Biomechanics of Running with a Commercially Available Non-Custom Athletic Footwear Insole
Bibliographic record
Abstract
A commercially available non-custom athletic footwear (AF) insole has recently been developed and is claimed to help prevent and treat running injuries such as patellofemoral pain syndrome (PFPS), plantar faciitis (PF), and tibial stress syndrome (TSS); however, no data are available to support whether these insoles influence biomechanical variables associated with these injuries. PURPOSE: To determine if the AF insole alters biomechanical variables associated with PFPS, PF and TSS. METHODS: Fifteen uninjured participants completed 5 trials running at 4m/s along a 30m runway with each of two conditions: (1) their own running shoe, control (CON), and (2) #1 with the AF insole placed within the shoe. As participants ran, 3D trajectories of the participant’s right lower leg and foot, and 3D ground reaction force data were recorded. Kinetic and kinematic data were used to calculate the following biomechanical variables: (1) knee abduction angular impulse during stance phase (associated with PFPS), (2) peak foot eversion angle during stance phase (associated with PF and TSS), and (3) peak foot eversion velocity during stance (associated with TSS). Two-tailed paired-samples t-tests were used to compare the CON and AF insole conditions. RESULTS: No significant differences were observed between the CON and AF insole conditions for knee abduction angular impulses (CON: 10.3±5.4 Nms, AF insole: 9.6±4.7 Nms, p=0.26), peak foot eversion angles (CON: 11.1±4.5 deg, AF insole: 10.8±4.3 deg, p=0.64) or peak foot eversion velocities (CON: 378±108 deg/s, AF insole: 408±122 deg/s, p=0.06). However, individually, many participants (7/15 for knee abduction angular impulse, 11/15 for peak foot eversion and eversion velocity) experienced relevant biomechanical changes of ± 10% or more with the AF insole. CONCLUSIONS: AF insoles have an influence on biomechanical variables associated with PFPS, PF or TSS for individual runners; however, their influence is not systematic across participants. Consequently, the AF insoles may offer benefit to some runners, but be detrimental to others.
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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".