Quantifying Children's Self‐Paced Physical Activity: Rethinking Accelerometer Calibration
Bibliographic record
Abstract
The purpose was to examine the ability of accelerometry (ACC) to estimate oxygen consumption (VO 2 ) for self‐paced physical activity (PA). Children's (n=15; 9.3±1.2 yrs.) VO 2 responses to paced treadmill (TM) activity and self‐paced PA using six games were determined using FITMATE. The vertical axis (V) and vector magnitude (VM) were used to quantify both paced and self‐paced PA with ACC (ActiGraph GT3X+ and expressed in counts/10sec). Linear regression and Bland‐Altman plots were used to compare VO 2 for continuous paced (TM) and intermittent self‐paced (games) PA by assessing ACC vertical axis (ACC‐V), ACC vector magnitude (ACC‐VM) and the relative contribution of each axes (using ANOVA; p=0.05). Paced (TM) PA showed positive relationships (r) for VO 2 plus ACC‐V and for VO 2 plus ACC‐VM of 0.90±0.03 and 0.89±0.05, respectively (p>0.05). Results for measured VO 2 showed higher VO 2 for self‐paced vs. paced (TM) PA between 100‐1000 cnts/10sec (ACC‐VM) (i.e., VO 2 at 300 cnts/10sec were 22 vs. 12 mLO 2 •kg ‐1 •min ‐1 , respectively (p<0.05). Thus, VO 2 estimates from TM‐derived equations (both ACC‐VM and ACC‐V) for self‐paced PA were under‐estimated compared to measured VO 2 for self‐paced PA (p<0.05); with low agreement (a dynamic bias especially as the intensity (>6METs) increased) as observed on Bland‐Altman plots. Comparing paced vs. self‐paced PA it was observed that the contribution of axis dominance to ACC‐VM for the two types of PA existed ‐ larger differences for paced (41±14%) and smaller differences for self‐paced (7±5%) (p<0.05). This study reveals that the poor estimates of VO 2 for intermittent self‐paced PA using equations from continuous paced TM PA is attributed to the presence of a dominant axis.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".