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 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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".