Objective: To evaluate item-factor structures of the 15 sensory items from the Survey of Pathways to Diagnosis and Services (Pathways) and examine the best fitting model. Methods: The study subjects were 1,968 children aged 6-17 years, who had ever been diagnosed with Autism Spectrum Disorder (ASD) and/or Intellectual Disability (ID). Factor analyses and item response theory models were used to determine the best fitting item-factor structure of the sensory items. The Strengths and Difficulties Questionnaires (SDQ) was used to test the concurrent validity of the sensory severity estimates. Results: A bifactor MIRT model (a general and four sensory factors) was selected as the best fitting model. All items statistically fitted to the bifactor model (p > .01) and showed moderate correlations with all five subscales of the SDQ (r s = .31 ~.51, p < .0001). The general sensory score differentiated the four different diagnostic groups (ASD, ID, ASD with ID, and no current symptoms) [F (3,1961) = 207.4, p < .0001]. Conclusion: The Pathways sensory items yielded reliable estimates of the general and each corresponding sensory factor by applying a bifactor MIRT model. The sensory score can be used as a valid sensory measure in the population survey.

The advance of information technology with governmental support allows researchers to utilize big data to conduct rigorous rehabilitation-related studies in both the United States and Korea. While big data research has been actively conducted in the United States, few Korean rehabilitation scientists currently conduct big data research. In this paper, we discuss the existing rehabilitation-related big data in Korea and introduce the current big data research trends in the United States. In addition, we provide potential research areas by discussing the previous big data studies in the United States, including 1) functional outcomes, 2) geographical variation, 3) linkage of a population-based cohort to hospital data, 4) cross-national comparison, and 5) scale development. We also discuss various statistical methods required in big data research and compare how these methods differ compared to the traditional research methods in rehabilitation research. Lastly, we discuss the importance of workforce development in rehabilitation-related big data research. We hope this paper will provide occupational therapy scientists and clinicians with practical and useful information about big data research and accelerate big data rehabilitation research communities in Korea.

Purpose: Breast cancer survivors(BCS) have memory, attention, processing speed, and/or executive function impairment affecting their quality of life(QOL) and function. Study aims were: (1) compare computer-assisted cognitive training with visual exercises to one with audio+ visual exercises, and (2) gain insight into the relationship between engagement in meaningful activities, cognitive deficits and QOL. Methods: This pilot study used an experimental design with 24 women. Pre and post measures assessed working memory, perceived cognition, QOL and engagement in meaningful activities. Results: Treatment effects were not significant for working memory or engagement in meaningful activities. Visual group participants scored significantly better on perceived cognitive function posttest test(M = 91.62, SD = 21.75) than pretest(M = 74.48, SD = 29.00), p = .010 and significantly higher on QOL posttest(M = 6.42, SD = 1.27) than pretest(M = 5.60, SD = 1.76), p = .004. Pretest perceived cognitive function was significantly associated with pretest(ρ = .617, p < .001) and posttest(ρ = .436, p < .05) QOL. Conclusion: Computer-assisted cognitive training with visual exercises appears to improve perceived cognitive function and QOL for BCS. A positive relationship exists between perceived cognitive function and QOL.