Modified sprint interval training protocols. Part II. Psychological responses
Bibliographic record
Abstract
Sprint-interval training (SIT) is a viable method to improve health and fitness. However, researchers have questioned the utility of SIT because of its strenuous nature. The current study aimed to determine if manipulating the sprint and recovery duration, while maintaining the 1:8 work to rest ratio, could uncover a more favourable SIT protocol. Nine healthy active males (age, 23.3 ± 3.0 years; body mass index, 22.4 ± 2.2 kg·m−2; maximal oxygen consumption, 48.9 ± 5.3 mL·kg−1·min−1) participated in 3 experimental running SIT sessions: (i) 30:240 (4 × 30-s efforts, 240-s recovery), (ii) 15:120 (8 × 15-s efforts, 120-s recovery), (iii) 5:40 (24 × 5-s efforts, 40-s recovery), and (iv) a final behavioural choice follow-up session. Affect, intentions, task self-efficacy, enjoyment, and preference were evaluated. Midway through exercise, affect became more positive for 5:40 compared with 30:240 (p < 0.05) and postexercise affect was greater for both 5:40 (p = 0.014) and 15:120 (p = 0.015) compared with 30:240. Participants expressed greater intentions to perform 5:40 3 and 5 times/week compared with 15:120 and 30:240 (p < 0.05). Participants felt more confident in their ability to perform 5:40 (p = 0.001) and 15:120 (p = 0.008) compared with 30:240. The 5:40 session was also rated as more enjoyable than 15:120 (p = 0.025) and 30:240 (p = 0.026). All participants preferred the 5:40 protocol. These data suggest that shorter sprints with more repetitions are perceived as more enjoyable and lead to greater intentions to engage in SIT.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".