MCrave: Continuous Estimation of Craving During Smoking Cessation

Overview

Journal Proc ACM Int Conf Ubiquitous Comput

Publisher Association for Computing Machinery

Date 2016 Dec 20

PMID 27990501

Citations 13

Authors

Soujanya Chatterjee

Karen Hovsepian

Hillol Sarker

Nazir Saleheen

Mustafa alAbsi

Gowtham Atluri

Emre Ertin

Cho Lam

Andrine Lemieux

Motohiro Nakajima

Bonnie Spring

David W Wetter

Santosh Kumar

Affiliations

Soon will be listed here.

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.

Citing Articles

Sensors for Smoking Detection in Epidemiological Research: Scoping Review.

Favara G, Barchitta M, Maugeri A, Magnano San Lio R, Agodi A JMIR Mhealth Uhealth. 2024; 12:e52383.

PMID: 39476379 PMC: 11561437. DOI: 10.2196/52383.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

Perski O, Li K, Pontikos N, Simons D, Goldstein S, Naughton F Nicotine Tob Res. 2023; 25(7):1330-1339.

PMID: 36971111 PMC: 10256890. DOI: 10.1093/ntr/ntad051.

mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels.

Ullah M, Chatterjee S, Fagundes C, Lam C, Nahum-Shani I, Rehg J Proc ACM Interact Mob Wearable Ubiquitous Technol. 2023; 6(3).

PMID: 36873428 PMC: 9979627. DOI: 10.1145/3550308.

Translation and psychometric validation of the Persian version of the Questionnaire on Smoking Urges for assessment of craving to smoke among university students.

Kazemitabar M, Garcia D PeerJ. 2021; 9:e12531.

PMID: 34900437 PMC: 8627126. DOI: 10.7717/peerj.12531.

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

Chatterjee S, Moreno A, Lizotte S, Akther S, Ertin E, Fagundes C Proc ACM Interact Mob Wearable Ubiquitous Technol. 2021; 4(1).

PMID: 34651096 PMC: 8513752. DOI: 10.1145/3380987.

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