1,721,000 research outputs found
Prolonged static stretching causes acute, non-metabolic fatigue and impairs exercise tolerance during severe intensity cycling
Full text linkWe tested the hypothesis that static stretching, an acute, non-metabolic fatiguing intervention, reduces exercise tolerance by increasing muscle activation and affecting muscle bioenergetics during cycling in the "severe" intensity domain. Ten active men (24±2 years, 74±11 kg, 176±8 cm) repeated an identical constant load cycling test, two tests were done in control conditions and two after stretching, that caused a 5% reduction of maximal isokinetic sprinting power output. We measured: i) oxygen consumption (VO2); ii) electromyography: iii) deoxyhemoglobin iv) blood lactate ([La-]); v) time to exhaustion (TTE) vi) perception of effort. Finally, VO2 and deoxyhemoglobin kinetics were determined. Force reduction following stretching was accompanied by augmented muscle excitation at a given workload (p=0.025), and a significant reduction in TTE (p=0.002). The time to peak of VO2 was reduced by stretching (p=0.034), suggesting an influence of the increased muscle excitation on the VO2 kinetics. Moreover, stretching was associated with a mismatch between O2 delivery and utilization during the on-kinetic, increased perception of effort and [La-], that are all compatible with an increased contribution of the glycolytic energy system to sustain the same absolute intensity. These results suggest a link between exercise intolerance and the decreased ability to produce force. Novelty bullets: • We provided the first characterization of the effects of prolonged stretching on the metabolic response during severe cycling. • Stretching reduced maximal force, augmented muscle activation in turn increasing the metabolic response to sustain exercise
Quantification of energy expenditure of military loaded runs: what is the performance of laboratory-based equations when applied to the field environment?
No full textINTRODUCTION: Performance during army loaded runs provides a synthetic indicator of a soldier's capacity to move while carrying loads and thereby remain able to execute a mission. The aim of this study was to estimate and compare the energy expenditure (EE) of army loaded runs, conducted in a field environment using laboratory-based equations and HR index (HRindex). METHODS: 45 Ranger recruits had HR monitored during three loaded runs (10, 15 and 20 km) in full military equipment in the field environment. EE was calculated using reference equations (EE-Eq) and estimates of oxygen consumption based on HRindex (EE-HRindex). Correspondence between EE-Eq and EE-HRindex estimates was evaluated using a two-way analysis of variance, correlation test and Bland-Altman analysis. RESULTS: EE-Eq relative to time and weight was significantly higher for the 10 km (0.175±0.016) compared with 15 and 20 km (0.163±0.016 and 0.160±0.013 kcal/kg/min, not different). The overall EE-Eq increased significantly with distance (1129±59, 1703±80 and 2250±115 kcal for 10, 15 and 20 km). EE-Eq was not different from and highly correlated with EE-HRindex, with a small and non-significant bias and good precision between methods. CONCLUSIONS: Our study provides the first comprehensive data on HR and EE during long-distance loaded army runs, in full combat equipment, in actual field conditions. Equation-based estimates of EE during these heavy-intensity activities were not significantly different from and highly correlated with HR-based estimates. This corroborates the general applicability of the predictive equations in the field environment. Furthermore, our study suggests that time-resolved HR-based estimates of EE during army runs can be used to evaluate for the effects of context specificity, individual variability and fatigue in movement economy
An Intensity-dependent Slow Component of HR Interferes with Accurate Exercise Implementation in Postmenopausal Women
Full text linkHeart rate (HR) targets are commonly used to administer exercise intensity in sport and clinical practice. Yet, as exercise protracts, a time-dependent dissociation between HR and metabolism can lead to a mis-prescription of the intensity ingredient of the exercise dose. Purpose: we tested the hypothesis that a slow component of HR (i.e. scHR) occurs in all intensity domains, greater than the slow component of oxygen uptake (scV[Combining Dot Above]O2), and we developed an equation to predict it across exercise intensities. Method: 18 healthy, postmenopausal women (54 ± 4 years) performed on a cycle-ergometer: i) a ramp incremental test for thresholds and V[Combining Dot Above]O2max detection; ii) 30-min constant-work exercise at 40, 50, 60, 70, and 80 %V[Combining Dot Above]O2max for the measurement of scHR, scV[Combining Dot Above]O2, stroke volume (SV) and body temperature (T°). scHR and scV[Combining Dot Above]O2 were compared by two-way RM-ANOVA (intensity and variable); Pearson correlation was calculated between the slow component of all variables, relative intensity, and domain; scHR (b·min-2) was predicted with a linear model based on exercise intensity relative to the respiratory compensation point (RCP). Results: A positive scHR was present in all domains, twice the size of scV[Combining Dot Above]O2 (p < 0.001) and significantly correlated with the slow components of V[Combining Dot Above]O2 (r2 = 0.46), T° (r2 = 0.52) and with relative intensity (r2 = 0.66). A linear equation accurately predicts scHR based on %RCP (r2 = 0.66, SEE = 0.15). Discussion: A mismatch exists between the slow components of HR and metabolic intensity. Whenever exercise is prescribed based on HR, target values should be adjusted over time to grant that the desired metabolic stimulus is maintained throughout the exercise session
