DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment.

A comprehensive assessment of cigarette smoking behavior and its effect on health requires a detailed examination of smoke exposure. We propose a CNN-LSTM-based deep learning architecture named DeepPuff to quantify Respiratory Smoke Exposure Metrics (RSEM). Smoke inhalations were detected from the breathing and hand gesture sensors of the Personal Automatic Cigarette Tracker v2 (PACT 2.

Relationships Between the Nicotine Metabolite Ratio and Laboratory Assessments of Smoking Reinforcement and Craving Among Adults in a Smoking Cessation Trial.

Introduction: People who metabolize nicotine more quickly are generally less successful at quitting smoking. However, the mechanisms that link individual differences in the nicotine metabolite ratio (NMR), a phenotypic biomarker of the rate of nicotine clearance, to smoking outcomes are unclear. We tested the hypotheses that higher NMR is associated with greater smoking reinforcement, general craving, and cue-induced cigarette craving in a treatment-seeking sample.

A preliminary randomized trial of reinforcement contingencies to improve compliance with ecological momentary assessment.

Ecological Momentary Assessment (EMA) methods are increasingly used by translational scientists to study real-world behavior and experience. The ability to draw meaningful conclusions from EMA research depends upon participant compliance with assessment completion. Most EMA studies provide financial compensation for compliance, but little empirical evidence addresses the impact of reinforcement parameters on the level of compliance.

An Evaluation of the Contextual Factors Associated With the Use of Either Tobacco or Electronic Cigarettes in Individuals Who Dual Use.

Introduction: Little is known about patterns of dual use of tobacco and electronic cigarettes (e-cigarettes), especially regarding the factors that lead people to choose either product in particular situations. Identifying contextual factors that are associated with product use would enhance understanding of the maintenance of dual product use.

Methods: Individuals who dual use (N = 102) completed ecological momentary assessment surveys via text message regarding the recent use of tobacco and e-cigarettes for 2 weeks.

Evaluating Declines in Compliance With Ecological Momentary Assessment in Longitudinal Health Behavior Research: Analyses From a Clinical Trial.

Background: Ecological momentary assessment (EMA) is increasingly used to evaluate behavioral health processes over extended time periods. The validity of EMA for providing representative, real-world data with high temporal precision is threatened to the extent that EMA compliance drops over time.

Objective: This research builds on prior short-term studies by evaluating the time course of EMA compliance over 9 weeks and examines predictors of weekly compliance rates among cigarette-using adults.