Energy Cost and Stride Pattern Variability of Elite Runners on the Treadmill (original) (raw)
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Energetically optimal stride frequency in running: the effects of incline and decline
The Journal of experimental biology, 2011
At a given running speed, humans strongly prefer to use a stride frequency near their 'optimal' stride frequency that minimizes metabolic cost. Although there is no definitive explanation for why an optimal stride frequency exists, elastic energy usage has been implicated. Because the possibility for elastic energy storage and return may be impaired on slopes, we investigated whether and how the optimal stride frequency changes during uphill and downhill running. Presuming a smaller role of elastic energy, we hypothesized that altering stride frequency would change metabolic cost less during uphill and downhill running than during level running. To test this hypothesis, we collected force and metabolic data as nine male subjects ran at 2.8 m s(-1) on the level, 3 deg uphill and 3 deg downhill. Stride frequency was systematically varied above and below preferred stride frequency (PSF ±8% and ±15%). Ground reaction force data were used to calculate potential, kinetic and total...
Stride frequency in relation to oxygen consumption in experienced and novice runners
European Journal of Sport Science, 2014
We hypothesised that experienced runners would select a stride frequency closer to the optimum (minimal energy costs) than would novice runners. In addition, we expected that optimal stride frequency could simply be determined by monitoring heart rate without measuring oxygen consumption ( _ V O 2 ). Ten healthy males (mean9s: 2492 year) with no running training experience and 10 trained runners of similar age ran at constant treadmill speed corresponding to 80% of individual ventilatory threshold. For two days, they ran at seven different stride frequencies (self-selected stride frequency9 18%) imposed by a metronome. Optimal stride frequency was based on the minimum of a second-order polynomial equation fitted through steady state _ V O 2 at each stride frequency. Running cost (mean9s) at optimal stride frequency was higher (P B0.05) in novice (236931 ml O 2 ×kg (1. km (1 ) than trained (189913 ml O 2 ×kg (1. km (1 ) runners. Self-selected stride frequency (mean9s; strides . min (1 ) for novice (77.892.8) and trained runners (84.495.3) were lower (P B0.05) than optimal stride frequency (respectively, 84.995.0 and 87.194.8). The difference between self-selected and optimal stride frequency was smaller (P B0.05) for trained runners. In both the groups optimal stride frequency established with heart rate was not different (P 0.3) from optimal stride frequency based on _ V O 2 . In each group and despite limited variation between participants, optimal stride frequencies derived from _ V O 2 and heart rate were related (r 0.7; P B0.05). In conclusion, trained runners chose a stride frequency closer to the optimum for energy expenditure than novices. Heart rate could be used to establish optimal stride frequency.
A study was done to compare energy expenditure per unit time for a group of students doing exercises on Treadmill and Track while running and walking. The effect of body mass on expenditure of energy in unit time was compared while running on Track and Treadmill and while walking on Track and Treadmill. The effect of speed on energy expenditure per unit time was also studied. The study reveals energy expenditure per unit time in the same person was more on Track walking and Track running when compared with Treadmill walking and Treadmill running in the study group taken. It also revealed that increased energy expenditure was necessary with increased body mass in both Track and Treadmill running and walking. When there is increase in the speed of the exercise, there was increased energy expenditure per unit time.
