Effects of Training With Free Weights Versus Machines on Muscle Mass, Strength, Free Testosterone, and Free Cortisol Levels
Bibliographic record
Abstract
Schwanbeck, SR, Cornish, SM, Barss, T, and Chilibeck, PD. Effects of training with free weights versus machines on muscle mass, strength, free testosterone, and free cortisol levels. J Strength Cond Res 34(7): 1851-1859, 2020-Free weights offer a more unstable training environment, which enhances muscle recruitment, whereas some machines have the advantage of using a "cam" pulley system that better matches strength curves. We compared the effect of training with free weights vs. machines on muscle mass, strength, free testosterone, and free cortisol concentrations. Forty-six subjects (26 women; 22 ± 3 years) were randomized to train using free weights or machines for 8 weeks (with each muscle group trained 2-3/weeks, 3-4 sets of 4-10 repetitions). Muscle thickness and strength were measured at 0 and 8 weeks. Salivary hormone concentrations were assessed before and at the end of workouts at the beginning, midway (4 weeks), and end (8 weeks) of the training intervention. Biceps and quadriceps muscle thickness increased (p < 0.01) with no difference between groups. There was a group × time interaction for machine bench press strength (p = 0.05) with the machine group increasing more than the free-weight group (13.9 vs. 8.6%). Free-weight bench press and squat, and Smith machine squat strength increased in both groups (11-19%; p < 0.01) with no difference between groups. Men in the free-weight group had a greater increase in free testosterone from before to after acute training sessions than men in the machine group and all women (p < 0.01); however, there was no differences between groups in free cortisol response to acute resistance exercise. Training sessions with free weights induced greater increases in free testosterone in men; however, training with free weights or machines resulted in similar increases in muscle mass and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".