The Submaximal Lateral Shuffle Test: A reliability and sensitivity analysis
Bibliographic record
Abstract
Lateral ankle stability and how it changes in different footwear has been investigated for years. Research, however, has shown a lack of reliability or sensitivity of available methodologies. This study aimed to evaluate the test-retest reliability and sensitivity of a novel lateral stability protocol, the Submaximal Lateral Shuffle Test (SLST). We recruited 11 and 40 participants to assess reliability and sensitivity of the SLST, respectively. Participants performed the SLST in footwear that differed in collar height and upper stiffness. ICC values showed good to excellent reliability in peak ankle angles and moments, ground reaction forces, impulses, stance time, and performance time. Significantly lower peak inversion and adduction angles and lower medio-lateral push off peak forces were found in the high cut shoes compared to the low cut shoes. The medio-lateral landing peak force showed lower forces in the high cut shoes. The smallest worthwhile change indicated meaningful differences in 70.0-82.5% of participants for inversion, adduction, medio-lateral landing peak, and push off peak forces. These results, however, were not systematic such that there was not a consistent direction of the difference for all participants. In conclusion, the SLST is a promising protocol to further investigate lateral stability in footwear.
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".