A domain-oriented approach to characterizing movement-evoked pain

Author:

Crow Joshua A.1ORCID,Joseph Verlin2,Miao Guanhong3,Goodin Burel R.4,Sibille Kimberly T.15,Cardoso Josue6,Bartley Emily J.7,Staud Roland8,Fillingim Roger B.17,Booker Staja Q.19ORCID

Affiliation:

1. Pain Research & Intervention Center of Excellence, University of Florida, Gainesville, FL, USA

2. College of Population Health, The University of New Mexico, Albuquerque, NM, USA

3. Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA

4. Department of Anesthesiology, School of Medicine, Washington University, St. Louis, MO, USA

5. Department of Physical Medicine & Rehabilitation, College of Medicine, University of Florida, Gainesville, FL, USA

6. The Pennsylvania State University, University Park, PA, USA

7. Department of Community Dentistry and Behavioral Science, College of Dentistry, University of Florida, Gainesville, FL, USA

8. Department of Rheumatology, College of Medicine, University of Florida, Gainesville, FL, USA

9. Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, FL, USA

Abstract

Abstract Introduction: Movement-evoked pain (MEP) impacts a substantial proportion of US adults living with chronic pain. Evidence suggests that MEP is influenced by numerous biopsychosocial factors and mediated by mechanisms differing from those of spontaneous pain. However, both characteristic and mechanistic knowledge of MEP remain limited, hindering effective diagnosis and treatment. Objectives: We asked (1) can chronic pain, functional, psychosocial, and behavioral measures be grouped into descriptive domains that characterize MEP? and (2) what relationships exist between biopsychosocial factors across multiple domains of MEP? Methods: We formed 6 characteristic domains from 46 MEP-related variables in a secondary analysis of data from 178 individuals (aged 45–85 years) with knee pain. Ratings of pain during 3 functional activities (ie, Balance, Walking, Chair Stand) were used as primary MEP variables. Pearson correlations were calculated to show linear relationships between all individual domain variables. Relationships between variables were further investigated through weighted correlation network analysis. Results: We observed a unique combination of pain characteristics associated with MEP apart from general pain. Notably, minutes doing physical activity were inversely associated with multiple variables within 4 of the 6 domains. Weighted correlation network analysis largely supported our classification of MEP domains. Additional interdomain relationships were observed, with the strongest existing between MEP, Mechanical Pain, and Multiple Pain Characteristics and Symptoms. Additional relationships were observed both within and between other domains of the network. Conclusion: Our analyses bolster fundamental understanding of MEP by identifying relevant mechanistic domains and elucidating biopsychosocial and interdomain relationships.

Funder

National Institute on Aging

National Institute of Arthritis and Musculoskeletal and Skin Diseases

National Center for Advancing Translational Sciences

Publisher

Ovid Technologies (Wolters Kluwer Health)

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