Publications
James M Rehg, Susan A Murphy and Santosh Kumar (eds.).
From Markers to Interventions: The Case of Just-in-Time Stress Intervention. pages 411–433, Springer International Publishing, 2017. URL, DOI BibTeX@inbook{Sarker2017, pages = "411--433", title = "From Markers to Interventions: The Case of Just-in-Time Stress Intervention", publisher = "Springer International Publishing", year = 2017, author = "Sarker, Hillol and Hovsepian, Karen and Chatterjee, Soujanya and Nahum-Shani, Inbal and Murphy, Susan A. and Spring, Bonnie and Ertin, Emre and al'Absi, Mustafa and Nakajima, Motohiro and Kumar, Santosh", editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh", abstract = "The use of sensor-based assessment of stress to trigger the delivery of just-in-time intervention has the potential to help people manage daily stress as it occurs in the person's natural environment. The challenge is to mine the continuous stream of sensor data and identify those few opportune moments for triggering an intervention---when there is sufficient confidence in the accuracy of the sensor-based stress markers, in order to limit interruptions to the daily lives. In this chapter, we describe the process of developing a real-time method to identify stress episodes, from a time series of stress markers, to inform the triggering of just-in-time stress-management interventions.", booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications", doi = "10.1007/978-3-319-51394-2_21", url = "https://doi.org/10.1007/978-3-319-51394-2_21" }Andrine Lemieux, Motohiro Nakajima, Soujanya Chatterjee, Hillol Sarker, Nazir Saleheen, Emre Ertin, Santosh Kumar and Mustafa al'Absi.
Unobtrusive Measurement of Stress and Smoking Psychophysiological Markers in the Natural Environment. 79(4):A150–A150, 2017. BibTeX@article{lemieux2017unobtrusive, author = "Andrine Lemieux and Motohiro Nakajima and Soujanya Chatterjee and Hillol Sarker and Nazir Saleheen and Emre Ertin and Santosh Kumar and Mustafa al'Absi", title = "Unobtrusive Measurement of Stress and Smoking Psychophysiological Markers in the Natural Environment", year = 2017, volume = 79, number = 4, pages = "A150--A150", booktitle = "PSYCHOSOMATIC MEDICINE" }Mustafa al'Absi, Inbal Nahum-Shani, Andrine Lemieux, David W Wetter, Joel Swendsen and Emre Ertin.
Harnessing mHealth Technology to Advance Intervention Research on Stress, Addiction, and Mental Health Disorders. 79(4):A149–A150, 2017. BibTeX@article{al2017harnessing, author = "Mustafa al'Absi and Inbal Nahum-Shani and Andrine Lemieux and David W Wetter and Joel Swendsen and Emre Ertin", title = "Harnessing mHealth Technology to Advance Intervention Research on Stress, Addiction, and Mental Health Disorders", year = 2017, volume = 79, number = 4, pages = "A149--A150", booktitle = "PSYCHOSOMATIC MEDICINE" }Hillol Sarker, Matthew Tyburski, Md Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David H Epstein, Kenzie L Preston, Debra C Furr-Holden, Adam Milam, Inbal Nahum-Shani, Mustafa and Santosh Kumar.
Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016, 4489–4501. URL, DOI BibTeX@inproceedings{Sarker:2016:FSS:2858036.2858218, author = "Sarker, Hillol and Tyburski, Matthew and Rahman, Md Mahbubur and Hovsepian, Karen and Sharmin, Moushumi and Epstein, David H. and Preston, Kenzie L. and Furr-Holden, C. Debra and Milam, Adam and Nahum-Shani, Inbal and al'Absi, Mustafa and Kumar, Santosh", title = "Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data", booktitle = "Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems", year = 2016, pages = "4489--4501", publisher = "ACM", abstract = "Management of daily stress can be greatly improved by de- livering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natu-ral environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the du-ration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.", doi = "10.1145/2858036.2858218", url = "http://doi.acm.org/10.1145/2858036.2858218" }Moushumi Sharmin, Andrew Raij, David Epstien, Inbal Nahum-Shani, Gayle J Beck, Sudip Vhaduri, Kenzie Preston and Santosh Kumar.
