TitleDevelopment and preliminary testing of a computerized adaptive assessment of chronic pain
Publication TypeJournal Article
Year of Publication2009
AuthorsAnatchkova, MD, Saris-Baglama, RN, Kosinski, M, Bjorner, JB
JournalJournal of Pain
Date PublishedSep
Publication Languageeng
ISBN Number1528-8447 (Electronic)1526-5900 (Linking)
Accession Number19595636
Keywords*Computers, *Questionnaires, Activities of Daily Living, Adaptation, Psychological, Chronic Disease, Cohort Studies, Disability Evaluation, Female, Humans, Male, Middle Aged, Models, Psychological, Outcome Assessment (Health Care), Pain Measurement/*methods, Pain, Intractable/*diagnosis/psychology, Psychometrics, Quality of Life, User-Computer Interface

The aim of this article is to report the development and preliminary testing of a prototype computerized adaptive test of chronic pain (CHRONIC PAIN-CAT) conducted in 2 stages: (1) evaluation of various item selection and stopping rules through real data-simulated administrations of CHRONIC PAIN-CAT; (2) a feasibility study of the actual prototype CHRONIC PAIN-CAT assessment system conducted in a pilot sample. Item calibrations developed from a US general population sample (N = 782) were used to program a pain severity and impact item bank (kappa = 45), and real data simulations were conducted to determine a CAT stopping rule. The CHRONIC PAIN-CAT was programmed on a tablet PC using QualityMetric's Dynamic Health Assessment (DYHNA) software and administered to a clinical sample of pain sufferers (n = 100). The CAT was completed in significantly less time than the static (full item bank) assessment (P < .001). On average, 5.6 items were dynamically administered by CAT to achieve a precise score. Scores estimated from the 2 assessments were highly correlated (r = .89), and both assessments discriminated across pain severity levels (P < .001, RV = .95). Patients' evaluations of the CHRONIC PAIN-CAT were favorable. PERSPECTIVE: This report demonstrates that the CHRONIC PAIN-CAT is feasible for administration in a clinic. The application has the potential to improve pain assessment and help clinicians manage chronic pain.