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Establishing the V<scp>o</scp><sub>2</sub> versus constant-work-rate relationship from ramp-incremental exercise: simple strategies for an unsolved problem

2019· article· en· W2978959867 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity Health NetworkUniversity of Calgary
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsWork rateRespiratory compensationWork (physics)Constant (computer programming)ChemistryReaction rate constantAnimal scienceMathematicsSteady state (chemistry)Time constantAnalytical Chemistry (journal)Heart rateAnaerobic exerciseThermodynamicsPhysicsKineticsInternal medicineMedicineBiologyPhysical therapyChromatographyPhysical chemistryComputer science

Abstract

fetched live from OpenAlex

The dissociation between constant work rate of O 2 uptake (V̇o 2 ) and ramp V̇o 2 at a given work rate might be mitigated during slowly increasing ramp protocols. This study characterized the V̇o 2 dynamics in response to five different ramp protocols and constant-work-rate trials at the maximal metabolic steady state (MMSS) to characterize 1) the V̇o 2 gain (G) in the moderate, heavy, and severe domains, 2) the mean response time of V̇o 2 (MRT), and 3) the work rates at lactate threshold (LT) and respiratory compensation point (RCP). Eleven young individuals performed five ramp tests (5, 10, 15, 25, and 30 W/min), four to five time-to-exhaustions for critical power estimation, and two to three constant-work-rate trials for confirmation of the work rate at MMSS. G was greatest during the slowest ramp and progressively decreased with increasing ramp slopes (from ~12 to ~8 ml·min −1 ·W −1 , P &lt; 0.05). The MRT was smallest during the slowest ramp slopes and progressively increased with faster ramp slopes (1 ± 1, 2 ± 1, 5 ± 3, and 10 ± 4, 15 ± 6 W, P &lt; 0.05). After “left shifting” the ramp V̇o 2 by the MRT, the work rate at LT was constant regardless of the ramp slope (~150 W, P &gt; 0.05). The work rate at MMSS was 215 ± 55 W and was similar and highly correlated with the work rate at RCP during the 5 W/min ramp ( P &gt; 0.05, r = 0.99; Lin’s concordance coefficient = 0.99; bias = −3 W; root mean square error = 6 W). Findings showed that the dynamics of V̇o 2 (i.e., G) during ramp exercise explain the apparent dichotomy existing with constant-work-rate exercise. When these dynamics are appropriately “resolved”, LT is constant regardless of the ramp slope of choice, and RCP and MMSS display minimal variations between each other. NEW &amp; NOTEWORTHY This study demonstrates that the dynamics of V̇o 2 during ramp-incremental exercise are dependent on the characteristics of the increments in work rate, such that during slow-incrementing ramp protocols the magnitude of the dissociation between ramp V̇o 2 and constant V̇o 2 at a given work rate is reduced. Accurately accounting for these dynamics ensures correct characterizations of the V̇o 2 kinetics at ramp onset and allows appropriate comparisons between ramp and constant-work-rate exercise-derived indexes of exercise intensity.

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.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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