mCrave: Continuous estimation of craving during smoking cessation

Soujanya Chatterjee; Karen Hovsepianr; Hillol Sarker; Nazir Saleheen; Mustafa Al'absiy; Gowtham Atluriy; Emre Ertin; Cho Lam; Andrine Lemieuxy; Motohiro Nakajimay; Bonnie Spring; David W. Wetter; Santosh Kumar

doi:10.1145/2971648.2971672

mCrave: Continuous estimation of craving during smoking cessation

Soujanya Chatterjee, Karen Hovsepianr, Hillol Sarker, Nazir Saleheen, Mustafa Al'absiy, Gowtham Atluriy, Emre Ertin, Cho Lam, Andrine Lemieuxy, Motohiro Nakajimay, Bonnie Spring, David W. Wetter, Santosh Kumar

Preventive Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

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.

Original language	English (US)
Title of host publication	UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	863-874
Number of pages	12
ISBN (Electronic)	9781450344616
DOIs	https://doi.org/10.1145/2971648.2971672
State	Published - Sep 12 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: Sep 12 2016 → Sep 16 2016

Publication series

Name	UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Other

Other	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/Territory	Germany
City	Heidelberg
Period	9/12/16 → 9/16/16

Funding

We thank Shahin Alan Samiei and Barbara Burch Kuhn from University of Memphis. We also thank study coordinators at Universities of Minnesota and Memphis. The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01CA190329, R01MD010362, R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative, and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative.

Keywords

Craving
Mobile health
Smoking cessation
Stress

ASJC Scopus subject areas

Information Systems
Hardware and Architecture
Computer Networks and Communications
Software
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2971648.2971672

Cite this

Chatterjee, S., Hovsepianr, K., Sarker, H., Saleheen, N., Al'absiy, M., Atluriy, G., Ertin, E., Lam, C., Lemieuxy, A., Nakajimay, M., Spring, B., Wetter, D. W., & Kumar, S. (2016). mCrave: Continuous estimation of craving during smoking cessation. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 863-874). (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2971648.2971672

Chatterjee, Soujanya ; Hovsepianr, Karen ; Sarker, Hillol et al. / mCrave : Continuous estimation of craving during smoking cessation. UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 863-874 (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

@inproceedings{cbf7703ed1b24336b46af0ef5a8e6b7a,

title = "mCrave: Continuous estimation of craving during smoking cessation",

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, Mobile health, Smoking cessation, Stress",

author = "Soujanya Chatterjee and Karen Hovsepianr and Hillol Sarker and Nazir Saleheen and Mustafa Al'absiy and Gowtham Atluriy and Emre Ertin and Cho Lam and Andrine Lemieuxy and Motohiro Nakajimay and Bonnie Spring and Wetter, {David W.} and Santosh Kumar",

note = "Funding Information: We thank Shahin Alan Samiei and Barbara Burch Kuhn from University of Memphis. We also thank study coordinators at Universities of Minnesota and Memphis. The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01CA190329, R01MD010362, R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative, and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

month = sep,

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doi = "10.1145/2971648.2971672",

language = "English (US)",

series = "UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

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Chatterjee, S, Hovsepianr, K, Sarker, H, Saleheen, N, Al'absiy, M, Atluriy, G, Ertin, E, Lam, C, Lemieuxy, A, Nakajimay, M, Spring, B, Wetter, DW & Kumar, S 2016, mCrave: Continuous estimation of craving during smoking cessation. in UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 863-874, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 9/12/16. https://doi.org/10.1145/2971648.2971672

mCrave: Continuous estimation of craving during smoking cessation. / Chatterjee, Soujanya; Hovsepianr, Karen; Sarker, Hillol et al.
UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 863-874 (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - mCrave

T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016

AU - Chatterjee, Soujanya

AU - Hovsepianr, Karen

AU - Sarker, Hillol

AU - Saleheen, Nazir

AU - Al'absiy, Mustafa

AU - Atluriy, Gowtham

AU - Ertin, Emre

AU - Lam, Cho

AU - Lemieuxy, Andrine

AU - Nakajimay, Motohiro

AU - Spring, Bonnie

AU - Wetter, David W.

AU - Kumar, Santosh

N1 - Funding Information: We thank Shahin Alan Samiei and Barbara Burch Kuhn from University of Memphis. We also thank study coordinators at Universities of Minnesota and Memphis. The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01CA190329, R01MD010362, R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative, and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative.

PY - 2016/9/12

Y1 - 2016/9/12

N2 - 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.

AB - 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.

KW - Craving

KW - Mobile health

KW - Smoking cessation

KW - Stress

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DO - 10.1145/2971648.2971672

M3 - Conference contribution

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T3 - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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ER -

Chatterjee S, Hovsepianr K, Sarker H, Saleheen N, Al'absiy M, Atluriy G et al. mCrave: Continuous estimation of craving during smoking cessation. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 863-874. (UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2971648.2971672

mCrave: Continuous estimation of craving during smoking cessation

Abstract

Publication series

Other

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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