Dynamic models of stress-smoking responses based on high-frequency sensor data

Sahar Hojjatinia, Elyse R. Daly, Timothy Hnat, Syed Monowar Hossain, Santosh Kumar, Constantino M. Lagoa, Inbal Nahum-Shani, Shahin Alan Samiei, Bonnie Spring, David E. Conroy*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

Original languageEnglish (US)
Article number162
Journalnpj Digital Medicine
Volume4
Issue number1
DOIs
StatePublished - Dec 2021

Funding

This research was supported by Grant U54 EB020404 awarded by the National Institute of Biomedical Imaging and Bioengineering, Grant R01 HL142732 awarded by the National Heart, Lung and Blood Institute, and Grant ECCS 1808266 awarded by the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the NSF.

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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