A large number of individuals experience mental health disorders, with cognitive behavioral therapy (CBT) emerging as a standard practice for reduction in psychiatric symptoms, including stress, anger, anxiety, and depression. However, CBT is associated with significant patient dropout and lacks the means to provide objective data regarding a patient's experience and symptoms between sessions. Emerging wearables and mobile health (mHealth) applications represent an approach that may provide objective data to the patient and provider between CBT sessions. Here, we describe the development of a classifier of real-time physiological stress in a healthy population (n = 35) and apply it in a controlled clinical evaluation for armed forces veterans undergoing CBT for stress and anger management (n = 16). Using cardiovascular and electrodermal inputs from a wearable device, the classifier was able to detect physiological stress in a non-clinical sample with accuracy greater than 90%. In a small clinical sample, patients who used the classifier and an associated mHealth application were less likely to discontinue therapy (p = 0.016, d = 1.34) and significantly improved on measures of stress (p = 0.032, d = 1.61), anxiety (p = 0.050, d = 1.26), and anger (p = 0.046, d = 1.41) compared to controls undergoing CBT alone. Given the large number of individuals that experience mental health disorders and the unmet need for treatment, especially in developing nations, such mHealth approaches have the potential to provide or augment treatment at low cost in the absence of in-person care.