Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions
Bibliographic record
Abstract
Objective monitors such as the activPAL characterize time when the thigh is horizontal as sedentary time. However, there are physiological differences between lying, bent-legged sitting, and straight-legged sitting. We introduce a three-monitor configuration to assess detailed sedentary postures and demonstrate its use in characterizing such positions in free-living conditions. We explored time spent in each sedentary posture between prolonged (>1 h) versus non-prolonged (<1 h) sedentary bouts. In total, 35 healthy adults (16♀, 24 ± 3 years; 24 h/day for 6.8 ± 1.0 days) wore an activPAL accelerometer on their thigh, torso, and shin. Hip and knee joint flexion angle estimates were determined during sedentary bouts using the dot-product method between the torso−thigh and thigh−shin, respectively. Compared to lying (69 ± 60 min/day) or straight-legged sitting (113 ± 100 min/day), most time was spent in bent-legged sitting (439 ± 101 min/day, p < 0.001). Most of the bent-legged sitting time was accumulated in non-prolonged bouts (328 ± 83 vs. 112 ± 63 min/day, p < 0.001). In contrast, similar time was spent in straight-legged sitting and lying between prolonged/non-prolonged bouts (both, p > 0.26). We document that a considerable amount of waking time is accumulated in lying or straight-legged sitting. This methodological approach equips researchers with a means of characterizing detailed sedentary postures in uncontrolled conditions and may help answer novel research questions on sedentariness.
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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 | 0.000 |
| 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".