Portable Metabolic Analyzer Research Articles

Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption.

Estimation of oxygen consumption (VO2) from accelerometer data is typically based on prediction equations developed in laboratory settings using steadily paced and controlled test activities. These equations may not capture the temporary changes in VO2 occurring in sporadic real-life physical activity. In this study, we introduced a novel floating epoch for accelerometer data analysis and hypothesized that an adaptive epoch length provides a more consistent estimation of VO2 in irregular activity conditions than a 6 s constant epoch. Two different activity tests were conducted: a progressive constant-speed test (CS) performed on a track and a 6 min back-and-forth walk test including accelerations and decelerations (AC/DC) performed as fast as possible. Twenty-nine adults performed the CS test, and sixty-one performed the AC/DC test. The data were collected using hip-worn accelerometers and a portable metabolic gas analyzer. General linear models were employed to create the prediction models for VO2 that were cross-validated using both data sets and epoch types as training and validation sets. The prediction equations based on the CS test or AC/DC test and 6 s epoch had excellent performance (R2 = 89%) for the CS test but poor performance for the AC/DC test (31%). Only the VO2 prediction equation based on the AC/DC test and the floating epoch had good performance (78%) for both tests. The overall accuracy of VO2 prediction is compromised with the constant length epoch, whereas the prediction model based on irregular acceleration data analyzed with a floating epoch provided consistent performance for both activities.

Accuracy Of Bioimpendance Analysis For Measuring Body Compositionand Resting Metabolism In Youth.

PURPOSE: This study assessed the accuracy of bioimpedance analysis (BIA) for measuring body composition and resting metabolic rate (RMR) against criterion measures in youth. METHODS: Youth of varying sex, age (child, 7-12 y; teen, 13-17 y), weight status (healthy weight, BMI < 85th %ile; overweight, BMI ≥ 85th %ile; obese, BMI ≥ 95th %ile), and pubertal status (years from peak height velocity) were recruited from Athens and surrounding counties of Southeast Ohio. Using paired t-tests, fat mass (FM; kg), fat free mass (FFM; kg), visceral adipose tissue (VAT; cm2) and RMR (kcal/day) measures from a multi-frequency, tetrapolar, eight-point BIA device were compared to dual-energy X-ray absorptiometry (DXA) and a portable metabolic analyzer, respectively. Relationships among all measures were assessed using correlation analyses. Accuracy was calculated as the root mean squared error (RMSE) and bias between methods. RESULTS: In total, 130 youth (11.9 ± 2.9 y; 68 female, 85 healthy weight, 77 children, 61 pre-pubertal) participated in the study. BIA measures of FM was greater in girls (16.0 ± 1.2 kg), and obese (27.6 ± 2.5 kg) compared to boys (13.2 ± 0.9 kg) and healthy weight youth (10.9 ± 0.4 kg). FFM was higher in boys (41.8 ± 1.8 kg), teens (49.2 ± 1.4 kg), and post-pubertal youth (52.0 ± 9.6 kg) compared to their counterparts (35.3 ± 1.2, 30.9 ± 0.9, and 28.5 ± 5.4 kg, respectively). BIA-estimated RMR was higher in boys (1276 ± 40 kcal/day), teens (1434 ± 29 kcal/day), and post-pubertal youth (1493 ± 207 kcal/day) compared to their counterparts (1133 ± 25, 1038 ± 19, 985 ± 116 kcal/day, respectively). Assessing the entire sample, BIA reported lower FFM (38.4 ± 1.1 kg) and higher FM (11.8 ± 0.8 kg) and VAT (50.4 ± 4.1 cm2) compared to DXA (32.6 ± 1.0 kg, 14.7 ± 0.8 kg, 45.8 ± 2.7 cm2), whereas estimated and measured RMR values were similar (1199 ± 24 vs. 1145 ± 254 kcal/day; p > 0.05). Correlations between methods ranged from r = 0.71 to 1.00 (p < 0.05). BIA overestimated FM by 2.8 kg and underestimated FFM by 5.7 kg. CONCLUSIONS: While measures between methods were statistically different, the magnitude of these differences were physiologically insignificant and strong relationships between BIA and criterion measures were observed. Future research should assess the sensitivity of BIA to body composition and RMR changes across growth trajectories.

