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Nahum-Shani I, Potter LN, Lam CY, Yap J, Moreno A, Stoffel R, Wu Z, Wan N, Dempsey W, Kumar S, Ertin E, Murphy SA, Rehg JM, Wetter DW. The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol. Contemp Clin Trials 2021; 110:106513. [PMID: 34314855 PMCID: PMC8824313 DOI: 10.1016/j.cct.2021.106513] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 11/30/2022]
Abstract
Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.
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Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America.
| | - Lindsey N Potter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Cho Y Lam
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Jamie Yap
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America
| | - Alexander Moreno
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Rebecca Stoffel
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Zhenke Wu
- School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Neng Wan
- Department of Geography, University of Utah, Salt Lake City, UT, United States of America
| | - Walter Dempsey
- School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN, United States of America
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States of America
| | - Susan A Murphy
- Departments of Statistics & Computer Science, Harvard University, Cambridge, MA, United States of America
| | - James M Rehg
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - David W Wetter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
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2
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Kwon S, Wan N, Burns RD, Brusseau TA, Kim Y, Kumar S, Ertin E, Wetter DW, Lam CY, Wen M, Byun W. The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings. Sensors (Basel) 2021; 21:s21041411. [PMID: 33670507 PMCID: PMC7922785 DOI: 10.3390/s21041411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/05/2021] [Accepted: 02/10/2021] [Indexed: 12/12/2022]
Abstract
MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.
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Affiliation(s)
- Sunku Kwon
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Neng Wan
- Department of Geography, University of Utah, Salt Lake City, UT 84112, USA;
| | - Ryan D. Burns
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Timothy A. Brusseau
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong;
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0SL, UK
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN 38152, USA;
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA;
| | - David W. Wetter
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Cho Y. Lam
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA; (D.W.W.); (C.Y.L.)
| | - Ming Wen
- Department of Sociology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Wonwoo Byun
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (R.D.B.); (T.A.B.)
- Correspondence: ; Tel.: +1-801-585-1119
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Ertin E, Sugavanam N, Holtyn AF, Preston KL, Bertz JW, Marsch LA, McLeman B, Shmueli-Blumberg D, Collins J, King JS, McCormack J, Ghitza UE. An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches. Front Psychiatry 2021; 12:674691. [PMID: 34248712 PMCID: PMC8264124 DOI: 10.3389/fpsyt.2021.674691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/14/2021] [Indexed: 12/18/2022] Open
Abstract
As digital technology increasingly informs clinical trials, novel ways to collect study data in the natural field setting have the potential to enhance the richness of research data. Cocaine use in clinical trials is usually collected via self-report and/or urine drug screen results, both of which have limitations. This article examines the feasibility of developing a wrist-worn device that can detect sufficient physiological data (i.e., heart rate and heart rate variability) to detect cocaine use. This study aimed to develop a wrist-worn device that can be used in the natural field setting among people who use cocaine to collect reliable data (determined by data yield, device wearability, and data quality) that is less obtrusive than chest-based devices used in prior research. The study also aimed to further develop a cocaine use detection algorithm used in previous research with an electrocardiogram on a chestband by adapting it to a photoplethysmography sensor on the wrist-worn device which is more prone to motion artifacts. Results indicate that wrist-based heart rate data collection is feasible and can provide higher data yield than chest-based sensors, as wrist-based devices were also more comfortable and affected participants' daily lives less often than chest-based sensors. When properly worn, wrist-based sensors produced similar quality of heart rate and heart rate variability features to chest-based sensors and matched their performance in automated detection of cocaine use events. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02915341.