Heart rate-index estimates oxygen uptake, energy expenditure and aerobic fitness in rugby players
Full text linkThe purpose of the study was to verify the suitability of heart rate-index (HRindex) in predicting submaximal oxygen consumption (VO2), energy expenditure (EE) and maximal oxygen consumption (VO2max) during treadmill running in rugby players. Fifteen professional rugby players (99.8 ± 12.7 kg, 1.85 ± 0.09 m) performed a running incremental test while VO2 (breath-by-breath) and heart rate (HR) were measured. HRindex was calculated (actual HR/resting HR) to predict submaximal and maximal VO2 ([(HRindex x 6)-5.0] x (3.5 body weight)) and EE. Measured and predicted VO2 and EE were compared by two-way RM-ANOVA (method, speed), correlation and Bland-Altman analysis. Measured and predicted VO2max were compared by paired t-test, correlation and Bland-Altman analysis. Submaximal VO2 and EE significantly increased (baseline VO2: 8.1 ± 1.6 ml·kg-1·min-1VO2max: 46.8 ± 4.3 ml·kg-1·min-1, baseline EE: 0.03 ± 0.01 kcal·kg-1·min-1, peak EE: 0.23 ± 0.03 kcal·kg-1·min-1) as a function of speed (p < 0.001 and p < 0.001 for VO2 and EE respectively) yet measured and predicted values at equal treadmill speeds were not significantly different (p = 0.17; p = 0.16) and highly correlated (r = 0.95; r = 0.94). The Bland-Altman analysis confirmed a non-significant bias between measured and estimated VO2 (measured: 40.3 ± 10.7, estimated: 40.7 ± 10.1 ml·kg-1·min-1, bias = 1.35 ml·kg-1·min-1, z = 1.12, precision = 3.39 ml·kg-1·min-1) and EE (measured: 20.0 ± 0.05 kcal·kg-1·min-1, estimated: 20.0 ± 0.05 kcal·kg-1·min-1, bias = 0.00 kcal·kg-1·min-1, z = 0.04, precision = 0.02 kcal·kg-1·min-1). Estimated and predicted VO2max were not statistically different (p = 0.91), highly correlated (r = 0.96), and showed a non-significant bias (bias = 0.17, z = 0.22, precision = 1.29 ml·kg-1·min-1). HRindex is a valid field method to track VO2, EE and VO2max during running in rugby players
Respiratory and muscular response to acute non-metabolic fatigue during ramp incremental cycling
No full textWe tested the hypothesis that acute, non-metabolic fatigue, by reducing maximal power output and possibly increasing muscle recruitment at a given exercise intensity, will reduce indexes of exercise tolerance during incremental cycling. Ten subjects performed three ramp incremental tests respectively after static stretching (STRC), dropjumps (DJ) or control (CTRL). Fatigue was assessed as reduction in maximal power output (sprintPO) during isokinetic sprints. During the ramps we measured: oxygen consumption (VO2), power output (PO), and surface electromyography. sprintPO was reduced after STRC and DJ (p = 0.007) yet not after CTRL. During the ramps, the interventions augmented muscle excitation vs CTRL (p ≤ 0.001). Peak PO and VO2 were reduced after STRC (302 ± 39W p = 0.033, 3365 ± 465 ml/min p = 0.015) and DJ (300 ± 37W p = 0.023, 3413 ± 476 ml/min p = 0.094) vs CTRL (314 ± 41W, 3505 ± 486 ml/min). Interventions were associated with early occurrence of the ventilatory thresholds and increased VO2vs CTRL (p = 0.029). The physiological response after acute non-metabolic fatigue suggests a link between exercise intolerance and the decreased ability to produce force
Application and performance of heart-rate-based methods to estimate oxygen consumption at different exercise intensities in postmenopausal women
No full textPurpose: Heart rate (HR) is a widespread method to estimate oxygen consumption ([Formula: see text]O2), exercise intensity, volume, and energy expenditure. Still, accuracy depends on lab tests or using indexes like HRnet and HRindex. This study addresses HR indexes' applicability in postmenopausal women (PMW), who constitute over 50% of the aging population and may have unique characteristics (e.g., heart size) affecting HR use. Methods: Fourteen PMW underwent a cycling ramp incremental test to establish the relationships between [Formula: see text]O2 (in MET) and absolute HR, HRnet, and HRindex. In a second group of ten PMW, population-specific and general equations were tested to predict MET and energy expenditure during six constant work exercises at various intensities. Pulmonary gas exchange and HR were continuously measured using a metabolic cart. Correlations, Bland-Altman analysis, and two-way RM-ANOVA were used to compare estimated and measured values. Results: Strong linear relationships between the three HR indexes and MET were found in Group 1. In Group 2, population-specific equations showed medium-to-high correlations, precision, and no significant biases when estimating MET and energy expenditure. HRnet and HRindex outperformed absolute HR in accuracy. General HR equations had similar correlations but exhibited larger biases and imprecision. Statistical differences between measured and estimated values were observed at all intensities with general equations. Conclusion: This investigation confirms the suitability of HR for estimating aerobic metabolism in one of the most significant aging populations. However, it emphasizes the importance of considering individual variability and developing population-specific models when utilizing HR to infer metabolism
Player's success prediction in rugby union: From youth performance to senior level placing
