Validity of ActivPAL CREA software detection of sitting and lying during free-living conditions
Bibliographic record
Abstract
Abstract Objective . Approaches to differentiate sitting and lying are available within the default activPAL software from a single thigh-worn monitor. Dual-monitor methods use multiple monitors positioned on the thigh and torso to characterize sitting versus lying. We evaluated the validity between these two methods to measure waking, sitting, and lying time in free-living conditions. We also examined if the degree-threshold distinguishing sitting/lying for the dual-monitor (<30° and <45°) impacted results. Approach . Thirty-five young adults (24 ± 3 years, 16 females) wore an activPAL 24 h per day on their thigh and torso during free-living conditions (average: 6.8 ± 1.0 d, 239 total). Data were processed using the default activPAL software (thigh-only) or a custom MATLAB program (dual-monitor). Main results . The single-monitor recorded less lying time (59 ± 99 min d −1 ) and more sitting time (514 ± 203 min d −1 ) than the dual-monitor method regardless of 30° (lying: 85 ± 94 min d −1 ; sitting: 488 ± 166 min d −1 ) or 45° lying threshold (lying: 170 ± 142, sitting: 403 ± 164 min d −1 ; all, p < 0.001). The single monitor lying time was weakly correlated to the dual-monitor (30°: ρ = 0.25, 45°: ρ = 0.21; both, p < 0.001), whereas sitting was moderate-strong (30°: ρ = 0.76, 45°: ρ = 0.58; both, p < 0.001). However, the mean absolute error was 81 min d −1 (30°) and 132 min d −1 (45°) for both lying and sitting. Significance . The method of differentiating sitting/lying from a single thigh-worn activPAL records more sitting time and less lying time compared to a dual-monitor configuration (regardless of degree-threshold) that considered the position of the torso. A further refinement of algorithms or implementation of multiple-monitor methods may be needed for researchers to derive detailed sedentary positions.
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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 | 0.001 |
| 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".