BACKGROUND:
Smoking cessation is the only evidence-based method to reduce tobacco-related health risks, but traditional interventions often face limited coverage. Digital interventions have potential, yet their comparative efficacy across various methodological frameworks and technology types remains unclear.

METHODS:
We conducted a frequentist random-effects network meta-analysis of 152 randomized controlled trials (RCTs), with 48.8% conducted in the US and 7.5% in China. Digital interventions were categorized by methodology and technology types, with cross-matched subgroup analyses performed.

RESULTS:
Personalized digital interventions significantly increased smoking cessation rates compared with standard care (RR 1.86 \[95% CI: 1.54-2.24]). Group-customized interventions demonstrated greater efficacy (RR 1.93 \[95% CI: 1.30-2.85]) compared to standard digital interventions (RR 1.50 \[95% CI: 1.31-1.72]). Among various technologies, text message-based interventions were most effective (RR 1.63 \[95% CI: 1.38-1.92]). Intervention effectiveness varied by age, with middle-aged individuals showing better outcomes than younger participants. Short- and medium-term interventions were more effective than long-term interventions. Sensitivity analyses confirmed these low-to-moderate strength findings.

CONCLUSIONS:
Digital interventions, particularly personalized and text message-based approaches, are effective for smoking cessation. However, limitations such as methodological heterogeneity, potential biases, inconsistent definitions, and limited long-term data persist. Future research should focus on large-scale trials assessing sustainability, population-specific responses, methodological standardization, and individual-level data integration.