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

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 impr...

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Autores principales:	Sahar Hojjatinia, Elyse R. Daly, Timothy Hnat, Syed Monowar Hossain, Santosh Kumar, Constantino M. Lagoa, Inbal Nahum-Shani, Shahin Alan Samiei, Bonnie Spring, David E. Conroy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/0ff1c740687e4d37a5624e752e6542da
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spelling	oai:doaj.org-article:0ff1c740687e4d37a5624e752e6542da2021-11-28T12:07:14ZDynamic models of stress-smoking responses based on high-frequency sensor data10.1038/s41746-021-00532-22398-6352https://doaj.org/article/0ff1c740687e4d37a5624e752e6542da2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00532-2https://doaj.org/toc/2398-6352Abstract 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.Sahar HojjatiniaElyse R. DalyTimothy HnatSyed Monowar HossainSantosh KumarConstantino M. LagoaInbal Nahum-ShaniShahin Alan SamieiBonnie SpringDavid E. ConroyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sahar Hojjatinia Elyse R. Daly Timothy Hnat Syed Monowar Hossain Santosh Kumar Constantino M. Lagoa Inbal Nahum-Shani Shahin Alan Samiei Bonnie Spring David E. Conroy Dynamic models of stress-smoking responses based on high-frequency sensor data
description	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.
format	article
author	Sahar Hojjatinia Elyse R. Daly Timothy Hnat Syed Monowar Hossain Santosh Kumar Constantino M. Lagoa Inbal Nahum-Shani Shahin Alan Samiei Bonnie Spring David E. Conroy
author_facet	Sahar Hojjatinia Elyse R. Daly Timothy Hnat Syed Monowar Hossain Santosh Kumar Constantino M. Lagoa Inbal Nahum-Shani Shahin Alan Samiei Bonnie Spring David E. Conroy
author_sort	Sahar Hojjatinia
title	Dynamic models of stress-smoking responses based on high-frequency sensor data
title_short	Dynamic models of stress-smoking responses based on high-frequency sensor data
title_full	Dynamic models of stress-smoking responses based on high-frequency sensor data
title_fullStr	Dynamic models of stress-smoking responses based on high-frequency sensor data
title_full_unstemmed	Dynamic models of stress-smoking responses based on high-frequency sensor data
title_sort	dynamic models of stress-smoking responses based on high-frequency sensor data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ff1c740687e4d37a5624e752e6542da
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Dynamic models of stress-smoking responses based on high-frequency sensor data

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