The Impact of Step Reduction on Muscle Health in Aging: Protein and Exercise as Countermeasures
Bibliographic record
Abstract
Declines in strength and muscle function with age-sarcopenia-contribute to a variety of negative outcomes including an increased risk of: falls, fractures, hospitalization, and reduced mobility in older persons. Population-based estimates of the loss of muscle after age 60 show a loss of ~1% per year while strength loss is more rapid at ~3% per year. These rates are not, however, linear as periodic bouts of reduced physical activity and muscle disuse transiently accelerate loss of muscle and declines in muscle strength and power. Episodic complete muscle disuse can be due to sickness-related bed rest or local muscle disuse as a result of limb immobilization/surgery. Alternatively, relative muscle disuse occurs during inactivity due to illness and the associated convalescence resulting in marked reductions in daily steps, often referred to as step reduction (SR). While it is a "milder" form of disuse, it can have a similar adverse impact on skeletal muscle health. The physiological consequences of even short-term inactivity, modeled by SR, show losses in muscle mass and strength, as well as impaired insulin sensitivity and an increase in systemic inflammation. Though seemingly benign in comparison to bed rest, periodic inactivity likely occurs, we posit, more frequently with advancing age due to illness, declining mental health and declining mobility. Given that recovery from inactivity in older adults is slow or possibly incomplete we hypothesize that accumulated periods of inactivity contribute to sarcopenia. Periodic activity, even in small quantities, and protein supplementation may serve as effective strategies to offset the loss of muscle mass with aging, specifically during periods of inactivity. The aim of this review is to examine the recent literature encompassing SR, as a model of inactivity, and to explore the capacity of nutrition and exercise interventions to mitigate adverse physiological changes as a result of SR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".