THE RELATIONSHIP BETWEEN HEART RATE KINETICS AND RELATIVE EXERCISE INTENSITY
Bibliographic record
Abstract
This study tested the hypothesis that a predictable relationship exists between HR kinetics and stepwise increments in exercise intensity, in 13 males exercising on a cycle ergometer (Age, 25 S109 4). Five step increment tests, each including a single increase in exercise intensity of 10% VO2 Reserve (VO2R), were conducted over two days with a minimum of 48 hours separating the testing days. VO2R represents the difference between peak VO2 and resting VO2. Each step increment test was 12 minutes in duration and included two 6 minute stages, a warm up stage and a target intensity stage. Each step increment test was labeled after its target intensity stage. The target intensity stages were 50, 60, 70, 80 and 90% VO2R (Δ = 10% VO2R) respectively. The power outputs representing these target intensities for each subject were derived from previous graded exercise tests (GXT). The temporal course of HR during the target intensity stages of each step increment test were analyzed using a monoexponential model in each subject. Both the absolute increase in HR (Δ HR) and the pattern of increase (t - time constant) were compared using an ANOVA with a post hoc Neuman Keuls Student Test. While there was no significant difference in the magnitude of the HR increase among the 5 targeted intensities, the pattern of HR increase changed significantly at the step increment test with a target intensity of 70% VO2R, (P < 0.05). No significant differences in HR kinetics existed among step increment tests below 70% VO2R (50 & 60% VO2R, p = NS) or above 70% VO2R (70, 80, 90% VO2R, p = NS). These results suggest that while there is no predictable relationship between pattern of HR rise and step increments in exercise intensity, HR kinetics during submaximal work intensities ie. < 70% VO2R appear significantly faster than equal step increments at higher intensities. These findings may reflect changes in autonomic drive observed at submaximal workload transitions.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
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".