Age‐related differences in agility are related to both muscle strength and corticospinal tract function
Bibliographic record
Abstract
Abstract Agility is essential for “healthy” aging, but neuromuscular contributions to age‐related differences in agility are not entirely understood. We recruited healthy (n = 32) non‐athletes (30–84 years) to determine: (1) if aging is associated with agility and (2) whether muscle strength or corticospinal tract function predicts agility. We assessed muscle strength via a validated knee extension test, corticospinal tract function via transcranial magnetic stimulation, and agility via spatiotemporal values (i.e., leg length‐adjusted hop length and hop length variability) collected during a novel propulsive bipedal hopping (agility) task on an electronic walkway. Pearson correlation revealed aging is associated with leg length‐adjusted hop length (r = −0.671, p < 0.001) and hop length variability (r = 0.423, p = 0.016). Further, leg length‐adjusted hop length and hop length variability correlated with quadriceps strength (r = 0.581, p < 0.001; r = −0.364, p = 0.048) and corticospinal tract function (r = −0.384, p = 0.039; r = 0.478, p = 0.007). However, hierarchical regressions indicated that, when controlling for sex, muscle strength only predicts leg length‐adjusted hop length (R2 = 0.345, p = 0.002), whereas corticospinal tract function only predicts hop length variability (R2 = 0.239, p = 0.014). Therefore, weaker quadriceps decrease the distance hopped, and deteriorating corticospinal tract function increases variability in hop length.
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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 | 0.001 |
| 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) | 0.002 | 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".