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.

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.