Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Abstract

BackgroundMobile phone‒based real-time ecological momentary assessments (EMAs) have been used to record health risk behaviors, and antecedents to those behaviors, as they occur in near real time.ObjectiveThe objective of this study was to determine if intensive longitudinal data, collected via mobile phone, could be used to identify imminent risk for smoking lapse among socioeconomically disadvantaged smokers seeking smoking cessation treatment.MethodsParticipants were recruited into a randomized controlled smoking cessation trial at an urban safety-net hospital tobacco cessation clinic. All participants completed in-person EMAs on mobile phones provided by the study. The presence of six commonly cited lapse risk variables (ie, urge to smoke, stress, recent alcohol consumption, interaction with someone smoking, cessation motivation, and cigarette availability) collected during 2152 prompted or self-initiated postcessation EMAs was examined to determine whether the number of lapse risk factors was greater when lapse was imminent (ie, within 4 hours) than when lapse was not imminent. Various strategies were used to weight variables in efforts to improve the predictive utility of the lapse risk estimator.ResultsParticipants (N=92) were mostly female (52/92, 57%), minority (65/92, 71%), 51.9 (SD 7.4) years old, and smoked 18.0 (SD 8.5) cigarettes per day. EMA data indicated significantly higher urges (P=.01), stress (P=.002), alcohol consumption (P<.001), interaction with someone smoking (P<.001), and lower cessation motivation (P=.03) within 4 hours of the first lapse compared with EMAs collected when lapse was not imminent. Further, the total number of lapse risk factors present within 4 hours of lapse (mean 2.43, SD 1.37) was significantly higher than the number of lapse risk factors present during periods when lapse was not imminent (mean 1.35, SD 1.04), P<.001. Overall, 62% (32/52) of all participants who lapsed completed at least one EMA wherein they reported ≥3 lapse risk factors within 4 hours of their first lapse. Differentially weighting lapse risk variables resulted in an improved risk estimator (weighted area=0.76 vs unweighted area=0.72, P<.004). Specifically, 80% (42/52) of all participants who lapsed had at least one EMA with a lapse risk score above the cut-off within 4 hours of their first lapse.ConclusionsReal-time estimation of smoking lapse risk is feasible and may pave the way for development of mobile phone‒based smoking cessation treatments that automatically tailor treatment content in real time based on presence of specific lapse triggers. Interventions that identify risk for lapse and automatically deliver tailored messages or other treatment components in real time could offer effective, low cost, and highly disseminable treatments to individuals who do not have access to other more standard cessation treatments.

Before and after: craving, mood, and background stress in the hours surrounding drug use and stressful events in patients with opioid-use disorder

“…For example, in smokers attempting to quit, Shiffman and Waters (2004) found that lapses followed rapid rises in negative mood, on the order of hours, but not on the order of whole days. Businelle and colleagues (2016) also found increases in specific risk factors within four hours of patients’ first smoking lapse. Our group has shown that, in cocaine and heroin users on opioid agonist maintenance, exposure to putative risk factors increases in the hours leading up to cocaine use, but less so for heroin use (Epstein et al 2009).…”

“…Researchers have been investigating the use of EMA data to predict future drug use. For example, Businelle and colleagues (2016) found that risk factors were higher within four hours of patients’ first smoking lapse and that EMA was promising as a tool to estimate the likelihood of lapse and automate delivery of tailored smoking cessation treatment. Mobile treatment might provide a path to improved treatment efficacy as well as making treatment available to areas where treatment programs are geographically remote and extend treatment beyond the usual duration of clinician contact.…”

“…Mobile treatment might provide a path to improved treatment efficacy as well as making treatment available to areas where treatment programs are geographically remote and extend treatment beyond the usual duration of clinician contact. Knowing the time course of changes in mood, stress and craving in the lives of people with substance-use disorders could help inform mobile “just in time” treatment, and, perhaps, predict when patients are at risk for drug use (Businelle et al 2016). …”

An Ecological Momentary Intervention for Smoking Cessation: Evaluation of Feasibility and Effectiveness

“…During the 2-week postquit period, participants received individually tailored automated messages based on their EMA responses (see [35] for a complete description of the lapse risk estimator). Level 1 messages were delivered when EMA responses indicated a low level of imminent smoking lapse risk, and message content focused on maintaining abstinence motivation and general cessation advice (eg, seeking social support for cessation, coping with various lapse triggers, and benefits of quitting).…”

Using mHealth to Increase Treatment Utilization Among Recently Incarcerated Homeless Adults (Link2Care): Protocol for a Randomized Controlled Trial (Preprint)

“…The EMA methodology that will be used in this study is similar to that developed by Shiffman and colleagues [51,67,68] and was used in previous studies conducted by the investigative team. [69][70][71][72][73] EMA items will assess numerous constructs that are hypothesized to be related to the study outcomes (see Table 3). The phone will audibly and visually cue EMAs for five minutes, 30 minutes after each participant's pre-set waking time.…”