Physiological and Metabolic Requirements, and User-Perceived Exertion of Immersive Virtual Reality Exergaming Incorporating an Adaptive Cable Resistance System: An Exploratory Study.

Objective: To measure metabolic and physiological demand, subjective fatigue, and enjoyment during a signature 30-minute immersive virtual reality (IVR) adaptive cable resistance exergaming session. Methods: Fourteen healthy college-aged individuals (seven females) were initially acquainted with the IVR equipment and gameplay dynamics. Participants then underwent a 30-minute IVR exergaming session performing six different cable resistance exercises. A portable metabolic gas exchange analyzer concurrently assessed energy expenditure (EE) through indirect calorimetry while a chest-worn monitor captured heart rate (HR). Participants subsequently completed questionnaires, including the Borg scale for rating of perceived exertion (RPE), Physical Activity Enjoyment Scale (PACES), and Simulator Sickness Questionnaire (SSQ). Results: The mean EE, mean metabolic equivalent, and average total calories expended during the 30-minute session were 14.7 (standard deviation [SD] 2.8) kcal/minute, 12.9 (SD 0.5), and 440 (SD 84) kcals respectively. The mean HR was 176 (SD 3.1) beats per minute (bpm) with a mean max HR of 188 SD (SD 2.9) bpm. The combined training volume among all participants was 16,102 kg (SD 4137). Participants classified the IVR training session to be "somewhat hard-to-hard" with a RPE score of 14 (SD 1) while indicating the session to be "enjoyable" with a PACES score of 4.31 (SD 0.36). The participants did not report any cybersickness symptoms, demonstrating an average total SSQ score of 24.04 (SD 24.13). Conclusions: IVR exergaming incorporating cable resistance training elicits high EE and physiological demand with high enjoyment scores while attenuating perceived fatigue. The potential for IVR to elicit these acute training effects over long-term training periods warrants further investigation into its contribution to fitness and health.

Differences in V˙O2max Measurements Between Breath-by-Breath and Mixing-Chamber Mode in the COSMED K5.

Automated metabolic analyzers are frequently utilized to measure maximal oxygen consumption (V˙O2max). However, in portable devices, the results may be influenced by the analyzer's technological approach, being either breath-by-breath (BBB) or dynamic micro mixing chamber mode (DMC). The portable metabolic analyzer K5 (COSMED, Rome, Italy) provides both technologies within one device, and the authors aimed to evaluate differences in V˙O2max between modes in endurance athletes. Sixteen trained male participants performed an incremental test to voluntary exhaustion on a cycle ergometer, while ventilation and gas exchange were measured by 2 structurally identical COSMED K5 metabolic analyzers synchronously, one operating in BBB and the other in DMC mode. Except for the flow signal, which was measured by 1 sensor and transmitted to both devices, the devices operated independently. V˙O2max was defined as the highest 30-second average. V˙O2max and V˙CO2@V˙O2max were significantly lower in BBB compared with DMC mode (-4.44% and -2.71%), with effect sizes being large to moderate (ES, Cohen d = 0.82 and 1.87). Small differences were obtained for respiratory frequency (0.94%, ES = 0.36), minute ventilation (0.29%, ES = 0.20), and respiratory exchange ratio (1.74%, ES = 0.57). V˙O2max was substantially lower in BBB than in DMC mode. Considering previous studies that also indicated lower V˙O2 values in BBB at high intensities and a superior validity of the K5 in DMC mode, the authors conclude that the DMC mode should be selected to measure V˙O2max in athletes.

Isolated finger flexor vs. exhaustive whole-body climbing tests? How to assess endurance in sport climbers?

