The Health Benefits of Resistance Exercise: Beyond Hypertrophy and Big Weights
Bibliographic record
Abstract
ABSTRACT It is well established that exercise is associated with a reduced risk of several chronic diseases. Currently, aerobic training (AT) receives primary attention in physical activity guidelines with a recommendation for ~150 min of moderate-to-vigorous AT weekly. In most physical activity guidelines, resistance training (RT) is termed a beneficial activity, with a recommendation to engage in strengthening activities twice weekly. However, we propose that the health benefits of RT are underappreciated. There is evidence, established and emerging, that RT can, in many respects, elicit similar health benefits to AT. When combined, AT and RT may yield ostensibly optimal health benefits versus performing either exercise exclusively. We discuss the health benefits of engaging in RT, including healthy aging, improved mobility, cognitive function, cancer survivorship, and metabolic health in persons with obesity and type 2 diabetes—all of which can influence morbidity and mortality. Many of the health benefits of RT can be achieved by lifting lighter loads to volitional failure, highlighting that the benefits of RT do not necessarily require lifting heavier weights. Accumulating evidence also shows a lower mortality risk in those who regularly perform RT. To optimize health, especially with aging, RT should be emphasized in physical activity guidelines in addition to AT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".