Cardiorespiratory and metabolic stress responses to acute high-intensity interval training anchored to critical power or maximal heart rate
Bibliographic record
Abstract
High intensity interval training (HIIT) involves repeated bouts of relatively hard work, commonly at intensities eliciting ≥ 80% of maximal heart rate (HRmax), interspersed with recovery periods. Anchoring intensity to HRmax can elicit a wide range of acute metabolic responses to exercise. Expressing intensity relative to metabolic thresholds such as critical power (CP) may reduce this variability. We therefore examined whether anchoring HIIT to CP reduced variability in change in [blood lactate] (ΔBLa−) compared to HRmax-based approach. Nineteen adults aged 23 ± 4 years completed two 4 × 4-min HIIT trials in a randomized, crossover manner at intensities equal to CP + 10% of work prime (CPHIIT) or ≥ 80% HRmax (HRHIIT). Variability in [ΔBLa−] from rest to exercise was not different between CPHIIT and HRHIIT (1.37 (0.42–1.62) vs. 1.32 (0.77–1.97) mM; p = 0.75). Workload was higher in CPHIIT vs. HRHIIT (192 ± 39 W vs. 180 ± 43 W; p = 0.001), as was exercise oxygen consumption, ventilation, respiratory exchange ratio, heart rate, and rating of perceived exertion (all p < 0.05). A CP-based HIIT protocol did not reduce variability of change in [ΔBLa−] compared to a traditional approach anchored to %HRmax. However, anchoring HIIT intensity to CP resulted in participants achieving higher workloads, eliciting higher cardiorespiratory and perceived stress which could translate to divergent training-induced responses.
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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 | 0.001 |
| 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".