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Feeding development in healthy infants: A comparative framework for children with Spinal Muscular Atrophy - The
Charlotte Dumitrascu1, Christina Pflug1, Jun Oh2
1Department of Voice, Speech and Hearing Disorders, University Dysphagia Center, University Medical Center Hamburg-Eppendorf, Germany.
International Journal of Pediatric Otorhinolaryngology
|April 9, 2025
Summary
This study established normative data for the Dysphagia in Spinal Muscular Atrophy (DySMA) tool in healthy infants. The DySMA effectively tracks feeding development, aiding in the assessment of children with Spinal Muscular Atrophy (SMA).
Area of Science:
- Pediatric Swallowing and Feeding
- Neuromuscular Disorders
- Clinical Diagnostics
Background:
- Limited normative data exists for swallowing parameters in healthy children, crucial for diagnosing conditions like Spinal Muscular Atrophy (SMA).
- Early detection of swallowing dysfunction in SMA is vital, even with disease-modifying therapies, necessitating reliable assessment tools.
Purpose of the Study:
- To generate normative data for the Dysphagia in Spinal Muscular Atrophy (DySMA) tool in a healthy infant population.
- To establish a benchmark for comparing swallowing and feeding abilities in infants with and without SMA.
Main Methods:
- Recruitment of healthy infants and toddlers aged 0-24 months, stratified into seven age groups.
- Assessment using the DySMA protocol by two certified speech-language pathologists.
- Descriptive statistical analysis and inter-rater reliability testing (ICC).
Main Results:
- 92 healthy children were analyzed, with DySMA scores ranging from 21 to 35.
- Excellent inter-rater reliability (ICC = .936) was demonstrated for the DySMA tool.
- Physiological development categories showed significant age-dependent changes, unlike pathology categories.
Conclusions:
- The DySMA tool accurately reflects physiological feeding development in infants and toddlers.
- Higher DySMA scores correlate with broader skill sets, while lower scores indicate limitations.
- This normative data enables the identification of deviations from typical development in children with SMA.

