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The Influence Of Height On Maximal Strength Relative To Lean Body Mass Among Strength Athletes.

2021· article· en· W3178694611 on OpenAlexaff
Alexander C. Pollock, Thomas Saïssi, Pierre-Marc Ferland, Alain Steve Comtois

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2021
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsLean body massBench pressSquatMathematicsLift (data mining)AnthropometryAnimal scienceAthletesStrength trainingPhysical therapyFootballMedicineBody weightResistance trainingInternal medicineBiologyComputer scienceGeography

Abstract

fetched live from OpenAlex

Previous studies have brought to attention the relationship between lean muscle mass and maximal strength in classic male powerlifters. PURPOSE: To evaluate the influence of height on maximal strength relative to lean body mass (MaxREL) among male powerlifters and American football players in the squat, the bench press, the deadlift, and the total. METHODS: Eighteen male junior drug-tested classic powerlifters (age: 21.2 ± 1.2 years, height: 174.1 ± 7 cm, body mass: 83.2 ± 12.4 kg, lean body mass: 68.3 ± 9.1 kg, squat: 199.9 ± 32.8 kg, bench press: 126.9 ± 20.3 kg, deadlift: 229.6 ± 33.3 kg, total: 556.4 ± 83 kg) and seventeen NCAA Division II American football players (age: 20.3 ± 1.2 years, height: 185.4 ± 8.1 cm, body mass: 111.8 ± 23 kg, lean body mass: 88.7 ± 13.1 kg, squat: 229.6 ± 29.8 kg, bench press: 148 ± 21 kg, deadlift: 224.9 ± 28.5 kg) were included in this study. Maximal strength in each lift was determined from either a powerlifting meet or testing from the sportsmen supervised strength and conditioning program. Athlete’s anthropometry was tape-measured while their lean body mass was measured with a bio-impedance scale. MaxREL was calculated by dividing maximal strength in each lift by athlete’s lean body mass. Linear regression analyses were computed and considered statistically significant at p < 0.05. RESULTS: The statistical analyses yielded the following correlations and prediction equations: Squat MaxREL = -0.0236*height (cm) + 7.0238 (r = -0.6, SE = 0.3, p = 0.001); Bench press MaxREL = -0.0128*height (cm) + 4.0688 (r = -0.54, SE = 0.19, p = 0.001); Deadlift MaxREL = -0.0426*height (cm) + 10.63 (r = -0.75, SE = 0.36, p = 0.001); Total MaxREL = -0.079*height (cm) + 21.722 (r = -0.75, SE = 0.72, p = 0.001). CONCLUSION: These results show that height has a negative influence on maximal strength relative to lean mass. In other words, the taller an athlete for a same lean body mass, the lower the strength potential. Possible explanations for these results, including body segment proportions are discussed. Future research should continue exploring the influence of anthropometry on maximal strength in homogeneous groups of high-level athletes to allowing the uncovering of body proportion associations and provide evidence to support strength and conditioning specialists.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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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Citations1
Published2021
Admission routes1
Has abstractyes

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