Future Research

1. ALTERNATIVE METHODS FOR ASSESSMENT OF CONVERSATIONAL LANGUAGE DEVELOPMENT: USE IN A CLINICAL POPULATION Rebekah Edgar, B.B.A., Felicia Engebrecht, B.A., Rachel Farmer, B.S., Andrea Gianniny, B.A. & Nancy Scherer, Ph. D. Department of Audiology and Speech-Language Pathology, East Tennessee State University, Johnson City, TN Abstract Introduction: Language samples have been an invaluable tool in assessing the acquisition of language for clinical populations. However, language sampling (LSA) is very time consuming and the sample may not be representative of the child’s use of language in everyday activities. A new method for collecting and analyzing language has been developed. The Language Environment Analysis (LENA) is a recorder which is worn by the child in special clothing and provides up to 16 hours of recorded language. The sample is obtained during the child’s natural everyday activities. Purpose: To compare data collected using a traditional language sample, which was analyzed using Systematic Analysis of Language Transcripts (SALT), to data collected and analyzed using the LENA device and software.Participants: The participants included 19 children, ranging in age from 13 to 36 months, with cleft lip and/or palate. Methods: Traditional language samples were collected using a standardized protocol and compared to weekend and weekday LENA samples. Measures of vocabulary and syntax were compared between the samples. Results: The results showed that there were no significant differences between LENA weekend and weekday samples, although there were large standard deviations for all measures. There was no significant correlation between Child Vocalization Count (LENA) and Total Number of Words (SALT), although it approached significance. The relationship between LENA measures and language samples indicated that for vocabulary, the two were related for children over 30 months of age but not for younger children. There was a significant correlation between Estimated Mean Length of Utterance (EMLU) from LENA and Mean Length of Utterance (MLU) from SALT. Discussion: LENA does provide an alternative method for screening language development in naturalistic environments, particularly syntactic development. However, this study suggests that vocabulary measures are not particularly reliable with LENA analyses until after 30 months of age. Clinical Implications: LENA is a promising tool to streamline the collection and analysis of language, though more research is needed to compare LENA measures to traditional assessment methods. • Methods • This study was a part of an ongoing study for children with cleft palate at East Tennessee State University & Vanderbilt University. • Participants: • 19 participants, 12 males & 7 females, 13-36 months of age • Primary palate repair at 12 months of age or earlier • Cognitive standard score ≤ 80 • Non-syndromic, monolingual, and no sensorineural hearing loss • PLS: Preschool Language Scale; CP: Cleft Palate; BCLP: Bilateral Cleft Lip & Palate; UCLP: Unilateral Cleft Lip & Palate; and MLU: Mean Length of Utterance • Procedures:Approximately 10 hours of data were recorded on a weekend day and weekday using the LENA digital language processor (DLP). Thirty minute language samples were collected following a standard protocol. • The LENA measures were: • Estimated Mean Length of Utterance (EMLU) • Child Vocalization Count (CVC) • The SALT measures were: • Mean Length of Utterance (MLU) • Total Number of Words • Vocabulary Measures: Child Vocalization Count (CVC-LENA) was compared to Total Number of Words (SALT). • Syntax Measures: Estimated Mean Length of Utterance (EMLU-LENA) was compared to Mean Length of Utterance (MLU) from SALT. • Methods, cont. • Statistical significance was derived from Wilcoxon Matched Pairs Signed Ranks tests. Relationships between vocabulary and syntax measures were also compared with correlation coefficients. Significance levels were set at ≤ 0.05. • Discussion • Vocabulary • For CVC and Total Number of Words, the relationship was stronger for children over 30 months of age. LENA collected a significantly higher amount of child vocalizations, which included both non-word vocalizations and words. Younger children are more likely to produce more non-word vocalization than older children. • Syntax • There is a high correlation between EMLU and MLU, • demonstrating that LENA collects data comparable to the language sample. LENA is an accurate, time efficient way to screen this aspect of language. LENA provides a naturalistic sample for language screening. • Results • Vocabulary • In Figure 1., Child Vocalization Count (CVC) was compared to Total Number of Words, and the trend lines show slopes that intersect at about 33 months. The slope of the LENA regression line was 5 and the slope of the SALT regression line was 16. The correlation coefficient between CVC and Total Number of Words was 0.42, and the p-value ≤ 0.06, which was not significant but approached significance. The data indicated that LENA and SALT were not highly associated for the younger participants; however, this relationship improved for the older children. • Syntax • Figure 2. displays the comparison of LENA Estimated Mean Length of Utterance (EMLU) to SALT Mean Length of Utterance (MLU). The slope of the LENA regression line was 0.13, and the slope of the SALT regression line was 0.12. The correlation coefficient between these variables was 0.73. The EMLU and MLU variables were not significantly different. Future Research A prospective study with a larger number of participants is necessary to further validate the relationship between LENA and language samples. This would also enable researchers to divide the children into two or more age groups to more precisely analyze the relationships between LENA and traditional language sampling measures. Conducting further research with other clinical populations would also be beneficial in exploring the versatility of LENA. References Kuehn, D. P., & Henne, L. J. (2003). Speech evaluation and treatment for patients with cleft palate. American Journal of Speech- Language Pathology, 12(1), 103-109. Kuehn, D. P., & Moller, K. T. (2000). Speech and language issues in the cleft palate population: The state of the art. Cleft Palate- Craniofacial Journal, 37(4), 1-35. Price, L. H., Hendricks, S., & Cook, C. (2010). Incorporating computer-aided language sample analysis into clinical practice. Language, Speech, and Hearing Services in Schools, 41(2), 206- 222. Research Behind Everything We Do. (2010).LENA Foundation. Retrieved November 14, 2010, from http://www.lenafoundation.org Resources. Salt Software. Retrieved November 14, 2010, from http://www.saltsoftware.com/resources Introduction Traditional language samples are generally reserved for children with severe language disorders due to the time and effort involved in collecting, transcribing, and analyzing the data (Price et al., 2010). One problem with traditional language sampling is that, though it can be set up as a play interaction between a parent and child, it is still conducted in a clinic, which does not produce the most naturalistic sample. Language sampling is an important part of treating children with cleft lip and palate (CLP), because it is the best way to identify areas in language and articulation that require intervention (Kuehn & Moller, 2000). However, language sampling is labor intensive and does not sample language in home contexts. Since many of these delays begin to emerge between 8 to 12 months (Kuehn & Henne, 2003), it is important to screen the child’s language skills early and often. Acknowledgements This study was funded by the National Institute on Deafness & Other Communicative Disorders. DC009654.