Effects of body mass and sex on kinematics and kinetics of the lower extremity during stair ascent and descent in older adults
Bibliographic record
Abstract
The effects of body mass and sex on lower limb biomechanics during ascent and descent were examined in participants aged 50 to 75 with normal weight (n = 19), overweight (n = 18), and obese (n = 8). Peak joint angles and joint moment of the lower limb were analyzed with the VICON motion analysis system. Results from multivariate analysis of variance showed that during descent, the overweight participants had significantly higher knee extensor moment (0.98 ± 0.30 N∙m/kg-1) than the normal-weight participants (0.70 ± 0.29 N∙m/kg-1). The obese group had significantly higher ankle abductor moment (0.21 ± 0.11 N∙m/kg-1) than the normal weight (0.12 ± 0.08 N∙m/kg-1) and overweight groups (0.09 ± 0.06 N∙m/kg-1). During ascent, the obese participants had significant higher hip flexor moment (0.42 ± 0.20 N∙m/kg-1) than overweight participants (0.22 ± 0.17 N∙m/kg-1). Significant sex differences were found in knee extension angles (4.2 ± 3.4° vs 7.0 ± 3.3°) during descent, plantar flexion angles during ascent (23.7 ± 5.3° vs 15.6 ± 3.7°) and descent (29.9 ± 5.0° vs 22.1 ± 7.9°), and ankle adduction angles (6.8 ± 4.8° vs 2.5 ± 2.5°) during ascent. It is concluded that body mass has significant impact on joint loading of lower limbs during stair walking. Being overweight and obese increased hip joint loading during ascent, and knee and ankle joint loading during descent in older adults. Sex difference in joint kinematics was presented during stair walking regardless of the body mass.
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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 | 0.000 | 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) | 0.001 | 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 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".