Eccentric Versus Concentric Training in Old Adults
Bibliographic record
Abstract
PURPOSE: We compared training with eccentric (i.e. lengthening) versus concentric (i.e. shortening) muscle contractions (hand gripping) for increasing muscle size and strength in the forearms of 25 older individuals (10 males, 15 females, mean age 63y). METHODS: We used a within-subjects design where each arm was randomized to a different training mode. Training was progressive up to 6 sets of 8 maximal repetitions on a Norm dynamometer, 3 times per week for 6 months. Forearm muscle size was assessed by ultrasound and peripheral quantitative computed tomography. Isometric, concentric, and eccentric grip torque was assessed on the Norm dynamometer. RESULTS: Eccentric training increased muscle thickness from a mean (SD) of 3.99 (0.51) to 4.19 (0.56) cm (+4.9%), which was greater than the concentric training increase from 3.99 (0.56) to 4.04 (0.58) cm (+1.6%) (p<0.01). Eccentric training increased muscle cross-sectional area from 3687 (1084) to 3787 (1090) mm2 (+3.0%), which was greater than the concentric training increase from 3691 (996) to 3739 (997) mm2 (+1.4%) (p=0.017). There was a contraction-type by time interaction for strength, where eccentric strength increased more (+30%) than concentric (+22%) and isometric (+17%) strength across arms (p<0.05). CONCLUSIONS: Eccentric training is more effective than concentric training for increasing muscle size in older individuals. Eccentric strength increased to a greater extent than other contraction types with eccentric or concentric training. Supported by the Canadian Institutes of Health Research
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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) | 0.001 | 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".