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Jung Eun Lee, David F. Stodden and Zan Gao

Background:

Few studies have examined young children’s leisure- and school-based energy expenditure (EE) and moderateto-vigorous physical activity (MVPA). The purpose of this study was to explore children’s estimated EE rates and time spent in MVPA in 3 time segments: at-school, after-school, and weekends.

Methods:

A total of 187 second and third grade children from 2 elementary schools participated in the study. Accelerometers were used to assess children’s 5-day EE and MVPA. Multiple 2 (Grade) × 2 (Gender) ANOVAs with repeated measures (Time) were conducted to examine the differences in the outcome variables.

Results:

Significant time effects on EE and MVPA were revealed. Children’s EE rate and minutes in MVPA per day were higher during after school and weekends than at school.

Conclusions:

Although children were more active outside of school, their MVPA during weekdays and weekends still fell far short of the recommended level of 60 minutes/day.

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Rudy J. Valentine, Michael J. Saunders, M. Kent Todd and Thomas G. St. Laurent

Carbohydrate–protein (CHO+Pro) beverages reportedly improve endurance and indices of muscle disruption, but it is unclear whether these effects are related to total energy intake or specific effects of protein.

Purpose:

The authors examined effects of CHO+Pro on time to exhaustion and markers of muscle disruption compared with placebo (PLA) and carbohydrate beverages matched for carbohydrate (CHO) and total calories (CHO+CHO).

Methods:

Eleven male cyclists completed 4 rides to exhaustion at 75% VO2peak. Participants consumed 250 ml of PLA, CHO (7.75%), CHO+CHO (9.69%), or CHO+Pro (7.75%/1.94%) every 15 min until fatigue, in a double-blind design.

Results:

Time to exhaustion was significantly longer (p < .05) in CHO+Pro (126.2 ± 25.4 min) and CHO+CHO (121.3 ± 36.8) than PLA (107.1 ± 30.3). CHO (117.5 ± 24.2) and PLA were not significantly different. Similarly, CHO+Pro was not significantly different from CHO and CHO+CHO. Postexercise plasma creatine kinase was lower after CHO+Pro (197.2 ± 149.2 IU/L) than PLA (407.4 ± 391.3), CHO (373.2 ± 416.6), and CHO+CHO (412.3 ± 410.2). Postexercise serum myoglobin was lower in CHO+Pro (47.0 ± 27.4 ng/mL) than all other treatments (168.8 ± 217.3, 82.6 ± 71.3, and 72.0 ± 75.8). Postexercise leg extensions at 70% 1RM were significantly greater 24 hr after CHO+Pro (11.3 ± 4.1) than PLA (8.8 ± 3.7), CHO (9.7 ± 4.3), and CHO+CHO (9.5 ± 3.6).

Conclusion:

These findings suggest that at least some of the reported improvements in endurance with CHO+Pro beverages might be related to caloric differences between treatments. Postexercise improvements in markers of muscle disruption with CHO+Pro ingestion appear to be independent of carbohydrate and caloric content and were elicited with beverages consumed only during exercise.

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Beth Glace, Christine Murphy and Malachy McHugh

The purpose of this study was to document eating strategies employed by runners during a 160-km race, and to identify eating patterns that predispose the runner to disturbed mental or gastrointestinal functioning. We monitored intake in 19 volunteers during the 12 hours pre-race. Intake was determined by interview with runners approximately every 12 km throughout the race. The mean finish time was 24.3 hours, with 4 runners not completing the race. Body mass decreased during the race, 75.9 ± 2.3 kg to 74.4 ± 2.2 kg (p < .001). Runners ingested 2643 kcals during the 12 hours prerace (68% carbohydrate) and 3.8 L of fluid. During the race 6047 kcal, 18 L of fluid, and 12 g of sodium were consumed. Gastrointestinal distress (GI) was experienced by half of the participants, but was unrelated to food or fluid intake. Upper GI symptoms were more prevalent than lower and occurred mainly after 88 km. Runners with GI distress tended to complete fewer training miles (p = .10) and to do shorter training runs (p = .08). Half of the volunteers reported mental status changes (MSC), such as confusion or dizziness. Runners with MSC had greater intake of total calories, carbohydrate, and fluid (p < .05) than runners without MSC. They also completed shorter training runs (p = .03). Caloric and moisture intake for all runners far exceeded intakes described previously. Although intake did not match energy expenditure, it may represent the upper limit for absorption during exercise, and very high food and/or fluid intake appears to lead to perturbed mental status.

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Kim Beals, Katherine A. Perlsweig, John E. Haubenstriker, Mita Lovalekar, Chris P. Beck, Darcie L. Yount, Matthew E. Darnell, Katelyn Allison and Bradley C. Nindl

essential amino acids throughout the day can help maintain lean body mass and muscle strength during periods of suboptimal caloric intake ( Pasiakos et al., 2013 ; Phillips, 2013 ; Rodriguez, 2013 ). Students consumed a mean fat intake of 1.6 ± 0.4 g·kg −1 ·day −1 (42% ± 5.5% total calories) (RC) and 1

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Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys and Michael J. Duncan

dominant hip yielded the strongest associations with METs or VO 2 (l·min −1 ), performing better than comparative ActiGraph or GENEActiv devices (Table  2 ). The total calories calculated using RT6 also revealed significant moderate associations with energy expenditure derived from breath

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Darryn S. Willoughby, Kaitlan N. Beretich, Marcus Chen and LesLee K. Funderburk

 > .05). There were no significant Group × Time interactions or main effects for group and time for the total calories, protein, carbohydrates, and fat, indicating that neither group underwent any significant dietary changes between the dietary assessments performed prior to and following the RT. Table 2

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Bill I. Campbell, Danielle Aguilar, Laurin Conlin, Andres Vargas, Brad Jon Schoenfeld, Amey Corson, Chris Gai, Shiva Best, Elfego Galvan and Kaylee Couvillion

. Considering that weight loss is a function of energy balance ( Thomas et al., 2009 ), these findings may seem counterintuitive. However, dietary protein has been shown to have a much higher thermic effect (25–30% of total calories) compared with less than 10% for carbohydrate or lipid ( Halton & Hu, 2004

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Nicolas Farina and Ruth G. Lowry

, Missouri, USA) activity monitor is a research grade, tri-axial (three-dimensional), piezoelectric accelerometer. It provides daily data in the form of total steps, active steps, active (moderate to vigorous physical activity [MVPA]) minutes, distance, active calories, and total calories expended. The

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Todd Miller, Stephanie Mull, Alan Albert Aragon, James Krieger and Brad Jon Schoenfeld

10% of the predicted RMR, intake was set at the RMR; if the RMR was greater than 10% over the predicted, intake was set at 10% below the measured RMR; if the RMR was greater than 10% below the predicted, intake was set at 10% above the measured RMR. Fat intake was set at 20% of total calories

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Megan Colletto and Nancy Rodriguez

.1 ± 0.03 .3 Note . Values are reported as means ± SEM. BMI = body mass index; BMD = bone mineral density. *Different between groups p  ≤ .05 or ** p  < .01. Dietary Intake and REE Calorie and macronutrient intake and REE are shown in Table  2 . There were no differences between groups in total calories