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Mobile phone-based interventions for smoking cessation
Open accessAbstract
BACKGROUND: Access to mobile phones continues to increase exponentially globally, outstripping access to fixed telephone lines, fixed computers and the Internet. Mobile phones are an appropriate and effective option for the delivery of smoking cessation support in some contexts. This review updates the evidence on the effectiveness of mobile phone-based smoking cessation interventions. OBJECTIVES: To determine whether mobile phone-based smoking cessation interventions increase smoking cessation in people who smoke and want to quit. SEARCH METHODS: For the most recent update, we searched the Cochrane Tobacco Addiction Group Specialised Register in April 2015. We also searched the UK Clinical Research Network Portfolio for current projects in the UK, and the ClinicalTrials.gov register for ongoing or recently completed studies. We searched through the reference lists of identified studies and attempted to contact the authors of ongoing studies. We applied no restrictions on language or publication date. SELECTION CRITERIA: We included randomised or quasi-randomised trials. Participants were smokers of any age who wanted to quit. Studies were those examining any type of mobile phone-based intervention for smoking cessation. This included any intervention aimed at mobile phone users, based around delivery via mobile phone, and using any functions or applications that can be used or sent via a mobile phone. DATA COLLECTION AND ANALYSIS: Review authors extracted information on risk of bias and methodological details using a standardised form. We considered participants who dropped out of the trials or were lost to follow-up to be smoking. We calculated risk ratios (RR) and 95% confidence intervals (CI) for each included study. Meta-analysis of the included studies used the Mantel-Haenszel fixed-effect method. Where meta-analysis was not possible, we presented a narrative summary and descriptive statistics. MAIN RESULTS: This updated search identified 12 studies with six-month smoking cessation outcomes, including seven studies completed since the previous review. The interventions were predominantly text messaging-based, although several paired text messaging with in-person visits or initial assessments. Two studies gave pre-paid mobile phones to low-income human immunodeficiency virus (HIV)-positive populations - one solely for phone counselling, the other also included text messaging. One study used text messages to link to video messages. Control programmes varied widely. Studies were pooled according to outcomes - some providing measures of continuous abstinence or repeated measures of point prevalence; others only providing 7-day point prevalence abstinence. All 12 studies pooled using their most rigorous 26-week measures of abstinence provided an RR of 1.67 (95% CI 1.46 to 1.90; I(2) = 59%). Six studies verified quitting biochemically at six months (RR 1.83; 95% CI 1.54 to 2.19). AUTHORS' CONCLUSIONS: The current evidence supports a beneficial impact of mobile phone-based smoking cessation interventions on six-month cessation outcomes. While all studies were good quality, the fact that those studies with biochemical verification of quitting status demonstrated an even higher chance of quitting further supports the positive findings. However, it should be noted that most included studies were of text message interventions in high-income countries with good tobacco control policies. Therefore, caution should be taken in generalising these results outside of this type of intervention and context.
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“Several factors suggest that mCessation may be a viable motivator to quit smoking in Samoa: the high acceptability of the mCessation program among Pacific populations in New Zealand [30], the empirical evidence of its efficacy [15,35], and increasing mobile phone coverage in Samoa [28].”
From Assessing the Cross-Cultural Adaptation and Translation of a Text-Based Mobile Smoking Cessation Program in Samoa (TXTTaofiTapaa): Pilot Study (2018) · cites this paper as [30]
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Abd-Alrazaq, A., Safi, Z., Alajlani, M., Warren, J., Househ, M., & Denecke, K. (2020) · Journal of Medical Internet Research · doi:10.2196/18301 · Cited by 150
“As mobile phones have become commonplace, a range of SMS text messaging–based interventions have been developed and trialed, with particular success in smoking cessation [3].”
BackgroundInterventions for improving modifiable risk factor control in the secondary prevention of stroke
Bridgwood, B. M., Lager, K. E., Mistri, A. K., Khunti, K., Wilson, A., & Modi, P. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009103.pub3 · Cited by 156
Developing and Pretesting a Text Messaging Program for Health Behavior Change: Recommended Steps
Abroms, L. C., Whittaker, R., Free, C., Alstyne, J. M. V., & Schindler-Ruwisch, J. M. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4917 · Cited by 155
Self-citationPalmer, M., Sutherland, J., Barnard, S., Wynne, A., Rezel, E., Doel, A., Grigsby-Duffy, L., Edwards, S., Russell, S., Hotopf, E., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0189801 · Cited by 136
Print-based self-help interventions for smoking cessation
Hartmann‐Boyce, J., Lancaster, T., & Stead, L. F. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001118.pub3 · Cited by 154
A Mobile App to Aid Smoking Cessation: Preliminary Evaluation of SmokeFree28
Ubhi, H. K., Michie, S., Kotz, D., Wong, W. C., & West, R. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3479 · Cited by 149
Smoking cessation, version 1.2016, NCCN clinical practice guidelines in oncology
Shields, P. G., Herbst, R. S., Arenberg, D., Benowitz, N. L., Bierut, L., Luckart, J. B., Cinciripini, P., Collins, B., David, S., Davis, J., et al. (2016) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2016.0152 · Cited by 146
How Smart are Smartphone Apps for Smoking Cessation? A Content Analysis
Hoeppner, B. B., Hoeppner, S. S., Seaboyer, L. M., Schick, M. R., Wu, G. W. Y., Bergman, B. G., & Kelly, J. F. (2015) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntv117 · Cited by 148
“We coded three such ways: (1) by being interactive, where the input provided by the user would result in specific feedback; (2) by being proactive, where the app reaches out to users after initial use, and (3) by being responsive to the quit attempt, where the functionality of the app changes after the quit day.”
