Depression is a silent disease that is increasing alarmingly due to the pace of society lifestyle. The symptoms of depression themselves cause patients to face psychological barriers that hinder the search for treatment. Currently, mobile devices are being used to monitor the behavior of people and, thus, identify if they have a healthy lifestyle or, even, if they have specific diseases. These devices are also being used to identify symptoms of depression. Nevertheless, most of the applications developed consume a lot of mobile resources and require the active participation of the user answering some test, which entails a barrier for the adoption of these applications. This paper presents an environment that passively monitors the user's context for automatically detecting symptoms of depression, reducing the possible barriers for the identification of this disease. This environment has been developed so that the monitored data can be reused by other systems, reducing the resource consumption. In addition, the presented system has been compared with popular mobile apps for assessing the mental health analyzing the resource consumption and the required interaction to users.