Adapting and Validating a Motor Intelligence Assessment Tool for Children with Intellectual Disabilities: Prioritizing Movement and Sensory-Motor Integration
Bibliographic record
Abstract
Background: Motor intelligence, which involves the integration of sensory input and motor output, plays a crucial role in the physical, cognitive, and social development of children with intellectual disabilities (ID). While validated tools exist to measure motor intelligence in typically developing children, there is a significant gap in reliable and adaptable assessments for children with ID. Assessing motor intelligence in this population is essential for identifying sensory-motor deficits and designing targeted interventions to enhance physical performance, promote participation in physical activities, and improve overall quality of life. Objective: To evaluate the reliability, validity, and sensitivity of the adapted tool in identifying sensory-motor deficits and movement priorities specific to this population. The ultimate goal is to provide a practical and effective assessment tool that can inform targeted interventions to improve motor performance, physical activity participation, and overall developmental outcomes for children with ID. Methods: A total of 100 children aged 9–12 years with mild-to-moderate intellectual disabilities (IQ range 50–70) were randomly selected from a special education school in Assiut province, Egypt. The study adapted an existing motor intelligence test battery, originally designed for typically developing children, to better suit the sensory-motor and cognitive abilities of children with ID. The adapted battery included tasks evaluating sensory-motor coordination, balance, motor planning, and movement prioritization. Modifications were made to simplify instructions, reduce task complexity, and incorporate visual and auditory cues to accommodate the unique needs of children with ID. Reliability and validity were assessed using Pearson’s correlation coefficients and t-tests, while factor analysis was conducted to identify key dimensions of motor intelligence in this population. Results: The motor intelligence test battery demonstrated high reliability (r = 0.813 to 0.999) and validity (t-values ranging from 7.98 to 9.33; p < 0.01). Tasks such as "Consecutive Jumps" (r = 0.980) and "Sound and Motion" (r = 0.915) showed excellent reliability, indicating their suitability for children with ID. However, tasks requiring more complex coordination, such as "Rolling Ball," exhibited moderate reliability (r = 0.529), suggesting the need for further refinement or alternative task designs for this population. Factor analysis revealed five distinct dimensions of motor intelligence, collectively explaining 35.65% of the variance, which aligned with the movement priorities and sensory-motor challenges specific to children with ID. Standardized score tables were developed to ensure fair and accurate interpretation of test results, accounting for the variability in motor abilities within this population. Conclusion: The adapted motor intelligence test battery proved to be a reliable and valid tool for assessing motor intelligence in children with intellectual disabilities. The modifications made to the original test battery ensured its appropriateness for this population, enabling the identification of sensory-motor deficits and movement priorities. The study highlights the importance of tailoring assessment tools to the unique needs of children with ID, ensuring accurate measurement and meaningful interpretation of results. The researcher recommends the inclusion of the adapted motor intelligence battery and the standardized score tables in related programs within intellectual schools to support the development of targeted interventions. These interventions can enhance motor performance, promote physical activity participation, and improve overall quality of life for children with ID.
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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 | 0.005 | 0.009 |
| 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 | 0.000 |
| 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".