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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".