Efficacy of digital interventions for smoking cessation by type and method: a systematic review and network meta-analysis

Abstract

Smoking cessation is the only evidence-based approach to reducing tobacco-related health risks, yet traditional interventions suffer from limited coverage. Although digital interventions show promise, their comparative efficacy across methodological frameworks and technology types remains unclear. Here we assessed digital interventions versus standard care via frequentist random-effects network meta-analysis of 152 randomized controlled trials (48.8% USA, 7.5% China). Interventions were categorized by methodology and technology type, with cross-matched subgroup analyses. Results showed that personalized interventions significantly improved smoking cessation rates compared with standard care (relative risk (RR) 1.86, 95% confidence interval (CI) 1.54–2.24), while group-customized interventions were more effective (RR 1.93, 95% CI 1.30–2.86) compared with standard digital interventions (RR 1.50, 95% CI 1.31–1.72). Among the various technology types, text message-based interventions were the most effective (RR 1.63, 95% CI 1.38–1.92). Intervention effectiveness was also influenced by age, with middle-aged individuals benefitting more than younger individuals. Short- and medium-term interventions were more effective than long-term interventions. Sensitivity analyses further confirmed these low-to-moderate findings. However, this study has some limitations, including methodological heterogeneity, potential bias and inconsistent definitions of numerical interventions. In addition, long-term follow-up data remain limited. Future studies require large-scale trials to assess long-term sustainability and population-specific responses, as well as standardization of methods and integration of data at the individual level.