Electronic Quality of Life Assessment Using Computer-Adaptive Testing

Chris Gibbons, Peter Bower, Karina Lovell, Jose M Valderas, Suzanne Skevington

Research output: Contribution to journalArticlepeer-review


Background: Quality of life (QoL) questionnaires are desirable for clinical practice but can be time-consuming to administer and interpret, making their widespread adoption difficult. Objective: Our aim was to assess the performance of the World Health Organization Quality of Life (WHOQOL)-100 questionnaire as four item banks to facilitate adaptive testing using simulated computer adaptive tests (CATs) for physical, psychological, social, and environmental QoL. Methods: We used data from the UK WHOQOL-100 questionnaire (N=320) to calibrate item banks using item response theory, which included psychometric assessments of differential item functioning, local dependency, unidimensionality, and reliability. We simulated CATs to assess the number of items administered before prespecified levels of reliability was met. Results: The item banks (40 items) all displayed good model fit (P>.01) and were unidimensional (fewer than 5% of t tests significant), reliable (Person Separation Index>.70), and free from differential item functioning (no significant analysis of variance interaction) or local dependency (residual correlations < +.20). When matched for reliability, the item banks were between 45% and 75% shorter than paper-based WHOQOL measures. Across the four domains, a high standard of reliability (alpha>.90) could be gained with a median of 9 items. Conclusions: Using CAT, simulated assessments were as reliable as paper-based forms of the WHOQOL with a fraction of the number of items. These properties suggest that these item banks are suitable for computerized adaptive assessment. These item banks have the potential for international development using existing alternative language versions of the WHOQOL items.
Original languageEnglish
Pages (from-to)e240
Issue number9
Publication statusPublished - 30 Sept 2016


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