Semantic feature effects on SFA treatment outcomes (Evans et al., 2020)
datasetposted on 23.06.2020, 17:35 authored by William S. Evans, Rob Cavanaugh, Michelle L. Gravier, Alyssa M. Autenreith, Patrick J. Doyle, William D. Hula, Michael Walsh Dickey
Purpose: Semantic feature analysis (SFA) is a naming treatment found to improve naming performance for both treated and semantically related untreated words in aphasia. A crucial treatment component is the requirement that patients generate semantic features of treated items. This article examined the role feature generation plays in treatment response to SFA in several ways: It attempted to replicate preliminary findings from Gravier et al. (2018), which found feature generation predicted treatment-related gains for both trained and untrained words. It examined whether feature diversity or the number of features generated in specific categories differentially affected SFA treatment outcomes.
Method: SFA was administered to 44 participants with chronic aphasia daily for 4 weeks. Treatment was administered to multiple lists sequentially in a multiple-baseline design. Participant-generated features were captured during treatment and coded in terms of feature category, total average number of features generated per trial, and total number of unique features generated per item. Item-level naming accuracy was analyzed using logistic mixed-effects regression models.
Results: Producing more participant-generated features was found to improve treatment response for trained but not untrained items in SFA, in contrast to Gravier et al. (2018). There was no effect of participant-generated feature diversity or any differential effect of feature category on SFA treatment outcomes.
Conclusions: Patient-generated features remain a key predictor of direct training effects and overall treatment response in SFA. Aphasia severity was also a significant predictor of treatment outcomes. Future work should focus on identifying potential nonresponders to therapy and explore treatment modifications to improve treatment outcomes for these individuals.
Supplemental Material S1. Participant demographics.
Supplemental Material S2. Language testing results.
Supplemental Material S3. Performance variables (summarized across all treated items).
Evans, W. S., Cavanaugh, R., Gravier, M. L., Autenreith, A. M., Doyle, P. J., Hula, W. D., & Dickey, M. W. (2020). Effects of semantic feature type, diversity, and quantity on semantic feature analysis treatment outcomes in aphasia. American Journal of Speech-Language Pathology. Advance online publication. https://doi.org/10.1044/2020_AJSLP-19-00112
Publisher Note: This article is part of the Special Issue: Select Papers From the 49th Clinical Aphasiology Conference.
This research was supported by VA Rehabilitation Research and Development Award I01RX000832 to Michael Walsh Dickey and Patrick J. Doyle and a VA Pittsburgh Healthcare System Geriatric Research Education and Clinical Center Pilot Project awarded to William S. Evans.
Read the peer-reviewed publication
languageaphasiasemanticfeaturetypediversityquantitysemantic feature analysisSFAoutcomestreatmentnamingperformancetreateduntreatedwordsreplicategainspredictchronic aphasialistssequentialgeneratedcodedcategoryaccuracyresponsepredictordirecteffectsinterventionseverityimpairmentpeople with aphasiaPWALanguageNeurology and Neuromuscular Diseases