Developing a Smartphone Application to Support Smoking Behavior Change through Social Comparison

Smokers' user experience of smoking cessation apps: A systematic review

OBJECTIVES

To explore how smokers view common functions and characteristics of smoking cessation apps.

DESIGN

Systematic review.

SEARCH SOURCES

CINAHL PLUS, MEDLINE, PsycINFO, EMBASE, IEEE Xplore, ACM Digital Library, and Google Scholar.

REVIEW METHODS

Seven digital databases were searched separately using relevant search terms. Search results were uploaded to Covidence. Inclusion and exclusion criteria were identified with the expert team in advance. Titles, abstracts, and full texts were screened by two reviewers independently. Any disagreements were discussed in research meetings. Pertinent data were extracted and analysed using qualitative content analysis. Findings were presented in a narrative approach.

RESULTS

28 studies were included in this review. The overarching themes were app functionality and app characteristics. Under app "functionality", six subthemes emerged: 1) education; 2) tracking; 3) social support; 4) compensation; 5) distraction, and 6) reminding. Under "app characteristics", five subthemes emerged: 1) simplification, 2) personalisation, 3) diverse content forms, 4) interactivity, and 5) privacy and security.

CONCLUSION

Understanding user needs and expectations is crucial for developing a programme theory for smoking cessation app interventions. Relevant needs identified in this review should be linked to broader theories of smoking cessation and app-based intervention.

Characterizing and Modeling Smoking Behavior Using Automatic Smoking Event Detection and Mobile Surveys in Naturalistic Environments: Observational Study

Background

There are 1.1 billion smokers worldwide, and each year, more than 8 million die prematurely because of cigarette smoking. More than half of current smokers make a serious quit every year. Nonetheless, 90% of unaided quitters relapse within the first 4 weeks of quitting due to the lack of limited access to cost-effective and efficient smoking cessation tools in their daily lives.

Objective

This study aims to enable quantified monitoring of ambulatory smoking behavior 24/7 in real life by using continuous and automatic measurement techniques and identifying and characterizing smoking patterns using longitudinal contextual signals. This work also intends to provide guidance and insights into the design and deployment of technology-enabled smoking cessation applications in naturalistic environments.

Methods

A 4-week observational study consisting of 46 smokers was conducted in both working and personal life environments. An electric lighter and a smartphone with an experimental app were used to track smoking events and acquire concurrent contextual signals. In addition, the app was used to prompt smoking-contingent ecological momentary assessment (EMA) surveys. The smoking rate was assessed based on the timestamps of smoking and linked statistically to demographics, time, and EMA surveys. A Poisson mixed-effects model to predict smoking rate in 1-hour windows was developed to assess the contribution of each predictor.

Results

In total, 8639 cigarettes and 1839 EMA surveys were tracked over 902 participant days. Most smokers were found to have an inaccurate and often biased estimate of their daily smoking rate compared with the measured smoking rate. Specifically, 74% (34/46) of the smokers made more than one (mean 4.7, SD 4.2 cigarettes per day) wrong estimate, and 70% (32/46) of the smokers overestimated it. On the basis of the timestamp of the tracked smoking events, smoking rates were visualized at different hours and were found to gradually increase and peak at 6 PM in the day. In addition, a 1- to 2-hour shift in smoking patterns was observed between weekdays and weekends. When moderate and heavy smokers were compared with light smokers, their ages (P<.05), Fagerström Test of Nicotine Dependence (P=.01), craving level (P<.001), enjoyment of cigarettes (P<.001), difficulty resisting smoking (P<.001), emotional valence (P<.001), and arousal (P<.001) were all found to be significantly different. In the Poisson mixed-effects model, the number of cigarettes smoked in a 1-hour time window was highly dependent on the smoking status of an individual (P<.001) and was explained by hour (P=.02) and age (P=.005).

Conclusions

This study reported the high potential and challenges of using an electronic lighter for smoking annotation and smoking-triggered EMAs in an ambulant environment. These results also validate the techniques for smoking behavior monitoring and pave the way for the design and deployment of technology-enabled smoking cessation applications.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2018-028284

Reveal Temporal Patterns of Smoking Behavior in Real Life Using Data Acquired through Automatic Tracking Systems

Accurately monitoring and modeling smoking behavior in real life settings is critical for designing and delivering appropriate smoking-cessation interventions through mHealth applications. In this paper, we inspect smoking patterns based on data collected from 52 volunteers during a 4-week period of their everyday lives. These data are acquired by an automatic data acquisition system comprising an electric lighter, two wearable sensors and one mobile phone, which together can automatically track smoking events, collect concurrent context and physiology, and trigger pop-up questionnaires. We visualize temporal patterns of smoking at the level of the week, day and time of the day. Statistical analysis on all subjects has demonstrated significant differences at the levels evaluated. Distinct emotions during smoking at individual level are also found. Quantified smoking patterns can upgrade our understanding of individual behaviors and contribute to optimizing intervention plans.