4 minutes of in-class high intensity interval activity improves selective attention in 9-11 year olds and may be linked to classroom behaviour
Bibliographic record
Abstract
The inclusion of adequate physical activity time within schools is difficult when curriculum time is already saturated with a focus on skills that improve academic achievement. Time efficient physical activity solutions that demonstrate their impact on academic achievement related outcomes are needed to prioritize physical activity within the school curricula. FUNtervals are 4-minute high intensity interval activities that use whole body actions to complement a story line. The purpose of this study was two-fold. 1) To explore whether FUNtervals can improve selective attention, an executive function posited to be essential for learning and academic success. 2) To examine whether this relationship is predicted by students’ classroom behaviour, an outcome with previously demonstrated improvements following FUNtervals. Over a 3-week period 7 grade 3-5 classes (n=88) were exposed to a single group, repeated cross-over design where each student’s selective attention on no-activity (NA) days was compared to FUNtervals (FUN). In week 1, students were familiarized with the d2 test of attention, FUNterval activities, and baseline off-task behaviour was observed. In both week 2 and week 3 students completed the d2 test of attention following either a FUNterval break or a no-activity break. The order of these breaks was randomized in week 2 and then repeated in the opposite order in week 3. Results showed students made fewer errors during the d2 test (% Error; MFUN=3.4 ±0.3% vs. MNA=4.4% ±0.5%, p= 0.001, ES= 0.26) following FUNtervals. Further, a significant relationship between baseline verbal classroom behaviour and changes in selective attention following FUNtervals was observed (EComm: R=.27, P=0.03; E%: R=.27, P=0.02; CP: R=.24, P=0.05). In supporting the priority of physical activity inclusion within schools, FUNtervals, time efficient and easily implemented physical activity breaks, can improve selective attention through improved accuracy, quality of work and a higher degree of carefulness in elementary school children. Acknowledgments: Shane Sures and Dr. Lucy LeMare
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".