Applied statistical methods for identifying features of heart rate that are associated with nicotine vaping.

Wearable devices have been increasingly adopted to collect physiological data such as heart rate that may infer momentary risk of substance use. Yet, innovative methods capable for handling these complex time series data as presented in the statistics or data science literature may not be accessible to substance use researchers. This study introduces a series of statistical methods to analyze heart rate data and identify features that are associated with nicotine vaping. Nontechnical description of the methods coupled with the information about open-source software packages that implemented these methods was provided. The analytical procedure included 5 steps: (1) de-noising by the singular spectrum analysis (SSA); (2) sleep region identification by the Sum of Single Effects (SuSiE) model; (3) repeated heart rate pattern identification by the matrix profile; (4) dimension reduction by the linear regression; and (5) comparing repeated heart rate patterns across non-vaping and vaping regions by the linear mixed model. Secondary analysis was conducted on heart rate and ecological momentary assessment (EMA) data collected from 35 young adult e-cigarette users (66% female) for 7 days. Effectiveness of the methods was demonstrated by graphical presentations showing that the extracted features characterize sleep patterns and heart rate changes before and after vaping events quite well. Secondary analysis found that heart rate was higher and changed faster before vaping. Statistical methods can effectively extract useful features from heart rate data that may inform momentary vaping risk and optimal timings for delivering messages in mobile-phone based interventions.