The Impact of Exercise Intensity Feedback Using Technology for Children During Active Play: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Most children do not engage in enough exercise at the recommended intensity. Using technological devices may increase the time children spend at greater intensities while exercising. OBJECTIVE: This study aimed to determine if children who are receiving instant feedback on their exercise intensity using technology would spend more time in moderate-vigorous intensity (≥70% of maximum capacity) during active play sessions. It also aimed to explore if the children's physical characteristics were associated with the average percentage of maximal heart rate (HR) reached during sessions. METHODS: Participants were asked to wear a HR monitor, attached around their chest, for 4 sessions out of the 15 sessions offered. Twenty children aged 5 to 11 years received feedback for 2 random sessions. When receiving feedback, color-coded intensity based on HR was projected onto a wall. Green corresponded to moderate intensity (≥70% of max HR) and red corresponded to a HR below moderate intensity. Age, anthropometric measures, muscle strength, body composition, physical activity level, and fitness level were measured. RESULTS: The average percentage of maximal HR during a session was similar whether feedback was provided (70.7%, SD 6.4%) or not (71.1%, SD 4.1%) with P=.93. No personal characteristics were associated with the average intensity recorded during the exercise sessions. CONCLUSIONS: Receiving instant exercise intensity feedback is not associated with a higher proportion of time spent at moderate intensity or above in children aged 5 to 11 years when involved in an active play program. Personal characteristics are not associated with the intensity recorded when participating in an active play program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".