Applying adaptive distributed practice to self-managed computer-based anomia treatment: A single-case experimental design

Abstract

IntroductionThere is a pressing need to improve computer-based treatments for aphasia to increase access to long-term effective evidence-based interventions. The current single case design incorporated two learning principles, adaptive distributed practice and stimuli variability, to promote acquisition, retention, and generalization of words in a self-managed computer-based anomia treatment. MethodsTwo participants with post-stroke aphasia completed a 12-week adaptive distributed practice naming intervention in a single-case experimental design. Stimuli variability was manipulated in three experimental conditions: high exemplar variability, low exemplar variability, and verbal description prompt balanced across 120 trained words. Outcomes were assessed at 1-week, 1-month, and 3-months post-treatment. Statistical comparisons and effect sizes measured in the number of words acquired, generalized, and retained were estimated using Bayesian generalized mixed-effect models. ResultsParticipants showed large and robust acquisition, generalization, and retention effects. Out of 120 trained words, participant 1 acquired ∼77 words (trained picture exemplars) and ∼63 generalization words (untrained picture exemplars of treated words). Similarly, participant 2 acquired ∼57 trained words and ∼48 generalization words. There was no reliable change in untrained control words for either participant. Stimuli variability did not show practically meaningful effects. ConclusionsThese case studies suggest that adaptive distributed practice is an effective method for re-training more words than typically targeted in anomia treatment research (∼47 words on average per Snell et al., 2010). Generalization across experimental conditions provided evidence for improved lexical access beyond what could be attributed to simple stimulus-response mapping. These effects were obtained using free, open-source flashcard software in a clinically feasible, asynchronous format, thereby minimizing clinical implementation barriers. Larger-scale clinical trials are required to replicate and extend these effects.