Isometric handgrip (IHG) training‐induced reductions in resting blood pressure: reactivity to a 2‐minute handgrip task identifies responders and non‐responders in young normotensive individuals (LB661)
Bibliographic record
Abstract
In people with hypertension, systolic blood pressure (SBP) reactivity to an isometric handgrip task (IHGT), but not a cold pressure task (CPT), is predictive of IHG training‐induced reductions in resting SBP. To investigate whether these findings could be extended to include young normotensives, resting BP and BP reactivity to an IHGT (2‐minute sustained contraction at 30% maximal voluntary contraction, MVC) and a CPT (2‐minute hand immersion in a cold water bath) were measured prior to and following 10 weeks of IHG training (4, 2‐minute IHG contractions at 30% MVC, using alternating hands, interspersed with 1‐minute rest periods, 3X/week) in 7 normotensive individuals (3 females, 4 males; age=25 ± 5.7 years). BP reactivity was derived by calculating the difference between peak stress BP and mean baseline resting BP. Significant training‐induced reductions in resting SBP (p<0.001) were strongly correlated with pre‐training SBP reactivity to the IHGT (r=‐0.8, p=0.03), but not the CPT (r= 0.2, p=0.6). These preliminary findings suggest that SBP reactivity to a short and simple handgrip task can be used as a predictive tool to identify who will respond best to IHG training. It might be possible to further explore how the IHG training stimulus could be optimized in non‐responders, to ensure that they too, experience reductions in resting BP. Grant Funding Source : Supported by University of Windsor Kinesiology seed grant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".