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. 2016 Sep:2016:863-874.
doi: 10.1145/2971648.2971672.

mCrave: Continuous Estimation of Craving During Smoking Cessation

Soujanya Chatterjee  1 , Karen Hovsepian  2 , Hillol Sarker  1 , Nazir Saleheen  1 , Mustafa al'Absi  3 , Gowtham Atluri  3 , Emre Ertin  4 , Cho Lam  5 , Andrine Lemieux  3 , Motohiro Nakajima  3 , Bonnie Spring  6 , David W Wetter  5 , Santosh Kumar  1
Affiliations

mCrave: Continuous Estimation of Craving During Smoking Cessation

Soujanya Chatterjee et al. Proc ACM Int Conf Ubiquitous Comput. 2016 Sep.

Abstract

Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.

Keywords: Craving; H.1.2. Models and Principles: User/Machine Systems; Mobile Health; Smoking Cessation; Stress.

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Figures

Figure 1
Figure 1
Distribution of Stress Inferences across hours of a day. Number of participants = 45
Figure 2
Figure 2
Analysis of Hour (time) of day, craving, and physiological stress.
Figure 3
Figure 3
Total number of self-report assessments during hours of day across all participants. Number of participants, n = 45
Figure 4
Figure 4
Craving distribution across hours of a day. Hours with High craving likelihood are marked with red. Hours with Low craving likelihood are marked with green
Figure 5
Figure 5
Median Stress likelihood associated with self-reported high craving is significantly greater than that associated with self-reported low craving during high vulnerable hours (marked with the star), however there is no significant difference between Median Stress likelihood associated with self-reported high craving and that associated with self-reported low craving during low vulnerable hours
Figure 6
Figure 6
Median Stress Likelihood associated with self-reported high craving (red) and self-reported low craving (green) during high vulnerable hours of day. Median Stress Likelihood associated with high craving (red) significantly greater than that associated with low craving (green)
Figure 7
Figure 7
Median Stress Likelihood associated with High craving (red) and low craving (green) during low vulnerable hours. No significant difference between Median Stress Likelihood associated with high craving (red) and that associated with low craving (green)
Figure 8
Figure 8
Receiver Operating Characteristic(ROC) curve
Figure 9
Figure 9
Model performance metrics for High Vulnerable, Low Vulnerable Hour, Baseline, Without Pairwise Feature
See this image and copyright information in PMC

References

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    1. World Health Organization. Tobacco – Fact Sheet. http://www.who.int/mediacentre/factsheets/fs339/en//, Accessed: March 2016.
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