Muscle Soreness and Neuromuscular Fatigue After Three Different Resistance Exercise Protocols: Comparison Between Men and Women
Bibliographic record
Abstract
ABSTRACT: Margoni, M, Bochicchio, G, Ferrari, L, and Pogliaghi, S. Muscle soreness and neuromuscular fatigue after three different resistance exercise protocols: Comparison between men and women. J Strength Cond Res 39(6): 625-633, 2025-This study evaluated the sex-related differences in the magnitude and time course of muscle soreness and neuromuscular fatigue after 3 different resistance training (RT) protocols, in both the upper and lower body. Sixteen recreational resistance-trained women ( n = 7) and men ( n = 9) performed 3 RT protocols, in randomized order as either power (POW, 4 × 5 at 50% 1 repetition maximum [1RM]), strength (STR, 4 × 2 at 90% 1RM), and hypertrophy (4 × 10 at 70% 1RM), involving 2 main exercises (back squat and bench press) at aim-specific training load, and 4 complementary exercises. Visual analog scale and load cell (1,000 Hz, AEP transducer, Italy) were used to assess muscle soreness and changes in maximal peak force, respectively, of upper and lower body pre-, post-, 24 h, 48, and 72 h after each protocol. Three-way RM ANOVA was run to compare muscle soreness and neuromuscular fatigue of the upper and lower body between sexes, within protocols and time. Men and women showed similar changes in muscle soreness and neuromuscular fatigue across all protocols and body parts ( p > 0.05). Moreover, both sexes exhibited higher neuromuscular fatigue in the lower body than the upper body, across all protocols ( p < 0.05). These results suggest that men and women show similar kinetics in muscle soreness and neuromuscular fatigue after 3 different RT protocols, with a greater impact experienced in the lower body. Therefore, designing RT programs on sex-specific performance kinetics may not be essential, although increasing upper body exercises volume and frequency can benefit both sexes.
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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) | 0.000 | 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) | 0.003 | 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".