Changes in multi‐segment foot biomechanics with a heat‐mouldable semi‐custom foot orthotic device
Bibliographic record
Abstract
BACKGROUND: Semi-custom foot orthoses (SCO) are thought to be a cost-effective alternative to custom-made devices. However, previous biomechanical research involving either custom or SCO has only focused on rearfoot biomechanics. The purpose of this study was therefore to determine changes in multi-segment foot biomechanics during shod walking with and without an SCO. We chose to investigate an SCO device that incorporates a heat-moulding process, to further understand if the moulding process would significantly alter rearfoot, midfoot, or shank kinematics as compared to a no-orthotic condition. We hypothesized the SCO, whether moulded or non-moulded, would reduce peak rearfoot eversion, tibial internal rotation, arch deformation, and plantar fascia strain as compared to the no-orthoses condition. METHODS: Twenty participants had retroreflective markers placed on the right limb to represent forefoot, midfoot, rearfoot and shank segments. 3D kinematics were recorded using an 8-camera motion capture system while participants walked on a treadmill. RESULTS: Plantar fascia strain was reduced by 34% when participants walked in either the moulded or non-moulded SCO condition compared to no-orthoses. However, there were no significant differences in peak rearfoot eversion, tibial internal rotation, or medial longitudinal arch angles between any conditions. CONCLUSIONS: A semi-custom moulded orthotic does not control rearfoot, shank, or arch deformation but does, however, reduce plantar fascia strain compared to walking without an orthoses. Heat-moulding the orthotic device does not have a measurable effect on any biomechanical variables compared to the non-moulded condition. These data may, in part, help explain the clinical efficacy of orthotic devices.
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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".