Given the exponential technological advancements in the domain of digital health, there is an urgent imperative to critically reassess the clinical effectiveness of smoking cessation interventions. Therefore, we conducted a NMA to capture these recent advances. Specifically, the investigation aims to quantify the impact of diverse digital modalities on abstinence outcomes, elucidate differential efficacies across device categories and generate methodological insights to substantively inform and advance evidence-based cessation frameworks.
The systematic search included 3,927 candidate articles, with 270 publications advancing to full-text scrutiny after preliminary title/abstract screening. The final analysis comprised 152 RCTs, including 94 trials (n = 63,955) reporting 7-day point prevalence abstinence and 58 trials (n = 66,402) documenting prolonged abstinence outcomes. The sample size totalled 117,642 individuals, characterized by a median age of 40.5 years interquartile range (IQR) 35.7-45.7 and male predominance (51.4%). Baseline cigarette consumption averaged 16.7 units daily (IQR 14.2-19.3). Study timelines spanned from 2005 to the present, with multinational representation; 48.8% were conducted in the USA and 7.5% in China, reflecting a recent acceleration in research activity. Industry sponsorship was identified in seven trials (Fig. 1 and Table 1). Full characteristics of included studies are presented in Supplementary Table 1.
Figures 2-4 show the comparative efficacy of digital interventions versus standard care on point prevalence abstinence. As the internal comparisons between other interventions lacked statistical significance, we focused primarily on comparisons with standard care. The league table results are provided in the Supplementary Information.
This category comprised 90 RCTs, involving 55,094 participants, graded as low-certainty evidence. Group-customized interventions exhibited superior cessation efficacy relative to standard care (relative risk (RR) 1.93, 95% confidence interval (CI) 1.30-2.86), corresponding to a 93% increased likelihood (low-quality evidence). Personalized interventions significantly elevated quit rates versus standard care (RR 1.86, 95% CI 1.54-2.24). Interactive modalities demonstrated RR of 1.50 (95% CI 1.27-1.78; low-quality evidence), paralleling standardized digital protocols (RR 1.50, 95% CI 1.31-1.72; very low-quality evidence), while placebo showed non-significant effects compared with standard care (RR 0.87, 95% CI 0.63-1.20).
The analysis included 81 RCTs involving 52,755 participants, with overall moderate-quality evidence. SMS interventions demonstrated the highest efficacy (RR 1.63, 95% CI 1.38-1.92), indicating a 63% increased smoking cessation success compared with standard care (low-quality evidence). Telephone interventions followed closely (RR 1.59, 95% CI 1.33-1.90), improving quit rates by 59% (low-quality evidence). Multicomponent and app-based interventions also significantly improved cessation rates, with RRs of 1.56 (95% CI 1.32-1.84; low-quality evidence) and 1.53 (95% CI 1.29-1.81; moderate-quality evidence), respectively. Email (RR 1.33, 95% CI 1.07-1.65) and web-based interventions (RR 1.30, 95% CI 1.13-1.49) had comparatively smaller effects but still increased quit rates by 33% and 30%, respectively (low-quality evidence). Face-to-face interventions showed no statistically significant benefit (RR 1.13, 95% CI 0.89-1.43; very low-quality evidence). Placebo groups also had lower quit rates than standard care (RR 0.88, 95% CI 0.67-1.15).
Building on the previous analysis, we conducted a cross-matched group analysis involving 94 RCTs (63,134 participants). Due to the limited studies on group-customized interventions, single-study nodes weakened network robustness. To enhance statistical power, we merged similar group-customized digital interventions into one composite category. Unmerged results, presented in the Supplementary Information, did not significantly differ.
Personalized apps showed a 77% higher quit rate compared with standard care (RR 1.77, 95% CI 1.38-2.28; low-quality evidence), while personalized websites had an RR of 1.71 (95% CI 1.28-2.29; very low-quality evidence). Standard SMS interventions increased quit success by 72% (RR 1.72, 95% CI 1.38-2.15; very low-quality evidence). The combined group-customized intervention doubled the likelihood of smoking cessation (RR 2.01, 95% CI 1.41-2.87; low-quality evidence).
Interactive SMS interventions more than doubled quit rates (RR 2.14, 95% CI 1.29-3.55; moderate-quality evidence). Interactive apps had an RR of 5.70 (95% CI 1.28-25.37); despite a large effect estimate, wide CIs indicated substantial uncertainty (very low-quality evidence). Interactive websites (RR 1.02, 95% CI 0.60-1.74), email (RR 1.16, 95% CI 0.83-1.62) and face-to-face interventions (RR 1.14, 95% CI 0.86-1.51) showed no significant benefits (very low-quality evidence).
