Comparative efficacy of digital interventions by methodological approach grouping. a, The network structure of included studies. Circles represent interventions, with area proportional to sample size or statistical weight. Lines indicate direct comparative evidence, with thickness proportional to the number of trials. b, Comparison of the relative efficacy of each intervention against standard care. The purple diamond denotes the pooled RR point estimate. The horizontal purple bar represents the 95% CI. A total of 90 independent RCTs were included (N = 55,094). Credit: Li et al. (Nature Human Behaviour, 2025).
Smoking remains one of the most deleterious habits for human health, as it is known to increase the risk of several life-threatening diseases, including lung and throat cancers, heart disease and strokes. While most smokers are well aware of its associated health risks, ceasing this habit can be a very difficult process.
Moreover, conventional programs for smoking cessation, such as those based on psychotherapy or nicotine replacement therapyare not financially or physically accessible for all individuals who wish to stop smoking. In recent years, behavioral scientists and psychologists have been working with engineers to create digital interventions that support people in their efforts to quit this unhealthy habit.
Researchers at Sichuan University in China have carried out a systematic review and meta-analysis of past research studies investigating the effectiveness of various digital interventions for smoking cessation. The results of their analyses, presented in a paper published in Nature Human Behaviorsuggest that personalized and group-customized technology-based programs could be particularly beneficial for smokers who wish to quit, with middle-aged individuals responding better than younger populations.
“Smoking cessation is the only evidence-based approach to reducing tobacco-related health risks, yet traditional interventions suffer from limited coverage,” Shen Li, Yiyang Li, and their colleagues wrote in their paper. “Although digital interventions show promise, their comparative efficacy across methodological frameworks and technology types remains unclear. We assessed digital interventions versus standard care via frequentist random-effects network meta-analysis of 152 randomized controlled trials (48.8% U.S., 7.5% China).”
As part of their study, the researchers reviewed over 100 past studies that evaluated different types of programs to help people stop smoking, which were either delivered in person in health care settings or using technology-based platforms. The authors categorized the interventions considered in their analyses based on the methods used to implement them and the technology they relied on. They also carried out further analyses to determine whether the effectiveness of the programs varied based on the age of participating smokers.
“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),” wrote Li, Li and their colleagues. “Among the various technology types, text message-based interventions were the most effective (RR 1.63, 95% CI 1.38–1.92).”
Overall, the findings of the team’s analyses suggest that personalized digital interventions were more successful than conventional programs offered by health care services in China or the United States. The programs that appeared to be most effective were those that involved a group of smokers and interventions delivered via text messages.
“Intervention effectiveness was also influenced by age, with middle-aged individuals benefitting more than younger individuals,” wrote the authors. “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.”
The recent work by Li, Li and their colleagues could potentially inform the future design and implementation of technology-based interventions aimed at reducing smoking rates in various geographical areas and thus improving public health. Nonetheless, the team’s analyses had some limitations which could be overcome in follow-up and further papers.
“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,” added Li, Li and their colleagues.
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More information:
Shen Li et al, Efficacy of digital interventions for smoking cessation by type and method: a systematic review and network meta-analysis, Nature Human Behaviour (2025). Two: 10.1038/S41562-025-02295-2.
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Most effective digital interventions to stop smoking identified (2025, October 1)
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