title: A new adaptive testing algorithm for shortening health literacy assessments.
abstract: BACKGROUND
  Low 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.
  
  
  METHODS
  We 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).
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  MDT-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.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Sasikiran
authors.initials: S
authors.lastname: Kandula
authors.forename: Jessica S
authors.initials: JS
authors.lastname: Ancker
authors.forename: David R
authors.initials: DR
authors.lastname: Kaufman
authors.forename: Leanne M
authors.initials: LM
authors.lastname: Currie
authors.forename: Qing
authors.initials: Q
authors.lastname: Zeng-Treitler
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003662:Decision Theory; D004521:Educational Measurement / Q000379:methods; D004522:Educational Status; D005260:Female; D057220:Health Literacy / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged