Individual and Group Responses of Cardiorespiratory Fitness to Running and Cycling Sprint Interval Training
Bibliographic record
Abstract
Abstract Digby, L, McCarthy, SF, Bornath, DPB, Copeland, JL, and Hazell, TJ. Individual and group responses of cardiorespiratory fitness to running and cycling sprint interval training. J Strength Cond Res 37(4): e313–e316, 2023—Sprint interval training (SIT) has gained popularity as an effective way to improve peak oxygen consumption (V̇O 2 peak) and subsequently health in a time-efficient manner. In addition, SIT has demonstrated improvements of ∼5–12% in V̇O 2 peak for both running and cycling protocols, although comparisons of differing modalities have yet to be examined. Therefore, this study sought to determine group and individual responses to running and cycling SIT while examining any crossover effects of running and cycling SIT when V̇O 2 peak is tested in different modes of exercise where 18 subjects completed either 3 weeks of cycling SIT (6 male, 3 female) or running SIT (5 male, 4 female) consisting of 4–6 repeated 30-second all-out bouts interspersed with 240 seconds of recovery. Cycling and running V̇O 2 peak tests were completed pretraining and post-training for the investigation of mode-specific cardiorespiratory fitness improvements. There were main effects of time for cycling V̇O 2 peak ( P = 0.022, = 0.499) and running V̇O 2 peak ( P = 0.080, = 0.334) that seem greater when testing in the same mode as training (∼+5.5%). A similar proportion of responders were identified in both training modes (∼67%) suggesting running and cycling SIT are both effective for improving cardiorespiratory fitness. These results suggest that the specificity of testing and training are important for SIT and that both running and cycling SIT are similarly effective at improving V̇O 2 peak.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".