Relationships Between Body Composition And Maximal Strength In Classic Powerlifters: An Update
Bibliographic record
Abstract
Powerlifting measures maximal strength through the squat, the bench press and the deadlift. The total of the heaviest weight lifted in each of these events determines the strongest powerlifter per weight class. PURPOSE: To present the relationships between body composition and maximal strength in classic powerlifters in order to update previously published results with the completion of a second data collection. METHODS: Thirty classic powerlifters (12 females and 18 males, age 29.5 ± 9.0 years, bodyweight 83.7 ± 20.2 kg, height 1.69 ± 0.08 m, Squat 181.1 ± 60.2 kg, Bench 112.7 ± 43.6 kg, Deadlift 206.9 ± 62.2 kg) body composition was analyzed by Dual-Energy X-Ray Absorptiometry (DXA Prodigy Advance, model #8743, GE Healthcare, Madison, Wisconsin USA). Most of the Absolute Maximal Strength (AMS) measures came from the 2018 or 2019 Quebec Powerlifting Federation Provincial Championship with powerlifters scanned 44.6 ± 25.4 days after their powerlifting meet. Relative Maximal Strength (RMS) was calculated with the Wilks formula; validated for both men and women and designed to compare both sexes. Subject’s characteristics are presented as means and standard deviations. Correlations between body composition and maximal strength measures were calculated using a 2-tailed Pearson correlation analysis and were considered statistically significant at p < 0.05. All statistical analyses were conducted using IBM SPSS Statistics 25. RESULTS: Results present multiple significant correlations (p < 0.05) between various body composition measurements and AMS and RMS. Total in kg (AMS) was significantly correlated (p < 0.01) with Years of Experience in Resistance Training (r = 0.49), Years of Experience in Powerlifting (YEP, r = 0.48), Bodyweight (r = 0.69), Height (r = 0.69), Body Mass Index (BMI, r = 0.52), Lean Body Tissue (LBT, r = 0.90), Relative LBT (LBT/Height, r = 0.92), Bone Mineral Content (BMC, r = 0.84) and Bone Mineral Density (BMD, r = 0.70). Total in Wilks points (RMS) was significantly correlated with YEP (r = 0.51, p < 0.01), LBT, r = 0.46, p < 0.05), Relative LBT (r = 0.50, p < 0.01), BMC (r = 0.45 p < 0.05) and BMD (r = 0.50 p < 0.01). CONCLUSION: The present results update our previously published results (year 1, n = 15) and help further understand the relationships between body composition variables and maximal strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".