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SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors

Authors:

Soujanya Chatterjee,

Alexander Moreno,

Steven Lloyd Lizotte,

Christopher P Fagundes,

David W. Wetter,

Santosh KumarAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 4, Pages 1 - 26

https://doi.org/10.1145/3380987

Published: 18 March 2020 Publication History

Abstract

Context plays a key role in impulsive adverse behaviors such as fights, suicide attempts, binge-drinking, and smoking lapse. Several contexts dissuade such behaviors, but some may trigger adverse impulsive behaviors. We define these latter contexts as 'opportunity' contexts, as their passive detection from sensors can be used to deliver context-sensitive interventions.

In this paper, we define the general concept of 'opportunity' contexts and apply it to the case of smoking cessation. We operationalize the smoking 'opportunity' context, using self-reported smoking allowance and cigarette availability. We show its clinical utility by establishing its association with smoking occurrences using Granger causality. Next, we mine several informative features from GPS traces, including the novel location context of smoking spots, to develop the SmokingOpp model for automatically detecting the smoking 'opportunity' context. Finally, we train and evaluate the SmokingOpp model using 15 million GPS points and 3,432 self-reports from 90 newly abstinent smokers in a smoking cessation study.

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Cited By

Favara GBarchitta MMaugeri AMagnano San Lio RAgodi A(2024)Sensors for Smoking Detection in Epidemiological Research: Scoping ReviewJMIR mHealth and uHealth10.2196/5238312(e52383)Online publication date: 30-Oct-2024
https://doi.org/10.2196/52383
Perski OLi KPontikos NSimons DGoldstein SNaughton FBrown J(2023)Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone AppNicotine and Tobacco Research10.1093/ntr/ntad05125:7(1330-1339)Online publication date: 27-Mar-2023
https://doi.org/10.1093/ntr/ntad051
Rigatti MChapman BChai PSmelson DBabu KCarreiro S(2023)Digital biomarker applications across the spectrum of opioid use disorderCogent Mental Health10.1080/28324765.2023.22403752:1Online publication date: 1-Aug-2023
https://doi.org/10.1080/28324765.2023.2240375
Show More Cited By

Index Terms

SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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Cited By

Favara GBarchitta MMaugeri AMagnano San Lio RAgodi A(2024)Sensors for Smoking Detection in Epidemiological Research: Scoping ReviewJMIR mHealth and uHealth10.2196/5238312(e52383)Online publication date: 30-Oct-2024
https://doi.org/10.2196/52383
Perski OLi KPontikos NSimons DGoldstein SNaughton FBrown J(2023)Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone AppNicotine and Tobacco Research10.1093/ntr/ntad05125:7(1330-1339)Online publication date: 27-Mar-2023
https://doi.org/10.1093/ntr/ntad051
Rigatti MChapman BChai PSmelson DBabu KCarreiro S(2023)Digital biomarker applications across the spectrum of opioid use disorderCogent Mental Health10.1080/28324765.2023.22403752:1Online publication date: 1-Aug-2023
https://doi.org/10.1080/28324765.2023.2240375
Nahum-Shani IWetter DMurphy S(2023)Adapting just-in-time interventions to vulnerability and receptivity: Conceptual and methodological considerationsDigital Therapeutics for Mental Health and Addiction10.1016/B978-0-323-90045-4.00012-5(77-87)Online publication date: 2023
https://doi.org/10.1016/B978-0-323-90045-4.00012-5
Xu MMoreno ANagesh SAydemir VWetter DKumar SRehg JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)PulseImputeProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602218(26874-26888)Online publication date: 28-Nov-2022
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