Algorithm-Driven Dosage Decisions (AD3): Optimizing Treatment for Children With Language Impairment Background This study was designed to provide recommended amounts of treatment to achieve the optimal amount of language gain for children with language impairment. Method The authors retrospectively analyzed treatment outcomes for 233 children for delivered dose, intensity, and cumulative intensity of therapy. The steps of the analytical ... Research Article
Newly Published
Research Article  |   December 09, 2016
Algorithm-Driven Dosage Decisions (AD3): Optimizing Treatment for Children With Language Impairment
 
Author Affiliations & Notes
  • Laura M. Justice
    The Ohio State University, Columbus
  • Jessica Logan
    The Ohio State University, Columbus
  • Hui Jiang
    The Ohio State University, Columbus
  • Mary Beth Schmitt
    Texas Tech University Health Sciences Center, Lubbock
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Laura M. Justice: justice.57@osu.edu
  • Editor: Krista Wilkinson
    Editor: Krista Wilkinson×
  • Associate Editor: Carol Miller
    Associate Editor: Carol Miller×
Article Information
Development / Language Disorders / Attention, Memory & Executive Functions / Newly Published / Research Article
Research Article   |   December 09, 2016
Algorithm-Driven Dosage Decisions (AD3): Optimizing Treatment for Children With Language Impairment
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2016_AJSLP-15-0058
History: Received May 20, 2015 , Revised November 11, 2015 , Accepted July 11, 2016
 
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2016_AJSLP-15-0058
History: Received May 20, 2015; Revised November 11, 2015; Accepted July 11, 2016

Background This study was designed to provide recommended amounts of treatment to achieve the optimal amount of language gain for children with language impairment.

Method The authors retrospectively analyzed treatment outcomes for 233 children for delivered dose, intensity, and cumulative intensity of therapy. The steps of the analytical process they applied to arrive at algorithms for recommended amounts of treatment were (1) multilevel modeling to predict children's language gains from the 3 intensity parameters and (2) extraction of regression weights to create a recommended amount of treatment.

Results Optimal outcomes can be identified using an equation specifying Ŷ = desired points of change (e.g., 0.6 SD units), V = child's baseline language skills, D = average number of minutes spent targeting language in a session, F = total number of sessions conducted across the year, and D × F = product of planned dose and frequency (cumulative intensity). Input of the values for Ŷ and V provides recommended amount of treatment.

Conclusions This study constitutes the first effort to provide empirical guidance on intensity of treatment for children with language impairment. The use of algorithm-driven dosage recommendations may be more effective than clinician judgment and trial and error, although these correlational results must be confirmed with experimental methods.

Acknowledgments
The original research was supported by the U.S. Department of Education, Institute of Education Sciences Grant R324A090012, awarded to Laura M. Justice. The authors are grateful to the numerous speech-language pathologists, families, and children who participated in the original research utilized in this study.
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