1,721,005 research outputs found
Mechanical and metabolic power in accelerated running-Part II: team sports
No full textPurpose: This manuscript is devoted to discuss the interplay between velocity and acceleration in setting metabolic and mechanical power in team sports. Methods: To this aim, an essential step is to assess the individual Acceleration-Speed Profile (ASP) by appropriately analysing training sessions or matches. This allows one to estimate maximal mechanical and metabolic power, including that for running at constant speed, and hence to determine individual thresholds thereof. Results: Several approaches are described and the results, as obtained from 38 official matches of one team (Italian Serie B, season 2020-2021), are reported and discussed. The number of events in which the external mechanical power exceeded 80% of that estimated from the subject's ASP ([Formula: see text]) was 1.61 times larger than the number of accelerations above 2.5 m s-2 ([Formula: see text]). The difference was largest for midfielders and smallest for attackers (2.30 and 1.36 times, respectively) due to (i) a higher starting velocity for midfielders and (ii) a higher external peak power for attackers in performing [Formula: see text]. From the energetic perspective, the duration and the corresponding metabolic power of high-demanding phases ([Formula: see text]) were essentially constant (6 s and 22 W kg-1, respectively) from the beginning to the end of the match, even if their number decreased from 28 in the first to 21 in the last 15-min period, as a consequence of the increased recovery time between [Formula: see text] from 26 s in the first to 37 s in the last 15-min period. Conclusion: These data underline the flaws of acceleration counting above fixed thresholds
Mechanical and metabolic power in accelerated running-PART I: the 100-m dash
No full textPurpose: Acceleration phases require additional mechanical and metabolic power, over and above that for running at constant velocity. The present study is devoted to a paradigmatic example: the 100-m dash, in which case the forward acceleration is very high initially and decreases progressively to become negligible during the central and final phases. Methods: The mechanical ([Formula: see text]) and metabolic ([Formula: see text]) power were analysed for both Bolt's extant world record and for medium level sprinters. Results: In the case of Bolt, [Formula: see text] and [Formula: see text] attain peaks of ≈ 35 and ≈ 140 W kg-1 after ≈ 1 s, when the velocity is ≈ 5.5 m s-1; they decrease substantially thereafter, to attain constant values equal to those required for running at constant speed (≈ 18 and ≈ 65 W kg-1) after ≈ 6 s, when the velocity has reached its maximum (≈ 12 m s-1) and the acceleration is nil. At variance with [Formula: see text], the power required to move the limbs in respect to the centre of mass (internal power, [Formula: see text]) increases gradually to reach, after ≈ 6 s a constant value of ≈ 33 W kg-1. As a consequence, [Formula: see text] ([Formula: see text]) increases throughout the run to a constant value of ≈ 50 W kg-1. In the case of the medium level sprinters, the general patterns of speed, mechanical and metabolic power, neglecting the corresponding absolute values, follow an essentially equal trend. Conclusion: Hence, whereas in the last part of the run the velocity is about twice that observed after ≈ 1 s, [Formula: see text] and [Formula: see text] are reduced to 45-50% of the peak values
Reply to Ettema letter: "Incorporating internal work leads to overestimations of total work in sprint running"
No full text[no abstract available
Force-Velocity Profiling in Sled-Resisted Sprint Running: Determining the Optimal Conditions for Maximizing Power
No full textThe measurement of power-velocity and force-velocity relationships offers valuable insight into athletic capabilities. The qualities underlying maximum power (i.e. optimal loading conditions) are of particular interest in individualized training prescription and the enhanced development of explosive performance. While research has examined these themes using cycle ergometers and specialized treadmills, the conditions for optimal loading during over-ground sprint running have not been quantified. This thesis aimed to assess whether force-velocity-power relationships and optimal loading conditions could be profiled using a sled-resisted multiple-trial method overground, if these characteristics differentiate between recreational athletes and highly-trained sprinters, and whether conditions for optimal loading could be determined from a single sprint. Consequently, this required understanding of the friction characteristics underlying sled-resisted sprint kinetics.
Chapter 3 presents a method of assessing these characteristics by dragging an instrumented sled at varying velocities and masses to find the conversion of normal force to friction force (coefficient of friction). Methods were reliable (intraclass correlation [ICC]>0.99; coefficient of variation [CV]<4.3%) and showed the coefficient of friction was dependent on sled towing velocity, rather than normal load. The ‘coefficient of friction-velocity’ relationship was plotted by a 2nd order polynomial regression (R²=0.999; P<0.001), with the subsequent equation presented for application in sled-resisted sprinting. Chapter 4 implements these findings, using multiple trials (6-7) of sled-resisted sprints to generate individual force-velocity and power-velocity relationships for recreational athletes (N=12) and sprinters (N=15). Data were very well fitted with linear and quadratic equations, respectively (R²=0.977-0.997; P<0.001), with all associated variables reliable (effect size [ES]=0.05-0.50; ICC=0.73-0.97; CV=1.0-5.4%). The normal loads that maximized power (mean±SD) were 78±6 and 82±8% of bodymass, representing an optimal force of 279±46 and 283±32 N at 4.19±0.19 and 4.90±0.18 m.s-1, for recreational and sprint athletes respectively. Sprinters demonstrated greater absolute and relative maximal power (17.2-26.5%; ES=0.97-2.13; P<0.02; likely), with much greater velocity production (maximum theoretical velocity, 16.8%; ES=3.66; P<0.001; most likely).
