Text Messaging-Based Interventions for Smoking Cessation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Tobacco use is one of the leading preventable global health problems producing nearly 6 million smoking-related deaths per year. Interventions delivered via text messaging (short message service, SMS) may increase access to educational and support services that promote smoking cessation across diverse populations. OBJECTIVE: The purpose of this meta-analysis is to (1) evaluate the efficacy of text messaging interventions on smoking outcomes, (2) determine the robustness of the evidence, and (3) identify moderators of intervention efficacy. METHODS: Electronic bibliographic databases were searched for records with relevant key terms. Studies were included if they used a randomized controlled trial (RCT) to examine a text messaging intervention focusing on smoking cessation. Raters coded sample and design characteristics, and intervention content. Summary effect sizes, using random-effects models, were calculated and potential moderators were examined. RESULTS: The meta-analysis included 20 manuscripts with 22 interventions (N=15,593; 8128 (54%) women; mean age=29) from 10 countries. Smokers who received a text messaging intervention were more likely to abstain from smoking relative to controls across a number of measures of smoking abstinence including 7-day point prevalence (odds ratio (OR)=1.38, 95% confidence interval (CI)=1.22, 1.55, k=16) and continuous abstinence (OR=1.63, 95% CI=1.19, 2.24, k=7). Text messaging interventions were also more successful in reducing cigarette consumption relative to controls (d+=0.14, 95% CI=0.05, 0.23, k=9). The effect size estimates were biased when participants who were lost to follow-up were excluded from the analyses. Cumulative meta-analysis using the 18 studies (k=19) measuring abstinence revealed that the benefits of using text message interventions were established only after only five RCTs (k=5) involving 8383 smokers (OR=1.39, 95% CI=1.15, 1.67, P<.001). The inclusion of the subsequent 13 RCTs (k=14) with 6870 smokers did not change the established efficacy of text message interventions for smoking abstinence (OR=1.37, 95% CI=1.25, 1.51, P<.001). Smoking abstinence rates were stronger when text messaging interventions (1) were conducted in Asia, North America, or Europe, (2) sampled fewer women, and (3) recruited participants via the Internet. CONCLUSIONS: The evidence for the efficacy of text messaging interventions to reduce smoking behavior is well-established. Using text messaging to support quitting behavior, and ultimately long-term smoking abstinence, should be a public health priority.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".