The aim of this article was to compare 2 portable devices (a heart-rate monitor and an electromagnetic-coil system) that evaluate 2 different physiological parameters—heart rate (HR) and ventilation (VE)—with the objective of estimating energy expenditure (EE). The authors set out to prove that VE is a more pertinent setting than HR to estimate EE during light to moderate activities (sitting and standing at rest and walking at 4, 5, and 6 km/hr). Eleven healthy men were recruited to take part in this study (27.6 ± 5.4 yr, 73.7 ± 9.7 kg). The authors determined the relationships between HR and EE and between VE and EE during light to moderate activities. They compared EE measured by indirect calorimetry (EEREF) with EE estimated by HR monitor (EEHR) and EE estimated by electromagnetic coils (EEMAG) in upright sitting and standing positions and during walking exercises. They compared EEREF with EEHR and EEMAG. The results showed no significant difference between the values of EEREF and EEMAG. However, they showed several significant differences between the values of EEREF and EEHR (for standing at rest and walking at 5 and 6 km/hr). These results showed that the electromagnetic-coil system seems to be more accurate than the HR monitor to estimate EE at rest and during exercise. Taking into consideration these results, it would be interesting to associate the parameters VE and HR to estimate EE. Furthermore, a new version of the electromagnetic-coil device was recently developed and provides the possibility to perform measurement under daily life conditions.
Steven Gastinger, Guillaume Nicolas, Anthony Sorel, Hamid Sefati and Jacques Prioux
Jean M. Nyakayiru, Kristin L. Jonvik, Philippe J.M. Pinckaers, Joan Senden, Luc J.C. van Loon and Lex B. Verdijk
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].
Alexander H.K. Montoye, Jordana Dahmen, Nigel Campbell and Christopher P. Connolly
Purpose: This purpose of this study was to validate consumer-based and research-grade PA monitors for step counting and Calorie expenditure during treadmill walking. Methods: Participants (n = 40, 24 in second trimester and 16 in third trimester) completed five 2-minute walking activities (1.5–3.5 miles/hour in 0.5 mile/hour increments) while wearing five PA monitors (right hip: ActiGraph Link [AG]; left hip: Omron HJ-720 [OM]; left front pants pocket: New Lifestyles NL 2000 [NL]; non-dominant wrist: Fitbit Flex [FF]; right ankle: StepWatch [SW]). Mean absolute percent error (MAPE) was used to determine device accuracy for step counting (all monitors) and Calorie expenditure (AG with Freedson equations and FF) compared to criterion measures (hand tally for steps, indirect Calorimetry for Calories). Results: For step counting, the SW had MAPE ≤ 10% at all walking speeds, and the OM and NL had MAPE ≤ 10% for all speeds but 1.5 miles/hour. The AG had MAPE ≤ 10% for only 3.0–3.5 miles/hour speeds, and the FF had high MAPE for all speeds. For Calories, the FF and AG had MAPE > 10% for all speeds, with the FF overestimating Calories expended. Trimester did not affect PA monitor accuracy for step counting but did affect accuracy for Calorie expenditure. Conclusion: The ankle-worn SW and hip-worn OM had high accuracy for measuring step counts at all treadmill walking speeds, whereas the NL had high accuracy for speeds ≥2.0 miles/hour. Conversely, the monitors tested for Calorie expenditure have poor accuracy and should be interpreted cautiously for walking behavior.
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
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
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
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