Heavy-, Severe-, and Extreme-, but Not Moderate-Intensity Exercise Increase Vo2max and Thresholds after 6 wk of Training
Bibliographic record
Abstract
INTRODUCTION: This study assessed the effect of individualized, domain-based exercise intensity prescription on changes in maximal oxygen uptake (V̇O 2max ) and submaximal thresholds. METHODS: Eighty-four young healthy participants (42 females, 42 males) were randomly assigned to six age, sex, and V̇O 2max -matched groups (14 participants each). Groups performed continuous cycling in the 1) moderate (MOD), 2) lower heavy (HVY1), and 3) upper heavy-intensity (HVY2) domain; interval cycling in the form of 4) high-intensity interval training (HIIT) in the severe-intensity domain, or 5) sprint-interval training (SIT) in the extreme-intensity domain; or no exercise for 6) control (CON). All training groups, except SIT, were work-matched. Training participants completed three sessions per week for 6 wk with physiological evaluations performed at PRE, MID, and POST intervention. RESULTS: Compared with the change in V̇O 2max (∆V̇O 2max ) in CON (0.1 ± 1.2 mL·kg -1 ·min -1 ), all training groups, except MOD (1.8 ± 2.7 mL·kg -1 ·min -1 ), demonstrated a significant increase ( P < 0.05). HIIT produced the highest increase (6.2 ± 2.8 mL·kg -1 ·min -1 ) followed by HVY2 (5.4 ± 2.3 mL·kg -1 ·min -1 ), SIT (4.7 ± 2.3 mL·kg -1 ·min -1 ), and HVY1 (3.3 ± 2.4 mL·kg -1 ·min -1 ), respectively. The ΔPO at the estimated lactate threshold ( θLT ) was similar across HVY1, HVY2, HIIT, and SIT, which were all greater than CON ( P < 0.05). The ΔV̇O 2 and ΔPO at θLT for MOD was not different from CON ( P > 0.05). HIIT produced the highest ΔPO at maximal metabolic steady state, which was greater than CON, MOD, and SIT ( P < 0.05). CONCLUSIONS: This study demonstrated that i) exercise intensity is a key component determining changes in V̇O 2max and submaximal thresholds and ii) exercise intensity domain-based prescription allows for a homogenous metabolic stimulus across individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".