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Record W2471527330

Energetics and metabolic economy of cross country skiing

2013· article· en· W2471527330 on OpenAlexaffvenue
Kevin Boldt

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2013
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCross countryPolingBreathingEnergeticsMathematicsMetabolic costPhysical medicine and rehabilitationAnimal scienceAnatomyPhysicsMedicineEngineeringBiologyEconomicsThermodynamicsElectrical engineeringDemographic economics
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION When using the two-skate technique, cross-country skiers move their skis in a skating motion while pushing with both poles simultaneously on every second footfall. Skiers often prefer to pole with either the left or right ski while synchronising their breathing to the movement of the poles. A locomotion-respiration coupling has been well observed when quadruped animals increase running speed [1]. That is, as the forelimbs are drawn back the animal exhales, and as the animal’s forelimbs extend the animal inhales [2]. A similar phenomenon has also been observed in cross-country skiers. Despite being very predictable, the effect of this coupling between poling and breathing on efficiency is unknown. The purpose of the present study was to evaluate the effect of reverse breathing and reverse poling techniques on efficiency and force production in skate cross-country skiing. We expect that skiers will be less efficient when poling on their non-preferred side and when breathing in a reversed pattern. METHODS Skiers (n=10) roller skied at a constant sub-threshold speed for four conditions each lasting four minutes. Metabolic efficiency was determined using the rate of oxygen uptake (O 2 ), where a higher O 2 indicates higher energy consumption and therefore lower efficiency. Force data were collected via strain gages in the poles and roller skis (128Hz). The first and last conditions, which acted as the controls, consisted of the athlete breathing normally and poling on their preferred side. In the second condition the athletes were asked to pole on their non-preferred side while maintaining normal breathing patterns. For the third condition, the athletes returned to poling on their preferred side while they reversed their breathing pattern. RESULTS O 2 increased significantly when the skiers switched from their preferred skiing technique to both the reverse poling ( p =0.047) and reverse breathing conditions ( p =0.097) (Table 1). The second control condition was not significantly different than any of the other conditions (Table 1). Impulse and timing were calculated from the forces measured in the poles and skis. When compared to the control condition, skiers mirrored their impulse production from each limb when poling was reversed. As such, there were no differences in impulse, mean cycle time, mean contact time, or mean limb recovery time when calculated relative to the poling side. Figure 1. Mean rate of oxygen consumption for all conditions. (SE=±1, n=9). DISCUSSION AND CONCLUSIONS The increase in O 2 for reverse poling and reverse breathing indicates that these two conditions are less metabolically efficient than the control condition. Previous work on respiration-locomotion coupling identified changes in abdominal pressure resulting from movement [3]. As a result, mechanical alteration of intra-abdominal pressure contributes to breathing, allowing the active respiratory muscles to perform less work and thus require less metabolic energy [1].  The reversal of this phenomenon may explain the decrease in efficiency observed for the reverse breathing condition. Force data from the reverse poling technique revealed no changes in gait mechanics. As cross-country skiing is a cyclical task, it is possible that despite symmetrical forces, there may have been subtle differences in technique that were not identified in this study. Future research should use electromyography to further examine changes in gait for reverse poling. REFERENCES Bramble &Jenkins, Sci. 262 (5131), 1993. Art et al., J. Vet. Med. 37 (10), 1990. Daffertshofer et al., Biol. Cybern. 90 , 2004.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.377
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2013
Admission routes2
Has abstractyes

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