Sport climbing requires high-intensity finger flexor contractions, along with a substantial whole-body systemic oxygen uptake ([Formula: see text]O2) contribution. Although fatigue is often localised to the finger flexors, the role of systemic ̇[Formula: see text]O2 and local aerobic mechanisms in climbing performance remains unclear. As such, the primary purpose of this study was to determine systemic and local muscle oxygen responses during both isolated finger flexion and incremental exhaustive whole-body climbing tests. The secondary aim was to determine the relationship of isolated and whole-body climbing endurance tests to climbing ability. Twenty-two male sport climbers completed a series of isometric sustained and intermittent forearm flexor contractions, and an exhaustive climbing test with progressive steepening of the wall angle on a motorised climbing ergometer. Systemic [Formula: see text]O2 and flexor digitorum profundus oxygen saturation (StO2) were recorded using portable metabolic analyser and near-infra red spectroscopy, respectively. Muscle oxygenation breakpoint (MOB) was identifiable during an incremental exhaustive climbing test with progressive increases in angle (82 ± 8% and 88 ± 8% [Formula: see text]O2 and heart rate climbing peak). The peak angle from whole-body treadwall test and impulse from isolated hangboard endurance tests were interrelated (R2 = 0.58-0.64). Peak climbing angle together with mean [Formula: see text]O2 and StO2 from submaximal climbing explained 83% of variance in self-reported climbing ability. Both systemic and muscle oxygen kinetics determine climbing-specific endurance. Exhaustive climbing and isolated finger flexion endurance tests are interrelated and suitable to assess climbing-specific endurance. An exhaustive climbing test with progressive wall angle allows determination of the MOB.

Maximal Oxygen Consumption Requirements in Professional North American Ice Hockey.

Ferland, P-M, Marcotte-L'Heureux, V, Roy, P, Carey, V, Charron, J, Lagrange, S, Leone, M, and Comtois, AS. Maximal oxygen consumption requirements in professional North American ice hockey. J Strength Cond Res 35(4): 1586-1592, 2021-This study was designed to measure preseason on-ice relative V̇o2max of professional ice-hockey players (n = 101 National Hockey League [NHL], 42 American Hockey League [AHL], 4 East Coast Hockey League [ECHL], and 15 Canadian Hockey League [CHL]) throughout 17 years and compare it between generations, league level, and position, and to verify if it was related to season and NHL career statistics. Relative V̇o2max was measured on ice with a portable metabolic analyzer (K4b2, Cosmed, Rome) with full hockey equipment, except for the helmet with either the Skating Multistage Aerobic Test or the 30-15 intermittent ice test tests. Relative V̇o2max results from both tests were compared between players of the same generation with an independent-samples T-test and were not significantly different. A one-way analysis of variance and post hoc pairwise tests were performed to detect significant differences between groups. Pearson correlations (two-tailed) were also performed between selected variables. All statistical significance was set at p < 0.05. Results show that there are no significant differences for relative V̇o2max between generations (2001-2003 vs. 2006 vs. 2015-2017), league level (NHL vs. AHL vs. ECHL vs. CHL), and position (winger, center, and defense), other than the ECHL being lower. There are also no significant relationships between V̇o2max values and hockey season and NHL career statistics. Thus, the results show that there is a minimal relative V̇o2max requirement to play North American ice hockey at the elite level (55.9 ± 5.2 ml·kg-1·min-1; n = 162). Future research should be directed toward comparing V̇o2max of elite and amateur ice-hockey players to confirm the minimal relative V̇o2max requirement to play North American ice hockey at the elite level.

Updating the Skating Multistage Aerobic Test and Correction for V[Combining Dot Above]O2max Prediction Using a New Skating Economy Index in Elite Youth Ice Hockey Players.

