New Insights Into Accelerometer-Measured Habitual Physical Activity and Sedentary Time During Early Recovery in Pediatric Concussion
Bibliographic record
Abstract
PURPOSE: Concussion management is shifting away from a rest-is-best approach, as data now suggest that exercise-is-medicine for this mild brain injury. Despite this, we have limited data on habitual physical activity following concussion. Therefore, our objective was to quantify accelerometer-measured physical activity and sedentary time in children with concussion (within the first month of injury) and healthy controls. We hypothesized that children with concussion would be less active than their healthy peers. METHODS: We performed a secondary analysis of prospectively collected accelerometer data. Our sample included children with concussion (n = 60, 31 females) and historical controls (n = 60) matched for age, sex, and season of accelerometer wear. RESULTS: Children with concussion were significantly more sedentary than controls (mean difference [MD], 38.3 min/d, P = .006), and spent less time performing light physical activity (MD, -19.5 min/d, P = .008), moderate physical activity (MD, -9.8 min/d, P < .001), and vigorous physical activity (MD, -12.0 min/d, P < .001); these differences were observed from 8:00 AM to 9:00 PM. Sex-specific analyses identified that girls with concussion were less active and more sedentary than both boys with concussion (P = .010) and healthy girls (P < .010). CONCLUSION: There is an activity deficit observed within the first month of pediatric concussion. Physical activity guidelines should address this while considering sex effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".