The effectiveness of weight loss programs for low back pain: a systematic review
Bibliographic record
Abstract
BACKGROUND: Low back pain has been associated with obesity or with being overweight. However, there are no high-quality systematic reviews that have been conducted on the effect of all types of weight loss programs focused on individuals with low back pain. Therefore, the present systematic review aims to evaluate the effectiveness of weight loss programs in reducing back pain and disability or increasing quality of life for individuals experiencing low back pain. MATERIALS AND METHODS: Searches for relevant studies were conducted on CINAHL, Web of Science, Ovid Medline, Ovid Embase and AMED. Studies were included if they were randomized controlled trials, non-randomized studies of intervention or quasi-experimental designs evaluating a weight loss program for persons with low back pain aimed at decreasing back pain and disability. The Effective Public Health Practice Project (EPHPP) Quality Assessment Tool was used to evaluate individual studies and GRADE was used to summarize the quality of the evidence. The review was prospectively registered; PROSPERO#: CRD42020196099. RESULTS: Eleven studies (n = 689 participants) including one randomized controlled trial, two non-randomized studies of intervention and eight single-arm studies were included (seven of which evaluated bariatric surgery). There was low-quality evidence that a lifestyle intervention was no better than waitlist for improving back pain and very low-quality evidence from single-arm studies that back pain improved from baseline after bariatric surgery. Most studies included were of poor quality, primarily due to selection bias, uncontrolled confounders, and lack of blinding, limiting the quality of evidence. CONCLUSION: There is very low-quality evidence that weight loss programs may improve back pain, disability, and quality of life in patients with LBP, although adherence and maintenance are potential barriers to implementation.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".