Interventions were ranked using P-scores to reflect their relative efficacy within the network, with higher scores denoting superior efficacy. In the methodological approach grouping, personalized interventions achieved the highest relative efficacy (P-score 0.88), whereas interactive interventions had moderate relative efficacy (P-score 0.53) (Fig. 5a). In the technological grouping, SMS-based interventions demonstrated the highest relative efficacy (P-score 0.87), followed by telephone interventions (P-score 0.81) and multicomponent interventions (P-score 0.78) (Fig. 5b). Within the cross-matched analysis, interactive apps exhibited the highest relative efficacy (P-score 0.95), followed by interactive SMS interventions (P-score 0.81) and group-customized interventions (P-score 0.80) (Fig. 5c). It is important to note that clinical recommendations should not rely exclusively on P-scores; they must comprehensively consider the consistency and robustness of findings, sample sizes and the methodological quality of included studies.
Participants were stratified into younger (<40 years) and middle-aged (≥40 years) groups to examine age-related differences in intervention effectiveness (Extended Data Figs. 1-3). Detailed comparative results are presented in the Supplementary Information.
In the younger group, personalized interventions significantly increased smoking cessation success (RR 1.70, 95% CI 1.27-2.27). Interactive (RR 1.38, 95% CI 1.09-1.74) and standard interventions (RR 1.32, 95% CI 1.08-1.62) also showed positive effects. Group-customized interventions showed uncertain results (RR 1.59, 95% CI 0.90-2.80). In the middle-aged group, effects were stronger. Group-customized interventions substantially increased quit rates (RR 2.66, 95% CI 1.49-4.75). Personalized interventions had the largest effects (RR 2.39, 95% CI 1.75-3.25), while standard (RR 1.84, 95% CI 1.48-2.29) and interactive interventions (RR 1.75, 95% CI 1.33-2.30) were also effective.
Among younger participants, app-based interventions (RR 2.10, 95% CI 1.36-3.23) significantly improved quit rates. SMS (RR 1.54, 95% CI 1.20-1.99), telephone (RR 1.56, 95% CI 1.05-2.33) and multicomponent interventions (RR 1.35, 95% CI 1.08-1.69) were effective, whereas email, face-to-face and web interventions did not reach statistical significance. In the middle-aged group, multicomponent interventions showed the highest efficacy (RR 2.13, 95% CI 1.57-2.89). SMS (RR 1.89, 95% CI 1.46-2.44), telephone (RR 1.83, 95% CI 1.44-2.31), app-based (RR 1.83, 95% CI 1.43-2.36), web-based (RR 1.67, 95% CI 1.32-2.13) and face-to-face interventions (RR 1.42, 95% CI 1.04-1.95) were all significantly effective. Email interventions had insufficient data for evaluation in this group.
In younger adults, personalized apps markedly improved quit rates (RR 3.37, 95% CI 1.76-6.47), despite wide CIs suggesting uncertainty. Interactive SMS (RR 2.38, 95% CI 1.26-4.49) and standard SMS interventions (RR 1.67, 95% CI 1.05-2.65) were effective. Email and face-to-face interventions showed no significant effects. For the middle-aged group, group-customized apps displayed substantial but imprecise effects (RR 8.04, 95% CI 1.75-37.00). Multicomponent (RR 2.34, 95% CI 1.66-3.31), personalized apps (RR 2.04, 95% CI 1.34-3.12) and SMS interventions (RR 1.91, 95% CI 1.44-2.53) significantly increased cessation success rates.
Interventions were classified into short-term (<3 months), medium-term (3-9 months) and long-term (>9 months) categories, with detailed visual representations provided in Extended Data Figs. 4-6.
In the methodological analysis, short-term interventions demonstrated the strongest efficacy. Personalized interventions showed the highest efficacy (RR 2.29, 95% CI 1.51-3.49), increasing quit rates by 129%. Group-customized (RR 1.98, 95% CI 1.04-3.75), interactive (RR 1.92, 95% CI 1.34-2.76) and standard interventions (RR 1.74, 95% CI 1.22-2.61) also significantly improved quit success. Medium-term interventions showed slightly reduced but significant effects, with RRs for group-customized at 2.05 (95% CI 1.17-3.58), personalized at 1.97 (95% CI 1.53-2.53), interactive at 1.37 (95% CI 1.10-1.72) and standard interventions at 1.40 (95% CI 1.17-1.67). By contrast, long-term intervention effects diminished and lost statistical significance, with RRs for interactive at 1.27 (95% CI 0.84-1.92), personalized at 1.26 (95% CI 0.75-2.12) and standard at 1.30 (95% CI 0.84-2.01).
