Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 h of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.