Orthotic Comfort Is Related to Kinematics, Kinetics, and EMG in Recreational Runners
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the relationship between differences in comfort and changes in lower extremity kinematic and kinetic variables and muscle activity in response to foot orthoses. METHODS: Twenty-one recreational runners volunteered for this study. Three orthotic conditions (posting, custom-molding, and posting and custom-molding) were compared with a control (flat) insert. Lower extremity kinematic, kinetic, and EMG data were collected for 108 trials per subject and condition in nine sessions per subject for overground running at 4 m.s-1. Comfort for all orthotic conditions was assessed in each session using a visual analog scale. The statistical tests used included repeated measures ANOVA, linear regression analysis, and discriminant analysis (alpha = 0.05). RESULTS: Comfort ratings were significantly different between orthotic conditions and the control condition ([lower, upper] confidence limits; posting: [-3.1, -0.8]; molding: [0.4, 3.4]; and posting and molding: [-1.1, 1.9]); 34.9% of differences in comfort were explained by changes in 15 kinematic, kinetic, and EMG variables. The 15 kinematic, kinetic, and EMG variables that partially explained differences in comfort classified 75.0% of cases correctly to the corresponding orthotic condition. DISCUSSION: In general, comfort is an important and relevant feature of foot orthoses. Evaluations of foot orthoses using comfort do not only reflect subjective perceptions but also differences in functional biomechanical variables. Future research should focus on defining the relationship between comfort and biomechanical variables for material modifications of footwear, different modes of locomotion, and the general population.
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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 | 0.002 |
| 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".