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Predicting the first smoking lapse during a quit attempt: A machine learning approach.

Emily T Hébert1, Robert Suchting2, Chaelin K Ra3

  • 1University of Texas Health Science Center (UTHealth) School of Public Health, Austin, TX, United States.

Drug and Alcohol Dependence
|October 23, 2020
PubMed
Summary

Identifying key predictors of smoking lapse is crucial for effective just-in-time adaptive interventions (JITAI). This study found that perceived odds of smoking, confidence, motivation, urge, and cigarette availability best predict lapse moments in smokers attempting to quit.

Keywords:
Just-in-time adaptive interventionMachine learningSmartphonesSmoking cessationmHealth

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Area of Science:

  • Behavioral Science
  • Digital Health
  • Addiction Research

Background:

  • Just-in-time adaptive interventions (JITAI) leverage mobile technology for timely smoking cessation support.
  • Effective JITAI depend on accurate decision rules to predict smoking lapse.
  • Identifying key lapse predictors is essential for optimizing JITAI efficacy.

Purpose of the Study:

  • To identify the strongest predictors of first smoking lapse in individuals undergoing a quit attempt.
  • To inform the development of more effective JITAI for smoking cessation.

Main Methods:

  • Participants (n=74) completed ecological momentary assessments via smartphones for 4 weeks post-quit.
  • A three-stage modeling process, including Cox regression and elastic net machine learning, analyzed 31 potential predictors.
  • Variable selection was refined using backwards elimination for parsimony.

Main Results:

  • Seven predictors were initially significant in univariate models.
  • The elastic net algorithm identified five key predictors: perceived odds of smoking, confidence, motivation, urge, and cigarette availability.
  • The final reduced model showed limitations in approximating the baseline model.

Conclusions:

  • Accurately predicting high-risk moments for smoking lapse is vital for JITAI development.
  • Data-driven variable selection approaches are valuable for identifying intervention targets.
  • This study highlights specific factors that can inform future JITAI design for smoking cessation.