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Shelby L. Francis, Ajay Singhvi, Eva Tsalikian, Michael J. Tansey and Kathleen F. Janz


Determining fitness is important when assessing adolescents with type 1 diabetes mellitus (T1DM). Submaximal tests estimate fitness, but none have been validated in this population. This study cross-validates the Ebbeling and Nemeth equations to predict fitness (VO2max (ml/kg/min)) in adolescents with T1DM.


Adolescents with T1DM (n = 20) completed a maximal treadmill test using indirect calorimetry. Participants completed one 4-min stage between 2.0 and 4.5 mph and 5% grade (Ebbeling/Nemeth protocol). Speed and grade were then increased until exhaustion. Predicted VO2max was calculated using the Ebbeling and Nemeth equations and compared with observed VO2max using paired t tests. Pearson correlation coefficients, 95% confidence intervals, coefficients of determination (R2), and total error (TE) were calculated.


The mean observed VO2max was 47.0 ml/kg/min (SD = 6.9); the Ebbeling and Nemeth mean predictions were 42.4 (SD = 9.4) and 43.5 ml/kg/min (SD = 6.9), respectively. Paired t tests resulted in statistically significant (p < .01) mean differences between observed and predicted VO2max for both predictions. The association between the Ebbeling prediction and observed VO2max was r = .90 (95% CI = 0.76, 0.96), R 2 = .81, and TE = 6.5 ml/kg/min. The association between the Nemeth prediction and observed VO2max was r = .81 (95% CI = 0.57, 0.92), R 2 = .66, and TE = 5.6 ml/kg/min.


The Nemeth submaximal treadmill protocol provides a better estimate of fitness than the Ebbeling in adolescents with T1DM.

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Christopher L. Melby, Kristen L. Osterberg, Alyssa Resch, Brenda Davy, Susan Johnson and Kevin Davy

Thirteen physically active, eumenorrheic, normal-weight (BMI ≤ 25 kg/m2) females, aged 18–30 years, completed 4 experimental conditions, with the order based on a Latin Square Design: (a) CHO/Ex: moderate-intensity exer-· cise (65% V̇O2peak) with a net energy cost of ~500 kcals, during which time the subject consumed a carbohydrate beverage (45 g CHO) at specific time intervals; (b) CHO/NoEx: a period of time identical to (a) but with subjects consuming the carbohydrate while sitting quietly rather than exercising; (c) NoCHO/ Ex: same exercise protocol as condition (a) during which time subjects consumed a non-caloric placebo beverage; and (d) NoCHO/NoEx: same as the no-exercise condition (b) but with subjects consuming a non-caloric placebo beverage. Energy expenditure, and fat and carbohydrate oxidation rates for the entire exercise/sitting period plus a 90-min recovery period were determined by continuous indirect calorimetry. Following recovery, subjects ate ad libitum amounts of food from a buffet and were asked to record dietary intake during the remainder of the day. Total fat oxidation (exercise plus recovery) was attenuated by carbohydrate compared to placebo ingestion by only ~4.5 g. There was a trend (p = .08) for a carbohydrate effect on buffet energy intake such that the CHO/Ex and CHO/NoEx energy intakes were lower than the NoCHO/Ex and NoCHO/NoEx energy intakes, respectively (mean for CHO conditions: 683 kcal; NoCHO conditions: 777 kcal). Average total energy intake (buffet plus remainder of the day) was significantly lower (p < .05) following the conditions when carbohydrate was consumed (CHO/Ex = 1470 kcal; CHO/NoEx = 1285 kcal) compared to the noncaloric placebo (NoCHO/Ex =1767 kcal; NoCHO/ NoEx = 1660 kcal). In conclusion, in young women engaging in regular exercise, ingestion of 45 g of carbohydrate during exercise only modestly suppresses total fat oxidation during exercise. Furthermore, the ingestion of carbohydrate with or without exercise resulted in a lower energy intake for the remainder of the day

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James Cameron Morehen, Warren Jeremy Bradley, Jon Clarke, Craig Twist, Catherine Hambly, John Roger Speakman, James Peter Morton and Graeme Leonard Close

Rugby League is a high-intensity collision sport competed over 80 min. Training loads are monitored to maximize recovery and assist in the design of nutritional strategies although no data are available on the total energy expenditure (TEE) of players. We therefore assessed resting metabolic rate (RMR) and TEE in six Super League players over 2 consecutive weeks in-season including one game per week. Fasted RMR was assessed followed by a baseline urine sample before oral administration of a bolus dose of hydrogen (deuterium 2H) and oxygen (18O) stable isotopes in the form of water (2H2 18O). Every 24 hr thereafter, players provided urine for analysis of TEE via DLW method. Individual training load was quantified using session rating of perceived exertion (sRPE) and data were analyzed using magnitude-based inferences. There were unclear differences in RMR between forwards and backs (7.7 ± 0.5 cf. 8.0 ± 0.3 MJ, respectively). Indirect calorimetry produced RMR values most likely lower than predictive equations (7.9 ± 0.4 cf. 9.2 ± 0.4 MJ, respectively). A most likely increase in TEE from Week 1 to 2 was observed (17.9 ± 2.1 cf. 24.2 ± 3.4 MJ) explained by a most likelyincrease in weekly sRPE (432 ± 19 cf. 555 ± 22 AU), respectively. The difference in TEE between forward and backs was unclear (21.6 ± 4.2 cf. 20.5 ± 4.9 MJ, respectively). We report greater TEE than previously reported in rugby that could be explained by the ability of DLW to account for all match and training-related activities that contributes to TEE.

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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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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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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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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