Emily T Hébert1, Elise M Stevens2, Summer G Frank2, Darla E Kendzor3, David W Wetter4, Michael J Zvolensky5, Julia D Buckner6, Michael S Businelle3. 1. Oklahoma Tobacco Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. Electronic address: emily-hebert@ouhsc.edu. 2. Oklahoma Tobacco Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. 3. Oklahoma Tobacco Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States; Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. 4. Department of Population Health Sciences and the Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States. 5. The University of Houston, College of Liberal Arts and Social Sciences, Department of Psychology, Houston, TX, United States. 6. Department of Psychology, Louisiana State University, Baton Rouge, LA, United States.
Abstract
BACKGROUND: Smartphone apps can provide real-time, tailored interventions for smoking cessation. The current study examines the effectiveness of a smartphone-based smoking cessation application that assessed risk for imminent smoking lapse multiple times per day and provided messages tailored to current smoking lapse risk and specific lapse triggers. METHODS: Participants (N=59) recruited from a safety-net hospital smoking cessation clinic completed phone-based ecological momentary assessments (EMAs) 5 times/day for 3 consecutive weeks (1week pre-quit, 2weeks post-quit). Risk for smoking lapse was estimated in real-time using a novel weighted lapse risk estimator. With each EMA, participants received messages tailored to current level of risk for imminent smoking lapse and self-reported presence of smoking urge, stress, cigarette availability, and motivation to quit. Generalized linear mixed model analyses determined whether messages tailored to specific lapse risk factors were associated with greater reductions in these triggers than messages not tailored to specific triggers. RESULTS: Overall, messages tailored to smoking urge, cigarette availability, or stress corresponded with greater reductions in those triggers than messages that were not tailored to specific triggers (p's=0.02 to <0.001). Although messages tailored to stress were associated with greater reductions in stress than messages not tailored to stress, the association was non-significant (p=0.892) when only moments of high stress were included in the analysis. CONCLUSIONS: Mobile technology can be used to conduct real-time smoking lapse risk assessment and provide tailored treatment content. Findings provide initial evidence that tailored content may impact users' urge to smoke, stress, and cigarette availability.
BACKGROUND: Smartphone apps can provide real-time, tailored interventions for smoking cessation. The current study examines the effectiveness of a smartphone-based smoking cessation application that assessed risk for imminent smoking lapse multiple times per day and provided messages tailored to current smoking lapse risk and specific lapse triggers. METHODS: Participants (N=59) recruited from a safety-net hospital smoking cessation clinic completed phone-based ecological momentary assessments (EMAs) 5 times/day for 3 consecutive weeks (1week pre-quit, 2weeks post-quit). Risk for smoking lapse was estimated in real-time using a novel weighted lapse risk estimator. With each EMA, participants received messages tailored to current level of risk for imminent smoking lapse and self-reported presence of smoking urge, stress, cigarette availability, and motivation to quit. Generalized linear mixed model analyses determined whether messages tailored to specific lapse risk factors were associated with greater reductions in these triggers than messages not tailored to specific triggers. RESULTS: Overall, messages tailored to smoking urge, cigarette availability, or stress corresponded with greater reductions in those triggers than messages that were not tailored to specific triggers (p's=0.02 to <0.001). Although messages tailored to stress were associated with greater reductions in stress than messages not tailored to stress, the association was non-significant (p=0.892) when only moments of high stress were included in the analysis. CONCLUSIONS: Mobile technology can be used to conduct real-time smoking lapse risk assessment and provide tailored treatment content. Findings provide initial evidence that tailored content may impact users' urge to smoke, stress, and cigarette availability.
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