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 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 | 0.001 |
| 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".