Detecting imminent smoking lapse risk: Prospective lapse risk algorithm versus participant retrospective self-report

Improving detection of smoking lapse risk factors could increase smoking cessation rates among socioeconomically disadvantaged adults, who are less likely to quit than the general population. This study used data from a randomized controlled trial that compared the efficacy of two smartphone-based smoking cessation interventions for socioeconomically disadvantaged adults. Daily ecological momentary assessments (EMAs) assessed current smoking lapse risk based on a previously developed algorithm. Participants were instructed to self-initiate EMAs when they were about to lapse and after a lapse. After self-reported lapses, participants were asked questions about the number of hours of awareness of heightened lapse risk prior to the lapse and coping skills that could have prevented the lapse. Overall, 157 participants self-initiated an EMA to report a smoking lapse during the 13-week post-quit study period. Participants reported detecting warning signs prior to 70.06 % of lapses; however, only 30 % of lapses were anticipated more than two hours in advance. The lapse risk algorithm detected elevated risk in 68.93 % of lapses that were preceded by an EMA within 24 h. A logistic mixed-effects model indicated that on average the algorithm detected heightened lapse risk earlier than participants reported they were aware of heightened lapse risk, AOR= 3.34, 95 % CI [1.50–7.42]. Participants most frequently endorsed coping with the urge to smoke and stress as skills that would have helped them prevent lapses. EMA-informed algorithms show promise for detecting heightened risk for smoking lapse before participant recognition, an important step for developing effective real-time smoking cessation interventions for socioeconomically disadvantaged adults.