Visualization of Time-series Sensor Data to Inform the Design of Just-in-time Adaptive Stress Interventions. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 505–516. URL, DOI BibTeX@inproceedings{Sharmin:2015:VTS:2750858.2807537, author = "Sharmin, Moushumi and Raij, Andrew and Epstien, David and Nahum-Shani, Inbal and Beck, J. Gayle and Vhaduri, Sudip and Preston, Kenzie and Kumar, Santosh", title = "Visualization of Time-series Sensor Data to Inform the Design of Just-in-time Adaptive Stress Interventions", booktitle = "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing", year = 2015, pages = "505--516", publisher = "ACM", abstract = "We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, inter-person variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.", doi = "10.1145/2750858.2807537", url = "http://doi.acm.org/10.1145/2750858.2807537" }Karen Hovsepian, Mustafa , Emre Ertin, Thomas Kamarck, Motohiro Nakajima and Santosh Kumar.
cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 493–504. URL, DOI BibTeX@inproceedings{Hovsepian:2015:CTG:2750858.2807526, author = "Hovsepian, Karen and al'Absi, Mustafa and Ertin, Emre and Kamarck, Thomas and Nakajima, Motohiro and Kumar, Santosh", title = "cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment", booktitle = "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing", year = 2015, pages = "493--504", publisher = "ACM", 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 wear-able 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 collec-tion, screening, cleaning, filtering, feature computation, nor-malization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently col-lected 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%.", doi = "10.1145/2750858.2807526", url = "http://doi.acm.org/10.1145/2750858.2807526" }S Vhaduri, A A Ali, M Sharmin, K Hovsepian and S Kumar.
Estimating Drivers' Stress from GPS Traces. In Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '14). 2014, 20:1–20:8. URL BibTeX@inproceedings{Vhaduri:2014:EDS:2667317.2667335, author = "S. Vhaduri and A.A. Ali and M. Sharmin and K. Hovsepian and S. Kumar", title = "Estimating Drivers' Stress from GPS Traces", booktitle = "Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '14)", year = 2014, pages = "20:1--20:8", publisher = "ACM", abstract = {Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days (for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.}, url = "http://doi.acm.org/10.1145/2667317.2667335" }M M Rahman, R Bari, A A Ali, M Sharmin, A Raij, K Hovsepian, S M Hossain, E Ertin, A Kennedy, D H Epstein, K L Preston, M Jobes, J G Beck, S Kedia, K D Ward, M al’Absi and S Kumar.
Are we there yet?: feasibility of continuous stress assessment via wireless physiological sensors. In Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 2014, 479–488. URL BibTeX@inproceedings{Rahman2014, author = "M.M Rahman and R. Bari and A.A. Ali and M. Sharmin and A. Raij and K. Hovsepian and S.M. Hossain and E. Ertin and A. Kennedy and D.H. Epstein and K.L. Preston and M. Jobes and J.G. Beck and S. Kedia and K.D. Ward and M. al’Absi and S. Kumar", title = "Are we there yet?: feasibility of continuous stress assessment via wireless physiological sensors", booktitle = "Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics", year = 2014, pages = "479--488", 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.", url = "http://dl.acm.org/citation.cfm?id=2649433" }S Potretzke, M Nakajima, T Cragin and M Absi.
Changes in circulating leptin levels during acute stress and associations with craving in abstinent smokers: a preliminary investigation. Psychoneuroendocrinology 47:232-40, 2014. URL BibTeX@article{Potretzke2014, author = "S. Potretzke and M. Nakajima and T. Cragin and M. al' Absi", title = "Changes in circulating leptin levels during acute stress and associations with craving in abstinent smokers: a preliminary investigation", journal = "Psychoneuroendocrinology", year = 2014, volume = 47, pages = "232-40", abstract = "Recent research suggests a role for the appetite hormone leptin in cigarette smoking. This study examined patterns of change in leptin in response to stress and associations with craving during the initial phase of a quit attempt. Thirty-six smokers (average age±SEM, 33.4±2.4) interested in smoking cessation set a quit day and were required to be abstinent for 24h. After, they completed a laboratory session including public speaking and cognitive challenges, and attended 4 weekly post-cessation assessments. Blood samples and self-report measures were collected throughout the laboratory session. The results indicated that leptin levels significantly increased following exposure to acute stress. We also found positive correlations between leptin and craving for cigarettes. No differences were observed in leptin levels between smokers who maintained abstinence for 4 weeks and those who relapsed during this period. These findings suggest that leptin levels may change in response to stress and that leptin could be a useful marker of craving for smoking.", url = "http://www.ncbi.nlm.nih.gov/pubmed/24954303" }M Nakajima and M al'Absi.
Nicotine withdrawal and stress-induced changes in pain sensitivity: a cross-sectional investigation between abstinent smokers and nonsmokers. Psychophysiology 51(10):1015-22, 2014. URL BibTeX@article{nakajima2014, author = "M. Nakajima and M. al'Absi", title = "Nicotine withdrawal and stress-induced changes in pain sensitivity: a cross-sectional investigation between abstinent smokers and nonsmokers", journal = "Psychophysiology", year = 2014, volume = 51, number = 10, pages = "1015-22", abstract = "Chronic smoking has been linked with alterations in endogenous pain regulation. These alterations may be pronounced when individuals quit smoking because nicotine withdrawal produces a variety of psychological and physiological symptoms. Smokers interested in quitting (n?=?98) and nonsmokers (n?=?37) completed a laboratory session including cold pressor test (CPT) and heat thermal pain. Smokers set a quit date and completed the session after 48?h of abstinence. Participants completed the pain assessments once after rest and once after stress. Cardiovascular and nicotine withdrawal measures were collected. Smokers showed blunted cardiovascular responses to stress relative to nonsmokers. Only nonsmokers had greater pain tolerance to CPT after stress than after rest. Lower systolic blood pressure was related to lower pain tolerance. These findings suggest that smoking withdrawal is associated with blunted stress response and increased pain sensitivity.", url = "http://www.ncbi.nlm.nih.gov/pubmed/24934193" }M al'Absi, M Nakajima, A Dokam, A Sameai, M Alsoofi, N S Khalil and Al M Habori.
