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Exercise Tolerance: New Insights From Single-leg Cycling Exercise

2019· article· en· W2954523863 on OpenAlexaff
Danilo Iannetta, Ahmad Qahtani, Louis Passfield, Martin J. MacInnis, Juan M. Murias

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCyclingIncremental exerciseCardiologyLeg muscleAnaerobic exerciseVO2 maxBlood lactateInternal medicineMedicineHeart ratePhysical therapyPhysical medicine and rehabilitationBlood pressure

Abstract

fetched live from OpenAlex

The biological factors determining the maximal exercise capacity are typically assessed during whole-body exercise (e.g. double-leg cycling), implicitly assuming that limbs contribute homogeneously to exercise tolerance. However, given the presence of limb dominance, it is possible that the dominant leg may achieve greater peak O2 uptake (V̇O2peak) and be able to sustain greater power outputs during prolonged dynamic exercise compared to the non-dominant leg. PURPOSE: To investigate peak power output (PPO), V̇O2peak, and maximal lactate steady-state (MLSS) during double-leg as well as during dominant and non-dominant and counter-weighted single-leg cycling exercise performed with the dominant and non-dominant legs. METHODS: Twelve men (30 ± 5 yrs) during 12 to 14 lab visits performed: (i) a ramp-incremental test to determine PPO, V̇O2peak, and maximal O2 extraction; and (ii) 30-min constant-load tests to determine MLSS. These tests were performed using both legs (DBL), the dominant leg only (SLd), and the non-dominant leg only (SLnd). Gas exchange variables were measured with a metabolic cart; local de-oxygenation ([HHb]) of the vastus lateralis (VL) was measured using a frequency-domain NIRS; capillary blood samples were analysed for lactate concentration ([Lac-]b). RESULTS: PPO for DBL, SLd, and SLnd was different in each condition: 329 ± 38, 181 ± 30, 168 ± 27 W, respectively (p < 0.05). V̇O2peak for DBL, SLd, and SLnd was different in each condition: 3.43 ± 0.34, 2.92 ± 0.42, 2.74 ± 0.38 L·min-1, respectively (p < 0.05). The [HHb] amplitude of the VL was greater in the dominant compared to the non-dominant leg during both DBL (18.6 ± 8.5 vs 15.4 ± 9.5 mMol) and SL (15.4 ± 9.5 vs 11.6 ± 7.7 mMol) ramp-exercise (p < 0.05). These amplitudes were highly correlated with the V̇O2peak values observed during DBL dominant and non-dominant (r = 0.86 and r=0.91, respectively), SLd (r=0.79), and SLnd (r=0.71) RI tests (p < 0.05). The PO at MLSS for DBL, SLd, and SLnd was different in each condition: 183 ± 32, 119 ± 25, and 111 ± 24 W, respectively (p < 0.05). The V̇O2, [Lac-]b, and RPE values during SLnd and SLd were similar (p > 0.05) despite this lower PO. CONCLUSIONS: These data indicate a heterogeneous exercise capacity of the exercising limbs that should be considered when evaluating exercise tolerance during double-leg exercise.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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
Published2019
Admission routes1
Has abstractyes

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