Hodges P1, Popovich J2, Aminpour P2, Gray S2, Lee A2, Cholewicki J2
1The University of Queensland, Brisbane, Australia, 2Michigan State University, East Lansing, United States
Background: Low back pain (LBP) is a very complex problem with abundant bio-psycho-social factors interacting to impact pain, disability, and quality of life. To understand the dynamics of such a complex problem, it needs to be studied in its entirety (systems approach). Although this concept is accepted by the scientific and clinical communities, much research to date is conducted by specialized teams focusing on isolated factors related to LBP. Such a reductionist approach may be useful in some circumstances, but it also creates barriers for integrating knowledge between disciplines and ultimately precludes the understanding of the dynamics of the entire LBP problem. To leverage the knowledge shared among various stakeholders, an innovative process called collaborative modeling was developed within the field of systems science specifically to enhance the understanding of complex, multi-factorial systems dynamics. This study assessed the feasibility of using this approach to integrate multidisciplinary knowledge of LBP.
Purpose: The purpose of this study was to develop acollaborative model, based on the opinions of a diverse group of LBP experts, designed to enhance understanding and to communicate the complexity of the LBP problem.
Methods: The study involved generating individual models that represented experts'understanding of the LBP problem using fuzzycognitive mapping (FCM), and five sequential phases of consultation and consensus with the participants to characterize and refine the interpretation of the FCMs: (1) Generation of individual FCMs of LBP by participants; (2) Proposal of Categories for clustering of model Components; (3) Preliminary evaluation of structure, composition and focal areas of individual participant's FCMs; (4) Refinement of Categories and Components with consensus meeting; (5) Generation of final structure and composition individual participant's FCMs. Descriptive statistics were applied to the structural and composition metrics of individual FCMs to aid in their interpretation.
Results: From a total of 38potential contributors, 29 (76%) agreed to participate. They represented 9 disciplines and 8 countries. The participants' models included a total of 729 Components, with an average of 25(SD=7) per model. After the final FCM refinement process (where Components from separate FCMs that used similar terms were combined, and Components from an FCM that included multiple terms were separated), there were 146 Components allocated to ten Categories. Although individual models varied considerably in their structure and composition, collectively, Components allocated to the “Psychology” Category were the most central for the group and for almost half (14/29) of the individual models.This was followed by “Social/Work/Contextual” and “Biomechanical” Categories.
Conclusion(s): The collaborative modeling process provides a strong foundation upon which to build a greater understanding and to communicate the complexity of the LBP problem. The next step is to build a meta-model and begin the task of disentangling the interactions among its Components.
Implications: This collaborative model will lead to an improved understanding of the complexity of LBP, and hopefully to improved outcomes for those suffering from this condition.
Keywords: Low back pain, bio-psycho-social, multidisciplinaryplinary
Funding acknowledgements: National Health and Medical Research Council (Australia); North American Spine Society
Purpose: The purpose of this study was to develop acollaborative model, based on the opinions of a diverse group of LBP experts, designed to enhance understanding and to communicate the complexity of the LBP problem.
Methods: The study involved generating individual models that represented experts'understanding of the LBP problem using fuzzycognitive mapping (FCM), and five sequential phases of consultation and consensus with the participants to characterize and refine the interpretation of the FCMs: (1) Generation of individual FCMs of LBP by participants; (2) Proposal of Categories for clustering of model Components; (3) Preliminary evaluation of structure, composition and focal areas of individual participant's FCMs; (4) Refinement of Categories and Components with consensus meeting; (5) Generation of final structure and composition individual participant's FCMs. Descriptive statistics were applied to the structural and composition metrics of individual FCMs to aid in their interpretation.
Results: From a total of 38potential contributors, 29 (76%) agreed to participate. They represented 9 disciplines and 8 countries. The participants' models included a total of 729 Components, with an average of 25(SD=7) per model. After the final FCM refinement process (where Components from separate FCMs that used similar terms were combined, and Components from an FCM that included multiple terms were separated), there were 146 Components allocated to ten Categories. Although individual models varied considerably in their structure and composition, collectively, Components allocated to the “Psychology” Category were the most central for the group and for almost half (14/29) of the individual models.This was followed by “Social/Work/Contextual” and “Biomechanical” Categories.
Conclusion(s): The collaborative modeling process provides a strong foundation upon which to build a greater understanding and to communicate the complexity of the LBP problem. The next step is to build a meta-model and begin the task of disentangling the interactions among its Components.
Implications: This collaborative model will lead to an improved understanding of the complexity of LBP, and hopefully to improved outcomes for those suffering from this condition.
Keywords: Low back pain, bio-psycho-social, multidisciplinaryplinary
Funding acknowledgements: National Health and Medical Research Council (Australia); North American Spine Society
Topic: Musculoskeletal: spine
Ethics approval required: No
Institution: Michigan State University
Ethics committee: Institutional Review Board
Reason not required: Exemption from ethical approval was granted by the IRB as the project was based on opinions of experts only
All authors, affiliations and abstracts have been published as submitted.