Optimal force and normal loading did not clearly differentiate between groups (unclear and likely small differences; P>0.05), and sprinters developed maximal power at much higher velocities (16.9%; ES=3.73; P<0.001; most likely). The optimal loading conditions for maximizing power appear individualized (range=69-96% of body-mass), and represent much greater resistance than current guidelines. Chapter 5 investigated the ability of a single sprint to predict optimal sled loading, using identical methods to Chapter 4 and a recently validated profiling technique using a single unloaded sprint. Power and maximal force were strongly correlated (r=0.71-0.86), albeit with moderate to large error scores (standardized typical error estimate [TEE]=0.53-0.71). Similar trends were observed in relative and absolute optimal force (r=0.50-0.72; TEE=0.71-0.88), with estimated optimal normal loading practically incomparable (bias=0.78-5.42 kg; r=0.70; TEE=0.73). However optimal velocity, and associated maximal velocity, were well matched between the methods (r=0.99; bias=0.4-1.4% or 0.00-0.04 m.s-1; TEE=0.12); highlighting a single sprint could conceivably be used to calculate the velocity for maximizing horizontal power in sled sprinting. Given the prevalence of resisted sprinting, practitioners and researchers should consider adopting these methods for individualized prescription of training loads for improved horizontal power and subsequent sprinting performance
Force-velocity Profiling in Sled-resisted Sprint Running: Determining the Optimal Conditions for Maximizing Power
No full textThe measurement of power-velocity and force-velocity relationships offers valuable insight
into athletic capabilities. The qualities underlying maximum power (i.e. optimal loading
conditions) are of particular interest in individualized training prescription and the enhanced
development of explosive performance. While research has examined these themes using cycle
ergometers and specialized treadmills, the conditions for optimal loading during over-ground
sprint running have not been quantified. This thesis aimed to assess whether force-velocitypower
relationships and optimal loading conditions could be profiled using a sled-resisted
multiple-trial method overground, if these characteristics differentiate between recreational
athletes and highly-trained sprinters, and whether conditions for optimal loading could be
determined from a single sprint. Consequently, this required understanding of the friction
characteristics underlying sled-resisted sprint kinetics. Chapter 3 presents a method of
assessing these characteristics by dragging an instrumented sled at varying velocities and
masses to find the conversion of normal force to friction force (coefficient of friction).
Methods were reliable (intraclass correlation [ICC]>0.99; coefficient of variation
[CV]<4.3%) and showed the coefficient of friction was dependent on sled towing velocity,
rather than normal load. The ‘coefficient of friction-velocity’ relationship was plotted by a
2nd order polynomial regression (R²=0.999; P<0.001), with the subsequent equation
presented for application in sled-resisted sprinting. Chapter 4 implements these findings,
using multiple trials (6-7) of sled-resisted sprints to generate individual force-velocity and
power-velocity relationships for recreational athletes (N=12) and sprinters (N=15). Data were
very well fitted with linear and quadratic equations, respectively (R²=0.977-0.997; P<0.001),
with all associated variables reliable (effect size [ES]=0.05-0.50; ICC=0.73-0.97; CV=1.0-
5.4%). The normal loads that maximized power (mean±SD) were 78±6 and 82±8% of bodymass,
representing an optimal force of 279±46 and 283±32 N at 4.19±0.19 and 4.90±0.18
m.s-1, for recreational and sprint athletes respectively. Sprinters demonstrated greater absolute
and relative maximal power (17.2-26.5%; ES=0.97-2.13; P<0.02; likely), with much greater
velocity production (maximum theoretical velocity, 16.8%; ES=3.66; P<0.001; most likely).
Optimal force and normal loading did not clearly differentiate between groups (unclear and
likely small differences; P>0.05), and sprinters developed maximal power at much higher
velocities (16.9%; ES=3.73; P<0.001; most likely). The optimal loading conditions for
maximizing power appear individualized (range=69-96% of body-mass), and represent much
greater resistance than current guidelines. Chapter 5 investigated the ability of a single sprint
to predict optimal sled loading, using identical methods to Chapter 4 and a recently validated
profiling technique using a single unloaded sprint. Power and maximal force were strongly
correlated (r=0.71-0.86), albeit with moderate to large error scores (standardized typical error
estimate [TEE]=0.53-0.71). Similar trends were observed in relative and absolute optimal
force (r=0.50-0.72; TEE=0.71-0.88), with estimated optimal normal loading practically
incomparable (bias=0.78-5.42 kg; r=0.70; TEE=0.73). However optimal velocity, and
associated maximal velocity, were well matched between the methods (r=0.99; bias=0.4-1.4%
or 0.00-0.04 m.s-1; TEE=0.12); highlighting a single sprint could conceivably be used to
calculate the velocity for maximizing horizontal power in sled sprinting. Given the prevalence
of resisted sprinting, practitioners and researchers should consider adopting these methods
for individualized prescription of training loads for improved horizontal power and
subsequent sprinting performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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