Habitual Physical Activity Levels Are Associated with Biomechanical Walking Economy in Children with Cerebral Palsy
Bibliographic record
Abstract
OBJECTIVE: To evaluate in children and adolescents with cerebral palsy the relationship between habitual physical activity and biomechanical treadmill walking economy and whether treadmill belt speed or walking time affect economy. DESIGN: Physical activity was measured in 11 subjects (10.6-16.3 yrs) with mild cerebral palsy using a triaxial accelerometer. To determine biomechanical walking economy, subjects' stride lengths and vertical sacral excursions were measured during each minute of three 3-min walks on a treadmill (at 60%, 75%, and 90% of individually determined fastest treadmill walking speed). RESULTS: Biomechanical walking economy at 60%, 75%, and 90% of (their) fastest speed each explained about half of the intersubject variance in daily physical activity (movement counts). A similar relationship was found between these biomechanical walking economy variables and movement counts at or above the 80th and 90th percentile (total minutes per day, number of 5-min bouts per day). Walking economy was 23.9% higher when subjects walked at 90% than when they walked at 60% of their fastest walking speed. No other speed-related effects on economy were found, nor did time affect economy. CONCLUSIONS: Within this population, those with high biomechanical treadmill walking economy are the more habitually physically active. Treadmill belt speed, but not walking time, affects biomechanical walking economy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".