Quality of Life Research

, Volume 22, Issue 8, pp 1907–1915 | Cite as

Psychometric properties of a computerized adaptive test for assessing mobility in older adults using novel video-animation technology

  • Edward H. IpEmail author
  • W. Jack Rejeski
  • Anthony P. Marsh
  • Ryan Barnard
  • Shyh-Huei Chen



This paper reports on the psychometric properties of a computerized adaptive test (CAT) version of the Mobility Assessment Tool (MAT) for older adults (MAT-CAT).


An item pool of 78 video-animation-based items for mobility was developed, and response data were collected from a sample of 234 participants aged 65–90 years. The video-animation-based instrument was designed to minimize ambiguity in the presentation of task demands. In addition to evaluating traditional psychometric properties including dimensionality, differential item functioning (DIF), and local dependence, we extensively tested the performance of several MAT-CAT measures and compared their performances with a fixed format.


Operationally, the MAT-CAT was sufficiently unidimensional and had acceptable levels of local independence. One DIF item was removed. Most importantly, the CAT measures showed that even starting with a single fixed item at the mean ability, the adaptive version delivered better performance than the fixed format in terms of several criteria including the standard error of estimate.


The MAT-CAT demonstrated satisfactory psychometric properties and superior performance to a fixed format. The video-animation-based adaptive instrument can be used for assessing mobility with specificity and precision.


Mobility Assessment Tool Item response theory Health-related quality of life Mobility disability Animation 



Differential item functioning


Mobility Assessment Tool


Health-related quality of life


Patient-reported outcome


Health Insurance Portability and Accountability Act of 1996


Computerized adaptive test



The research has been supported by the following grants from the National Institute of Aging: P30 AG21332.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Edward H. Ip
    • 1
    Email author
  • W. Jack Rejeski
    • 2
  • Anthony P. Marsh
    • 2
  • Ryan Barnard
    • 1
  • Shyh-Huei Chen
    • 1
  1. 1.Department of Biostatistical SciencesWake Forest School of MedicineWinston-SalemUSA
  2. 2.Department of Health and Exercise ScienceWake Forest UniversityWinston-SalemUSA

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