Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data

Abstract Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to “push” content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to...

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Autores principales:	David H. Epstein, Matthew Tyburski, William J. Kowalczyk, Albert J. Burgess-Hull, Karran A. Phillips, Brenda L. Curtis, Kenzie L. Preston
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a813c79a2e594dbc8539be094313d3c1
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spelling	oai:doaj.org-article:a813c79a2e594dbc8539be094313d3c12021-12-02T13:34:33ZPrediction of stress and drug craving ninety minutes in the future with passively collected GPS data10.1038/s41746-020-0234-62398-6352https://doaj.org/article/a813c79a2e594dbc8539be094313d3c12020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0234-6https://doaj.org/toc/2398-6352Abstract Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to “push” content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy—as high as 0.93 by the end of 16 weeks of tailoring—but this was driven mostly by correct predictions of absence. For predictions of presence, “believability” (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based “digital phenotyping” inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.David H. EpsteinMatthew TyburskiWilliam J. KowalczykAlbert J. Burgess-HullKarran A. PhillipsBrenda L. CurtisKenzie L. PrestonNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
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 David H. Epstein Matthew Tyburski William J. Kowalczyk Albert J. Burgess-Hull Karran A. Phillips Brenda L. Curtis Kenzie L. Preston Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
description	Abstract Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to “push” content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy—as high as 0.93 by the end of 16 weeks of tailoring—but this was driven mostly by correct predictions of absence. For predictions of presence, “believability” (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based “digital phenotyping” inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.
format	article
author	David H. Epstein Matthew Tyburski William J. Kowalczyk Albert J. Burgess-Hull Karran A. Phillips Brenda L. Curtis Kenzie L. Preston
author_facet	David H. Epstein Matthew Tyburski William J. Kowalczyk Albert J. Burgess-Hull Karran A. Phillips Brenda L. Curtis Kenzie L. Preston
author_sort	David H. Epstein
title	Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
title_short	Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
title_full	Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
title_fullStr	Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
title_full_unstemmed	Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data
title_sort	prediction of stress and drug craving ninety minutes in the future with passively collected gps data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a813c79a2e594dbc8539be094313d3c1
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Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data

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