Technological Innovations Enabling Automatic, Context-Sensitive Ecological Momentary Assessment

Health-related behavior, subjective states, cognitions, and interpersonal experiences are inextricably linked to context. Context includes information about location, time, past activities, interaction with other people and objects, and mental, physiological, and emotional states. Most real-time data collection methodologies require that subjects self-report information about contextual influences, notwithstanding the difficulty they have identifying the contextual factors that are influencing their behavior and subjective states. Often these assessment methodologies ask subjects to report on their activities or thoughts long after the actual events, thereby relying on retrospective recall and introducing memory biases. The “gold standard” alternative to these self-report instruments is direct observation. Direct observation in a laboratory setting, however, artificially constrains behavior. Direct observation is also typically too costly and invasive for long-term, large-sample-size studies of people in their natural environments. Technological innovations are creating new opportunities to capture accurate, real-time data with minimal intrusiveness using techniques such as electronic Ecological Momentary Assessment (EMA). Other chapters in this collection discuss the benefits, challenges, and versatility of electronic EMA as it is being used in current research. This chapter, however, looks toward the future. New technologies will enable two significant extensions to current EMA methodologies. First, most EMA studies to date have used intermittent collection of self-report data. New technologies will enable EMA studies that combine continuous data collection of subject activities and physiological states with intermittent self-report data collection. Second, new technologies will enable EMA studies where a computer automatically triggers context-sensitive intermittent self-reports based upon analysis of the continuous data stream. Intermittent self-reports can be tied to the observation of particular activities or states that are specified by the researcher but automatically detected by the computer.