ArticlePain reports2024
A domain-oriented approach to characterizing movement-evoked pain.
Article in Pain reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Rethinking measurement of movement-evoked pain with digital technology.The journal of pain · 2026Article
- Rethinking Measurement of Movement-Evoked Pain with Digital Technology.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.