High-intensity Interval Training In Overweight And Sedentary Adults
Bibliographic record
Abstract
High-intensity interval training (HIIT) is well-established as a methodology for improving metabolic health and performance parameters. Research also demonstrates that HIIT is generally well-tolerated in a variety of populations. Less established is the impact of HIIT on intentions to exercise. PURPOSE: Investigate intentions toward engagement in HIIT exercise among overweight and insufficiently active adults. METHODS: 48 overweight-to-obese participants (mean BMI = 28, mean VO2 peak = 29 ml/kg/min) completed four counterbalanced trials comprised of a 30-minute continuous trial at 33% peak power (CONT) and three 20-minute interval trials that alternated between 85% and 15% peak power using 1:1 work-to-recovery ratios: 15 secs (INT-15), 30 secs (INT-30), and 60 secs (INT-60). RESULTS: Data was analyzed using RM ANOVA and pairwise comparisons. Intention to engage in each trial regularly in the near future was neutral-to-positive (4.9-6.0 on a 1-9 scale) with greater intention to complete INT-30 trials than CONT (p < 0.05; ES = 0.4) or INT-60 (p < 0.05; ES = 0.4) trials. Additional analyses revealed positive intentions to engage in exercise of any kind in the near future (7.2-7.4 on a 1-9 scale) but no difference between trials (p > 0.10). CONCLUSIONS: Findings indicate that 15-sec and 30-sec HIIT trials produce future intentions to exercise that are equal to or greater than intentions to engage in moderate continuous exercise. These results combined with findings for affective and enjoyment responses to exercise provide justification for the utilization of shorter interval trials as a relatively time-efficient alternative to lower intensity continuous exercise when promotion of intention to exercise is a desired outcome.
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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) | 0.000 | 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) | 0.001 | 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".