Effects of High-Heeled Shoes and Asymmetrical Load Carrying on Lower-Extremity Kinematics During Walking in Young Women
Bibliographic record
Abstract
BACKGROUND: Asymmetrical load carrying and wearing high-heeled shoes are very common. Biomechanics studies on the combined effects of high-heeled shoe wearing and asymmetrical load carrying are lacking. We sought to identify changes in lower-extremity joint kinematics associated with the effect of shoes and asymmetrical load carrying during walking. METHODS: Fifteen healthy young women (mean ± SD: age, 24.67 ± 3.54 years; body weight, 54.96 ± 6.67 kg; and height, 162.2 ± 3.91 cm) who habitually wore high-heeled shoes participated in the study. They were asked to walk under nine combined conditions of three heights of shoe heels (0, 3, and 9 cm) and three carried loads (0%, 5%, and 10% of body weight). Temporospatial parameters and maximal joint angles in the sagittal and frontal planes of the hip, knee, and ankle on both limbs were studied. RESULTS: It was found that high-heeled shoe wearing and asymmetrical load carrying altered temporospatial parameters and joint kinematics. With increased heel height and load weight, cadence decreased and stride length increased. The knee flexion angle increased with an increase in heel height, and the load served only to exacerbate the changes. Changes in the hip angle were mostly caused by asymmetrical load carrying, whereas angle changes in the ankle were mostly caused by an increase in heel height. CONCLUSIONS: This study demonstrated that when high-heeled shoe wearing and asymmetrical load carrying are combined, changes at each joint are much greater than with high-heeled shoe wearing or load carrying alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".