Current methods to assess depression and then ultimately select appropriate treatment have many limitations. They are usually based on having a clinician rate scales, which were developed in the 1960s. Their main drawbacks are lack of objectivity, being symptom-based and not preventative, and requiring accurate communication. This work explores new technology to assess depression, including its increase or decrease, in an automatic, more objective, pre-symptomatic, and cost-effective way using wearable sensors and smart phones for 24/7 monitoring of different personal parameters such as physiological data, voice characteristics, sleep, and social interaction. We aim to enable early diagnosis of depression, prevention of depression, assessment of depression for people who cannot communicate, better assignment of a treatment, early detection of treatment remission and response, and anticipation of post-treatment relapse or recovery.