Ankle and knee extensor muscle effort during locomotion in young and older athletes: Implications for understanding age-related locomotor decline
Bibliographic record
Abstract
Age-related reduction in muscle force generation capacity is similarly evident across different lower limb muscle groups, yet decline in locomotor performance with age has been shown to depend primarily on reduced ankle extensor muscle function. To better understand why ageing has the largest detrimental effect on ankle joint function during locomotion, we examined maximal ankle and knee extensor force development during a two-leg hopping test in older and young men, and used these forces as a reference to calculate relative operating efforts for the knee and ankle extensors as participants walked, ran and sprinted. We found that, across locomotion modes in both age groups, ankle extensors operated at a greater relative effort compared to knee extensors; however, slightly less pronounced differences between ankle and knee extensor muscle efforts were present among older men, mainly due to a reduction in the ankle extensor force generation during locomotion modes. We consider these findings as evidence that reduced ankle push-off function in older age is driven by a tendency to keep ankle extensor effort during locomotion lower than it would otherwise be, which, in turn, may be an important self-optimisation strategy to prevent locomotor-induced fatigue of ankle extensor muscles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".