Determining the Optimal Interval Exercise Intensity Dose for Improving Exercise Performance
Bibliographic record
Abstract
Short duration interval training is a time-efficient exercise strategy that can improve fitness through changes in metabolic, cardiovascular and performance related variables. Studies have examined the positive effects of maximal intensity exercise (≥100% VO2max) on metabolic and performance variables in recreationally active individuals and trained athletes. The intensity of interval training required to cause improvements in aerobic fitness is unknown. This study will look at the performance related adaptations that occur with three different exercise intensities of interval training matched in terms of duration, frequency and type of exercise. Sixteen recreationally active individuals, both males (n=9, age = 23.3±3.3, VO2max = 44.2±6.5) and females (n=6, age = 21.5±0.7, VO2max = 39.7±5.4) were randomly assigned to one of three groups who trained using intervals at 120% VO2max (n=5), 90% VO2max (n=4) or 65% VO2max (n=7). The participants performed 14 days of training spread over 4 weeks that consisted of 8-12 repeats of 1-minute cycling at the prescribed intensity with 1-minute active recovery between intervals. Training increased time to fatigue in the 90% VO2max and 120% VO2max group however not in the 65% VO2max group. The changes in VO2max before and after training were significant in the 90% VO2max group and the 120% VO2max group, however not between the 120% VO2max and 90% VO2max group (p≤0.05). Interval training at 90% and 120% VO2max stimulates analogous improvements in fitness. These results may be important for diseased or sedentary populations where very high-intensity and long duration exercise may not be well tolerated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".