Psychophysiological measures have potential to aid the discipline of user research, but are currently under-utilized. Currently, across both academia and industry there is a need to increase the quality and quantity of feedback garnered from individuals during user tasks. Psychophysiological measures are beneficial in that they can collect data objectively, unobtrusively, and in real-time. The work put forth in this dissertation focuses on two separate contexts in which psychophysiological measures are used to increase the overall quality of user research data. The first context is described in Chapters 2 and 3, in which electrodermal activity (EDA) within a high fidelity combine simulator is used as a measure of mental effort. Due to both the natural complexity of operating a combine harvester and the relative lack of understanding of combine operators today, using psychophysiological measures within this environment serves to better understand the user without compromising the experience. The second context is described in Chapters 4 and 5, in which consumer level hardware is used to measure the emotional states of workplace employees. The hardware captured electrodermal activity and heart rate data from participants while they also submitted their emotional states as training data. These data were used to build a general emotion detection model which was then tested in real-time over the course of four weeks. Additionally, emotion reporting is explored through the lens of personality and models were built and evaluated to determine what, if any influence personality plays in emotional self-report. Both mental effort within the combine simulator and emotion detection using everyday technology seek to improve the overall understanding of the user and support the use of psychophysiological measures within user research.