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Showing content from https://pubmed.ncbi.nlm.nih.gov/27751985/ below:

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

. 2016 Oct 17;18(10):e275. doi: 10.2196/jmir.6307. Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Affiliations

Affiliation

¹ Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. michael-businelle@ouhsc.edu.

PMID: 27751985
PMCID: PMC5088341
DOI: 10.2196/jmir.6307

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

Michael S Businelle et al. J Med Internet Res. 2016.

. 2016 Oct 17;18(10):e275. doi: 10.2196/jmir.6307. Affiliation

¹ Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States. michael-businelle@ouhsc.edu.

PMID: 27751985
PMCID: PMC5088341
DOI: 10.2196/jmir.6307

Item in Clipboard

Abstract

Background: Mobile 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.

Objective: The 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.

Methods: Participants 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.

Results: Participants (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.

Conclusions: Real-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.

Keywords: ecological momentary assessment; mhealth; mobile app; smartphone; smoking cessation; socioeconomic disadvantage, risk estimation.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures
Figure 1

Number of lapse risk factors…

Figure 1

Number of lapse risk factors by imminent lapse status.

Figure 1

Number of lapse risk factors by imminent lapse status.

Figure 2

Receiver operator characteristics curve for…

Figure 2

Receiver operator characteristics curve for weighted and unweighted risk estimators.

Figure 2

Receiver operator characteristics curve for weighted and unweighted risk estimators.

Figure 3

Weighted lapse risk scores by…

Figure 3

Weighted lapse risk scores by lapse status.

Figure 3

Weighted lapse risk scores by lapse status.