An initial application of computerized adaptive testing (CAT) for measuring disability in patients with low back pain
1 Department of Biostatistics, Faculty of Medicine, Ankara University, Ankara, Turkey
2 Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Ankara University, Ankara, Turkey
3 Department of Rehabilitation Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, UK
Citation and License
BMC Musculoskeletal Disorders 2008, 9:166 doi:10.1186/1471-2474-9-166Published: 18 December 2008
Recent approaches to outcome measurement involving Computerized Adaptive Testing (CAT) offer an approach for measuring disability in low back pain (LBP) in a way that can reduce the burden upon patient and professional. The aim of this study was to explore the potential of CAT in LBP for measuring disability as defined in the International Classification of Functioning, Disability and Health (ICF) which includes impairments, activity limitation, and participation restriction.
266 patients with low back pain answered questions from a range of widely used questionnaires. An exploratory factor analysis (EFA) was used to identify disability dimensions which were then subjected to Rasch analysis. Reliability was tested by internal consistency and person separation index (PSI). Discriminant validity of disability levels were evaluated by Spearman correlation coefficient (r), intraclass correlation coefficient [ICC(2,1)] and the Bland-Altman approach. A CAT was developed for each dimension, and the results checked against simulated and real applications from a further 133 patients.
Factor analytic techniques identified two dimensions named "body functions" and "activity-participation". After deletion of some items for failure to fit the Rasch model, the remaining items were mostly free of Differential Item Functioning (DIF) for age and gender. Reliability exceeded 0.90 for both dimensions. The disability levels generated using all items and those obtained from the real CAT application were highly correlated (i.e. > 0.97 for both dimensions). On average, 19 and 14 items were needed to estimate the precise disability levels using the initial CAT for the first and second dimension. However, a marginal increase in the standard error of the estimate across successive iterations substantially reduced the number of items required to make an estimate.
Using a combination approach of EFA and Rasch analysis this study has shown that it is possible to calibrate items onto a single metric in a way that can be used to provide the basis of a CAT application. Thus there is an opportunity to obtain a wide variety of information to evaluate the biopsychosocial model in its more complex forms, without necessarily increasing the burden of information collection for patients.