Allisse, M, Bui, HT, Léger, L, Comtois, A-S, and Leone, M. Updating the skating multistage aerobic test and correction for V[Combining Dot Above]O2max prediction using a new skating economy index in elite youth ice hockey players. J Strength Cond Res 34(11): 3182-3189, 2020-A number of field tests, including the skating multistage aerobic test (SMAT), have been developed to predict V[Combining Dot Above]O2max in ice hockey players. The SMAT, like most field tests, assumes that participants who reach a given stage have the same oxygen uptake, which is not usually true. Thus, the objectives of this research are to update the V[Combining Dot Above]O2 values during the SMAT using a portable breath-by-breath metabolic analyzer and to propose a simple index of skating economy to improve the prediction of oxygen uptake. Twenty-six elite hockey players (age 15.8 ± 1.3 years) participated in this study. The oxygen uptake was assessed using a portable metabolic analyzer (K4b) during an on-ice maximal shuttle skate test. To develop an index of skating economy called the skating stride index (SSI), the number of skating strides was compiled for each stage of the test. The SMAT enabled the prediction of the V[Combining Dot Above]O2max (ml·kg·min) from the maximal velocity (m·s) and the SSI (skating strides·kg) using the following regression equation: V[Combining Dot Above]O2max = (14.94 × maximal velocity) + (3.68 × SSI) - 24.98 (r = 0.95, SEE = 1.92). This research allowed for the update of the oxygen uptake values of the SMAT and proposed a simple measure of skating efficiency for a more accurate evaluation of V[Combining Dot Above]O2max in elite youth hockey players. By comparing the highest and lowest observed SSI scores in our sample, it was noted that the V[Combining Dot Above]O2 values can vary by up to 5 ml·kg·min. Our results suggest that skating economy should be included in the prediction of V[Combining Dot Above]O2max to improve prediction accuracy.

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds.

This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption (, ), and between Stryd PO and external mechanical power (, ). Stryd power meter underestimated PO () whereas GCT and LSS values did not show any significant differences with the reference measures (, , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.

On-ice Energy Cost And Lactate Production During An All-out Anaerobic Test In Ice Hockey Players

The lack of ice hockey-specific lactic anaerobic assessment tools limits the ability of coaches to better track and develop their players. PURPOSE: Establish two predictive equations for assessing indirectly 1) the O2 energy expenditure, and 2) the maximum lactate concentration following an all-out on-ice skating effort. METHODS: Twenty male elite ice hockey players participated in this study (age=15.7±1.0 year). The maximal anaerobic skating test (MAST) consisted of skating back and forth on an 18.2m course at maximal speed with abrupt stops at each end for a total of 12 shuttles (average time=52.0±2.0s). The O2 energy cost was measured using a portable metabolic analyzer (Cosmed K4b2) and the maximum post-exercise lactate concentration at 1, 3, 5, 15, and 20 min was measured with a Lactate Pro analyzer. The independent variables used to estimate O2 consumption were body mass, time, heart rate, and the number of skating strides and skating stride index both measured at the 2nd shuttle. For the lactate concentration estimation, the independent variables used were time, heart rate, and the number of skating strides and skating stride index both measured at the 6th shuttle. RESULTS: Correlation coefficients for both equations were r=0.87 and standard error of estimate (SEE) were 6.2% and 6.8% respectively for O2 uptake and lactate production, indicating that validity of the regression algorithms were excellent. Particularly for the estimation of the lactate level, the removal of variables in relation to the skating efficiency reduces the correlation to 0.49 and increases the SEE to 10.5% thus indicating the importance of considering an index of skating efficiency during this type of evaluation. CONCLUSIONS: To our knowledge, there is no specific ice hockey field test allowing the indirect estimation of O2 cost and lactate concentration in a purely anaerobic test. Thus, using simple and easy-to-measure variables, coaches will be able to monitor more effectively their players’ progress in an effort to optimize their individual on-ice anaerobic performance.

The COSMED K5 in Breath-by-Breath and Mixing Chamber Mode at Low to High Intensities.

