Many complementary and integrative health (CIH) approaches have been shown to be effective for chronic
pain and included in guidelines. This evidence of effectiveness is built on hundreds of studies representing
millions of research dollars, and the ability to analyze and better compare results across these studies is
essential to obtain the full value of this investment. However, useful across-study comparisons which would
allow better understanding and targeting of these interventions are hampered by at least two challenges: the
lack of common outcome measures and the inability to meaningfully stratify or classify patients.
To address the first challenge, in Aim 1 this study will develop and evaluate crosswalks or links between
components of the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS®) short
form (PROMIS-29) and common legacy measures used for chronic pain. In particular, we will create
crosswalks/links for the two most commonly used measures for CLBP, including the Roland-Morris Disability
Questionnaire (RMDQ) and the Oswestry Disability Index (ODI). In addition, depending on data availability and
input from our Advisory Council we will create at least two other crosswalks/links between the PROMIS-29 and
other legacy measures for CLBP (e.g., the Back Pain Functional Scale) or legacy measures for other types of
chronic pain (e.g., the Neck Disability Index for chronic neck pain).
To address the second challenge, in Aim 2 we will evaluate and refine the chronic pain impact stratification
scheme proposed by the NIH Research Task Force on chronic low back pain. The proposed scheme uses the
Impact Stratification Score (ISS) which is calculated using 9 items from the PROMIS-29. We will first evaluate
the ISS and its properties to assess whether they are stable across different samples and determine whether
they can be improved. After we have finalized the components and calculation of the ISS, we will examine its
effect on the impacts of chronic pain (e.g., health-related quality of life, healthcare utilization, worker
productivity) to identify meaningful cut-points to use to stratify chronic pain patients into subgroups who exhibit
different levels of chronic pain impact.
Three types of data will be used in the analyses to address Aims 1 and 2: data from three large in-house
existing datasets, data collected from a national convenience sample using Amazon’s Mechanical Turk
(MTurk) crowdsourcing platform, and data from the probability-based nationally representative
KnowledgePanel. Aim 3 will evaluate crowdsourcing as a reliable, efficient method to collect data on
individuals with chronic pain through the comparison of its results to what was found using KnowledgePanel.