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Record W1986491914 · doi:10.1519/jsc.0b013e31818dc44e

Parasympathetic Modulation and Running Performance in Distance Runners

2009· article· en· W1986491914 on OpenAlexfundaboutno aff
Daniel Boullosa, José Tuimil, Anthony S. Leicht, Juan J Crespo-Salgado

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2009
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
FundersUniversité de Montréal
KeywordsHeart rateMedicineCardiologyPhysical therapyInternal medicinePsychologyPhysical medicine and rehabilitationBlood pressure

Abstract

fetched live from OpenAlex

This study examined the relationships between basal heart rate (BHR) and heart rate recovery (HRR), parasympathetic modulation parameters, with running performance in distance runners. It was hypothesized that greater parasympathetic modulation would be significantly associated with greater running performance. Twelve well-trained endurance runners (23.2 +/- 3.3 years; 175.6 +/- 5.8 cm; 65.2 +/- 6.7 kg) performed the Université de Montréal Track Test (UMTT) until volitional exhaustion (total final time, TUMTT), with the highest completed stage recorded as the maximal aerobic speed (MAS). More than 48 hours afterwards, participants ran at the MAS until volitional exhaustion, with maximal running time (Tlim) recorded. Maximum heart rate was significantly greater for the UMTT compared with Tlim (p = 0.004). Significant correlations were exhibited between MAS and BHR (r = -0.845, p = 0.001); mean drop in heart rate at the first minute of recovery after the UMTT (r = 0.617, p = 0.033) and Tlim (r = 0.787, p = 0.002); and mean drop in heart rate at the second minute of recovery after the UMTT (r = 0.630, p = 0.028). These results support previous reports that endurance training results in greater running performance and greater parasympathetic modulation before and after exercise. We suggest that coaches consider HRR and BHR for the monitoring of training for endurance performance.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.027
GPT teacher head0.324
Teacher spread0.298 · 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

Citations32
Published2009
Admission routes2
Has abstractyes

Explore more

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