The portable metabolic analyzer COSMED K5 (Rome, Italy) allows for switching between breath-by-breath (BBB) and dynamic micro-mixing chamber (DMC) modes. This study aimed to evaluate the reliability and validity of the K5 in BBB and DMC at low, moderate, and high metabolic rates. Two K5 simultaneously operated in BBB or DMC, whereas (i) a metabolic simulator (MS) produced four different metabolic rates (repeated eight times), and (ii) 12 endurance-trained participants performed bike exercise at 30%, 40%, 50%, and 85% of their individual power output at V˙O2max (repeated three times). K5 data were compared with predicted simulated values and consecutive Douglas bag measurements. Reliability did not differ significantly between BBB and DMC, whereas the typical error and intraclass correlation coefficients for oxygen uptake (V˙O2), carbon dioxide output (V˙CO2), and minute ventilation (V˙E) ranged from 0.27% to 6.18% and from 0.32 to 1.00 within four metabolic rates, respectively. Validity indicated by mean differences ranged between 0.61% and -2.05% for V˙O2, 2.99% to -11.04% for V˙CO2, and 0.93% to -6.76% for V˙E compared with MS and Douglas bag at low to moderate metabolic rates and was generally similar for MS and bike exercise. At high rates, mean differences for V˙O2 amounted to -4.63% to -7.27% in BBB and -0.38% to -3.81% in DMC, indicating a significantly larger difference of BBB at the highest metabolic rate. The K5 demonstrated accurate to acceptable reliability in BBB and DMC at all metabolic rates. Validity was accurate at low and moderate metabolic rates. At high metabolic rates, BBB underestimated V˙O2, whereas DMC showed superior validity. To test endurance athletes at high workloads, the DMC mode is recommended.

Cross-Validation and Comparison of Energy Expenditure Prediction Models Using Count-Based and Raw Accelerometer Data in Youth

Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.

Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p&lt;.0001), 8,6% for FO (r=.69; P&lt;.001), and 11.6% for AC (r=.81; p&lt;.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p&lt;.0001), 8,6% for FO (r=.69; P&lt;.001), and 11.6% for AC (r=.81; p&lt;.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

Pole Dancing for Fitness: The Physiological and Metabolic Demand of a 60-Minute Class.

Nicholas, JC, McDonald, KA, Peeling, P, Jackson, B, Dimmock, JA, Alderson, JA, and Donnelly, CJ. Pole dancing for fitness: The physiological and metabolic demand of a 60-minute class. J Strength Cond Res 33(10): 2704-2710, 2019-Little is understood about the acute physiological or metabolic demand of pole dancing classes. As such, the aims of this study were to quantify the demands of a standardized recreational pole dancing class, classifying outcomes according to American College of Sports Medicine (ACSM) exercise-intensity guidelines, and to explore differences in physiological and metabolic measures between skill- and routine-based class components. Fourteen advanced-level amateur female pole dancers completed three 60-minute standardized pole dancing classes. In one class, participants were fitted with a portable metabolic analysis unit. Overall, classes were performed at a mean VO2 of 16.0 ml·kg·min, total energy cost (EC) of 281.6 kcal (4.7 kcal·min), metabolic equivalent (METs) of 4.6, heart rate of 131 b·min, rate of perceived exertion (RPE) of 6.3/10, and blood lactate of 3.1 mM. When comparing skill- and routine-based components of the class, EC per minute (4.4 vs. 5.3 kcal·min), peak VO2 (21.5 vs. 29.6 ml·kg·min), METs (4.3 vs. 5.2), and RPE (7.2 vs. 8.4) were all greater in the routine-based component (p < 0.01), indicating that classes with an increased focus on routine-based training, as compared to skill-based training, may benefit those seeking to exercise at a higher intensity level, resulting in greater caloric expenditure. In accordance with ASCM guidelines, an advanced-level 60-minute pole dancing class can be classified as a moderate-intensity cardiorespiratory exercise; when completed for ≥30 minutes, ≥5 days per week (total ≥150 minutes) satisfies the recommended level of exercise for improved health and cardiorespiratory fitness.

