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Pain Research and Treatment
Volume 2016 (2016), Article ID 7478509, 8 pages
http://dx.doi.org/10.1155/2016/7478509
Research Article

Pain Predicts Function One Year Later: A Comparison across Pain Measures in a Rheumatoid Arthritis Sample

1New York University College of Dentistry, New York, NY 10010, USA
2University of Wisconsin at Madison, Madison, WI 53705, USA

Received 16 December 2015; Accepted 6 March 2016

Academic Editor: Stefan Evers

Copyright © 2016 Vivian Santiago et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Guidance is limited on best measures and time periods to reference when measuring pain in order to predict future function. Objective. To examine how different measures of pain predict functional limitations a year later in a sample of rheumatoid arthritis patients. Methods. Logistic regression analyses were conducted using baseline and one-year data (). Pain intensity in the last 24 hours was measured on a 0–10 numerical rating scale and in the last month using an item from the Arthritis Impact Measurement Scale 2 (AIMS2). AIMS2 also provided frequency of severe pain, pain composite scores, and patient-reported limitations. Physician-rated function was also examined. Results. Composite AIMS2 pain scale performed best, predicting every functional outcome with the greatest magnitude, a one-point increase in pain score predicting 21% increased odds of limitations (combined patient and physician report). However, its constituent item—frequency of severe pain in the last month—performed nearly as well (19% increased odds). Pain intensity measures in last month and last 24 hours yielded inconsistent findings. Conclusion. Although all measures of pain predicted some functional limitations, predictive consistency varied by measure. Frequency of severe pain in the last month provided a good balance of brevity and predictive power.