Concurrent tobacco and khat use is associated with blunted cardiovascular stress response and enhanced negative mood: a cross-sectional investigation. Human Psychophmarmacology 29(4):307-314, 2014. URL BibTeX@article{alabsi2014, author = "M. al'Absi and M. Nakajima and A. Dokam and A. Sameai and M. Alsoofi and N.S. Khalil and M. Al Habori", title = "Concurrent tobacco and khat use is associated with blunted cardiovascular stress response and enhanced negative mood: a cross-sectional investigation", journal = "Human Psychophmarmacology", year = 2014, volume = 29, number = 4, pages = "307-314", abstract = "Objectives Khat (Catha edulis), an amphetamine-like plant, is widely used in East Africa and the Arabian Peninsula and is becoming a growing problem in other parts of the world. The concurrent use of tobacco and khat is highly prevalent and represents a public health challenge. We examined for the first time associations of the concurrent use of tobacco and khat with psychophysiological responses to acute stress in two sites in Yemen. Methods Participants (N?=?308; 135 women) included three groups: users of khat and tobacco, users of khat alone, and a control group (nonsmokers/nonusers of khat). These individuals completed a laboratory session in which blood pressures (BP), heart rate, and mood measures were assessed during rest and in response to acute stress. Results Concurrent use of khat and tobacco was associated with attenuated systolic BP, diastolic BP, and heart rate responses to laboratory stress (ps < 0.05) and with increased negative affect relative to the control group (ps < 0.05). Conclusions Results demonstrated blunted cardiovascular responses to stress and enhanced negative affect in concurrent khat and tobacco users. These findings extend previous studies with other substances and suggest that adverse effects of khat use may lie in its association with the use of tobacco. Copyright © 2014 John Wiley & Sons, Ltd.", url = "http://onlinelibrary.wiley.com/doi/10.1002/hup.2403/full" }L Wanner and M B Srivastava.
ViRUS: Virtual Function Replacement Under Stress. In 6th Workshop on Power-Aware Computing and Systems (HotPower 14). 2014. URL BibTeX@inproceedings{187114, author = "L. Wanner and M.B. Srivastava", title = "ViRUS: Virtual Function Replacement Under Stress", booktitle = "6th Workshop on Power-Aware Computing and Systems (HotPower 14)", year = 2014, publisher = "USENIX Association", abstract = "In this paper we introduce ViRUS: Virtual function Replacement Under Stress. ViRUS allows the runtime system to switch between blocks of code that perform equivalent functionality at different Quality-of- Service levels when the system is under stress be it in the form of scarce energy resources, temperature emergencies, or various sources of environmental and process variability with the ultimate goal of energy efficiency. We demonstrate ViRUS with a framework for transparent function replacement in shared libraries and a polymorphic version of the standard C math library in Linux. Case studies show how ViRUS can tradeoff upwards of 4% degradation in application quality for a band of upwards of 50% savings in energy consumption.", url = "http://blogs.usenix.org/conference/hotpower14/workshop-program/presentation/wanner" }Emre Ertin, Nathan Stohs, Santosh Kumar, Andrew Raij, Mustafa and Siddharth Shah.
AutoSense: Unobtrusively Wearable Sensor Suite for Inferring the Onset, Causality, and Consequences of Stress in the Field. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. 2011, 274–287. URL, DOI BibTeX@inproceedings{Ertin:2011:AUW:2070942.2070970, author = "Ertin, Emre and Stohs, Nathan and Kumar, Santosh and Raij, Andrew and al'Absi, Mustafa and Shah, Siddharth", title = "AutoSense: Unobtrusively Wearable Sensor Suite for Inferring the Onset, Causality, and Consequences of Stress in the Field", booktitle = "Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems", year = 2011, series = "SenSys '11", pages = "274--287", address = "New York, NY, USA", publisher = "ACM", abstract = "The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.", acmid = 2070970, doi = "10.1145/2070942.2070970", isbn = "978-1-4503-0718-5", keywords = "deployment experiences, mobile health, psychological stress monitoring, wearable physiological sensors", location = "Seattle, Washington", numpages = 14, url = "http://doi.acm.org/10.1145/2070942.2070970" }