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Affiliation(s)
- Emre Ertin
- Dreese Lab, Department of Electrical and Computer Engineering, Ohio State University, Columbus, OH, United States
| | - Nithin Sugavanam
- Dreese Lab, Department of Electrical and Computer Engineering, Ohio State University, Columbus, OH, United States
| | - August F Holtyn
- Center for Learning and Health, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Kenzie L Preston
- National Institute on Drug Abuse Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
| | - Jeremiah W Bertz
- National Institute on Drug Abuse Intramural Research Program, National Institutes of Health, Baltimore, MD, United States
| | - Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Bethany McLeman
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | | | | | | | | | - Udi E Ghitza
- Center for the Clinical Trials Network, National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, United States
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4
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Nakajima M, Lemieux AM, Fiecas M, Chatterjee S, Sarker H, Saleheen N, Ertin E, Kumar S, al'Absi M. Using novel mobile sensors to assess stress and smoking lapse. Int J Psychophysiol 2020; 158:411-418. [DOI: 10.1016/j.ijpsycho.2020.11.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 11/02/2020] [Accepted: 11/10/2020] [Indexed: 11/30/2022]
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Wiernik BM, Ones DS, Marlin BM, Giordano C, Dilchert S, Mercado BK, Stanek KC, Birkland A, Wang Y, Ellis B, Yazar Y, Kostal JW, Kumar S, Hnat T, Ertin E, Sano A, Ganesan DK, Choudhoury T, al’Absi M. Using Mobile Sensors to Study Personality Dynamics. European Journal of Psychological Assessment 2020. [DOI: 10.1027/1015-5759/a000576] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.
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Affiliation(s)
| | - Deniz S. Ones
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Benjamin M. Marlin
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MS, USA
| | - Casey Giordano
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Stephan Dilchert
- Department of Management, Zicklin School of Business, Baruch College, CUNY, New York, NY, USA
| | | | | | - Adib Birkland
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Yilei Wang
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Brenda Ellis
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Yagizhan Yazar
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Jack W. Kostal
- Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, TN, USA
| | - Timothy Hnat
- Department of Computer Science, University of Memphis, TN, USA
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, OH, USA
| | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Deepak K. Ganesan
- College of Information and Computer Sciences, University of Massachusetts, Amherst, MS, USA
| | | | - Mustafa al’Absi
- Department of Family Medicine & Biobehavioral Health, Medical School, University of Minnesota-Duluth, Duluth, MN, USA
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6
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Marsch LA, Campbell A, Campbell C, Chen CH, Ertin E, Ghitza U, Lambert-Harris C, Hassanpour S, Holtyn AF, Hser YI, Jacobs P, Klausner JD, Lemley S, Kotz D, Meier A, McLeman B, McNeely J, Mishra V, Mooney L, Nunes E, Stafylis C, Stanger C, Saunders E, Subramaniam G, Young S. The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network. J Subst Abuse Treat 2020; 112S:4-11. [PMID: 32220409 PMCID: PMC7134325 DOI: 10.1016/j.jsat.2020.02.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/30/2020] [Accepted: 02/08/2020] [Indexed: 01/17/2023]
Abstract
The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN's efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first "prescription digital therapeutic" authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare.
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Affiliation(s)
- Lisa A Marsch
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA; Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA.
| | - Aimee Campbell
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA; Department of Psychiatry, Columbia University, 1051 Riverside Dr, New York, NY 10032, USA
| | - Cynthia Campbell
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, USA
| | - Ching-Hua Chen
- Computational Health Behavior and Decision Science Research, IBM Thomas J. Watson Research, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
| | - Emre Ertin
- The Ohio State University College of Engineering, 2070 Neil Ave, Columbus, OH 43210, USA
