A new adaptive testing algorithm for shortening health literacy assessments

BMC Medical Informatics and Decision Making

Research Article

David R Kaufman¹  Sasikiran Kandula²  Qing Zeng-Treitler³  Jessica S Ancker⁴  Leanne M Currie⁵ 

[1] Department of Biomedical Informatics, Columbia University, New York, NY, USA;Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA;Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA;Veteran Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA;Departments of Pediatrics and Public Health, Weill Cornell Medical College, New York, NY, USA;School of Nursing, University of British Columbia, Vancouver, BC, Canada;

关键词: Health Literacy;    Item Response Theory;    Computerize Adaptive Testing;    Calibration Scheme;    Health Literacy Questionnaire;   

DOI  :  10.1186/1472-6947-11-52

 received in 2011-01-24, accepted in 2011-08-06,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundLow health literacy has a detrimental effect on health outcomes, as well as ability to use online health resources. Good health literacy assessment tools must be brief to be adopted in practice; test development from the perspective of item-response theory requires pretesting on large participant populations. Our objective was to develop a novel classification method for developing brief assessment instruments that does not require pretesting on large numbers of research participants, and that would be suitable for computerized adaptive testing.MethodsWe present a new algorithm that uses principles of measurement decision theory (MDT) and Shannon's information theory. As a demonstration, we applied it to a secondary analysis of data sets from two assessment tests: a study that measured patients' familiarity with health terms (52 participants, 60 items) and a study that assessed health numeracy (165 participants, 8 items).ResultsIn the familiarity data set, the method correctly classified 88.5% of the subjects, and the average length of test was reduced by about 50%. In the numeracy data set, for a two-class classification scheme, 96.9% of the subjects were correctly classified with a more modest reduction in test length of 35.7%; a three-class scheme correctly classified 93.8% with a 17.7% reduction in test length.ConclusionsMDT-based approaches are a promising alternative to approaches based on item-response theory, and are well-suited for computerized adaptive testing in the health domain.

【 授权许可】

Unknown   
© Kandula et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.