Mobile phone text messaging and app-based interventions for smoking cessation
Open accessAbstract
BACKGROUND: Mobile phone-based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face-to-face support. In addition, mCessation can be automated and therefore provided affordably even in resource-poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012. OBJECTIVES: To determine whether mobile phone-based smoking cessation interventions increase smoking cessation rates in people who smoke. SEARCH METHODS: For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018. SELECTION CRITERIA: Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone-based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six-month follow-up. DATA COLLECTION AND ANALYSIS: We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta-analyses of the most stringent measures of abstinence at six months' follow-up or longer, using a Mantel-Haenszel random-effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only. MAIN RESULTS: = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings. AUTHORS' CONCLUSIONS: There is moderate-certainty evidence that automated text message-based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate-certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.
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Participants with missing outcome data will be classified as smokers.
“All those with missing outcome data will be coded as smokers, as is standard in digital intervention smoking cessation trials.66,67”
From Efficacy of smartphone applications to help cancer patients quit smoking: Protocol of the Quit2Heal randomized controlled trial (2023) · cites this paper as [66]
The available systematic reviews highlight low certainty regarding the efficacy of smoking cessation Apps.
“the available systematic reviews of the literature highlight the existence of low certainty regarding the efficacy of smoking cessation Apps.”
From Evaluation of effectiveness and acceptability of a psychological treatment for smoking cessation combined with a smartphone App: A pilot study (2024) · cites this paper as Whittaker et al., 2019
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- Authors
- Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., Gu, Y., & Dobson, R.
- Affiliations
- University of Auckland (NZ)
- UNSW Sydney (AU)
- The George Institute for Global Health (AU)
- Stockton University (US)
- Venue
- Cochrane Database of Systematic Reviews
- Publisher
- Wiley
- Published
- 2019-10-22
- Volume
- 10
- Pages
- CD006611-CD006611
- ISSN
- 1361-6137 (ISSN-L)
- 1465-1858
- PMC
- PMC6804292
- OpenAlex
- W2982082714
- Cited by
- 452
- References
- 129
- Citations by year
- 2026: 39 · 2025: 56 · 2024: 71 · 2023: 76 · 2022: 76 · 2021: 86 · 2020: 44 · 2019: 4
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cochrane Database of Systematic Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- ResearchSpace (University of Auckland) (submittedVersion)
- Funders
- Bill and Melinda Gates Foundation
- American Heart Association (K12DA00167, 14CRP18200010)
- China Medical Board (15-226)
- Pfizer
- World Health Organization
- George Washington University
- Center for Drug Use and HIV Research
- National Institute for Health and Care Research (16/114/20, SRP/16/114/20)
- National Institutes of Health (14CRP18200010)
- Milken Institute School of Public Health, George Washington University
- Health Research Council of New Zealand
- National Institute on Drug Abuse (14CRP18200010, K12 DA00167, K12DA000167)
- National Cancer Institute (K07CA124579)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Keywords
- smoking cessation, mobile phone intervention, text messaging, mobile application, automated intervention, abstinence rates, contingency management, resource-limited settings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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