| | - Udi Ghitza
- The National Institute on Drug Abuse, 6001 Executive Blvd, Rockville, MD 20852, USA
| | - Chantal Lambert-Harris
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Saeed Hassanpour
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - August F Holtyn
- Psychiatry and Behavioral Sciences, Johns Hopkins Medicine, 5255 Loughboro Road, N.W., Washington, DC 20016, USA
| | - Yih-Ing Hser
- Department of Psychiatry and Behavioral Sciences at the UCLA Integrated Substance Abuse Programs, 11075 Santa Monica Blvd., Ste. 200, Los Angeles, CA 90025, USA
| | - Petra Jacobs
- The National Institute on Drug Abuse, 6001 Executive Blvd, Rockville, MD 20852, USA
| | - Jeffrey D Klausner
- Epidemiology UCLA Fielding School of Public Health, Box 951772, Los Angeles, CA 90095-1772, USA
| | - Shea Lemley
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - David Kotz
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Andrea Meier
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Bethany McLeman
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Jennifer McNeely
- Department of Population Health, Department of Medicine, NYU School of Medicine, 227 East 30th Street, Seventh Floor, New York, NY 10016, USA
| | - Varun Mishra
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Larissa Mooney
- Resnick Neuropsychiatric Hospital at UCLA, Ronald Reagan UCLA Medical Center, 150 Medical Plaza Driveway, Los Angeles, CA 90095, USA
| | - Edward Nunes
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA; Department of Psychiatry, Columbia University, 1051 Riverside Dr, New York, NY 10032, USA
| | | | - Catherine Stanger
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Elizabeth Saunders
- Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, 46 Centerra Dr, Lebanon, NH 03766, USA
| | - Geetha Subramaniam
- The National Institute on Drug Abuse, 6001 Executive Blvd, Rockville, MD 20852, USA
| | - Sean Young
- University of California, Irvine, UC Institute for Prediction Technology, Donald Bren Hall: 6135, Irvine, CA 92697, USA
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Chatterjee S, Moreno A, Lizotte SL, Akther S, Ertin E, Fagundes CP, Lam C, Rehg JM, Wan N, Wetter DW, Kumar S. SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors. Proc ACM Interact Mob Wearable Ubiquitous Technol 2020; 4. [PMID: 34651096 DOI: 10.1145/3380987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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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Affiliation(s)
| | | | | | | | - Emre Ertin
- The Ohio State University, Columbus, OH, 43210, USA
| | | | - Cho Lam
- University of Utah, Salt Lake City, UT, 84112, USA
| | - James M Rehg
- Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Neng Wan
- University of Utah, Salt Lake City, UT, 84112, USA
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Holtyn AF, Bosworth E, Marsch LA, McLeman B, Meier A, Saunders EC, Ertin E, Ullah MA, Samiei SA, Hossain M, Kumar S, Preston KL, Vahabzadeh M, Shmueli-Blumberg D, Collins J, McCormack J, Ghitza UE. Towards detecting cocaine use using smartwatches in the NIDA clinical trials network: Design, rationale, and methodology. Contemp Clin Trials Commun 2019; 15:100392. [PMID: 31245651 PMCID: PMC6582185 DOI: 10.1016/j.conctc.2019.100392] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/01/2019] [Accepted: 06/03/2019] [Indexed: 11/19/2022] Open
Abstract
Cocaine use in clinical trials is often measured via self-report, which can be inaccurate, or urine drug screens, which can be intrusive and burdensome. Devices that can automatically detect cocaine use and can be worn conveniently in daily life may provide several benefits. AutoSense is a wearable, physiological-monitoring suite that can detect cocaine use, but it may be limited as a method for monitoring cocaine use because it requires wearing a chestband with electrodes. This paper describes the design, rationale, and methodology of a project that seeks to build upon and extend previous work in the development of methods to detect cocaine use via wearable, unobtrusive mobile sensor technologies. To this end, a wrist-worn sensor suite (i.e., MotionSense HRV) will be developed and evaluated. Participants who use cocaine (N = 25) will be asked to wear MotionSense HRV and AutoSense for two weeks during waking hours. Drug use will be assessed via thrice-weekly urine drug screens and self-reports, and will be used to isolate periods of cocaine use that will be differentiated from other drug use. The present study will provide information on the feasibility and acceptability of using a wrist-worn device to detect cocaine use.
