Cardiorespiratory fitness and muscular endurance responses immediately and 2 months after a whole-body Tabata or vigorous-intensity continuous training intervention
Bibliographic record
Abstract
Young adults (52 females, 16 males; age = 21 ± 3 years; V̇O2peak: 41 ± 6 mL/(kg·min)) were randomized into 3 groups: (i) no-exercise control (CTL; n = 15), (ii) Tabata (n = 27), or (iii) vigorous-intensity continuous training (VICT; n = 26) groups for a 4-week supervised training period (4 sessions/week). V̇O2peak, time-to-fatigue (TTF), 5 km time-trial performance (TT), and muscular endurance were assessed at baseline, post-training (POST), and 2-month follow-up (FU). Response confidence intervals (CI) were used to classify individuals as likely responders (R; CI > 0). Both exercise interventions increased TTF and TT at POST (both p < 0.01), but these benefits were maintained at FU after VICT only (p < 0.01). Push-up performance was increased at POST and FU (both p < 0.01) after Tabata. VICT resulted in a greater proportion of TTF R versus both groups at POST (CTL: 1/15; VICT: 19/26; Tabata: 9/27) and versus Tabata at FU (3/15; 13/26; 4/27). VICT also had a greater proportion of TT R versus CTL at POST (2/15; 17/26; 10/27). Tabata had a greater proportion of R for maximum push-up repetitions versus both groups at POST (3/15; 6/26; 18/27) and versus CTL at FU (2/15; 10/26; 18/27). Collectively, VICT appears to be more effective for improving cardiorespiratory fitness, whereas whole-body Tabata confers larger improvements in push-up performance following short-term training. Novelty: Vigorous-intensity continuous training elicits larger improvements in cardiorespiratory fitness versus whole-body Tabata. Individual response profiles parallel group-level changes in cardiorespiratory fitness and muscular endurance.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".