Measuring the Psychobiological Correlates of Daily Experience in Adolescents

Child development in real time: The power of ambulatory assessment for investigating dynamic developmental processes and behavior longitudinally

Ambulatory assessment methods used to capture "real-world" microprocesses through self-report or passive data collection are used to assess child and adolescent behavior in context. This chapter begins by introducing the researcher to ambulatory assessment methods and describes these methods for use in child and adolescent developmental and behavioral research. Next, the importance of attention to timing is discussed. We then suggest appropriate analytic methods for putting ambulatory assessment data to best use to answer developmental research questions. We end with comments on the ethics of ambulatory assessment data and some concluding remarks for researchers wanting to use these methods in their own work.

A Usability Study of Physiological Measurement in School Using Wearable Sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.