What Matters in Semantic Feature Analysis: Practice-Related Predictors of Treatment Response in Aphasia

摘要: Purpose This study investigated the predictive value of practice-related variables—number of treatment trials delivered, total treatment time, average number of trials per hour, and average number of participant-generated features per trial—in response to semantic feature analysis (SFA) treatment. Method SFA was administered to 17 participants with chronic aphasia daily for 4 weeks. Individualized treatment and semantically related probe lists were generated from items that participants were unable to name consistently during baseline testing. Treatment was administered to each list sequentially in a multiple-baseline design. Naming accuracy for treated and untreated items was obtained at study entry, exit, and 1-month follow-up. Results Item-level naming accuracy was analyzed using logistic mixed-effect regression models. The average number of features generated per trial positively predicted naming accuracy for both treated and untreated items, at exit and follow-up. In contrast, total treatment time and average trials per hour did not significantly predict treatment response. The predictive effect of number of treatment trials on naming accuracy trended toward significance at exit, although this relationship held for treated items only. Conclusions These results suggest that the number of patient-generated features may be more strongly associated with SFA-related naming outcomes, particularly generalization and maintenance, than other practice-related variables. Supplemental Materials https://doi.org/10.23641/asha.5734113