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Eric T. Trexler, Katie R. Hirsch, Bill I. Campbell and Abbie E. Smith-Ryan

The purpose of the current study was to evaluate changes in body composition, metabolic rate, and hormones during postcompetition recovery. Data were collected from natural physique athletes (7 male/8 female) within one week before (T1) competition, within one week after (T2), and 4–6 weeks after (T3) competition. Measures included body composition (fat mass [FM] and lean mass [LM] from ultrasongraphy), resting metabolic rate (RMR; indirect calorimetry), and salivary leptin, testosterone, cortisol, ghrelin, and insulin. Total body water (TBW; bioelectrical impedance spectroscopy) was measured at T1 and T2 in a subsample (n = 8) of athletes. Significant (p < .05) changes were observed for weight (T1 = 65.4 ± 12.2 kg, T2 = 67.4 ± 12.6, T3 = 69.3 ± 13.4; T3 > T2 > T1), LM (T1 = 57.6 ± 13.9 kg, T2 = 59.4 ± 14.2, T3 = 59.3 ± 14.2; T2 and T3 > T1), and FM (T1 = 7.7 ± 4.4 kg, T2 = 8.0 ± 4.4, T3 = 10.0 ± 6.2; T3 > T1 and T2). TBW increased from T1 to T2 (Δ=1.9 ± 1.3 L, p < .01). RMR increased from baseline (1612 ± 266 kcal/day; 92.0% of predicted) to T2 (1881 ± 329, 105.3%; p < .01) and T3 (1778 ± 257, 99.6%; p < .001). Cortisol was higher (p < .05) at T2 (0.41 ± 0.31 μg/dL) than T1 (0.34 ± 0.31) and T3 (0.35 ± 0.27). Male testosterone at T3 (186.6 ± 41.3 pg/mL) was greater than T2 (148.0 ± 44.6, p = .04). RMR changes were associated (p ≤ .05) with change in body fat percent (ΔBF%; r = .59) and T3 protein intake (r= .60); male testosterone changes were inversely associated (p≤ .05) with ΔBF%, ΔFM, and Δweight (r=-0.81–-0.88). TBW increased within days of competition. Precompetition RMR suppression appeared to be variable and markedly reversed by overfeeding, and reverted toward normal levels following competition. RMR and male testosterone increased while FM was preferentially gained 4–6 weeks postcompetition.

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Berit Steenbock, Marvin N. Wright, Norman Wirsik and Mirko Brandes

provide energy expenditure (EE) prediction models from raw accelerometry data established against indirect calorimetry, (2) to compare two linear and two machine learning models, and (3) to compare accuracy of different accelerometers placed on the hips, thigh, and wrists. Methods Study Participants To

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Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson and John R. Sirard

various features of the accelerometer data to estimate PA and SB. Algorithms to estimate PA from accelerometer data often rely on laboratory calibration studies that use indirect calorimetry as a criterion measure for activity intensity. Laboratory calibration protocols require participants to complete

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Paula B. Costa, Scott R. Richmond, Charles R. Smith, Brad Currier, Richard A. Stecker, Brad T. Gieske, Kimi Kemp, Kyle E. Witherbee and Chad M. Kerksick

, fat, and protein in grams (g) and normalized to body mass. EA was computed in units of kJ/kg fat-free mass (FFM) based on Loucks et al. 3 Resting Metabolic Rate Resting metabolic rate was assessed using indirect calorimetry (TrueOne 2400 Metabolic Measurement System; ParvoMedics, Murray, UT). All data

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Jennifer L. Huberty, Jeni L. Matthews, Meynard Toledo, Lindsay Smith, Catherine L. Jarrett, Benjamin Duncan and Matthew P. Buman

types of yoga, poses and sequences may help individuals meet physical activity recommendations. The Oxycon Mobile measures ventilation, oxygen uptake, and respiratory exchange ( Rosdahl, Gullstrand, Salier-Eriksson, Johansson, & Schantz, 2010 ) and uses indirect calorimetry techniques to accurately

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Kathryn J. DeShaw, Laura Ellingson, Yang Bai, Jeni Lansing, Maria Perez and Greg Welk

examined validity in controlled and semi-structured lab settings ( Evenson, Goto, & Furberg, 2015 ). An advantage of these settings is that it enables the use of more robust criterion measures such as portable, indirect calorimetry systems. However, results vary widely based on the nature of activities

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.6 kg/m 2 ). RMR (indirect calorimetry, fasted state), VO 2max (graded treadmill exercise test with spirometry), body composition (dual x-ray absorptiometry), and PAL (combined heart rate and movement sensor) was determined. Group differences were tested by independent t -tests and Mann

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Giovanni Mario Pes, Maria Pina Dore, Alessandra Errigo and Michel Poulain

been developed recently that enable an acceptable estimation of energy expenditure during the activity (indirect calorimetry, double-labeled water turnover), they require the use of time-consuming procedures and expensive equipment difficult to apply in nonagenarians. Such methods may be used only

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and VO2peak, (utilising indirect calorimetry), venous blood sampling, cardiac scanning, strength (1-RM bench press/back squat), power (force velocity profile) and total mood disturbance (TMD) via a profile of mood states (POMS) assessments were made at regular intervals. Training consisted of specific

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Scott E. Crouter, Paul R. Hibbing and Samuel R. LaMunion

and AP estimates of SB compare in youth. In addition, studies that have compared both monitors have tended to not use a criterion measure of direct observation or indirect calorimetry. Three studies have compared the AP inclinometer function to AG count-based estimates of SB in youth, and have found