Regarding the technology types, short-term interventions achieved the highest efficacy with multicomponent (RR 2.36, 95% CI 1.38-4.05) and telephone (RR 2.27, 95% CI 1.49-3.48) interventions leading. SMS (RR 1.86, 95% CI 1.24-2.80) and web-based methods (RR 1.68, 95% CI 1.17-2.41) also performed well. Medium-term outcomes indicated sustained efficacy primarily in app-based (RR 1.61, 95% CI 1.23-2.11) and SMS interventions (RR 1.51, 95% CI 1.24-1.84), along with multicomponent and telephone interventions maintaining RRs above 1.40. Long-term effects showed a general decrease but remained significant for multicomponent (RR 1.56, 95% CI 1.15-2.11).
Cross-matched group analysis showed varied temporal efficacy. In short-term interventions, telephone-based methods exhibited high efficacy (RR 3.24, 95% CI 1.60-6.56), although the wide CIs warrant cautious interpretation. Personalized web (RR 2.75, 95% CI 1.35-5.61) and SMS interventions (RR 2.06, 95% CI 1.23-3.46) were also effective. Medium-term analyses highlighted personalized web (RR 1.94, 95% CI 1.39-2.71), personalized app (RR 2.17, 95% CI 1.47-3.19) and SMS (RR 1.57, 95% CI 1.24-1.99) interventions with maintained efficacy. In the long-term, personalized app (RR 1.77, 95% CI 1.38-2.28), SMS (RR 1.72, 95% CI 1.38-2.15) and multicomponent methods (RR 1.61, 95% CI 1.31-1.98) remained significantly effective, although efficacy was reduced compared with short-term outcomes.
The results for prolonged abstinence rates demonstrated substantial concordance with point prevalence abstinence findings. Personalized and interactive digital interventions also showed high quit success rates in promoting prolonged abstinence, with SMS, telephone and multicomponent interventions performing well. However, we remain cautious about the robustness of the prolonged abstinence rate results for two main reasons. First, the network connectivity was relatively sparse, and the number of studies for certain interventions was limited, which may affect result reliability. Second, the biochemical verification methods used in current smoking cessation studies are inadequate for accurately detecting prolonged abstinence beyond 1 month, which may lead to reporting bias. Detailed statistical results, forest plots, network diagrams and P-scores are provided in the Supplementary Information.
In the network meta-regression analysis, we evaluated the effect of various covariates on intervention outcomes, including biochemical verification, baseline daily smoking amount, gender, age, use of smoking cessation medication, financial incentives and year of publication. The results showed that none of these covariates was statistically significant for the primary outcomes (P > 0.05), suggesting that these factors may not be the primary determinants of intervention effectiveness. Detailed regression analysis results can be found in the Supplementary Information.
To test the robustness of the results, we conducted a sensitivity analysis and reanalysed the data using Bayesian methods. The results of the Bayesian analysis were consistent with the frequentist analysis, showing no significant changes in the effects or significance of the primary interventions, further validating the reliability of the results. In addition to the Bayesian analysis, we assessed the impact of including studies involving individuals with mental health conditions on overall results and heterogeneity. After excluding these studies, the results remained stable, supporting our conclusions. Detailed sensitivity analysis results can be found in the Supplementary Information.
By comparing the adjusted funnel plots, we found clear indications of publication bias. The funnel plots showed asymmetry, with smaller studies tending to report larger intervention effects, which may result in overestimating the overall effect. The Egger's regression test reached statistical significance (P < 0.05), indicating a high risk of publication bias. This bias may arise because newer digital interventions remain underexplored. Positive findings are more likely to be published, while negative or non-significant results are often overlooked, contributing to a positive reporting bias in the literature.
The risk-of-bias assessment indicated a high overall risk of bias in the included studies, stemming primarily from missing outcome data, potential selection bias and challenges in implementing blinding. According to the Cochrane Smoking Cessation Group, the nature of smoking cessation interventions makes it difficult to achieve full blinding for participants and researchers, inevitably introducing some bias. However, some studies managed to ensure blinding of outcome assessors, which helped reduce the risk of detection bias. Variations in randomization procedures, allocation concealment and outcome reporting across studies may have compromised the reliability of the pooled results. Detailed bias risk assessment figures and descriptions are available in the Supplementary Information.
The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) assessment indicated the quality of evidence for the primary outcomes. The quality of evidence in the methodological group was rated as 'low', the digital intervention type group as 'moderate' and the cross-matching group as 'low'. The quality of evidence for specific intervention methods ranged from 'moderate' to 'very low', primarily limited by a high risk of bias, insufficient transferability of study designs and the presence of publication bias. Details for the assessment are provided in the tables in the Supplementary Information.