Reliability of a multi‐segment foot model in a neutral cushioning shoe during treadmill walking
Bibliographic record
Abstract
BACKGROUND: Detailed kinematics of the foot has been frequently reported on in the literature, specifically using various multi-segment foot models. It is important to identify the reliability of a multi-segment foot model in a population of mixed genders and activity levels, while walking in commonly used footwear. The main objective of this study was to investigate the between-day reliability and within-session variability of the Oxford Foot Model (OFM) while walking in a neutral cushioning shoe. METHODS: A 7-camera Vicon motion capture system was used along with 29 passive reflective markers, placed on the participant to examine the multi-segment foot kinematics of the left foot using the OFM. Windows were cut in New Balance 840 shoes following reports from a previous investigation to maintain shoe integrity during testing. Two walking sessions on separate days were collected for 12 healthy participants, with an average total of 22 gait cycles per day. RESULTS: ICCs ranged from 0.020 to 0.964 for between-day reliability, and within-session ICC values ranged from 0.268 to 0.985. Between-day ICC values of the relative measures (excursion and range of motion (ROM)) were higher than the absolute angle measures (angle at foot strike and peak angle). Largest differences were measured in the transverse plane, and the smallest differences in the sagittal plane. Bland-Altman plots revealed best agreement in the frontal and sagittal planes. SEM values ranged from 0.04 to 3.5 for the between-day reliability. CONCLUSIONS: Between-day reliability and within-session variability were comparable to previous studies for adults walking barefoot and shod. This research demonstrates that the OFM can produce reliable data when applied to the assessment of a shod foot.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".