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 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) | 0.002 | 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".