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Background An individual’s mental health is best captured by considering the overall associations of biological, behavioral, and social functions that comprise the framework of individual experience. As such, accessing data on various health indicators concurrently can influence prediction of disease progression or change in response to treatment. Data generated passively by smartphones to measure human behavior has generated significant research interest and has increasingly been utilized in psychiatric disorders. In schizophrenia, passive and continuous assessment of how an individual uses their mobile device may give rise to clinically useful markers that can be used to improve treatment processes, adapt treatment choices, identify early risk for relapse to initiate clinical intervention, and develop new clinical models. A promising approach is to leverage current advances in mobile technology, data analytics and machine learning to enable automated and fast phenotyping of digital data. . In this context, the workflow for phenotyping (passive data collection → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. Digital phenotyping can be used in conjunction with standard care to reduce time to recognition and acknowledgement that worsening of a symptom needs to be addressed, to reduce time to receiving appropriate level of care, to increase ability to analyze and collect data from a variety of sources to improve mental health needs assessment and delivery of services, and to advance outcome measurement through comparison of passive and active data sets. Aim The aim of this pilot study is to test whether a smartphone digital phenotyping application can help detect early signs of treatment failure or response in individuals with chronic schizophrenia after discharge from hospital. Methods 17 individuals with DSM-5 schizophrenia were provided with a smartphone and digital phenotyping app, MindStrong Health app, following discharge from an inpatient psychiatric facility. The participants were followed for 6 months with monthly rater administered evaluations assessing neurocognition (Brief Assessment of Cognition in Schizophrenia (BACS)), symptomatology (PANSS; CGI-S/I), quality of life (SF-36), healthcare utilization, alcohol/drug use, level of clinical insight and depression (Calgary Depression Rating Scale, CDSS). Digital phenotyping data included gestures (swipes, taps, other), orientation (the way the phone is pointing), acceleration (sudden movements of the phone), keystroke patterns with characters encoded, number of calls made, number of emails sent, number of text messages sent, and location information from the GPS. Predictive models were built using multiple machine learning techniques - random forest plots, linear regression and gradient boosting, to predict the target scores based on phone usage patterns. Results Of the 17 enrolled participants, 10 provided analyzable data (i.e., had at least 22 target days with data). There was a gradual reduction of passive data generation due to either non-use of the smart phone or due to non-recharging of the device. The mean PANSS score was 80.12 (14.56). BACS scores corresponding to motor speed (token motor task), verbal fluency (category instances, letter fluency), and attention and processing speed (symbol coding) were found to be highly correlated with a composite digital phenotyping marker while scores on the PANSS or CDSS were not. Discussion The study provides a basis for using smartphone-base mobile apps to use as an augmentation within clinical practice to gather further information on patients outside the clinic setting, focusing on their behavior in the ‘real world.’ In particular, the cognitive data derived from the digital device correlated well with rater administered traditional cognitive ratings. Collecting digital data can provide a much needed window into the lives of patients in between normally scheduled visits, while minimizing costs and inconvenience to the patient. Studies with larger sample sizes are required to assess relationships with relapse, rehospitalizations and treatment failure.