Validity and Reliability of the New Portable Metabolic Analyzer PNOE.

Assessment of the oxygen and carbon dioxide content of expired air during exercise is critical for determining cardiorespiratory status. The purpose of this study was to compare the new portable metabolic analyzer PNOE with COSMED – Quark CPET, a previously validated stationary metabolic cart.Methods: A total of 22 subjects (17 male and 5 female) aged 32.3 ± 11.1 years took part in the study. Breath by breath gas exchange was measured by both devices during a four-stage incremental protocol on a cycle ergometer. On a separate day, 10 participants repeated the trial to assess the reliability of the PNOE metabolic cart.Results: Strong correlations were obtained in VO2 (r = 0.98, p < 0.001), VCO2 (r = 0.98, p < 0.001), VE (r = 0.98, p < 0.001), and RQ (r = 0.91, p < 0.001), between the two devices. Bland-Altman plots revealed a mean difference of 34.0 ± 118 ml/min and 36.4 ± 110 ml/min in VO2 and VCO2 analysis, respectively. There were no significant differences in VO2, VCO2, VE, or RQ between the two devices. Intraclass correlation coefficient was high between the two trials for VO2 (r = 0.98, p < 0.001), VCO2 (r = 0.98, p < 0.001), VE (r = 0.99, p < 0.001), and RQ (r = 0.93, p < 0.001).Conclusions: Our data indicate that the portable metabolic cart PNOE can accurately determine respiratory gases over a wide range of exercise intensities, in healthy individuals, in a controlled laboratory setting.

Acute Physiological And Perceptual Responses To Body-weight Circuit And Treadmill-based High-intensity Interval Exercise In Children

Research examining acute responses to non-laboratory-based high-intensity interval exercise (HIIE), such as body-weight exercise, in children is lacking. PURPOSE: To compare the acute physiological and perceptual responses between body-weight circuit (CIRC) and treadmill high-intensity interval running (TM) in children. METHODS: Physically active boys (n=17; age = 9.7 ± 1.3 years) completed a graded exercise test to determine maximal heart rate (HRmax), peak oxygen uptake (VO2peak) and maximal aerobic speed (MAS). Time-matched (8-min) CIRC and TM were completed in a randomized order on separate days within a 1 to 2-week period. CIRC consisted of two sets of 4 bouts of 30 s of maximal repetitions of mountain climbers, jump squats, jumping jacks, and burpees, whereas TM included 30 s bouts at 100% MAS. Both had a 30 s recovery between bouts. HR and gas exchange data were continuously assessed using a portable metabolic analyzer and HR monitor, and blood lactate concentration (BLa) was measured pre- and post-exercise. Global affect (PANAS) and exercise enjoyment (PACES) were assessed at baseline and post-exercise. Rating of perceived exertion (RPE), affect (Feeling Scale) and enjoyment (Exercise Enjoyment Scale) were recorded pre- and post-exercise and at 38% and 75% of session completion. RESULTS: Mean peak HR and VO2 were 87% HRmax and 74% VO2peak for CIRC and 89% HRmax and 80% VO2peak for TM, with a significant difference in peak VO2 between regimens (p<0.05). Yet, there were no differences in session HR, VO2 or VE (p>0.05). Post-exercise BLa was significantly higher following CIRC vs. TM (mean difference = 3.0 ± 2.2 mM, p<0.05). RPE, affect and enjoyment responses did not differ between regimens during exercise, and affect did not differ from pre- to post-exercise within or between regimens (p>0.05). Post-exercise enjoyment was significantly lower after CIRC vs. pre-exercise (mean difference = 3.9 ± 1.1, p<0.05). CONCLUSION: Although HR was similar, there was a higher peak VO2 in response to TM, paralleling previous work in adults. The greater BLa accumulation in CIRC may be explained by greater muscle recruitment required for multi-joint, dynamic movements and could explain the reduced post-exercise enjoyment. Future research should investigate higher volume protocols utilizing different body-weight exercises.