Metabolic Power, Time of Foot Contact, and Cost Coefficient During Grade Running
Medicine and Science in Sports and Exercise, 1998
Trained endurance runners appear to fine-tune running mechanics to minimize metabolic cost. Referred to as self-optimization, the support for this concept has primarily been collated from only a few gait (e.g., stride frequency, length) and physiological (e.g., oxygen consumption, heart rate) characteristics. To extend our understanding, the aim of this study was to examine the effect of manipulating ground contact time on the metabolic cost of running in trained endurance runners. Additionally, the relationships between metabolic cost, and leg stiffness and perceived effort were examined. Ten participants completed 5 × 6-min treadmill running conditions. Self-selected ground contact time and step frequency were determined during habitual running, which was followed by ground contact times being increased or decreased in four subsequent conditions whilst maintaining step frequency (2.67 ± 0.15 Hz). The same self-selected running velocity was used across all conditions for each participant (12.7 ± 1.6 km • h −1). Oxygen consumption was used to compute the metabolic cost of running and ratings of perceived exertion (RPE) were recorded for each run. Ground contact time and step frequency were used to estimate leg stiffness. Identifiable minimums and a curvilinear relationship between ground contact time and metabolic cost was found for all runners (r 2 = 0.84). A similar relationship was observed between leg stiffness and metabolic cost (r 2 = 0.83). Most (90%) runners self-selected a ground contact time and leg stiffness that produced metabolic costs within 5% of their mathematical optimal. The majority (n = 6) of self-selected ground contact times were shorter than mathematical optimals, whilst the majority (n = 7) of self-selected leg stiffness' were higher than mathematical optimals. Metabolic cost and RPE were moderately associated (r s = 0.358 p = 0.011), but controlling for condition (habitual/manipulated) weakened this relationship (r s = 0.302, p = 0.035). Both ground contact time and leg stiffness appear to be self-optimized characteristics, as trained runners were operating at or close to their mathematical optimal. The majority of runners favored a self-selected gait that may rely on elastic energy storage and release due to shorter ground contact times and higher leg stiffness's than optimal. Using RPE as a surrogate measure of metabolic cost during manipulated running gait is not recommended.
2021
Purpose The objective of this study was to investigate the changes in metabolic variables, running energetics and spatiotemporal gait parameters during an 80.5 km treadmill ultramarathon and establish which key predictive variables best determine ultramarathon performance. Methods Twelve participants (9 male and 3 female, age 34 ± 7 years, and maximal oxygen uptake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\dot{V}$$\end{document}V˙O2max) 60.4 ± 5.8 ml·kg−1·min−1) completed an 80.5 km time trial on a motorised treadmill in the fastest possible time. Metabolic variables: oxygen consumption (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} ...
The Effects of Running Speed on the Metabolic and Mechanical Energy Costs of Running
2000
THE EFFECTS OF RUNNING SPEED ON THE METABOLIC AND MECHANICAL ENERGY COSTS OF RUNNING. Chad Harris, Mark Debeliso, Kent J. Adams. JEPonline. 2003;6(3):28-37. This study assessed the influence of speed on the metabolic and the mechanical cost to run a given distance. Trained male runners (n=12) performed 2 treadmill run trials of 8 min duration at each of 6 speeds
The Effect Of Stride Frequency Variations On Running Performance At The Velocity Of Vo2Max
Medicine & Science in Sports & Exercise
Running economy(RE) is considered to be a critical factor to improve running performance. Stride frequency(SF) is an important variable for determining RE. The importance of SF has gained more attention in recent years, especially for recreational runners. However, no previous research has investigated the interaction between running performance and SF at the velocity of VO2max. PURPOSE: To investigate the effect of five different SF variations on running performance until volitional fatigue at the velocity of VO2max. METHODS: Fourteen male recreational runners (Age = 25.8 ± 4.96 years, Height = 171 ± 6.2cm, Body Mass = 71.9 ± 7.5kg) measured VO2max (54 ± 5.6 ml/kg/min) and preferred stride frequency (PSF; 89.3 ± 4 / min) through a graded exercise test (GXT) and running session, respectively. Running speed was determined based on each individual's VO2max via the metabolic equation for gross VO2 in metric units by ACSM. Participants ran on the treadmill (0% grade) with five SF conditions (PSF, ±5%, ±10%) until time to exhaustion. Data were analyzed using a one way ANOVA with repeated measures and Tukey HSD post hoc. RESULTS: The total running performance (time, distance), energy expenditures (kcal), and oxygen consumption (VO2) were statistically significant among SF variations (p<0.05). Additionally, the respiratory exchange ratio iii (RER), respiratory rate (RR), and ventilation (VE) were no statistically significant (p>0.05). CONCLUSION: The SF variations have a significant influence on running performance. The relationship between SF variations and other variables (RER, RR, VE) were possibly related to the central governor theory to delay the onset of fatigue. These results suggest that recreational runners could use a 105% of PSF to improve running performance with the better RE.