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Affiliation(s)
- August F. Holtyn
- Johns Hopkins University School of Medicine, 5200 Eastern Ave, Baltimore, MD, 21224, USA
- Corresponding author.
| | - Eugene Bosworth
- Johns Hopkins University School of Medicine, 5200 Eastern Ave, Baltimore, MD, 21224, USA
| | - Lisa A. Marsch
- Geisel School of Medicine at Dartmouth, 46 Centerra Parkway, Suite 315, Lebanon, NH, 03766, USA
| | - Bethany McLeman
- Geisel School of Medicine at Dartmouth, 46 Centerra Parkway, Suite 315, Lebanon, NH, 03766, USA
| | - Andrea Meier
- Geisel School of Medicine at Dartmouth, 46 Centerra Parkway, Suite 315, Lebanon, NH, 03766, USA
| | - Elizabeth C. Saunders
- Geisel School of Medicine at Dartmouth, 46 Centerra Parkway, Suite 315, Lebanon, NH, 03766, USA
| | - Emre Ertin
- Ohio State University, 512 Dreese Lab, 2015 Neil Avenue, Columbus, OH, 43210, USA
| | - Md Azim Ullah
- Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K), The University of Memphis FedEx Institute of Technology, Suite 335, Memphis, TN, 38152, USA
- The University of Memphis, Department of Computer Science, 375 Dunn Hall, Memphis, TN, 38152, USA
| | - Shahin Alan Samiei
- Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K), The University of Memphis FedEx Institute of Technology, Suite 335, Memphis, TN, 38152, USA
| | - Monowar Hossain
- Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K), The University of Memphis FedEx Institute of Technology, Suite 335, Memphis, TN, 38152, USA
| | - Santosh Kumar
- Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K), The University of Memphis FedEx Institute of Technology, Suite 335, Memphis, TN, 38152, USA
- The University of Memphis, Department of Computer Science, 375 Dunn Hall, Memphis, TN, 38152, USA
| | - Kenzie L. Preston
- National Institute on Drug Abuse Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | - Massoud Vahabzadeh
- National Institute on Drug Abuse Intramural Research Program, 251 Bayview Blvd, Baltimore, MD, 21224, USA
| | | | - Julia Collins
- Emmes Corporation, 401 N Washington, Suite 700, Rockville, MD, 20850, USA
| | - Jennifer McCormack
- Emmes Corporation, 401 N Washington, Suite 700, Rockville, MD, 20850, USA
| | - Udi E. Ghitza
- National Institute on Drug Abuse, 6001 Executive Boulevard, Rm 3105, MSC 9557, Bethesda, MD, 20892, USA
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9
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Kumar S, Abowd G, Abraham WT, al'Absi M, Chau DHP, Ertin E, Estrin D, Ganesan D, Hnat T, Hossain SM, Ives Z, Kerr J, Marlin BM, Murphy S, Rehg JM, Nahum-Shani I, Shetty V, Sim I, Spring B, Srivastava M, Wetter D. Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K). IEEE Pervasive Comput 2017; 16:18-22. [PMID: 29276451 PMCID: PMC5739587 DOI: 10.1109/mprv.2017.29] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- Santosh Kumar
- is a professor and Moss Chair of Excellence in computer science at the University of Memphis and director of the MD2K Center of Excellence
| | - Gregory Abowd
- is a Regents' Professor and J.Z. Liang Chair in the School of Interactive Computing at the Georgia Institute of Technology
| | - William T Abraham
- is Chair of Excellence in Cardiovascular Medicine and Chief of Cardiovascular Medicine at the Ohio State University College of Medicine. He leads MD2K's congestive heart failure studies
| | - Mustafa al'Absi
- is a Max & Mary La Due Pickworth Chair and professor of behavioral medicine and the founding director of Duluth Medical Research Institute at the University of Minnesota Medical School
| | - Duen Horng Polo Chau
- is an assistant professor at the Georgia Institute of Technology's School of Computational Science and Engineering
| | - Emre Ertin
- is a research associate professor with the Department of Electrical and Computer Engineering at the Ohio State University. He is the sensors lead for MD2K
| | - Deborah Estrin
- is a professor of computer science at Cornell Tech in New York City, professor of public health at Weill Cornell Medical College, and co-founder of Open mHealth
| | - Deepak Ganesan
- is a professor in the Department of Computer Science at the University of Massachusetts, Amherst
| | - Timothy Hnat
- is the chief software architect for MD2K at the University of Memphis
| | | | - Zachary Ives
- is a professor of computer and information science and associate dean of the School of Engineering and Applied Science, University of Pennsylvania
| | - Jacqueline Kerr
- is an associate professor of family and preventive medicine at the University of California, San Diego
| | - Benjamin M Marlin
- is an assistant professor at the University of Massachusetts, Amherst
| | - Susan Murphy
