Muscle Synergy Analysis of Patients With Chronic Low Back Pain During Functional Tasks
Bibliographic record
Abstract
Chronic low back pain (LBP) significantly impairs daily functional movements. Persistent pain may trigger alterations in neuromuscular control, particularly muscle coordination. However, muscle synergy patterns during specific functional tasks in patients with LBP remain unclear. We recruited 36 participants, including 18 patients with chronic LBP (the LBP group) and 18 healthy participants (the control group). Surface electromyography signals were recorded from ten trunk and lower-limb muscles during three common functional tasks: sit-to-stand, trunk flexion, and lifting. Muscle synergies were extracted via non-negative matrix factorization. Cosine similarity analysis and statistical parametric mapping were applied to evaluate spatial (motor modules) and temporal (motor primitives) differences between groups. Compared to the control group, participants with chronic LBP exhibited a reduced number of muscle synergies during sit-to-stand, indicating adaptive reorganization of motor modules. Although spatial muscle synergy structures were largely conserved between groups, significant temporal differences emerged in trunk flexion, particularly during eccentric phases. Patients with LBP showed prolonged and temporally shifted activation of spinal extensors and hip-pelvic stabilizers, suggesting compensatory mechanisms to mitigate spinal loading. Muscle contribution patterns during lifting tasks also differed significantly between groups, despite similar temporal activation. In conclusion, patients with chronic LBP demonstrate distinct muscle synergy adaptations characterized by reduced complexity, altered timing during eccentric trunk movements, and modified lower-limb recruitment strategies. Trunk flexion emerged as a particularly sensitive task for identifying neuromuscular deficits in LBP. These findings provide targeted insights for clinical rehabilitation, emphasizing eccentric trunk control, motor control timing, and lower-limb muscle retraining.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".