Efficacy of Mobile Health in Patients With Low Back Pain: Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Low back pain is one of the most common health problems and a main cause of disability, which imposes a great burden on patients. Mobile health (mHealth) affects many aspects of people's lives, and it has progressed rapidly, showing promise as an effective intervention for patients with low back pain. However, the efficacy of mHealth interventions for patients with low back pain remains unclear; thus, further exploration is necessary. OBJECTIVE: The purpose of this study was to evaluate the efficacy of mHealth interventions in patients with low back pain compared to usual care. METHODS: This was a systematic review and meta-analysis of randomized controlled trials designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. We searched for studies published in English before October 2020 in the PubMed, EMBASE, Web of Science, and Cochrane Library databases. Two researchers independently scanned the literature, extracted data, and assessed the methodological quality of the included studies. Bias risks were assessed using the Cochrane Collaboration tool. We used RevMan 5.4 software to perform the meta-analysis. RESULTS: A total of 9 studies with 792 participants met the inclusion criteria. The simultaneous use of mHealth and usual care showed a better reduction in pain intensity than usual care alone, as measured by the numeric rating scale (mean difference [MD] -0.85, 95% CI -1.29 to -0.40; P<.001), and larger efficacy in reducing disability, as measured by the Rolland-Morris Disability Questionnaire (MD -1.54, 95% CI -2.35 to -0.73; P<.001). Subgroup analyses showed that compared with usual care, mHealth using telephone calls significantly reduced pain intensity (MD -1.12, 95% CI -1.71 to -0.53; P<.001) and disability score (MD -1.68, 95% CI -2.74 to -0.63; P<.001). However, without the use of telephone calls, mHealth had no obvious advantage over usual care in improving pain intensity (MD -0.48, 95% CI -1.16 to 0.20; P=.16) and the disability score (MD -0.41, 95% CI -1.88 to 1.05; P=.58). The group that received a more sensitive feedback intervention showed a significantly reduced disability score (MD -4.30, 95% CI -6.95 to -1.69; P=.001). CONCLUSIONS: The use of simultaneous mHealth and usual care interventions has better efficacy than usual care alone in reducing pain intensity and disability in patients with low back pain. Moreover, the results of subgroup analysis revealed that mHealth using telephone calls might play a positive role in improving pain intensity and disability in patients with 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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".