Cannabis use and stressful life events during the perinatal period: cross-sectional results from Pregnancy Risk Assessment Monitoring System (PRAMS) data, 2016

Aims: We aimed to determine the association between stressful life events (SLEs) in the year prior to childbirth with (1) pre-pregnancy cannabis use, (2) cessation of cannabis use during pregnancy and (3) postpartum relapse to cannabis use. Design: We used data from the Pregnancy Risk Assessment Monitoring System (PRAMS) 2016, a cross-sectional, population-based surveillance system. Setting: Mailed and telephone surveys conducted in five states—Alaska, Colorado, Maine, Michigan and Washington—in the United States. Participants: Women (n = 6061) who delivered a live infant within the last 6 months and had data on cannabis use. Measurements: Self-reported data included SLEs (yes/no response for 14 individual events in the 12 months prior to childbirth) and cannabis use [yes/no prior to pregnancy, during pregnancy, and at the time of the survey (approximately 2–6 months postpartum)]. The associations between SLEs and cannabis use (primary outcomes) were examined in logistic regression models adjusted for maternal demographics (e.g. age, race, education), geography (i.e. state of residence) and cigarette smoking. Findings: Pre-pregnancy, 16.4% (997/6061) of respondents endorsed using cannabis, with 36.4% (363/997) continuing cannabis use during pregnancy. Among the 63.6% (634/997) who did not report use during pregnancy, 23.2% (147/634) relapsed to cannabis use during the postpartum. Nine of the 14 possible SLEs were associated with increased odds of pre-pregnancy cannabis use [e.g. husband/partner or mother went to jail, adjusted odds ratio (aOR) = 2.16, 95% confidence interval (CI) = 1.30–3.62] and four were associated with increased odds of continued cannabis use during pregnancy (e.g. husband/partner lost job, aOR = 2.19, 95% CI = 1.21–3.96). The odds of postpartum relapse to cannabis were significantly associated with two SLEs (husband/partner said they did not want pregnancy, aOR = 2.86, CI = 1.10–7.72; husband/partner or mother went to jail, aOR = 0.37, 95% CI = 0.13–1.00). Conclusions: Stressful life events during the year prior to childbirth appear to be linked to greater odds of women\u27s cannabis use during the perinatal period, especially during pre-pregnancy

puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation

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

Using novel mobile sensors to assess stress and smoking lapse

Mobile sensors can now provide unobtrusive measurement of both stress and cigarette smoking behavior. We describe, here, the first field tests of two such methods, cStress and puffMarker, that were used to examine relationships between stress and smoking behavior and lapse from a sample of 76 smokers motivated to quit smoking. Participants wore a mobile sensors suite, called AutoSense, which collected continuous physiological data for 4 days (24-hours pre-quit and 72-hours post-quit) in the field. Algorithms were applied to the physiological data to create indices of stress (cStress) and first lapse smoking episodes (puffMarker). We used mixed effects interrupted autoregressive time series models to assess changes in heart rate (HR), cStress, and nicotine craving across the 4-day period. Self-report assessments using ecological momentary assessment (EMA) of mood, withdrawal symptoms, and smoking behavior were also used. Results indicated that HR and cStress, respectively, predicted smoking lapse. These results suggest that measures of traditional psychophysiology, such as HR, are not redundant with cStress; both provide important information. Results are consistent with existing literature and provide clear support for cStress and puffMarker in ambulatory clinical research. This research lays groundwork for sensor-based markers in developing and delivering sensor-triggered, just-in-time interventions that are sensitive to stress-related lapser risk factors

Center of excellence for mobile sensor data-to-knowledge (MD2K)

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

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

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