Investigating the test-retest reliability of Illinois Agility Test for wheelchair users
Bibliographic record
Abstract
The Illinois Agility Test (IAT) is a standard agility course used to assess and train able-bodied athletes as well as wheelchair-sport athletes and rehabilitation of wheelchair users. It has been shown to be a reliable and valid tool to assess the able-bodied population, but the reliability of this test for assessing wheelchair propulsion has never been shown. The purpose of this study is to investigate the test-retest reliability of IAT to assess wheelchair propulsion. In this paper, the test-retest reliability of using IAT for wheelchair users is found for peak and average velocity, acceleration, tangential and total force of the push, each for the left and the right wheel. Each of these variables was found for thirty-two decisive points throughout the IAT path. The Intra-class Correlation Coefficient (ICC) was found to be very strong for 15 out of 16 variables. The average ICC was 89% and the average 95% confidence interval was [44% 96%]. In addition, thirty-seven other significant propulsion parameters were found that are clinically important, such as the number of pushes participants take to go around cones on the right, relative to turning around the cones on the left. Also, all thirty-seven variables were compared between the two sessions using four separate MANOVAs; the results showed no significant difference between IAT performed in the two sessions which were at least one week apart. This, in turn, backs the reliability of IAT for wheelchair users. These results are sufficient evidence to show that IAT is a reliable tool to test wheelchair agility for fifteen variables tested for non-wheelchair users. Since experienced wheelchair users are much more consistent in wheelchair propulsion compared to non-wheelchair-users, the results of this study show that IAT can be used as a reliable tool to assess and train wheelchair users, both for clinical and athletic applications.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".