Assessing the criterion validity of the activPAL CREA v1.3 algorithm and ActiPASS 2023.12 software for detecting steps during a progressive treadmill-based laboratory protocol
Bibliographic record
Abstract
Thigh-worn accelerometry is commonly implemented to measure step cadence. The default activPAL CREA algorithm is a valid measure of cadence during walking, but its validity during running is unknown. The ActiPASS software is designed to analyse tri-axial accelerometry data from various brands. We tested the validity of CREA v1.3 and ActiPASS 2023.12 to measure step cadence against manually-counted steps. Forty-five participants (26♀, 23.4 ± 4.6 years) completed 5 walking (6 min each, 2–4 mph) and 5 running treadmill (5–7 mph) stages (423 total stages completed). Based on equivalence testing, walking cadence (stages 1–5: 92–124 steps/min) from CREA was statistically equivalent (zone: <±2.2% of the manually-counted mean) to manual counts (92–125 steps/min). However, CREA underpredicted cadence during running stages (stages 6–10: 143–135 steps/min) by ~ 11–20 steps/min (p < 0.001). The ActiPASS-derived cadences were equivalent (zone: <±3.3%) to manual counts for all walking stages (99–127 steps/min) except Stage 1 (zone: ±10.5%). ActiPASS underpredicted cadences during running (stages 6–10: 137–153 steps/min) by ~ 10–16 steps/min (p < 0.001) compared to manual counts (stages 6–10: 154–164 steps/min). The CREA v1.3 algorithm is a valid measure of cadence during walking while ActiPASS 2023.12 is a valid measure of cadence during medium-fast walking. Further research is required to improve step cadence estimation across ambulation speeds.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".