  Health risks from cigarette smoking -- the leading cause of preventable death
in the United States -- can be substantially reduced by quitting. Although most
smokers are motivated to quit, the majority of quit attempts fail. A number of
studies have explored the role of self-reported symptoms, physiologic
measurements, and environmental context on smoking risk, but less work has
focused on the temporal dynamics of smoking events, including daily patterns
and related nicotine effects. In this work, we examine these dynamics and
improve risk prediction by modeling smoking as a self-triggering process, in
which previous smoking events modify current risk. Specifically, we fit smoking
events self-reported by 42 smokers to a time-varying semi-parametric Hawkes
process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP
achieves superior prediction performance compared to related and existing
models, with the incorporation of time-varying predictors having greatest
benefit over longer prediction windows. Moreover, the impact function
illustrates previously unknown temporal dynamics of smoking, with possible
connections to nicotine metabolism to be explored in future work through a
randomized study design. By more effectively predicting smoking events and
exploring a self-triggering component of smoking risk, this work supports
development of novel or improved cessation interventions that aim to reduce
death from smoking.