Journal of Applied Physiology, 2013
Although most of the literature on locomotion energetics and biomechanics is about constant-speed experiments, humans and animals tend to move at variable speeds in their daily life. This study addresses the following questions: 1) how much extra metabolic energy is associated with traveling a unit distance by adopting acceleration/deceleration cycles in walking and running, with respect to constant speed, and 2) how can biomechanics explain those metabolic findings. Ten males and ten females walked and ran at fluctuating speeds (5 Ϯ 0, Ϯ 1, Ϯ 1.5, Ϯ 2, Ϯ 2.5 km/h for treadmill walking, 11 Ϯ 0, Ϯ 1, Ϯ 2, Ϯ 3, Ϯ 4 km/h for treadmill and field running) in cycles lasting 6 s. Field experiments, consisting of subjects following a laser spot projected from a computer-controlled astronomic telescope, were necessary to check the noninertial bias of the oscillating-speed treadmill. Metabolic cost of transport was found to be almost constant at all speed oscillations for running and up to Ϯ2 km/h for walking, with no remarkable differences between laboratory and field results. The substantial constancy of the metabolic cost is not explained by the predicted cost of pure acceleration/deceleration. As for walking, results from speed-oscillation running suggest that the inherent within-stride, elastic energy-free accelerations/decelerations when moving at constant speed work as a mechanical buffer for among-stride speed fluctuations, with no extra metabolic cost. Also, a recent theory about the analogy between sprint (level) running and constant-speed running on gradients, together with the mechanical determinants of gradient locomotion, helps to interpret the present findings. running economy; speed oscillation Address for reprint requests and other correspondence:
Journal of Strength and Conditioning Research, 2014
The relationships between 30-m sprint time and performance on a nonmotorized treadmill (TM) test and a vertical jump test were determined in this investigation. Seventy-eight physically active men and women (22.9 6 2.7 years; 73.0 6 14.7 kg; 170.7 6 10.4 cm) performed a 30-second maximal sprint on the curve nonmotorized TM after 1 familiarization trial. Pearson product-moment correlation coefficients produced significant (p # 0.05) moderate to very strong relationships between 30-m sprint time and body mass (r = 20.37), %fat (r = 0.79), peak power (PP) (r = 20.59), relative PP (r = 20.42), time to peak velocity (r = 20.23) and TM sprint times at 10 m (r = 0.48), 20 m (r = 0.59), 30 m (r = 0.67), 40 m (r = 0.71), and 50 m (r = 0.75). Strong relationships between 30-m sprint time and peak (r = 20.479) and mean vertical jump power (r = 20.559) were also observed. Subsequently, stepwise regression was used to produce two 30-m sprint time prediction models from TM performance (TM1: body mass + TM data and TM2: body composition + TM data) in a validation group (n = 39), and then crossvalidated against another group (n = 39). As no significant differences were observed between these groups, data were combined (n = 72) and used to create the final prediction models (TM1: r 2 = 0.75, standard error of the estimate (SEE) = 0.27 seconds; TM2: r 2 = 0.84, SEE = 0.22 seconds). These final movementspecific models seem to be more accurate in predicting 30-m sprint time than derived peak (r 2 = 0.23, SEE = 0.48 seconds) and mean vertical jump power (r 2 = 0.31, SEE = 0.45 seconds) equations. Consequently, sprinting performance on the TM can significantly predict short-distance sprint time. It, therefore, may be used to obtain movement-specific measures of sprinting force, velocity, and power in a controlled environment from a single 30-second maximal sprinting test.