Intra-rater reliability of footwear-related comfort assessments
Bibliographic record
Abstract
Comfort is an important aspect of athletic footwear since it has been associated with health and performance benefits. Footwear comfort is also a key consideration in orthotic therapy during the prescription/fitting process of foot orthoses. However, little is known about the actual ability of individuals to reliably assess footwear-related comfort. Therefore, the main objective of this study was to determine the intra-rater reliability of footwear comfort assessments. Ninety healthy male adults completed one familiarization and two testing sessions on different days. During each session, participants performed running trials to assess comfort of six different shoe insoles using Visual Analog Scales (VASs) and Yes–No questions. For the VAS, intra-rater reliability was determined using intra-class correlation coefficients. Cronbach's alpha was calculated to obtain the intra-rater reliability based on the Yes–No questions. For 31.1% of the participants a reliable assessment based on the VAS (intra-class correlation coefficient ≥ 0.7) was obtained. Using Yes–No questions (Cronbach's alpha ≥ 0.7), 46.7% of the participants had a reliable comfort assessment. The majority of individuals did not seem able to reliably assess footwear comfort, and the reason for this poor reliability remains unclear. However, when using footwear comfort in orthotic therapy, scientific research projects, or footwear development one should account for this low reliability of comfort assessments. This may be done by (1) simplifying the measure used to assess comfort, (2) only selecting individuals that were pre-tested for reliable assessments, and/or (3) establishing mean comfort ratings for an individual across multiple testing sessions.
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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 | 0.052 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".