Development and validation of an activPAL accelerometry count-based model of physical activity intensity in adults
Bibliographic record
Abstract
The activPAL linear cadence-metabolic equivalents (METs) equation poorly estimates activity intensity. The magnitude of acceleration in three directional planes may be a superior predictor of activity intensity than stepping cadence, with accelerometry count thresholds developed in children/adolescent populations. We extracted the proprietary accelerometer-derived information to develop a counts-METs model and cross-validates it in laboratory and free-living conditions. Forty adults (25±6 years) wore an activPAL during a 7-stage progressive treadmill protocol (criterion: indirect calorimetry). Tri-axial accelerometry-derived activity counts (vector magnitude) and METs data from a subset of participants (n = 20) were modelled (R2=0.76) and the regression equation evaluated in the remaining participants (n = 20). Thirty-two of these participants wore the activPAL during free-living conditions (n = 192-d; criterion: PiezoRxD monitor). The absolute percent error of the counts-METs model in the laboratory cross-validation was 18±13%, with equivalence testing determinining equivalent MET values to indirect calorimetry during the slowest (1.5 mph) and fastest (4.0-4.5 mph) stages. In free-living conditions, the model accurately quantified light- and moderate-intensity physical activity but underestimated vigorous-intensity activity (6.5±11.3 vs. 5.5±20.8 mins/day; p < 0.001). We developed and present a data analysis method using the activPAL tri-axial accelerometry counts to improve estimations of physical activity intensity in controlled laboratory settings and uncontrolled free-living settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".