Validity of Activity Trackers in Estimating Energy Expenditure During High-Intensity Functional Training

ABSTRACTPurpose: The purpose of this study was to evaluate the agreement of five commercially available accelerometers in estimating energy expenditure while performing an acute bout of high-intensity functional training (HIFT). Methods: Participants (n = 47; average age: 28.5 ± 11.6 years) consisted of recreationally active, healthy adults. Each participant completed a session of HIFT: a 15-minute workout consisting of 12 repetitions each of air-squats, sit-ups, push-ups, lunges, pull-ups, steps-ups, and high-knees; performed circuit-style by completing as many rounds as possible. During this session, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move). Results: Four of the five activity trackers reported lower (p < .05) total EE values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcal) was not significantly different from, and most closely estimated caloric expenditure compared to the PMA (144.99 ± 37.13 kcal) (p = .056). A repeated-measures ANOVA showed that all activity trackers were significantly different from the reference measure (PMA) (p < .05). Systematic relative agreement between the activity trackers was calculated, exhibiting a significant ICC = 0.426 (F [46,230] = 5.446 [p < .05]). Conclusion: The wrist- and hip-mounted activity trackers did not accurately assess energy expenditure during HIFT exercise. With the exception of the ActiGraph GT3X, the remaining four activity trackers showed inaccurate estimates of the amount of kilocalories expended during the HIFT exercise bout compared to the PMA.

Measured and predicted resting energy expenditure in wheelchair rugby athletes

Objective: Report measured resting energy expenditure (REE) in wheelchair rugby athletes and evaluate agreement between REE and the prediction models of Chun, Cunningham, Harris-Benedict, Mifflin, Nightingale and Gorgey, and Owen.Design: Cohort-based validation study.Setting. Paralympic team training camp.Participants: Fourteen internationally competitive athletes who play wheelchair rugby, 13 of whom had cervical spinal cord injuries (SCI).Outcome Measures: A portable metabolic analyzer was used to measure REE following an overnight fast and dual-energy X-ray absorptiometry (DXA) was used to assess lean body mass for the prediction equations.Results: REE in the current sample was 1735 ± 257 kcal × day−1 ranging from 1324 to 2068 kcal × day−1. Bland–Altman analyses revealed negative mean bias but similar limits of agreement between measured REE and scores predicted by Chun, Cunningham, Mifflin, Nightingale and Gorgey, and Owen models in elite athletes who play wheelchair rugby.Conclusion: Prediction models regressed on persons with and without SCI under-predicted REE of competitive wheelchair rugby athletes. This outcome may be explained by the higher REE/fat-free mass (FFM) ratio of current athletes compared to less active samples. Findings from the current study will help practitioners to determine nutrient intake needs on training days of varied intensity.

PL-014 The gaseous metabolism characteristics of overweight adult women during exercise stress test

