Prediction of One Repetition Maximum Strength From Multiple Repetition Maximum Testing and Anthropometry
Bibliographic record
Abstract
The purpose of this study was to quantify the decrease in the load lifted from 1 to 5, 10, and 20 repetitions to failure for the flat barbell bench press (chest press; CP) and plate-loaded leg press (LP). Furthermore, we developed prediction equations for 1 repetition maximum (RM) strength from the multiple RM tests, including anthropometric data, gender, age, and resistance training volume. Seventy subjects (34 men, 36 women), 18-69 years of age, completed 1, 5, 10, and 20RM testing for each of the CPs and LPs. Regression analyses of mean data revealed a nonlinear decrease in load with increasing repetition number (CP: linear S(y.x) = 2.6 kg, nonlinear S(y.x) = 0.2 kg; LP: linear S(y.x) = 11.0 kg, nonlinear S(y.x) = 2.6 kg, respectively). Multiple regression analyses revealed that the 5RM data produced the greatest prediction accuracy, with R(2) data for 5, 10, and 20RM conditions being LP: 0.974, 0.933, 0.915; CP: 0.993, 0.976, and 0.955, respectively. The regression prediction equations for 1RM strength from 5RM data were LP: 1RM = 1.0970 x (5RM weight [kg]) + 14.2546, S(y.x) = 16.16 kg, R(2) = 0.974; CP: 1RM = 1.1307 x (5RM weight) + 0.6999, S(y.x) = 2.98 kg, R(2) = 0.993. Dynamic muscular strength (1RM) can be accurately estimated from multiple repetition testing. Data reveal that no more than 10 repetitions should be used in linear equations to estimate 1RM for the LP and CP actions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".