Effects of Acute Long- versus Short-Interval High-Intensity Interval Training on Attention and Psychological States in a Sample of Male and Female Adolescents: A Pilot Study
Bibliographic record
Abstract
The aim of this study was to assess the effect of acute short- versus long-interval high-intensity interval training (HIIT) on cognitive performance and psychological states in secondary school students. Fifteen secondary school students (nine males and six females: mean age = 16.2 ± 0.4 years, mean Body Mass Index = 21.2 ± 1.5 kg/m2, and maximum oxygen uptake = 42.2 ± 5.9 mL/kg/min) participated in the current study. They performed one of the following three sessions in a randomized order: (i) a long-interval HIIT (LIHIIT), (ii) a short-interval HIIT (SIHIIT), and (iii) a control condition (CC). Cognitive performance and perceived exertion were assessed pre and immediately post each condition using the d2 test and the Rating of Perceived Exertion (RPE) tool, respectively. Mood state was quantified using the Brunel Mood Scale (BRUMS) questionnaire immediately post each condition. The findings reported higher concentration performance in the SIHIIT compared to the LIHIIT condition (p = 0.043) and the CC (p < 0.001) and in the LIHIIT compared to the CC (p = 0.023). Moreover, the total count of errors was higher in the CC than in the LIHIIT (p = 0.01) and in the SIHIIT conditions (p < 0.001) and in the LIHIIT than in the SIHIIT condition (p = 0.03). RPE value was higher in the LIHIIT and SIHIIT conditions than in the CC (both p < 0.001), whereas no statistically significant difference between LIHIIT and SIHIIT conditions (p = 0.24) was found. Regarding the BRUMS, a significant difference between conditions in the fatigue subscale was found, being higher in LIHIIT with respect to SIHIIT (p = 0.03) and CC (p < 0.05). Vigor differed between conditions, with a higher value than in the LIHIIT (p = 0.04) and CC (p < 0.001). All the remaining subscales did not significantly differ between conditions (p > 0.05). Practitioners may implement short-interval HIIT prior to any tasks that require high levels of visual attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".