From Markers to Interventions - The Case of Just-in-Time Stress Intervention

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. This dissertation takes a first step in modeling users\u27 availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. Delay in responding to a prompt is used to objectively measure availability. Presented work compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. Findings suggest that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. Users are least available at work and during driving, and most available when walking outside. Proposed model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. In addition to assessing the availability of a person, 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. This dissertation proposes a time series pattern mining method to detect stress episodes in a time series of discontinuous and rapidly varying stress data. This model is applied to two separate human subject studies on physiological, GPS, and activity data collected from 91 (38+53) users in their natural environment to discover patterns of stress in real life. Findings suggest that the duration and the type of a prior stress episode predict the duration and the type of the next stress episode. Stress in mornings and evenings is lower than during the day. The work then analyzes the relationship between stress and objectively rated disorder in the surrounding neighborhood and suggests a model to identify the proactive or reactive timing for JITI

Can Wearable Sensors Help Assess the Reliability of Self-Reports in Mobile Health Studies?

Self-report in the form of Ecological Momentary Assessment (EMA) has been the primary instrument to collect measurements from participants in their natural environment. Given numerous sources of biases and inaccuracies in self-report, assessing and improving the reliability of self-report has been the subject of continuing research. However, to date, there exist only limited lab based methods to check the veracity of collected self-report data. Increasing adoption of sensors in field studies that sometimes can passively measure the same phenomena that have been traditionally included in EMA self-report has opened up a new opportunity to assess the reliability of self-reports.In this paper, we use data collected in a week-long field study with wearable sensors to first investigate whether lack of agreement between self-reported location and GPS-inferred location can be used to predict the reliability of self-reports. We find this not be the case, primarily because lack of agreement on location results from sensitivity of some participants to reporting locations and it does not indicate lack of care in completing self-reports. We then investigate whether contexts of the participants, such as place (from GPS), activity level (inferred from accelerometers), or stress (inferred from physiological sensors) are associated with low reliability. We find that not being at home or work does not predict reliability of self-report, nor does the context when participants are engaged in physical activity at the time of receiving the self-report prompts. However, we do find that if the participants are stressed at the time of receiving a self-report prompt, then reliability of self-reported data is low. This implies that unless demanded by the study protocol, self-report prompts should be avoided when participants are under stress

Opportunities and Challenges in Designing Participant-Centric Smoking Cessation System

Smoking is one of the most challenging behavioral health problems. In the past, failed quit attempts have been attributed to factors including stress, presence of smoking cues, and negative affect-most of which were self-reported and prone to recall-bias. The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. In our research, we collected physiological data utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). We also collected self-report measures (n=3120), which offer rich contextual information about users\u27 social, emotional, geographical, and physiological conditions. Analysis of collected data informed the design of MyQuitPal, a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. The design of MyQuitPal is also grounded on theories of long term health-behavior change. We believe our research advances understanding of complexities and opportunities surrounding the design of smoking cessation systems

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

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

mCrave

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

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