While the majority of studies reporting ergogenic effects of dietary nitrate have used a multiday supplementation protocol, some studies suggest that a single dose of dietary nitrate before exercise can also improve subsequent performance. We aimed to compare the impact of acute and 6-day sodium nitrate supplementation on oxygen uptake (V̇O2) and time-trial performance in trained cyclists. Using a randomized, double-blind, cross-over design, 17 male cyclists (25 ± 4 y, V̇O2peak 65 ± 4 ml·kg-1·min-1, Wmax 411 ± 35 W) were subjected to 3 different trials; 5 days placebo and 1 day sodium nitrate supplementation (1-DAY); 6 days sodium nitrate supplementation (6-DAY); 6 days placebo supplementation (PLA). Nitrate was administered as 1097 mg sodium nitrate providing 800 mg (~12.9 mmol) nitrate per day. Three hours after ingestion of the last supplemental bolus, indirect calorimetry was performed while subjects performed 30 min of exercise at 45% Wmax and 30 min at 65% Wmax on a cycle ergometer, followed by a 10 km time-trial. Immediately before exercise, plasma [nitrate] and [nitrite] increased to a similar extent during the 6-DAY and 1-DAY trial, but not with PLA (plasma nitrite: 501 ± 205, 553 ± 278, and 239 ± 74 nM, respectively; p < .001). No differences were observed between interventions in V̇O2 during submaximal exercise, or in time to complete the time-trial (6-DAY: 1004 ± 61, 1-DAY: 1022 ± 72, PLA: 1017 ± 71 s; p = .28). We conclude that both acute and 6-days of sodium nitrate supplementation do not alter V̇O2 during submaximal exercise or improve time-trial performance in highly trained cyclists, despite increasing plasma [nitrate] and [nitrite].
Jean M. Nyakayiru, Kristin L. Jonvik, Philippe J.M. Pinckaers, Joan Senden, Luc J.C. van Loon and Lex B. Verdijk
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.
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.
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
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
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
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
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
.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
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