Neuromechanical Responses After Biofeedback Training in Participants With Chronic Low Back Pain: An Experimental Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate changes in neuromechanical responses and clinical outcomes in chronic low back pain participants after 4 sessions of biofeedback training. METHODS: Twenty-one participants took part in an electromyography biofeedback 4-session training program aimed at reducing lumbar paraspinal muscle activity during full trunk flexion. The sessions consisted of ~46 trunk flexion-extension divided into 5 blocks. The effects of training blocks and sessions on lumbar flexion-relaxation ratio and lumbopelvic ranges of motion were assessed. Changes in disability (Oswestry Disability Index), pain intensity (numerical rating scale), and fear of movement (Tampa Scale for Kinesiophobia) were also evaluated. RESULTS: Analyses of variance revealed a significant block effect for which an increase in the flexion-relaxation ratio and the lumbar range of motion between block 1 and the other blocks for sessions 1 and 2 (P < .0001) was observed. However, no significant session or interaction effect was observed. Among clinical outcomes, only fear of movement significantly decreased between the baseline (mean [SD], 33.05 [7.18]) and the fourth session (29.80 [9.88]) (P = .02). There was no significant correlation between clinical outcomes and neuromechanical variables. CONCLUSION: Biofeedback training led to decreases in lumbar paraspinal muscle activity in full trunk flexion and increases in lumbopelvic range of motion in participants with chronic nonspecific low back pain. Although the neuromechanical changes were mostly observed at the early stage of the program, the presence of a decrease in the fear of movement suggests that the participants' initially limited ROMs may have been modulated by fear avoidance behaviors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".