Evidence map›Paper›PMID 40897803›Full record

SynthesisNature human behaviour2025

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

Shen Li, Yiyang Li, Chenhao Xu, Siheng Tao, Haozhen Sun, Jiaqing Yang, Yilin Wang, Sheyu Li, Xuelei Ma

Abstract readSystematic ReviewNetwork Meta-Analysis
In one paragraph

Synthesis in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  8. Observational
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  16. Strengthening tobacco cessation across the primary health sector in the Americas: progress, gaps, and opportunities since 2007.Revista panamericana de salud publica = Pan American journal of public health · 2026
    Article
  17. Article
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  19. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shen Li *Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.ORCID http://orcid.org/0009-0000-7099-087X
Yiyang Li *Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Chenhao Xu *West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.
Siheng TaoDepartment of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Haozhen SunDepartment of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Jiaqing YangDepartment of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
Yilin WangWest China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.
Sheyu LiDepartment of Endocrinology and Metabolism, Laboratory of Diabetes and Metabolism Research, Cochrane China Centre, MAGIC China Centre, Chinese Evidence-Based Medicine Centre, West China Hospital, Sichuan University, Chengdu, China. lisheyu@scu.edu.cn.ORCID http://orcid.org/0000-0003-0060-0287
Xuelei MaDepartment of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China. drmaxuelei@gmail.com.ORCID http://orcid.org/0000-0002-9148-5001

Funding

National Natural Science Foundation of China (National Science Foundation of China) 72342014
6 · The paper itself

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.

Indexed as

Smoking CessationHumansRandomized Controlled Trials as TopicText Messaging

Identifiers

PMID40897803
PMCPMC12545192

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.