Effects of lumbar stabilisation and treadmill exercise on function in patients with chronic mechanical low back pain
Bibliographic record
Abstract
Background/Aims: Multifidus muscle is an important contributor to low back pain as it atrophies and is inhibited during the first episode of low back pain. This study aims to compare the effectiveness of lumbar stabilisation exercise with treadmill walking on multifidus muscle activation, pain intensity and functional disability among people with chronic mechanical low back pain. Methods: Individuals with chronic mechanical low back pain were randomised into two groups: Group 1 (n=25) performed lumbar stabilisation exercises following the McGill protocol; and Group 2 (n=25) followed the modified Bruce treadmill walking protocol. Participants walked or exercised three times a week for 8 weeks. Pain intensity (measured using a visual analogue scale), functional disability (using the Oswestry Disability Index Questionnaire) and amplitude of multifidus muscle activation (using a surface electromyography machine) were assessed at baseline and at the end of week 8. Data were analysed using descriptive statistics, paired and independent t-tests at α≤0.05. Findings: There were no significant differences between the groups' anthropometric and clinical characteristics at baseline. Both groups demonstrated significant reductions in pain intensity and functional disability and increases in multifidus muscle activation at the end of the study (all P<0.05). Group 1 had lower pain intensity (2.60 ± 0.48 vs 4.50 ± 0.12) and functional disability (24.20 ± 4.06 vs 40.00 ± 10.56) and significantly higher multifidus muscle activation levels (40.00 ± 4.16 vs 26.95 ± 4.04; P<0.05) than Group 2 at the end of week 8. Conclusions: Lumbar stabilisation exercises are more effective than treadmill walking exercises in activating the multifidus muscle, reducing pain intensity and functional disability in individuals with chronic mechanical low back pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".