Eccentric Training Improves Body Composition by Inducing Mechanical and Metabolic Adaptations: A Promising Approach for Overweight and Obese Individuals
Bibliographic record
Abstract
Skeletal muscle generates force by either shortening (concentrically) or lengthening (eccentrically). Eccentric (ECC) exercise is characterized by a lower metabolic demand and requires less muscle activity than concentric (CON) exercise at the same level of exerted force. However, the specific effect of ECC training vs. CON training on lean and fat mass remains underexplored. The first aim of this paper was to review the available evidence regarding the effects of ECC training on whole body and segmental lean and fat mass and, when possible, compare these with the effects of CON training. The second aim was to provide some insights into the main mechanical, physiological, and metabolic adaptations of ECC training that contribute to its effects on body composition. The third aim was to determine the beneficial effects of ECC exercise on health-related parameters in overweight and obese patients. ECC training is an effective modality to improve lean mass, but when matched for load or work, the difference between ECC and CON trainings seems unclear. A few studies reported that ECC training is also efficient at reducing fat mass. By increasing post-exercise resting energy expenditure, modifying metabolic substrate, and improving both blood lipid profile and insulin resistance, ECC training is a potential exercise modality for individuals with chronic conditions such as those who are overweight and obese. Further investigations using standardized experimental conditions, examining not only segmental but also whole body composition, are required to compare ECC and CON trainings.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".