Criterion Validity of Accelerometers in Determining Knee-Flexion Angles During Sitting in a Laboratory Setting
Bibliographic record
Abstract
Introduction : Device-based monitors often classify all sedentary positions as the sitting posture, but sitting with bent or straight legs may exhibit unique physiological and biomechanical effects. The classifications of the specific nuances of sitting have not been understood. The purpose of this study was to validate a dual-monitor approach from a trimonitor configuration measuring knee-flexion angles compared to motion capture (criterion) during sitting in laboratory setting. Methods : Nineteen adults (12 ♀ , 24 ± 4 years) wore three activPALs (torso, thigh, tibia) while 14 motion capture cameras simultaneously tracked 15 markers located on bony landmarks. Each participant completed a 45-s supine resting period and eight, 45-s seated trials at different knee flexion angles (15° increment between 0° and 105°, determined via goniometry), followed by 15 s of standing. Validity was assessed via Friedman’s test (adjusted p value = .006), mean absolute error, Bland–Altman analyses, equivalence testing, and intraclass correlation. Results : Compared to motion capture, the calculated angles from activPALs were not different during 15°–90° (all, p ≥ .009), underestimated at 105° ( p = .002) and overestimated at 0°, as well as the supine position (both, p < .001). Knee angles between 15° and 105° exhibited a mean absolute error of ∼5°, but knee angles <15° exhibited larger degrees of error (∼10°). A proportional (β = −0.12, p < .001) bias was observed, but a fixed (0.5° ± 1.7°, p = .405) bias did not exist. In equivalence testing, the activPALs were statistically equivalent to motion capture across 30°–105°. Strong agreement between the activPALs and motion capture was observed (intraclass correlation = .97, p < .001). Conclusions : The usage of a three-activPAL configuration detecting seated knee-flexion angles in free-living conditions is promising.
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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".