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Record W3017017719 · doi:10.4236/ape.2020.102008

Influencing Factors of Pacing Variations and Performance in a 44-Kilometer Mountain Trail Race

2020· article· en· W3017017719 on OpenAlexaff
Alain Groslambert, Bertrand Baron, Théo Ouvrard, L. Desmoulins, E Lacroix, Philippe Gimenez, Sidney Grosprêtre, Fred Grappe

Bibliographic record

VenueAdvances in Physical Education · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSprintPerceived exertionHeart rateTreadmillAnimal scienceMathematicsPhysical therapyMedicinePsychologyPhysicsInternal medicineBiologyBlood pressure

Abstract

fetched live from OpenAlex

Objective: This study aimed to analyse the changes in the biomechanical and psychophysiological responses, and the body mass of trailers in a small-distance (44 km and 1520 m elevation gain) trail competition performed in tropical conditions. Methods: Ten trained trailers (8 men, 2 females; age: 42.0 ± 5 years, body mass: 65.57 ± 5.4 kg, height: 174.9 ± 5.9 cm BMI: 21.71 ± 2.1, Maximal Aerobic Speed (MAS): 16.6 ± 2.1 km·h-1) volunteered to take part in the competition, comprising eight laps of 5.5 km. At the end of each lap, the trailers had to stop for 10 min to perform tests measuring 1) the maximal horizontal force (F0), theoretical maximal running velocity (V0) and maximal power output (PO) during a 30-m sprint; 2) the vertical oscillations and maximal relative force during a 30-s treadmill submaximal run; 3) the perceived exertion and pleasure; and 4) body mass. The pacing, stride variations and heart rate were continuously recorded during the race. Results: The variations of PO (W·kg-1) during the 30-m sprint and perceived pleasure were significant (p = 0.003 and p = 0.02, respectively) influencing factors of pacing. A significant decrease (p the first and last laps. Fraction of MAS and MAS were significantly (p = 0.004 and p = 0.04, respectively) related to the trail performance. Conclusions: Training programmes could be proposed that include the increase of MAS, fraction of MAS and lower limb PO. During the competition, it could be interesting to plan a drinking programme to avoid potential thermoregulatory impairment, as well as psychological strategies to increase pleasure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.309
Teacher spread0.295 · 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 teacher head, 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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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