Standing Still or Standing Out: Distinguishing Passive and Active Standing Is a Step in the Right Direction
Bibliographic record
Abstract
Standing is a solution to reduce or break-up sedentary time (sitting/reclining/lying while awake); however, the measurable health benefits of standing are conflicting. A recent article in the Journal for the Measurement of Physical Behaviour has demonstrated that the thigh-worn activPAL inclinometer can distinguish between passive (no movement) and active (structured micromovements) standing using a machine learning model in lab-based and free-living environments. The predictive model extends beyond previous research by considering three-dimensional aspects of movement into the decision tree model. The ability to characterize these distinct postures is increasingly important to understand the physiological difference between passive and active standing. Notably, active standing, when stepping is not feasible, may be superior to passive standing for improving metabolic activity, reducing fatigue, and enhancing blood flow. Applied to free-living settings, active standing could help mitigate or attenuate some adverse cardiometabolic effects of stationary activity, thereby yielding positive cardiovascular outcomes. As standing gains recognition as a potentially important health behavior, distinguishing between passive and active standing offers a unique opportunity to clarify the health impacts of standing time, contributing to the evidence base. This evidence may contribute to more detailed activity guidelines and support public health initiatives to promote active standing. These advancements have the potential to enhance our understanding of standing behaviors’ health impacts and the possible divergent physiological effects of active versus passive standing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".