Reliability and Validity of the Vividness of Motor Imagery Questionnaire-2 in Individuals With Neurological Motor Impairments: A Preliminary Study
Bibliographic record
Abstract
Purpose: Motor imagery (MI) is a cost-effective technique in neurorehabilitation to promote motor learning. The Vividness of Motor Imagery Questionnaire-2 (VMIQ-2) is a reliable and valid tool to assess MI ability in athletes and healthy individuals to improve motor performance. However, its reliability and validity have not been established in individuals with neurological motor impairments. Therefore, this study estimated the test-retest reliability of VMIQ-2 and its concurrent validity with the Kinesthetic and Visual Imagery Questionnaire-10 (KVIQ-10). Methods: A convenience sample of 33 individuals diagnosed with neurological motor impairments was recruited. The researcher administered the KVIQ-10 and the self-reported VMIQ-2 was completed electronically twice, 5–14 days apart. The scores of VMIQ-2 were correlated with KVIQ-10 to determine its concurrent validity, and the test-retest reliability of the total VMIQ-2 and its subscales was also calculated. Bland-Altman analysis was used to identify the differences between the two measures and a two-way mixed random effects model was used to establish the intra-class correlation coefficient (ICC) between them. Results: The VMIQ-2 and KVIQ-10 demonstrated a statistically significant negative correlation with a moderate effect size ( r = −0.36). The test-retest reliability of VMIQ-2 was high (ICC: 0.87) and the test-retest reliability of each subscale also demonstrated moderate-to-high reliability. The ICC values indicated poor reliability between the two measures while the Bland-Altman analysis indicated no consistent bias of using VMIQ-2 or KVIQ-10 in individuals with neurological motor impairments. Conclusions: Unlike the currently utilized tools, the VMIQ-2 is the first self-reported, reliable, and valid MI assessment tool for individuals with neurological motor impairments. The uniqueness of the VMIQ-2 is that it distinguishes between the different MI perspectives (i.e., internal visual and external visual). Additionally, VMIQ-2 could be an efficient, time-saving electronic measurement tool for use in clinics and telerehabilitation to assess clients' appropriateness for motor imagery practice.
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 | 0.004 | 0.011 |
| 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 | 0.000 |
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".