The Ecological Momentary Assessment (EMA) method is used to repeatedly collect an individual's momentary self-assessments of states (e.g., stress, depression), along other data streams from smartphone sensors and wearable devices. But sometimes individuals can not reliably self-assess their states (e.g. poor self awareness, cognitive impediments or social desirability) and close friends could bridge the gap indicating how they perceive observable states of the target individual. The thesis introduces and examines the feasibility of the Peer-ceived Momentary Assessment (PeerMA) method that collects peer-based assessments, in addition to EMA- based assessments. We used PeerMA in three field studies about real life phenomena deriving human factors that influence the acceptance of the method, and methodological aspects that affect its applicability in broader contexts. While modeling human states, we showed that peer-based assessments can enhance the classification accuracy of algorithms that are trained to predict the individual's self-assessment of the state issued via EMA.