ArticlePilot and feasibility studies2018
MapMySmoke: feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting.
Article in Pilot and feasibility studies, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT02932917 (MapMySmoke), which is not on this map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
MapMySmoke: Feasibility of a New Quit Cigarette Smoking Mobile Phone Application Using Integrated Geo-positioning Technology, and Motivational Messaging Within Primary Care Setting
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Effectiveness, feasibility, and acceptability of behaviour change tools used by family doctors: a global systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2023Pooled it
- The associations between smartphone addiction and self-esteem, self-control, and social support among Chinese adolescents: A meta-analysis.Frontiers in psychology · 2022Pooled it
- Relative effectiveness of a full versus reduced version of the 'Smoke Free' mobile application for smoking cessation: an exploratory randomised controlled trial.F1000Research · 2018Trial
- Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support.Scientific reports · 2026Article
- Relative importance of temporal and location features in predicting smoking events.NPJ digital medicine · 2025Article
- Real-time prediction of smoking activity using machine learning based multi-class classification model.Multimedia tools and applications · 2022Article
- Are Machine Learning Methods the Future for Smoking Cessation Apps?Sensors (Basel, Switzerland) · 2021Article
- Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events.Sensors (Basel, Switzerland) · 2020Article
- Mobile Applications for the Treatment of Tobacco Use and Dependence.Current addiction reports · 2019Article
- Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research.Addictive behaviors · 2018Review
Corrections and comments
- Erratum issuedErratum to:2017
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately 11,000 people die in Scotland each year as a result of smoking-related causes. Quitting smoking is relatively easy; maintaining a quit attempt is a very difficult task with success rates for unaided quit attempts stubbornly remaining in the single digits. Pharmaceutical treatment can improve these rates by lowering the overall reward factor of nicotine. However, these and related nicotine replacement therapies do not operate on, or address, the spatial and contextual aspects of smoking behaviour. With the ubiquity of smartphones that can log spatial, quantitative and qualitative data related to smoking behaviour, there exists a person-centred clinical opportunity to support smokers attempting to quit by first understanding their smoking behaviour and subsequently sending them dynamic messages to encourage health behaviour change within a situational context.
methodsWe have built a smartphone app-MapMySmoke-that works on Android and iOS platforms. The deployment of this app within a clinical National Health Service (NHS) setting has two distinct phases: (1) a 2-week logging phase where pre-quit patients log all of their smoking and craving events; and (2) a post-quit phase where users receive dynamic support messages and can continue to log craving events, and should they occur, relapse events. Following the initial logging phase, patients consult with their general practitioner (GP) or healthcare provider to review their smoking patterns and to outline a precise, individualised quit attempt plan. Our feasibility study consists of assessment of an initial app version during and after use by eight patients recruited from an NHS Fife GP practice. In addition to evaluation of the app as a potential smoking cessation aid, we have assessed the user experience, technological requirements and security of the data flow.
resultsIn an initial feasibility study, we have deployed the app for a small number of patients within one GP practice in NHS Fife. We recruited eight patients within one surgery, four of whom actively logged information about their smoking behaviour. Initial feedback was very positive, and users indicated a willingness to log their craving and smoking events. In addition, two out of three patients who completed follow-up interviews noted that the app helped them reduce the number of cigarettes they smoked per day, while the third indicated that it had helped them quit. The study highlighted the use of pushed notifications as a potential technology for maintaining quit attempts, and the security of collection of data was audited. These initial results influenced the design of a planned second larger study, comprised of 100 patients, the primary objectives of which are to use statistical modelling to identify times and places of probable switches into smoking states, and to target these times with dynamic health behaviour messaging.
conclusionsWhile the health benefits of quitting smoking are unequivocal, such behaviour change is very difficult to achieve. Many factors are likely to contribute to maintaining smoking behaviour, yet the precise role of cues derived from the spatial environment remains unclear. The rise of smartphones, therefore, allows clinicians the opportunity to better understand the spatial aspects of smoking behaviour and affords them the opportunity to push targeted individualised health support messages at vulnerable times and places.
trial registrationClinicalTrial.gov, NCT02932917.
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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.