Efficacy of mobile health interventions in the conservative management of chronic low back pain in low- and middle-income countries: a systematic review, meta-analysis, and trial sequential analysis
Bibliographic record
Abstract
Chronic low back pain (CLBP) is a major global health issue, particularly severe in low- and middle-income countries (LMICs), where health care resources and accessibility are limited. Mobile health (mHealth) interventions offer a promising solution by leveraging technology to deliver health care services remotely. This review aims to evaluate the effectiveness of mHealth interventions in managing CLBP in LMICs. A comprehensive search of electronic databases was performed for studies published until June 2024, evaluating mHealth interventions for CLBP in LMICs. Primary outcomes measured were pain intensity and disability, while secondary outcomes included quality of life (QoL). Risk of bias was assessed using Cochrane risk-of-bias tool (RoB2), and quality of evidence was evaluated using GRADE. Robustness of meta-analysis results was assessed via trial sequential analysis (TSA). Seven studies met the inclusion criteria. The mHealth interventions significantly reduced the overall pain intensity (MD = -1.11, 95% CI: -1.75, -0.46) and disability (MD = -6.59, 95% CI: -10.65, -2.54). Subgroup analysis indicated greater effectiveness of short-term interventions (<6 weeks) in reducing pain and Oswestry disability index (ODI) vs long-term interventions (>6 weeks). mHealth interventions notably reduced pain and ODI scores vs unsupervised programs but showed no significant difference compared to in-person programs. The z-score line remained within TSA boundaries. mHealth interventions show potential in reducing pain and disability among patients with CLBP in LMICs, although with inconclusive impact on QoL. The high heterogeneity and limited number of studies underscore the need for further research with greater sample size to validate these findings and explore the long-term benefits and implementation challenges of mHealth in resource-constrained settings.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.046 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".