The Kinesthetic and Visual Imagery Questionnaire (KVIQ) for Assessing Motor Imagery in Persons with Physical Disabilities: A Reliability and Construct Validity Study
Bibliographic record
Abstract
PURPOSE: To benefit from mental practice training after stroke, one must be able to engage in motor imagery, and thus reliable motor imagery assessment tools tailored to persons with sensorimotor impairments are needed. The aims of this study were to (1) examine the test-retest reliability of the Kinesthetic and Visual Imagery Questionnaire (KVIQ-20) and its short version (the KVIQ-10) in healthy subjects and subjects with stroke, (2) investigate the internal consistency of both KVIQ versions, and (3) explore the factorial structure of the two KVIQ versions. METHODS: The KVIQ assesses on a five-point ordinal scale the clarity of the image (visual: V subscale) and the intensity of the sensations (kinesthetic: K subscale) that the subjects are able to imagine from the first-person perspective. Nineteen persons who had sustained a stroke (CVA group) and 46 healthy persons (CTL group) including an age-matched (aCTL: n = 19) control group were assessed twice by the same examiner 10 to 14 days apart. The test-retest reliability was assessed using intraclass correlation coef- ficients (ICCs). The internal consistency (Cronbach alpha) and the factorial structure of both KVIQ versions were studied in a sample of 131 subjects. RESULTS: In the CVA group, the ICCs ranged from 0.81 to 0.90, from 0.73 to 0.86 in the aCTL group, and from 0.72 to 0.81 in the CTL group. When imagining movements of the affected and unaffected limbs (upper and lower limbs combined) ICCs in the CVA group ranged, respectively, from 0.71 to.87 and from 0.86 to 0.94. Likewise, when imagining movement of the dominant and nondominant limbs, ICCs in the aCTL group ranged, respectively, from 0.75 to 0.89 and from 0.81 to.92. Cronbach a values were, respectively, 0.94 (V) and 0.92 (K) for the KVIQ-20 and 0.89 (V) and 0.87(K) for the KVIQ-10. The factorial analyses indicated that two factors explained 63.4% and 67.7% of total variance, respectively. CONCLUSION: Both versions of the KVIQ present similar psychometric properties that support their use in healthy individuals and in persons post-stroke. Because the KVIQ-10 can be administered in half the time, however, it is a good choice when assessing persons with physical disabilities.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".