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SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors.

Soujanya Chatterjee1, Alexander Moreno2, Steven Lloyd Lizotte3

  • 1University of Memphis, Memphis, TN, 38152, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|October 15, 2021
PubMed
Summary
This summary is machine-generated.

Researchers identified "opportunity" contexts that trigger impulsive behaviors like smoking lapse. Their new SmokingOpp model uses GPS data to detect these contexts, aiding smoking cessation interventions.

Keywords:
ContextGPS tracesInterventionMobile HealthSmoking Cessation

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Area of Science:

  • Behavioral Science
  • Digital Health
  • Machine Learning

Background:

  • Impulsive behaviors are influenced by environmental contexts, with some contexts triggering rather than preventing them.
  • These 'opportunity' contexts can be leveraged for timely, context-sensitive interventions.
  • Smoking cessation is a key area where understanding opportunity contexts is crucial.

Purpose of the Study:

  • To define and operationalize the concept of 'opportunity' contexts for impulsive behaviors.
  • To apply this concept to smoking cessation, identifying specific triggers for smoking.
  • To develop and validate a model for automatically detecting smoking opportunity contexts.

Main Methods:

  • Defined 'opportunity' contexts based on self-reported smoking allowance and cigarette availability.
  • Utilized Granger causality to establish the link between opportunity contexts and smoking occurrences.
  • Developed the SmokingOpp model by extracting features from GPS traces, including novel 'smoking spot' locations.

Main Results:

  • Demonstrated the clinical utility of opportunity contexts in predicting smoking occurrences.
  • Successfully developed the SmokingOpp model for automated detection of smoking opportunity contexts.
  • Validated the model using a large dataset of GPS points and self-reports from 90 smokers.

Conclusions:

  • 'Opportunity' contexts are significant predictors of impulsive smoking behavior.
  • The SmokingOpp model offers a novel, data-driven approach to identify these contexts.
  • This research provides a foundation for developing effective, context-aware smoking cessation interventions.