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. 2021 Nov 23;4(1):162.
doi: 10.1038/s41746-021-00532-2.

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

Sahar Hojjatinia  1 , Elyse R Daly  2 , Timothy Hnat  3 , Syed Monowar Hossain  3 , Santosh Kumar  3 , Constantino M Lagoa  1 , Inbal Nahum-Shani  4 , Shahin Alan Samiei  3 , Bonnie Spring  2 , David E Conroy  5   6
Affiliations

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

Sahar Hojjatinia et al. NPJ Digit Med. .

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.

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Conflict of interest statement

B.S. serves on the Scientific Advisory Board for Actigraph. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Data chunks are uninterrupted time series for variables in the system.
These two plots represent changes in a stress probabilities and b a binary smoking outcome for one data chunk.
Fig. 2
Fig. 2
System configuration for dynamical model that integrates a linear system and a nonlinearity.
Fig. 3
Fig. 3. Different responses of the identified system for one data chunk.
a Response of the system to predictor (stress), ycause, b intrinsic response of the system, yintrinsic, and c system response, which is the sum of response to predictor and intrinsic response, ysystem.
Fig. 4
Fig. 4. Pulse response of a representative of all clusters.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 5
Fig. 5. Pulse response for participants in cluster 1, instant increase followed by a sharp decrease in smoking odds.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 6
Fig. 6. Pulse response for participants in cluster 2, a delayed increase followed by a sharp decrease in smoking odds.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 7
Fig. 7. Pulse response for participants in cluster 3, two rounds of increases in smoking odds.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 8
Fig. 8. Pulse response for participants in cluster 4, just one delayed increase in smoking odds in response to stress.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 9
Fig. 9. Pulse response for participants in cluster 5, instant decrease in the odds of smoking followed by a sharp delayed increase.
The vertical line at the 10-min mark on the x-axis reflects the time when the stress probability returned to zero.
Fig. 10
Fig. 10
Discretization of a unit circle provides an example of gridding the unit circle used in the software.
See this image and copyright information in PMC

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