SABLE: Sensor-Based Assessment of Behavioral Lifestyles and Experiences

University Of Texas At Austin, Austin TX

Investigators

Abstract

This research project will develop and evaluate SABLE, a smartphone-based toolkit for unobtrusively capturing human behaviors as they unfold in the course of everyday life. These behaviors, such as activity, sociability, and sleep are associated with such significant outcomes as physical health (e.g., heart disease, obesity), mental health (e.g., depression, anxiety), subjective well-being (e.g., mood, stress), and performance (e.g., academic, occupational). When it comes to measuring behaviors in the real world, however, researchers are surprisingly bad at measuring what people do. The problem is that collecting data on objective behaviors in the real world is almost impossible to do, especially if it must be done without affecting the behavior one is trying to record. With the knowledge and consent of participants, SABLE will use data collected by sensors routinely embedded in smartphones to yield high-fidelity portraits of how behaviors are played out in everyday life. Substantively, the data will illuminate previously unmapped dynamic patterns of human behavior. Methodologically, SABLE will advance techniques for inferring complex behaviors. SABLE will be made available to the research and applied communities, allowing researchers and practitioners to measure everyday behavior unobtrusively with high levels of ecological validity. Data gathered by SABLE will allow researchers to address long-standing questions about the psychosocial patterns of day-to-day life and to identify behaviors that predict consequential life outcomes. SABLE will facilitate new smartphone-based interventions designed to improve lifestyles through self-insight and behavior change. The project will be integrated into psychology and computer science coursework, and it will provide education, training, and mentorship opportunities for more than 50 undergraduate and graduate research apprentices. Behaviors constitute the independent or dependent variables of many studies in the social and behavioral sciences and many more in the health sciences, but the predominant technology for assessing behavior, self-reports, are subject to an array of limitations ranging from susceptibility to memory constraints and deliberate malingering to being time consuming and disruptive. The advent of smartphones and their ubiquity offer an opportunity to revolutionize the way social scientists collect behavioral data. The investigators will use mobile-sensing methods to address basic questions about the structure and contours of behavior as they play out in everyday life and develop automated behavioral classifier models (e.g., watching TV alone at home) derived from smartphone sensing data. The investigators will build on existing sensing software to permit automated data collection from a broad array of smartphones, with the data delivered to social-science researchers in an easy-to-access format. The investigators will explore answers to basic questions about the structure and contours of behavior and linguistic expression in everyday life. They will create a set of sensor-based classifiers that researchers can use to extract behavioral inferences from mobile-sensor data, and they will create an open-source online resource for these materials. By providing the means to automatically and unobtrusively assess psychological characteristics from people's smartphones, this project will catalyze the integration of behavioral data in all areas of research and applied settings that involve human participants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.