The Relationship Between Pain-Related Threat and Motor Behavior in Nonspecific Low Back Pain: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Although pain-related fear and catastrophizing are predictors of disability in low back pain (LBP), their relationship with guarded motor behavior is unclear. The aim of this meta-analysis was to determine the relationship between pain-related threat (via pain-related fear and catastrophizing) and motor behavior during functional tasks in adults with LBP. METHODS: This review followed PRISMA guidelines. MEDLINE, Embase, PsychINFO, and CINAHL databases were searched to April 2021. Included studies measured the association between pain-related fear or pain catastrophizing and motor behavior (spinal range of motion, trunk coordination and variability, muscle activity) during movement in adults with nonspecific LBP. Studies were excluded if participants were postsurgery or diagnosed with specific LBP. Two independent reviewers extracted all data. The Newcastle-Ottawa Scale was used to assess for risk of bias. Correlation coefficients were pooled using the random-effects model. RESULTS: Reduced spinal range of motion during flexion tasks was weakly related to pain-related fear (15 studies, r = -0.21, 95% CI = -0.31 to -0.11) and pain catastrophizing (7 studies, r = -0.24, 95% CI = -0.38 to -0.087). Pain-related fear was unrelated to spinal extension (3 studies, r = -0.16, 95% CI = -0.33 to 0.026). Greater trunk extensor muscle activity during bending was moderately related to pain-related fear (2 studies, r = -0.40, 95% CI = -0.55 to -0.23). Pain catastrophizing, but not fear, was related to higher trunk activity during gait (2 studies, r = 0.25, 95% CI = 0.063 to 0.42). Methodological differences and missing data limited robust syntheses of studies examining muscle activity, so these findings should be interpreted carefully. CONCLUSION: This study found a weak to moderate relationship between pain-related threat and guarded motor behavior during flexion-based tasks, but not consistently during other movements. IMPACT: These findings provide a jumping-off point for future clinical research to explore the advantages of integrated treatment strategies that target both psychological and motor behavior processes compared with traditional approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".