Are we there yet?

Rahman, Md. Mahbubur; Bari, Rummana; Ali, Amin Ahsan; Sharmin, Moushumi; Raij, Andrew; Hovsepian, Karen; Hossain, Syed Monowar; Ertin, Emre; Kennedy, Ashley; Epstein, David H.; Preston, Kenzie L.; Jobes, M.; Beck, J. Gayle; Kedia, Satish; Ward, Kenneth D.; Al’Absi, Mustafa; Kumar, Santosh

doi:10.1145/2649387.2649433

Cited by 45 publications

(11 citation statements)

References 47 publications

(42 reference statements)

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“…All were participating in a 46-week natural-history study of stress, geographical location, and drug use. Our prior publications of data from a subset of participants in this study focused primarily on ambulatory physiological monitoring (Hossain et al 2014; Kennedy et al 2015; Rahman et al 2014) and an examination of the relationship between stress and drug use (Furnari et al 2015). …”

Section: Methodsmentioning

confidence: 99%

Context and craving during stressful events in the daily lives of drug-dependent patients

et al. 2017

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Rationale Knowing how stress manifests in the lives of people with substance-use disorders could help inform mobile “just in time” treatment. Objectives To examine discrete episodes of stress, as distinct from the fluctuations in background stress assessed in most EMA studies. Methods For up to 16 weeks, outpatients on opioid-agonist treatment carried smartphones on which they initiated an entry whenever they experienced a stressful event (SE) and when randomly prompted (RP) three times daily. Participants reported the severity of stress and craving and the context of the report (location, activities, companions). Decomposition of covariance was used to separate within-person from between-person effects; reffect sizes below are within-person. Results Participants (158 of 182; 87%) made 1,787 stress-event entries. Craving for opioids increased with stress severity (reffect = 0.50). Stress events tended to occur in social company (with acquaintances, 0.63, friends, 0.17, or on the phone, 0.41) rather than with family (spouse, −0.14; child, −0.18), and in places with more overall activity (bars, 0.32; outside, 0.28; walking, 0.28) and more likelihood of unexpected experiences (with strangers, 0.17). Being on the internet was slightly protective (−0.22). Our prior finding that being at the workplace protects against background stress in our participants was partly supported in these stressful-event data. Conclusions The contexts of specific stressful events differ from those we have seen in prior studies of ongoing background stress. However, both are associated with drug craving.

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Section: Methodsmentioning

confidence: 99%

Context and craving during stressful events in the daily lives of drug-dependent patients

et al. 2017

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“…In addition to refining architectural choices to minimize data losses, we also developed mDebugger [21, 22] to discover deficiencies in study protocol or participant compliance. Fixing them have led to substantial improvements in data yield.…”

Section: Lessons Learnedmentioning

confidence: 99%

mCerebrum

Hossain

Hnat

Saleheen

et al. 2017

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems

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The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage. CCS Concepts • Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; • Computer systems organization → Embedded and cyber-physical systems; ACM Reference format Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In Proceedings of SenSys ′17, Delft, Netherlands, November 6–8, 2017, 14 pages.

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“…Due to sensor detachment, displacement, loosening, and wireless loss between phone and the sensor, some of the ECG data were not of acceptable quality. We computed the amount of unacceptable ECG data using a method proposed in [55] and discarded them. Acceptable ECG data were obtained 10.54 hours per day on average (around 10,447 hours of data in total); these were the data we used for stress inference.…”

Section: Data Descriptionmentioning

confidence: 99%

“…To isolate data affected by activity, we first detect physical activity from chest-worn 3-axis accelerometer data, using an existing model [55]. Second, we estimate the time it takes for physiology to recover from the effect of a just concluded activity episode.…”

Section: Reducing the Impact Of Confounding Factorsmentioning

confidence: 99%

“…First, stress measurements obtained from sensors usually have to be inferred from physiological data, which by their very nature is rapidly varying, similar to real-time tracking of stock prices. Second, unlike stock-price data, the time series of stress is discontinuous due to factors such as sensor detachment and wireless losses [51, 55]. Third, sensor measurements are frequently confounded by physical activity (23% of the time [55]), that need to be filtered out for an accurate assessment of stress.…”

Section: Introductionmentioning

confidence: 99%

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Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

Sarker

Tyburski

Rahman

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Management of daily stress can be greatly improved by delivering 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 natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration 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.

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Are we there yet?

Cited by 45 publications

References 47 publications

Context and craving during stressful events in the daily lives of drug-dependent patients

Context and craving during stressful events in the daily lives of drug-dependent patients

mCerebrum

Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

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