The report is a product of the VA/HSR&D Evidence Synthesis Program.
Rapid Evidence Review: Measures for Patients with Chronic Musculoskeletal Pain
Chronic musculoskeletal pain is a major source of disability and morbidity for Veterans in the U.S., affecting approximately 60% of Veterans with chronic health conditions in VA primary care. Management of chronic musculoskeletal pain remains challenging. Groups ranging from pain expert coalitions to the National Institutes of Health and the Institute of Medicine have recently called for more focused and strategic pain therapy research. As these organizations note, successful development and testing of interventions to improve chronic musculoskeletal pain depends on the use of valid, reliable, and responsive measures of pain and pain-related outcomes.
In 2016, HSR&D held a state-of-the-art (SOTA) conference on Non-pharmacological Approaches to Chronic Musculoskeletal Pain Management and convened a group of researchers to develop a short set of core outcome measures for prospective pain research. The SOTA group requested a rapid evidence review to describe and compare key psychometric qualities (including minimally important difference (MID), test-retest reliability, validity, and responsiveness) of commonly used pain measures, particularly those that might be suitable for clinical trials of non-pharmacological treatments of chronic musculoskeletal pain. These qualities would not be the only criteria for selecting core measures, but could serve as a basic requirement of measures considered candidates for wide implementation. The evidence review focused on English-language versions of 17 measures of pain intensity/severity and/or pain-related functional interference for persons with chronic musculoskeletal pain, identified by the review operational partners and the SOTA Planning Committee. Links to brief information on individual measures are provided, but are not definitive or complete sources.
- Brief Pain Inventory (BPI)
- Defense & Veterans Pain Rating Scale (DVPRS)
- Graded Chronic Pain Scale (GCPS)
- Hip Osteoarthritis Outcomes Scale (HOOS)
- Knee Osteoarthritis Outcomes Scale (KOOS)
- McGill Pain Questionnaire (MPQ)
- West-Haven-Yale Multidimensional Pain Inventory (WHYMPI/MPI)
- Numeric Rating Scale (NRS)
- Oswestry Disability Index (ODI)
- Patient Global Impression of Change (PGIC)
- PEG A three-item scale assessing [P] pain intensity, [E] enjoyment of life, and [G] general activity)
- Patient-Reported Outcomes Measurement Information System - Pain Interference (PROMIS-PI)
- Roland-Morris Disability Questionnaire (RMDQ)
- SF-36 Bodily Pain Scale (SF-36 BPS)
- Visual Analogue Scale (VAS)
- Western Ontario and McMaster Universities Arthritis Index (WOMAC)
- Wong-Baker Faces® Pain Rating Scale
Investigators with VA's Evidence-based Synthesis Program site located in Minneapolis, MN searched multiple data sources [MEDLINE, Google Scholar, the National Center for Biotechnology Information (NCBI), and PubMed] from 2000 through January 2017 for articles relevant to this evidence review. Of 1,635 abstracts identified as potentially relevant, 318 articles underwent full-text review, and 43 met inclusion criteria. Six of the 43 studies included Veteran populations. Eight studies provided MID estimates for 8 of the 17 measures.
Summary of Findings
Among the assessed multi-item pain measures, the ODI, RMDQ, and SF-36 BPS had the most complete psychometric evidence within chronic musculoskeletal pain populations. BPI, GCPS, MPI/WHYMPI, MPQ, PEG, PROMIS-PI and WOMAC also had evidence for several key psychometric properties. Many measures merit consideration if they fit project needs and feasibility. Researchers' final choice of measures should consider population characteristics, pain site and type, recall period of interest and intervention length, analytic goals, and study resources.
- Of the measures focused primarily on pain severity, the greatest reporting of psychometric properties was found for the NRS and VAS, followed by the MPQ. NRS and VAS, however, single-item measures and question content varied across studies.
- Of the measures of pain interference, the greatest reporting of psychometric properties was found for the ODI, PROMIS-PI, and RMDQ.
- MID assessment methods differed and were often based on statistical rather than patient-noticeable differences.
- Reliability, internal consistency, concurrent or criterion validity, discriminant validity, and responsiveness differed widely but generally were in the fair to excellent range.
- Feasibility, delivery mode, and public availability differed widely.
- Generalizability of psychometric findings is limited by demographic under-reporting and lack of diversity in study populations.
Limitations of current chronic musculoskeletal pain measurement research relate to variations in: 1) definition and reporting of chronic musculoskeletal pain and pain-related diagnoses, 2) methods of assessing psychometric outcomes, and 3) reporting on demographics of patient populations.
Research into pain measurement would be strengthened by consistent definitions of chronic musculoskeletal pain, standardized methods of assessing psychometric outcomes, and comprehensive descriptions of patient populations. Moreover, estimates of measure validity are difficult to compare within or across measures. Future research could clarify assumptions that support the validity of these measures and identify gaps requiring conceptual research. Research is needed to validate patient-reported pain outcome measures in diverse chronic musculoskeletal pain populations – and to develop a framework for determining measure selection that incorporates feasibility and applicability.
Focusing on this review, a cyberseminar titled "Measures for Patients with Chronic Musculoskeletal Pain: A Rapid Evidence Review" will be held on December 5, 2017 from 11:00am to 12:00pm (EST). Register here.
View the full report
Goldsmith ES, Murdoch M, Taylor B, Greer N, MacDonald R, McKenzie LG, Rosebush C, Wilt TJ. Rapid Evidence Review: Measures for Patients with Chronic Musculoskeletal Pain. VA ESP Project #09-009; 2017.
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