No full textThe study questioned if and to what extent specific anthropometric and functional characteristics measured in youth draft camps, can accurately predict subsequent career progression in rugby union. DESIGN: Original research. METHODS: Anthropometric and functional characteristics of 531 male players (U16) were retrospectively analysed in relation to senior level team representation at age 21-24. Players were classified as International (Int: National team and international clubs) or National (Nat: 1st, 2nd and other divisions and dropout). Multivariate analysis of variance (one-way MANOVA) tested differences between Int and Nat, along a combination of anthropometric (body mass, height, body fat, fat-free mass) and functional variables (SJ, CMJ, t15m, t30m, VO2max). A discriminant function (DF) was determined to predict group assignment based on the linear combination of variables that best discriminate groups. Correct level assignment was expressed as % hit rate. RESULTS: A combination of anthropometric and functional characteristics reflects future level assignment (Int vs. Nat). Players' success can be accurately predicted (hit rate=81% and 77% for Int and Nat respectively) by a DF that combines anthropometric and functional variables as measured at ∼15 years of age, percent body fat and speed being the most influential predictors of group stratification. CONCLUSIONS: Within a group of 15 year-olds with exceptional physical characteristics, future players' success can be predicted using a linear combination of anthropometric and functional variables, among which a lower percent body fat and higher speed over a 15m sprint provide the most important predictors of the highest career success
Performance and anthropometrics of classic powerlifters: which characteristics matter?
No full textThe purpose of this study is: (a) provide normative performance and anthropometric data of Southern European classic powerlifters of both sexes; (b) determine the possible relationships between these variables and performance; and (c) develop population-specific predictive equations for single lifts and overall powerlifting performance. During an unofficial national-level competition, we recruited 74 athletes (51 men and 23 women) and recorded their individual, anthropometric, and performance characteristics and divided them into sex and 2 performance categories based on their Wilks points. Weaker (<370 Wilks points) and stronger (>370 Wilks points) athletes of both sexes were compared by two-way analysis of variance. Simple correlation and multiple linear regression between individual/anthropometric characteristics and performance were modeled. We applied a step-forward multiple linear regression model to predict single lifts and overall performance. All parameters significantly differed between sexes (p < 0.05 for all comparisons). Stronger male athletes had a significantly larger neck (42 ± 2.8 cm; effect size [ES] = 0.59), and flexed (40.6 ± 3.3 cm; ES = 1.18) and relaxed upper-arm (37.5 ± 3.1 cm; ES = 1.34) and thigh girths (63.6 ± 7.0 cm; ES = 0.77) compared to weaker male athletes. Furthermore, stronger women had significantly larger flexed (32.6 ± 3.3 cm; ES = 0.88) and relaxed upper-arm (33 ± 1.5 cm; ES = 2.28) and chest girths (99.3 ± 9.2 cm; ES = 1.10) compared to weaker female athletes. A combination of experience, fat mass, and upper-limb and lower-limb muscle mass indexes can accurately and precisely predict overall and individual lift performance (r ≥ 0.83 for all the predictions). This is the first study to provide normative performance and anthropometric data in Southern European male and female powerlifters
Attrition in Italian Ranger trainees during special forces training program: a preliminary investigation
No full textPurpose This pilot observational study aimed at determining the causes of attrition during the Italian Army- Rangers training program identifying possible predictor characteristics. Methods In 103 male recruits (26 ± 2 years) we measured anthropometric and functional characteristics and monitored drop-out date and cause in the first 6 months of the program. The possible association of anthropometric or functional parameters with dropout was evaluated (unpaired t-test, successful vs dropouts). Results Anthropometric (body weight 77 ± 7 Kg, stature 178 ± 7 cm, fat mass 12 ± 3 %) and functional characteristics (2 km-run 448 ± 22 s, number of pull-ups 12 ± 3, number of dips 19 ± 5, number of push-ups in 60 s 41 ± 10, number of sit-ups in 60 s 45 ± 5) were similar to those reported in the literature for special forces in Europe. 42 recruits (41 %) abandoned the program, the main cause of dropout being voluntary withdraw for personal reasons (60 %); 30 % of recruits were excluded from the program for medical reasons; 10 % for technical reasons (e.g. fail of technical exams or physical requirements, discipline issues). Significant differences between successful and dropout groups were detected only in % of body fat (11 ± 3 vs 13 ± 3) and the number of pull-ups (12 ± 3 vs 11 ± 4). Conclusions Ours are the first available data on Italian Army-Ranger trainees. Our data suggest targeting individual motivation, self-efficacy and resilience upon admittance to the program as potential factors affecting dropout for personal reasons. Furthermore, optimal physical preparation practices (including gradual overload and injury prevention strategies) and optimal medical treatment could potentially reduce attrition for medical and technical reasons
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