Web-Based Interventions for Chronic Back Pain: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain is one of the most common presenting complaints to a physician's office. Treatment is often challenging and recovery depends on various factors, often resulting in significant investments of time and resources. OBJECTIVE: The aim of this review is to determine which Web-based interventions aimed at chronic low back pain are of benefit to patients. METHODS: Randomized controlled trials (RCTs) studying Web-based interventions directed at adults with chronic low back pain were included. Retrospective studies, narrative reviews, nonrandomized trials, and observational studies were excluded. Electronic databases and bibliographies were searched. RESULTS: In total, nine unique RCTs were identified (total participants=1796). The number of patients randomized in each trial ranged from 51 to 580. Four trials studied online cognitive behavioral therapy (CBT) and five trials studied other Web-based interventions with interactive features. Empowerment/control was improved in six studies. Use of CBT was associated with reduced catastrophization among patients. Mixed results were reported with regards to reduction in pain levels and disability, although some studies showed promise in reducing disability in the short term. One study that measured health care utilization reported reduced utilization with the use of moderated email discussion. CONCLUSIONS: Limited data are available regarding effective Web-based interventions to improve outcomes for patients with chronic low back pain. Nine RCTs with small sample sizes were identified in this review. Online CBT appears to show some promise in terms of reducing catastrophization and improving patient attitudes. Further research in this area with larger-scale studies focusing on appropriate outcomes appears to be a priority.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".