Effects of intensified training with insufficient recovery on joint level and single muscle fibre mechanical function: the role of myofibrillar Ca <sup>2+</sup> sensitivity
Bibliographic record
Abstract
Intense exercise training with insufficient recovery time is associated with reductions in neuromuscular performance. However, it is unclear how single muscle fibre mechanical function and myofibrillar Ca 2+ sensitivity contribute to these impairments. We investigated the effects of overload training on joint-level neuromuscular performance and cellular-level mechanical function. Fourteen athletes (4 female and 10 male) underwent a 3-week intensified training protocol consisting of up to 150% of their regular training hours with three additional high-intensity training sessions per week. Neuromuscular performance of the knee extensors was assessed via maximal voluntary contraction (MVC) force, electrically evoked twitch contractions, and a force-frequency relationship. Muscle biopsies were taken from the vastus lateralis to assess single fibre mechanical function. Neither MVC force nor twitch parameters were altered following training (all p > 0.05), but a rightward shift in the force–frequency curve was observed with average reduction in force of 6%–27% across frequencies 5–20 Hz (all p < 0.05). In single fibres, maximal force output was not reduced following training, but there was a rightward shift in the force–pCa curve driven by a 6% reduction in Ca 2+ sensitivity ( p < 0.05). These data indicate intensified training leads to impaired Ca 2+ sensitivity at the single fibre level, which in part explains impaired neuromuscular function at the joint level during lower frequencies of activation. This is an important consideration for athletes, as performance is often assessed at maximal levels of activation, and these underlying impairments in force generation may be less obvious.
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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) | 0.001 | 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 | 0.001 |
| 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".