Classroom-based high-intensity interval activity improves off-task behaviour in primary school students
Bibliographic record
Abstract
This study examined the effects of an acute bout of brief, high-intensity interval exercise on off-task classroom behaviour in primary school students. A grade 4 class (n = 24) and a grade 2 class (n = 20) were exposed to either a no-activity break or an active break that consisted of "FUNtervals", a high-intensity interval protocol, on alternating days for 3 weeks. No-activity days consisted of a 10-min inactive break while FUNterval days consisted of a 4-min FUNterval completed within a 10-min break from regular class activities. Off-task behaviour was observed for 50 min after each no-activity/FUNterval break, with the amount of time students spent off-task (motor, passive, and verbal behaviour) being recorded. When comparing no-activity breaks with FUNtervals the grade 4 class demonstrated reductions in both passive (no activity = 29% ± 13% vs. FUNterval = 25% ± 13%, p < 0.05, effect size (ES) = 0.31) and motor (no activity = 31% ± 16% vs. FUNterval = 24% ± 13%, p < 0.01, ES = 0.48) off-task behaviour following FUNtervals. Similarly, in the grade 2 class, passive (no activity = 23% ± 14% vs. FUNterval = 14% ± 10%, p < 0.01, ES = 0.74), verbal (no activity = 8% ± 8% vs. FUNterval = 5% ± 5%, p < 0.05, ES = 0.45), and motor (no activity = 29% ± 17% vs. FUNterval = 14% ± 10%, p < 0.01, ES = 1.076) off-task behaviours were reduced following FUNtervals. In both classrooms the effects of physical activity were greatest in those students demonstrating the highest rates of off-task behaviour on no-activity days. These data demonstrate that very brief high-intensity bouts of exercise can improve off-task behaviour in grade 2 and 4 students, particularly in students with high rates of such behaviour.
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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 | 0.000 |
| 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 | 0.001 |
| 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".