Assessing the usefulness of acute physiological responses following resistance exercise: sensitivity, magnitude of change, and time course of measures
Bibliographic record
Abstract
A variety of strategies exist to modulate the acute physiological responses following resistance exercise aimed at enhancing recovery and/or adaptation processes. To assess the true impact of these strategies, it is important to know the ability of different measures to detect meaningful change. We investigated the sensitivity of measures used to quantify acute physiological responses to resistance exercise and constructed a physiological profile to characterise the magnitude of change and the time course of these responses. Eight males accustomed to regular resistance exercise performed experimental sessions during a "control week", void of an exercise stimulus. The following week, termed the "exercise week", participants repeated this sequence of experimental sessions, and they also performed a bout of lower-limb resistance exercise following the baseline assessments. Assessments were conducted at baseline and at 2, 6, 24, 48, 72, and 96 h after the intervention. On the basis of the signal-to-noise ratio, the most sensitive measures were maximal voluntary isometric contraction, 20-m sprint, countermovement jump peak force, rate of force development (100-200 ms), muscle soreness, Daily Analysis Of Life Demands For Athletes part B, limb girth, matrix metalloproteinase-9, interleukin-6, creatine kinase, and high-sensitivity C-reactive protein with ratios >1.5. Clear changes in these measures following resistance exercise were determined via magnitude-based inferences. These findings highlight measures that can detect real changes in acute physiological responses following resistance exercise in trained individuals. Researchers investigating strategies to manipulate acute physiological responses for recovery and/or adaptation can use these measures, as well as the recommended sampling points, to be confident that their interventions are making a worthwhile impact.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".