 This systematic review identifies and characterizes sensing applications and public data sets for digital phenotyping of mental health, DPMH, from a technical perspective. A total of 31 sensing apps and eight data sets were identified, with sensing apps exploring different context data sources to support DPMH studies, while general-purpose sensing apps focus only on contextual data collection. The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, and sleep. While there is growth in proposals for DPMH sensing apps, the review shows that the combined evidence for high-quality features for mental states remains limited, highlighting the need for further research to reach the needed maturity for applications in clinical settings. This article was authored by Jean P. Mendes, Ivan Armora, Pepin van de Ven and others.