Using big-data to validate theories of rehabilitation in aphasia
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1
Boston University, United States
Introduction. While the evidence for efficacy of rehabilitation of language disorders is fairly robust and conclusive (Allen, et al., 2012; Brady, et al., 2012; Kelly, Brady, & Enderby, 2010; Cherney, et al., 2008), a major limitation identified by these reviews is that the sample size of patients in each of the interventions have been modest (5-20 patients). As technology moves our field forward, we can now collect and analyze larger sets of data to validate theories of rehabilitation. As a first step, we report data from a recently completed study examining the effectiveness of software platform (Constant Therapy) to deliver, monitor and analyze treatment for individuals with aphasia (Des Roches et al., 2015).
Methods. Fifty one individuals with language and cognitive deficits were administered standardized tests (Western Aphasia Battery, Boston Naming Test, Pyramids and Palm Trees, and Cognitive Linguistic Quick Test) prior to initiation and following completion of therapy. Forty-two experimental patients used the iPad-based therapy once a week with the clinician and up to six days a week for home practice. Nine control patients practiced therapy on the iPad once per week with the clinician only. Thirty-eight therapy tasks were divided into language and cognitive activities that were developed (Des Roches et al., 2015), 28 of these tasks included buttons that revealed a hint to assist the patient answer the item. The assigned therapy tasks were tailored to that individual’s language and cognitive impairment profile based on an initial baseline assessment. Each task was practiced until accuracy on task reached 100% on multiple occasions at which point that task was replaced with the task at the next level of difficulty. The 51 patients each completed a 10 week program leading to total of 3327 therapy sessions across patients.
Analysis and Results: Mixed regression models showed that both the experimental and control groups improved but experimental participants showed more significant changes on the therapy tasks and standardized tests than control patients. Additionally, the more severe patients benefited from the simpler language and cognitive tasks whereas the less severe patients improved on tasks that required a combination of language/cognitive processing. Correlations between starting severity scores and the amount of change on standardized tests showed that participants with lower initial scores showed more improvement on the standardized tests than participants with higher initial scores. In addition, because participants were able to self-administer the hints for individual items, the relationship between levels of hint use and participants’ corresponding accuracy showed that hint use often negatively predicted performance. Correlations between frequency of hint use and participant language/cognitive impairment profiles showed significant negative relationships between hint use and performance on standardized tests indicating that the more severe the participants the more often they utilized hints to complete the therapy tasks.
Conclusions: Analysis at the patient, therapy task and item level allow an evaluation of the efficacy of rehabilitation and individual responsiveness to rehabilitation. In addition, preliminary results from a larger data set collected anonymously from over 2,000 independent patient users is also presented.
Acknowledgements
This presentation is part of a symposium-
The rise of big-data in aphasiology: an opportunity for theory development
References
Allen, L., et al., Therapeutic interventions for aphasia initiated more than six months post stroke: a review of the evidence. Topics in Stroke Rehabilitation, 2012. 19(6): p. 523-35.
Brady, M.C., et al., Speech and language therapy for aphasia following stroke. The Cochrane database of systematic reviews, 2012. 5: p. CD000425.
Cherney, L.R., et al., Evidence-based systematic review: Effects of intensity of treatment and constraint-induced language therapy for individuals with stroke-induced aphasia. Journal of Speech, Language, and Hearing Research, 2008. 51(5): p. 1282-1299.
Des Roches CA, Balachandran I, Ascenso EM, Tripodis Y and Kiran S (2015) Effectiveness of an impairment-based individualized rehabilitation program using an iPad-based software platform. Front. Hum. Neurosci. 8:1015. doi: 10.3389/fnhum.2014.01015
Kelly, H., M.C. Brady, and P. Enderby, Speech and language therapy for aphasia following stroke. The Cochrane database of systematic reviews, 2010(5): p. CD000425.
Keywords:
Aphasia,
Big-data,
Clinical Decision Support Systems,
Rehabilitation,
Intervention Studies
Conference:
Academy of Aphasia 53rd Annual Meeting, Tucson, United States, 18 Oct - 20 Oct, 2015.
Presentation Type:
symposium
Topic:
Not student first author
Citation:
Kiran
S and
Des Roches
CA
(2015). Using big-data to validate theories of rehabilitation in aphasia.
Front. Psychol.
Conference Abstract:
Academy of Aphasia 53rd Annual Meeting.
doi: 10.3389/conf.fpsyg.2015.65.00053
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Received:
01 May 2015;
Published Online:
24 Sep 2015.
*
Correspondence:
Prof. Swathi Kiran, Boston University, Boston, United States, kirans@bu.edu