The Kinesthetic and Visual Imagery Questionnaire Is a Reliable Tool for Individuals With Parkinson Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It is not known whether individuals with Parkinson disease (PD) can practice movements mentally. Before this question can be addressed, a reliable imagery assessment tool must be established. The recently developed Kinesthetic and Visual Imagery Questionnaire (KVIQ) is valid for non-disabled individuals and individuals with stroke. We have extended this work by examining the test-retest reliability and concurrent validity of the KVIQ in individuals with PD. METHODS: Eleven individuals with mild to moderate PD were assessed, while on medication, by the same examiner at 2 sessions (5-12 days apart). Test-retest reliability was measured using intraclass correlation coefficients (ICCs). To examine concurrent validity, KVIQ scores from the second session were compared with a gold standard, the revised Movement Imagery Questionnaire, using Spearman rank order correlation coefficients. RESULTS: There was no significant difference between total KVIQ scores for the test-retest sessions (P > 0.05). Overall, test-retest reliability of the KVIQ was good (ICC = 0.87), and reliability of the subscale of the KVIQ for indexing visual imagery and kinesthetic imagery was also good (ICC = 0.82 and 0.95, respectively). However, the subscale indexing axial visual imagery showed less reliability (ICC = 0.74), suggesting that individuals with PD were not as reliable when imagining axial visual movements as they were for imagining limb movements. Concurrent validity between the second session KVIQ score and the revised Movement Imagery Questionnaire score (gold standard) was excellent (rho = 0.93). CONCLUSION: Our data support the conclusion that the KVIQ is a reliable and valid test for indexing mental imagery ability in individuals with PD. The KVIQ is easy to administer, and the movements (both real and imagined) required are appropriate for individuals with neuropathology. Our data suggest that the KVIQ is a good choice for clinicians who may wish to index motor imagery ability before implementing imagery as a rehabilitation intervention.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".