Effect Of Weekly Set Progressions On Muscle Hypertrophy Adaptations In Healthy Females
Bibliographic record
Abstract
Resistance training (RT) volume is an essential training variable underpinning muscle mass accretion. However, the effects of cycling weekly sets number on muscle hypertrophy adaptations remains unclear, particularly in females. PURPOSE: We investigated the effect of increasing weekly sets every two weeks versus performing a constant set volume on lateral thigh thickness in healthy females over a 12-week study period. METHODS: Twenty healthy females (age 27 ± 4 years; RT experience 2 ± 1 years; prior quadriceps-focused training volume 22 ± 8 sets/wk) were recruited. To mitigate a potential confounding effect from previous training volume, participants underwent a two-week volume reduction phase, followed by a 2-week familiarization phase, similarly increasing their weekly sets. Subsequently, they started their quadriceps-focused training intervention. Participants were randomly allocated to a Constant Group (CON; n = 9) set at 22 weekly sets or; Progression Group (PROG; n = 11) that increased two sets per week every two weeks. The PROG group started at 16 sets and finished at 26 sets per week. The average number of weekly sets was 22 for CON and 21 for PROG. Participants performed a lower-limb training program (barbell back squat, leg press 45, seated knee extension, Romanian deadlift and seated knee flexion) twice a week (6-8 and 10-12 repetitions, respectively) for 12 weeks. Intensity of effort was fixed at two repetitions in reserve/set, with only the last set of each exercise performed to concentric failure. Lateral thigh muscle thickness (MT) at 50% of the femur length was assessed at baseline and after 12 weeks of the training program. RESULTS: There were no differences in volume load after the intervention (p = 0.540) and no time by group interaction (p = 0.881). We observed a main effect of time for MT changes (p = 0.035) but no group effects (p = 0.910). CONCLUSIONS: Our findings indicate that progressively adding two weekly sets compared to maintaining a fixed volume, at least under equated volume load conditions, does not elicit greater hypertrophic adaptations in the lateral thigh muscles of healthy females over a 12-week training period. AE, GO and MFB would like to thank for Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES - Finance Code 001) for their respective scholarships.
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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 | 0.000 | 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) | 0.002 | 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".