Adjunctive Smart Phone Based Smoking Cessation Treatment

Research Supporting Study Objectives. Tobacco use is the leading cause of preventable death in the U.S. Over 443,000 deaths per year are attributable to smoking, with the leading causes of smoking-attributable death being cancer, cardiovascular diseases, and respiratory disease. Although the prevalence of smoking has declined to 20.6% among U.S. adults, 31.1% of those living in poverty are currently smoking. Numerous studies have shown that low SES and financial strain are associated with a reduced likelihood of smoking cessation. As such, lung cancer incidence is far greater among those with low education and who are living in poverty, relative to their higher SES counterparts. Although individuals of low SES are just as likely to attempt smoking cessation, they are less likely to succeed. As such, smoking cessation interventions for low SES populations have had limited success (e.g., biologically confirmed 7-day point prevalence abstinence rates of 7-13% at 6-month follow-up. Innovative smoking cessation interventions are needed to help reduce the rate of smoking in socioeconomically disadvantaged populations of smokers.

Ecological Momentary Assessment and Smoking Cessation Treatments. To date, most studies that have examined smoking and smoking cessation in socioeconomically disadvantaged smokers have used focus groups or traditional lab/clinic based assessment methodology. In the latter scenario, study participants arrive at a lab or clinic for their baseline visit and are asked to answer questions about their "average" or "recent" (e.g., over the past 2 weeks) mood, level of stress, and smoking urges. Participants return to the lab/clinic for follow-up visits and are asked to report thoughts, feelings, and activities that occurred weeks or even months earlier (e.g., "How stressed were you when you smoked your first cigarette after your quit date?"). This type of assessment methodology may result in biased and/or inaccurate estimates due to recall biases and errors in memory and only offers a gross understanding of how cessation symptoms effect smoking lapses and relapse. A more nuanced picture of these symptoms may offer important insights that may be used to create or improve cessation interventions for socioeconomically disadvantaged smokers, who face unique and substantial challenges in quitting smoking.

Ecological momentary assessment (EMA), in which handheld devices (e.g., smart phones) are used to capture moment to moment experience, is currently the most accurate way to measure phenomena in real time in natural settings. EMAs are often used to assess individuals at multiple time points throughout a day. Thus, momentary changes in key variables can be tracked and potentially used to initiate novel interventions. EMA research may facilitate a better understanding of the mechanisms involved in successful cessation attempts, those affecting smoking lapses, and those implicated in the transition from lapse to relapse. Although multiple studies have identified momentary predictors of smoking relapse to our knowledge, no studies have used a participant's responses to EMAs to automatically prompt tailored smoking cessation interventions. Using smart phones to detect high relapse risk situations and automatically deliver tailored smoking cessation treatments may help socioeconomically disadvantaged smokers quit.