Acute work rate adjustments during high-intensity interval training in a hot and temperate environment
Bibliographic record
Abstract
Heart rate drifts upward over time during interval exercise and during exercise in hot conditions. As such, work rate must be lowered to maintain target heart rate. The purpose was to characterize acute work rate adjustments during high-intensity interval training based on target heart rate. Seven humans (three females) completed five study visits: a graded exercise test on a cycle ergometer to measure maximal heart rate (HRmax) in ∼22 °C and four trials performed in ∼22 °C (TEMP) or ∼35 °C (HOT), consisting of an 8 min warm-up at 70% HRmax followed by one (15TEMP and 15HOT) or five (43TEMP and 43HOT) rounds of high-intensity interval training (one round = 4 min work at 90% HRmax and 3 min recovery at 70% HRmax) totaling 15 min or 43 min of exercise, respectively. Work rate was lowered 33 ± 20 W ( p = 0.005) in 43TEMP and 56 ± 30 W ( p = 0.003) in 43HOT between the first and fifth work intervals. Thermal strain (0.2 °C higher rectal temperature, p = 0.01) and cardiovascular strain (6 beats·min–1 larger increase in heart rate from first to fifth recovery interval, p = 0.01) were greater in 43HOT versus 43TEMP. Using target heart rate during high-intensity interval training may reduce the training stimulus, especially in hot environments, but it may also limit thermal strain and enable participants to complete the prescribed workout despite the heat.
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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) | 0.001 | 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".