk for glucose intolerance. RESEARCH DESIGN AND METHODS Inactive older (≥60 years of age) participants (N = 10) were recruited from the community and were nonsmoking, with a BMI <35 kg/m2 and a fasting blood glucose concentration between 105 and 125 mg dL−1. Participants completed three randomly ordered exercise protocols spaced 4 weeks apart. Each protocol comprised a 48-h stay in a whole-room calorimeter, with the first day serving as the control day. On the second day, participants engaged in either 1) postmeal walking for 15 min or 45 min of sustained walking performed at 2) 10:30 a.m. or 3) 4:30 p.m. All walking was on a treadmill at an absolute intensity of 3 METs. Interstitial glucose concentrations were determined over 48 h with a continuous glucose monitor. Substrate utilization was measured continuously by respiratory exchange (VCO2/VO2). RESULTS Both sustained morning walking (127 ± 23 vs. 118 ± 14 mg dL−1) and postmeal walking (129 ± 24 vs. 116 ± 13 mg dL−1) significantly improved 24-h glycemic control relative to the control day (P < 0.05). Moreover, postmeal walking was significantly (P < 0.01) more effective than 45 min of sustained morning or afternoon walking in lowering 3-h postdinner glucose between the control and experimental day. CONCLUSIONS Short, intermittent bouts of postmeal walking appear to be an effective way to control postprandial hyperglycemia in older people.