Frontiers in Psychology (May 2015)
A novel approach for the analysis of treatment effects and training schedules in acquired dysgraphia.
Abstract
Introduction. While considerable research has examined the optimal intensity of aphasia treatment (Robey, 1998), there has been little work on the optimal distribution of practice within the treatment period. Research on learning and memory indicates that studied material is remembered longer when the same amount of study is distributed across multiple sessions rather than being concentrated (Pashler, et al, 2007). In this study we examine the effectiveness of distributed compared to “clustered” treatment schedules for individuals with acquired dysgraphia. Using the multiple regression approach of generalized Linear Mixed-Effects Models (LMEMs) (Barr et al., 2013) we evaluate the effectiveness of training words according to different training schedules. In addition to modeling the main effects of treatment while controlling for variables like word length and frequency, LMEMs address problems in repeated measures designs such as uneven spacing of measurements (Barr et al., 2013) and can take into account random variability in the treatment items. Thus, LMEM has a number of features that make this a promising approach to evaluating data from rehabilitation studies. Methods. Behavioral spelling treatment was administered to 5 individuals (2 female) with acquired dysgraphia resulting from a single left hemisphere stroke; ages were 46-81 and all were at least 2 years post stroke at enrollment. Study phases: pre-training evaluation, spelling training, post-training evaluation, 3 month waiting period, and follow-up evaluation. Individualized training word sets were selected for each participant depending on type and severity of dysgraphic deficit (training: n=40). Treatment used the spell-study-spell technique (Rapp, 2005) and both training schedules were applied within participant on different word subsets. In the distributed training schedule words were trained once per session (every 2-3 sessions), while in the clustered schedule, words were trained 3-4 times per session (every 6-8 sessions), for the same total number of training trials per word per schedule type (total training trials = 13-21). LMEM was used to analyze accuracy data for each individual. Predictor variables were included to evaluate the main effects of session, word type (generalization, training) , training schedule (distributed, clustered) and interactions between these predictors; by-item random intercepts and slopes were included as well. Results. 1) 5/5 individuals significantly improved their accuracy over the training period 2) 4/5 individuals showed larger training effects for distributed vs. clustered training (2 were significant or marginally so) 3) 4 /5 individuals showed significant generalization (improvement on untrained items) 4) 3 of the 4 individuals with follow-up data showed no significant learning loss for trained items at 3-month follow-up. Discussion. Using LMEM analysis techniques we analyzed a complex treatment data set for individual participants. The significant effects of training, generalization and maintenance of learning gains 3-months after training support other findings of successful dysgraphia treatment in the chronic stage. Findings also provide support for the importance of training schedule, an area that requires continued investigation. Analysis of additional participants and time points is anticipated. Acknowledgements. The work is part of a multi-site, NIDCD-supported project examining language recovery neurobiology in aphasia (DC006740). We thank Abigail Lo and Alexandra Gordon.
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