Commentary to ‘Movement‐evoked pain is not associated with pain at rest or physical function in knee osteoarthritis’
Bibliographic record
Abstract
Movement-evoked pain (MEP), or pain that occurs with or is provoked by movement, is prevalent in individuals with knee osteoarthritis (KOA) and is associated with mobility impairment, clinical pain and poor health outcomes (Butera et al., 2024). Recent studies and literature reviews show that MEP is distinguishable from pain-at-rest (PAR) and is driven by shared and distinct mechanisms, underscoring the importance of studying and assessing these constructs (Butera et al., 2024). The article by Lozano-Meca et al. (2024), recently published in the European Journal of Pain, entitled ‘Movement-evoked pain is not associated with pain at rest or physical function in knee osteoarthritis’, examined correlations between PAR, MEP and physical performance in adults with KOA. We commend the authors for their work to distinguish MEP from PAR, and also take this opportunity to provide our methodological perspective on MEP and KOA to clarify some misinterpretations of our own cited work. First, the assertion that associations between MEP and PAR have not been studied in KOA is inaccurate; multiple recent studies have examined this relationship in KOA and other musculoskeletal conditions (Fullwood et al., 2021). Second, based on their cross-sectional analysis in a relatively small sample (N = 59) with minimally-to-moderately severe radiographic KOA (KL grade ≤ II), the authors concluded that MEP was not associated with PAR or physical performance (Lozano-Meca et al., 2024). This may be interpreted as counterintuitive and inconsistent with prior work. We propose several plausible reasons for these findings, some of which were highlighted by the authors. In their study, MEP was operationalized as pain occurring in response to four walking tasks (Lozano-Meca et al., 2024). While our paper is cited to support their definition of MEP (Fullwood et al., 2021), we must clarify that MEP is not limited to pain during walking. Particular to KOA, individuals commonly report pain during squatting, ascending/descending stairs, and standing from a sitting position; indicating MEP is best characterized by assessing pain over a variety of tasks that are meaningful to the individual. Also, in the current study the average report of MEP was relatively low, implying that walking on a flat surface may not provoke pain (Lozano-Meca et al., 2024). Having less pain with movement or movement-evoked hypoalgesia (Butera et al., 2024) is an important consideration which was not discussed. Findings may also be due to measuring MEP after each walking task rather than calculating an aggregate pain score across all tasks, which is superior (Knox et al., 2023). Interestingly, PAR (average self-reported pain over the past 7 days) was high, which implies differential pain phenotypes may be represented. As highlighted by the authors, recall measures of pain are limited in their ability to assess PAR, yet this approach was used in the current study. Alternatively, we suggest measuring PAR as current pain intensity while seated immediately prior to each performance task as a baseline, allowing for direct comparisons between PAR and MEP. Prior studies investigating MEP in KOA have relied on larger, more heterogeneous samples with better representation of varying radiographic severity. We bring up this point because the sample employed by Lozano-Meca et al. (2024) was predominantly minimal-moderate radiographic severity. Also, a substantial proportion of patients in their study used assistive devices (Lozano-Meca et al., 2024). While typically allowed in physical performance testing and MEP assessments, assistive devices potentially influence MEP and functional performance and thus need to be controlled for in the analysis to ensure confidence in the measures and findings. In our prior work examining MEP in KOA, we also controlled for other potentially confounding factors including age, sex, race, KL score, body mass index and socioeconomic status, which are important biopsychosocial components of pain. While the title of the article states there are no relationships between MEP and PAR, we found it interesting that there were statistically significant, albeit small, correlations between performance on the 10 m walk test and MEP, as well as PAR and the MEP recorded for the timed-up-and-go test and the 10 m walk test (Lozano-Meca et al., 2024). As noted, the negative correlation between the 10 m walk test time and MEP was surprising, as it suggests that greater MEP was associated with faster walking times. Upon further consideration, it is possible that those individuals not using assistive devices or those with faster speeds may have experienced greater pain due to increased effort/exertion. Future studies of MEP-related factors, such as motivation and perceived effort, may provide additional information to better understand the nuances of MEP and function. Ultimately, the study by Lozano-Meca et al. contributes to the emerging science of MEP and highlights the need for consensus regarding definitions and standardization of provocation tasks intended to capture the impact of pain on natural human movements (Butera et al., 2024). We support the authors' conclusion that PAR and MEP are indeed distinct constructs and that both should be considered for optimizing pain management. However, study findings should be interpreted with caution and additional research is needed to replicate these findings and better understand MEP and its impact on KOA. The authors have no financial disclosures to report.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".