- is the H.E. Robbins Distinguished University Professor of Statistics, professor of psychiatry, and research professor at the Institute of Social Research at the University of Michigan
| | - James M Rehg
- is a professor in the School of Interactive Computing at the Georgia Institute of Technology. He is deputy director of MD2K and leads MD2K's data science research
| | - Inbal Nahum-Shani
- is an assistant professor at the Institute for Social Research at the University of Michigan
| | - Vivek Shetty
- is a professor of oral and maxillofacial surgery and an assistant vice-chancellor for research at the University of California, Los Angeles. He heads the MD2K Training Core
| | - Ida Sim
- is a professor of medicine and co-director of biomedical informatics at the University of California, San Francisco. She leads MD2K's consortium activities
| | - Bonnie Spring
- is a professor of preventive medicine, psychology, psychiatry, and public health at Northwestern University. She leads MD2K's smoking cessation studies
| | - Mani Srivastava
- is a professor of electrical engineering and computer science at the University of California, Los Angeles
| | - Dave Wetter
- is the Jon M. and Karen Huntsman Presidential Professor in Population Health Sciences at the Huntsman Cancer Institute at the University of Utah
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10
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Chatterjee S, Hovsepian K, Sarker H, Saleheen N, al'Absi M, Atluri G, Ertin E, Lam C, Lemieux A, Nakajima M, Spring B, Wetter DW, Kumar S. mCrave: Continuous Estimation of Craving During Smoking Cessation. Proc ACM Int Conf Ubiquitous Comput 2016; 2016:863-874. [PMID: 27990501 DOI: 10.1145/2971648.2971672] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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.
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11
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Saleheen N, Ali AA, Hossain SM, Sarker H, Chatterjee S, Marlin B, Ertin E, al’Absi M, Kumar S. puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation. Proc ACM Int Conf Ubiquitous Comput 2015; 2015:999-1010. [PMID: 26543927 PMCID: PMC4631252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
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12
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Kumar S, Abowd GD, Abraham WT, al'Absi M, Beck JG, Chau DH, Condie T, Conroy DE, Ertin E, Estrin D, Ganesan D, Lam C, Marlin B, Marsh CB, Murphy SA, Nahum-Shani I, Patrick K, Rehg JM, Sharmin M, Shetty V, Sim I, Spring B, Srivastava M, Wetter DW. Center of excellence for mobile sensor data-to-knowledge (MD2K). J Am Med Inform Assoc 2015; 22:1137-42. [PMID: 26555017 DOI: 10.1093/jamia/ocv056] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 04/27/2015] [Indexed: 11/13/2022] Open
Abstract
Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.
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Affiliation(s)
- Santosh Kumar
- Computer Science, University of Memphis, Memphis, TN
| | - Gregory D Abowd
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA
| | | | - Mustafa al'Absi
- Duluth Medical Research Institute (DMRI), University of Minnesota Medical School, Duluth, MN
| | | | - Duen Horng Chau
- School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Tyson Condie
- Computer Science, University of California, Los Angeles, CA
| | - David E Conroy
- Preventive Medicine, Northwestern University, Chicago, IL
| | - Emre Ertin
- Electrical & Computer Engineering, The Ohio State University, Columbus, OH
| | | | - Deepak Ganesan
- Computer Science, University of Massachusetts, Amherst, MA
| | - Cho Lam
- Psychology, Rice University, Houston, TX
| | | | - Clay B Marsh
- Health Sciences, West Virginia University, Morgantown, WV
| | | | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Kevin Patrick
- The Qualcomm Institute, University of California, San Diego, La Jolla, CA
| | - James M Rehg
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA
| | | | - Vivek Shetty
- School of Dentistry, University of California, Los Angeles, CA
| | - Ida Sim
- Medicine, University of California, San Francisco, CA
| | - Bonnie Spring
- Preventive Medicine, Northwestern University, Chicago, IL
| | - Mani Srivastava
- Electrical Engineering, University of California, Los Angeles, CA
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13
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Hovsepian K, al'Absi M, Ertin E, Kamarck T, Nakajima M, Kumar S. cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. Proc ACM Int Conf Ubiquitous Comput 2015; 2015:493-504. [PMID: 26543926 DOI: 10.1145/2750858.2807526] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets - in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.
