Short-Term Heavy Resistance Training Eliminates Age-Related Deficits in Muscle Mass and Strength in Healthy Older Males
Bibliographic record
Abstract
The objective of this investigation was to determine whether short-term heavy resistance training (RT) in healthy older men could eliminate deficits in muscle mass and strength (ST) compared with healthy younger men. Seventeen older men (60-71 yr) performed supervised RT for 22 weeks. Before and after RT, measurements were made for lean tissue mass (LTM), muscle thickness (MT), and ST (leg and bench press 1 repetition maximum) and were compared with values of younger men (n = 22-60 for the different measures, 18-31 yr). Before training, older men had significantly lower (p < 0.05) LTM (58.4 ± 7.0 kg), MT (3.4 ± 0.7 cm), and ST (leg press = 168 ± 33 kg; bench press = 75 ± 18 kg) compared with younger men (LTM 64.3 ± 7.1 kg; MT 4.0 ± 0.8 cm; leg press = 231 ± 54 kg; bench press = 121 ± 31 kg). All deficits were eliminated after 22 weeks of RT (LTM = 60.5 ± 7.6 kg; MT = 4.0 ± 0.7 cm; leg press = 222 ± 48 kg; bench press = 107 ± 19 kg). Short-term, heavy RT in healthy older men is sufficient to overcome deficits in muscle mass and ST when compared with healthy younger men. The practical application from this research is that healthy older men can be prescribed a whole-body heavy RT program to substantially increase muscle mass and ST to levels similar to young, active individuals.
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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 | 0.000 |
| 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".