Minimalist Training: Is Lower Dosage or Intensity Resistance Training Effective to Improve Physical Fitness? A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Findings from original research, systematic reviews, and meta-analyses have demonstrated the effectiveness of resistance training (RT) on markers of performance and health. However, the literature is inconsistent with regards to the dosage effects (frequency, intensity, time, type) of RT to maximize training-induced improvements. This is most likely due to moderating factors such as age, sex, and training status. Moreover, individuals with limited time to exercise or who lack motivation to perform RT are interested in the least amount of RT to improve physical fitness. OBJECTIVES: The objective of this review was to investigate and identify lower than typically recommended RT dosages (i.e., shorter durations, lower volumes, and intensity activities) that can improve fitness components such as muscle strength and endurance for sedentary individuals or beginners not meeting the minimal recommendation of exercise. METHODS: Due to the broad research question involving different RT types, cohorts, and outcome measures (i.e., high heterogeneity), a narrative review was selected instead of a systematic meta-analysis approach. RESULTS: It seems that one weekly RT session is sufficient to induce strength gains in RT beginners with < 3 sets and loads below 50% of one-repetition maximum (1RM). With regards to the number of repetitions, the literature is controversial and some authors report that repetition to failure is key to achieve optimal adaptations, while other authors report similar adaptations with fewer repetitions. Additionally, higher intensity or heavier loads tend to provide superior results. With regards to the RT type, multi-joint exercises induce similar or even larger effects than single-joint exercises. CONCLUSION: The least amount of RT that can be performed to improve physical fitness for beginners for at least the first 12 weeks is one weekly session at intensities below 50% 1RM, with < 3 sets per multi-joint exercise.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".