Mobile sensors can now provide unobtrusive measurement of both stress and cigarette smoking behavior. We describe, here, the first field tests of two such methods, cStress and puffMarker, that were used to examine relationships between stress and smoking behavior and lapse from a sample of 76 smokers motivated to quit smoking. Participants wore a mobile sensors suite, called AutoSense, which collected continuous physiological data for 4 days (24-hours pre-quit and 72-hours post-quit) in the field. Algorithms were applied to the physiological data to create indices of stress (cStress) and first lapse smoking episodes (puffMarker). We used mixed effects interrupted autoregressive time series models to assess changes in heart rate (HR), cStress, and nicotine craving across the 4-day period. Self-report assessments using ecological momentary assessment (EMA) of mood, withdrawal symptoms, and smoking behavior were also used. Results indicated that HR and cStress, respectively, predicted smoking lapse. These results suggest that measures of traditional psychophysiology, such as HR, are not redundant with cStress; both provide important information. Results are consistent with existing literature and provide clear support for cStress and puffMarker in ambulatory clinical research. This research lays groundwork for sensor-based markers in developing and delivering sensor-triggered, just-in-time interventions that are sensitive to stress-related lapser risk factors.
Bibliographical noteFunding Information:
The authors wish to acknowledge their support by the National Science Foundation under award numbers IIS-1231754 , ACI-1640813 and IIS-1722646 , National Institute of Health grants R01DA016351 and R01DA027232 under the trans-NIH OppNet initiative R01DA035502 (by NIDA), National Institutes of Health grant R01DA016351 , National Institutes of Health and National Institute on Drug Abuse grant R01DA027232 , and funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative U54EB020404 (by NIBIB ). We thank Shahin Alan Samiei for his help with data management (University of Memphis) and the study coordinators at Universities of Minnesota and Memphis. The authors declare no conflict of interest in this study.
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