MethodUtilizing Digital Health Technologies for Patient Education in Lifestyle Medicine
Kuwabara, A. M., Su, S., & Krauss, J. (2019) · American Journal of Lifestyle Medicine · doi:10.1177/1559827619892547 · Cited by 169
Kaplan, R. M., & Stone, A. A. (2013) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143736 · Cited by 144
Text Messaging Interventions on Cancer Screening Rates: A Systematic Review
Uy, C., López, J., Trinh‐Shevrin, C., Kwon, S. C., Sherman, S. E., & Liang, P. S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7893 · Cited by 143
Liu, J., Davidson, E., Bhopal, R., White, M., Johnson, M., Netto, G., Deverill, M., & Sheikh, A. (2012) · Health Technology Assessment · doi:10.3310/hta16440 · Cited by 237
Mobile phone-based interventions for improving contraception use
Smith, C. E., Gold, J., Ngo, T. D., Sumpter, C., & Free, C. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011159.pub2 · Cited by 136
Mobile phone-based behavioural interventions for health: A systematic review
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Sherifali, D., Nerenberg, K. A., Wilson, S., Semeniuk, K., Ali, M. U., Redman, L. M., & Adamo, K. B. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8006 · Cited by 122
Naughton, F., Prevost, A. T., Gilbert, H. M., & Sutton, S. R. (2012) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntr254 · Cited by 135
A Survey of Smartwatches in Remote Health Monitoring
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“Examples of effective smartphone-based healthcare applications include those for the self-management of long-term illnesses [5], smoking cessation [6], family planning/contraception [7], and psychological therapies [8], among other clinical research applications [9–12].”
BackgroundCheung, Y. T. D., Chan, C. H. H., Lai, C.-K. J., Chan, W. F. V., Wang, M. P., Li, H. C. W., Chan, S. S. C., & Lam, T. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4829 · Cited by 127
Home-Based Cardiac Rehabilitation
Thomas, R. J., Beatty, A. L., Beckie, T. M., Brewer, L. C., Brown, T. M., Forman, D. E., Franklin, B. A., Keteyian, S. J., Kitzman, D. W., Regensteiner, J. G., et al. (2019) · Journal of Cardiopulmonary Rehabilitation and Prevention · doi:10.1097/hcr.0000000000000447 · Cited by 127
Telemedicine interventions for gestational diabetes mellitus: A systematic review and meta-analysis
Rasekaba, T. M., Furler, J., Blackberry, I., Tacey, M., Gray, K., & Lim, K. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.07.007 · Cited by 115
“[18] Whittaker R, et al.”
BackgroundSmoking-Cessation Interventions for U.S. Young Adults
Villanti, A. C., McKay, H., Abrams, D. B., Holtgräve, D. R., & Bowie, J. V. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.08.009 · Cited by 124
Lyzwinski, L. N. (2014) · Journal of Personalized Medicine · doi:10.3390/jpm4030311 · Cited by 124
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Haug, S., Schaub, M. P., Venzin, V., Meyer, C., & John, U. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2636 · Cited by 122
Chen, C., Haddad, D., Selsky, J., Hoffman, J. E., Kravitz, R. L., Estrin, D., & Sim, I. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2152 · Cited by 121
Nglazi, M. D., Bekker, L.-G., Wood, R., Hussey, G. D., & Wiysonge, C. S. (2013) · BMC Infectious Diseases · doi:10.1186/1471-2334-13-566 · Cited by 119
Akanbi, M. O., Carroll, A. J., Achenbach, C. J., O’Dwyer, L., Jordan, N., Hitsman, B. L., Bilaver, L., McHugh, M., & Murphy, R. L. (2018) · Addiction · doi:10.1111/add.14518 · Cited by 119
“A recent Cochrane meta-analysis of 12 studies that evaluated m-Health interventions for smoking cessation reported greater quit rates in the intervention group [56]; however, all the included studies were from high-income countries.”
ResultAbroms, L. C., Ahuja, M., Kodl, Y., Thaweethai, L., Sims, J., Winickoff, J. P., & Windsor, R. A. (2012) · Journal of Health Communication · doi:10.1080/10810730.2011.649159 · Cited by 117
Metadata
- Authors
- Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., & Gu, Y.
- Affiliations
- University of Auckland (NZ)
- Queen Mary University of London (GB)
- The George Institute for Global Health (AU)
- Stockton University (US)
- Venue
- Cochrane Database of Systematic Reviews
- Publisher
- Wiley
- Published
- 2016-04-10
- Volume
- 4
- Pages
- CD006611-CD006611
- ISSN
- 1361-6137 (ISSN-L)
- 1465-1858
- PMC
- PMC6485940
- OpenAlex
- W2123622898
- Cited by
- 1,079
- References
- 92
- Citations by year
- 2026: 15 · 2025: 31 · 2024: 43 · 2023: 56 · 2022: 56 · 2021: 71 · 2020: 113 · 2019: 122 · 2018: 121 · 2017: 96 · 2016: 86
- FWCI
- 16.62
- Type
- journal article
- Language
- en
- Update notices
- new_version (2019-10-22)
- Open access
- bronze
- doi.org
- Locations
- Cochrane Database of Systematic Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Smoking Behavior and Cessation
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Keywords
- smoking cessation, mobile phone intervention, text messaging, low-income populations, abstinence, point prevalence, video messaging, telephone quitline, risk of bias, loss to follow-up, cost-effectiveness, implementation barriers, implementation facilitators
- MeSH terms
- Adult, Counseling, Humans, Randomized Controlled Trials as Topic, Smoking Cessation, Cell Phone, Text Messaging
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.
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