Objective Overweight was a global public health problem. In recent years, the number of overweight people in China had been increasing. Being overweight had a serious impact on health. 31.1% of overweight people had aggregation of risk factors for cardiovascular metabolic diseases. And overweight people were more likely to suffer from some diseases, such as hypertension, diabetes, dyslipidemia and arthritis. This study compared the gas metabolism index differences between overweight and normal weight women when they did exercise under different load, and summarized gas metabolism characteristics of overweight women, in order to lay the foundation for instructing overweight women to do exercise scientifically, reduce the risk factors of chronic diseases such as cardiovascular disease, and enhance and improve physical fitness and health.&#x0D; Methods Adult women between 20 and 30 years were taken as subjects. After measuring their height and weight, they were divided into normal weight group (BMI=18~23.9kg/m2) and overweight group (BMI＞24kg/m2) according to body mass index (BMI). There were 15 participants in each group. After the baseline test, using modified Bruce treadmill protocol, the air metabolism indexes of two groups were determined by Cortex MetaMax 3B portable gas metabolic analyzer, including oxygen uptake(VO2), minute ventilation(MV), breathing frequency(BF), expiratory end-tidal CO2concentration(ETCO2), expiratory end-tidal O2concentration (ETO2), arterial blood carbon dioxide partial pressure (PaCO2), carbon dioxide output(VCO2), oxygen pulse and maximal voluntary ventilation(MMV), etc. The differences of gas metabolism indexes among resting, exercise, and recovery stages were compared and analyzed.&#x0D; Results (1) Most of indexes such as VO2, VCO2, and MV rose gradually with the load increase during exercise stress test except for ETO2and PaCO2. VO2, PaCO2, VCO2and ETCO2of overweight group were significantly lower than normal weight group during the same load. PaCO2of overweight group at grade 4 was significant lower than normal weight group by 5.6 mmHg (P&lt;0.05). VCO2of overweight group at grade 5 was significant lower than normal weight group by 0.6L/min (P&lt;0.05). ETCO2 of overweight group at grade 3 and 4 were significant lower than normal weight group about 0.5% and 0.6% respectively (P&lt;0.05). (2) During recovery stage, most of indexes decreased gradually, such as MV and BF, while ETO2presented a rising trend. At a certain time during the recovery stage, ETCO2of overweight group was significantly lower than normal weight group (5.3% vs 5.8%), while MMV, MV and oxygen pulse were significantly higher than normal weight group (P&lt;0.05). MMV of overweight group at 2, 3 and 4 minutes were significant lower than normal weight group by1L/min, 1L/min and 0.9L/min, at the same time, MV of overweight group were significant lower than normal weight group by17.8L/min, 20.1L/min and 16.9L/min. The oxygen pulse of overweight group during whole 5 minutes recovery period were significantly higher than normal weight group by 2.7L/min, 3.9L/min, 3.9L/min, 2.9L/min and 2.0L/min. (3) The gaseous metabolism between two groups was significantly different when they did 7.1 and 10.2 METs exercise.&#x0D; Conclusions Although there was no difference in gas metabolism between overweight and normal weight adult women in resting state, the respiratory function of overweight women was weaker than normal weight women during exercise, especially at the intensities of 7.1 and 10.2 METs. During the recovery period after exercise stress test, the recovery rate of gas metabolism in overweight adult women was slower than that of normal weight women.

Energy Balance Coexists With Disproportionate Macronutrient Consumption Across Pretraining, During Training, and Posttraining Among Indian Junior Soccer Players.

To evaluate energy expenditure, energy intake, and nutrient adequacy of Indian junior soccer players. Forty junior national-level soccer players (Under-12 and Under-16 age groups) were assessed for 3-day weighed food records and 3-day energy expenditure. Energy and nutrient intake was analyzed from food records, and energy expenditure was measured using a portable metabolic analyzer and activity records. Nutrient adequacy was determined by comparing intake with prevailing recommendations. Players exhibited no significant difference between energy intake (boys = 3062 [340.9] and girls = 2243 [320.3]kcal·d-1) and expenditure (boys = 2875 [717.3] and girls = 2442 [350.3]kcal·d-1). Across age groups, the Under-12 boys showed positive energy balance as against energy deficits in Under-16. Girls showed energy deficits, although not significant. There were 58% of girls showing energy availability <30kcal·kg-1 fat-free mass, of which 37% were Under-16 players. Carbohydrates contributed to >60% of energy expenditure among 95.2% boys and 73.7% girls. Among 52.4% boys and 47.4% girls, <25% of energy expenditure was contributed by fat. More than 95% players consumed <1g·kg-1 carbohydrates pretraining and 100% of them consumed >1.2g·kg-1 carbohydrates posttraining. Junior soccer players consumed more than recommended carbohydrates in the diet, although not aligning with the pretraining, during training, and posttraining meal requirements. Considering the energy deficits observed among Under-16 players, a suitable dietary modification is warranted.