Adapting and Validating a Motor Intelligence Assessment Tool for Children with Intellectual Disabilities: Prioritizing Movement and Sensory-Motor Integration
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».