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Record W4407395103 · doi:10.1123/ijspp.2024-0291

Association Between Reactive Strength Index and Metabolic Cost of Running in Male Master Athletes

2025· article· en· W4407395103 on OpenAlexaff
Maxence Tanneau, Nicolas Berryman, Iñigo Mujika, Anh Phong Nguyen, Darío F Cappa, Jeanne Barraud, Laurent Bosquet

Bibliographic record

VenueInternational Journal of Sports Physiology and Performance · 2025
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAthletesMedicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To examine the association between reactive strength index (RSI) and metabolic cost of running (MCr) in master athletes. We hypothesized that individuals with a higher RSI would exhibit a lower MCr, that both variables would be associated, and that age would moderate this relationship. METHODS: Forty-five male master athletes participated to this study. A median split was performed to constitute 2 age groups (group 1, 47.7 [1.7] y old; group 2, 56.3 [4.6] y old). Participants underwent a single 45-minute session during which RSI was assessed with drop jumps, and MCr, during a continuous exercise of submaximal intensity performed on a treadmill. RESULTS: A small negative association was found between net MCr and RSI in the overall sample (RSI mean r = -.38, P < .05; RSI max r = -.38, P < .05). Group 1 exhibited a lower net MCr (P < .01, d = -0.84) and higher RSI (P < .01, d > 0.82) than group 2. However, the association between the 2 measures was verified specifically in group 2 (RSI from a 20-cm drop jump, r = -.53, P < .01; RSI mean, r = -.50, P < .05; RSI maximum, r = -.57, P < .01). CONCLUSIONS: RSI is associated with net MCR in master athletes, particularly in older participants. Additional intervention studies are now required to further explore this association.

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 score0.031
Threshold uncertainty score0.257

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 communication0.0000.000
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 head0.014
GPT teacher head0.282
Teacher spread0.268 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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