Three Weeks of Overload Training Increases Resting Muscle Sympathetic Activity
Bibliographic record
Abstract
PURPOSE: Overload training is hypothesized to alter autonomic regulation, although interpretations using indirect measures of heart rate variability are conflicting. The aim of the present study was to examine the effects of overload training on muscle sympathetic nerve activity (MSNA), a direct measure of central sympathetic outflow, in recreational endurance athletes. METHODS: Measurements of heart rate variability, cardiac baroreflex sensitivity (BRS), MSNA (microneurography), and sympathetic BRS were obtained in 17 healthy triathletes and cyclists after 1 wk of reduced training (baseline) and again after 3 wk of either regular (n = 7) or overload (n = 10) training. RESULTS: After training, the changes (Δ) in peak power output (10 ± 10 vs -12 ± 9 W, P < 0.001), maximal heart rate (-2 ± 4 vs -8 ± 3 bpm, P = 0.006), heart rate variability (SD of normal-to-normal intervals, 27 ± 31 vs -3 ± 25 ms; P = 0.04), and cardiac BRS (7 ± 6 vs -2 ± 8 ms·mm Hg, P = 0.02) differed between the control and overload groups. The change in MSNA burst frequency (-2 ± 2 vs 4 ± 5 bursts per minute, P = 0.02) differed between groups. Across all participants, the changes in resting MSNA and peak power output were correlated negatively (r = -0.51, P = 0.04). No between-group differences in resting heart rate or blood pressure were observed (all P > 0.05). CONCLUSIONS: Overload training increased MSNA and attenuated increases in cardiac BRS and heart rate variability observed with regular training. These results support neural adaptations after overload training and suggest that increased central sympathetic outflow may be linked with decreased exercise performance.
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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) | 0.000 | 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".