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14
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Rahman M, Bari R, Ali AA, Sharmin M, Raij A, Hovsepian K, Hossain SM, Ertin E, Kennedy A, Epstein DH, Preston KL, Jobes M, Beck JG, Kedia S, Ward KD, al'Absi M, Kumar S. Are We There Yet? Feasibility of Continuous Stress Assessment via Wireless Physiological Sensors. ACM BCB 2014; 2014:479-488. [PMID: 25821861 PMCID: PMC4374173 DOI: 10.1145/2649387.2649433] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors - a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.
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15
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Hossain SM, Ali AA, Rahman M, Ertin E, Epstein D, Kennedy A, Preston K, Umbricht A, Chen Y, Kumar S. Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity. IPSN 2014; 2014:71-82. [PMID: 25531010 PMCID: PMC4269159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
A variety of health and behavioral states can potentially be inferred from physiological measurements that can now be collected in the natural free-living environment. The major challenge, however, is to develop computational models for automated detection of health events that can work reliably in the natural field environment. In this paper, we develop a physiologically-informed model to automatically detect drug (cocaine) use events in the free-living environment of participants from their electrocardiogram (ECG) measurements. The key to reliably detecting drug use events in the field is to incorporate the knowledge of autonomic nervous system (ANS) behavior in the model development so as to decompose the activation effect of cocaine from the natural recovery behavior of the parasympathetic nervous system (after an episode of physical activity). We collect 89 days of data from 9 active drug users in two residential lab environments and 922 days of data from 42 active drug users in the field environment, for a total of 11,283 hours. We develop a model that tracks the natural recovery by the parasympathetic nervous system and then estimates the dampening caused to the recovery by the activation of the sympathetic nervous system due to cocaine. We develop efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then apply our model on the recovery segments from these windows. Our model achieves 100% true positive rate while keeping the false positive rate to 0.87/day over (9+ hours/day of) lab data and to 1.13/day over (11+ hours/day of) field data.
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Affiliation(s)
| | | | | | - Emre Ertin
- Dept of Electrical & Computer Engineering, The Ohio State University
| | | | | | | | - Annie Umbricht
- Dept. of Psychiatry and Behavioral Sciences, Johns Hopkins University
| | - Yixin Chen
- Dept of Computer Science and Engg., Washington University in St. Louise
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16
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Ramakrishnan N, Ertin E, Moses RL. Enhancement of coupled multichannel images using sparsity constraints. IEEE Trans Image Process 2010; 19:2115-2126. [PMID: 20236892 DOI: 10.1109/tip.2010.2045701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We consider the problem of joint enhancement of multichannel images with pixel based constraints on the multichannel data. Previous work by Cetin and Karl introduced nonquadratic regularization methods for SAR image enhancement using sparsity enforcing penalty terms. We formulate an optimization problem that jointly enhances complex-valued multichannel images while preserving the cross-channel information, which we include as constraints tying the multichannel images together. We pose this problem as a joint optimization problem with constraints. We first reformulate it as an equivalent (unconstrained) dual problem and develop a numerically-efficient method for solving it. We develop the Dual Descent method, which has low complexity, for solving the joint optimization problem. The algorithm is applied to both an interferometric synthetic aperture radar (IFSAR) problem, in which the relative phase between two complex-valued images indicate height, and to a synthetic multimodal medical image example.
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Affiliation(s)
- Naveen Ramakrishnan
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
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17
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Ertin E, Austin CD, Sharma S, Moses RL, Potter LC. GOTCHA experience report: three-dimensional SAR imaging with complete circular apertures. ACTA ACUST UNITED AC 2007. [DOI: 10.1117/12.723245] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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