Effect of Motivational Interviewing and Exercise on Chronic Low Back Pain: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background The prevalence of chronic low back pain (CLBP) and its concomitant cost implications have continued to rise across the globe. Currently, there is no effective treatment for CLBP that leads to long‐term improvement. Hence, there is growing recognition of the need for behaviour techniques including motivational interviewing (MI) to address CLBP. Objective To determine the effect of MI and exercise on pain in individuals with CLBP. Method We searched for trials in seven databases from inception to April 2024. Trials were included if MI was used alone or in addition to an exercise programme for improving CLBP in adults aged (≥ 18 years). Results From 3062 records retrieved, we included three randomized controlled trials (RCTs). Only one study was rated as having a low risk of bias. There is no evidence to support the benefit of MI and exercise on improving pain (SMD‐0.23, 95% CI‐0.55 to 0.09, I2 = 0%, p = 0.16), disability (MD‐1.80, 95% CI‐4.55 to 0.94, I2 = 85%, p = 0.20) and physical functioning (SMD 0.00, 95% CI‐1.31 to 1.32, I2 = 93%, p = 0.99). Conclusion There is insufficient evidence to support the effect of MI and exercise on pain in individuals with CLBP. More large‐scale RCTs are needed in evaluating the effectiveness of MI and exercise in individuals with CLBP.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".