Effect of isometric handgrip exercise on heart rate variability with and without somatosensory stimulation
Bibliographic record
Abstract
Sub‐motor somatosensory electrical stimulation (STIM) of skeletal muscle increases heart rate variability (HRV). Short duration (<30 sec) isometric handgrip exercise (HG) increases heart rate (HR) and decreases HRV largely through vagal withdrawal. The current study aimed to determine whether a gated effect exists when HG and STIM are combined. We tested the hypothesis that changes due to STIM would dominate over HG induced changes. Twelve volunteers (7 female; aged 24±2 yr.) performed 3 protocols: 1) four × 20s HG at 30, 40 & 50% of their maximal voluntary contraction (MVC), 2) seven × 30s STIMs, and 3) a combination of HG+STIM. Compared to rest, STIM increased HRV (Rest: 61±80ms 2 , STIM: 69±84ms 2 , p<0.05). In contrast, HRV decreased at all HG levels (ΔHRV@30%: −39±51ms 2 , 40%: −29±39ms 2 , 50%: −25±39ms 2 ). The effects of HG were reversed during HG+STIM (ΔHRV@30%STIM: −1.7±40ms 2 , 40%STIM: 5.5±37ms 2 , 50%STIM: −4.2±19ms 2 , p<0.05). Heart rate did not change during STIM (Rest: 59±7bpm; STIM: 58±6bpm). However HR increased above baseline and progressively at each level of HG (50% vs. 30%, p<0.001; 50% vs. 40%, p<0.001; 40% vs. 30%, p<0.05). Heart rate was not different between HG and HG+STIM, p=0.095. The results indicate somatosensory stimulation can reverse the changes in HRV induced by handgrip suggesting muscle afferent signals may gate descending signals affecting heart rate variability. Funded by CIHR.
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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".