Effect of Asymmetrical Load Carrying on Joint Kinetics of the Lower Extremity During Walking in High-Heeled Shoes in Young Women
Bibliographic record
Abstract
BACKGROUND: Carrying a load asymmetrically and walking in high-heeled shoes are common in women. Knowledge of the effects of the two combined conditions on lower-limb kinetics is lacking. We sought to examine the effects of walking in high-heeled shoes and carrying an asymmetrical load on the joint kinetics of the lower extremity in young women. METHODS: Fifteen participants were asked to walk in flat-heeled and 9-cm high-heeled shoes and to asymmetrically carry loads of 0% body weight (BW), 5% BW, and 10% BW. The three-dimensional joint moments of the hip, knee, and ankle in each of the walking conditions were studied through ground reaction force measurements and motion analysis. RESULTS: Walking in high-heeled shoes and asymmetrically carrying a load of 5% or 10% BW resulted in significant differences in ankle joint moments of the loaded and unloaded lower limbs. Compared with walking in flat-heeled shoes, walking in high-heeled shoes and carrying a load asymmetrically significantly increased hip extension, hip abduction, knee extension, and knee adduction moments and decreased ankle plantar moment of the loaded leg. Walking in high-heeled shoes carrying a load of 10% BW resulted in greater significant changes in hip abduction, knee extension, and ankle dorsiflexion moments in the loaded leg than did carrying a load of 5% BW. CONCLUSIONS: These findings indicate that walking in high-heeled shoes and asymmetrical load carrying create significant differences in joint loading between the two limbs and alter lower-extremity kinetics.
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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) | 0.002 | 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".