Abstract Tailoring care to match patients to the treatment most effective for them has the potential to accelerate recovery and meaningfully reduce the growing burden of depression. A key barrier to tailoring care is the absence of objective, real-time methods to effectively predict and assess treatment response. Mobile technology holds promise to overcome this barrier. Specifically, smartphones and wearable sensors collect passive, continuous and objective measures of constructs central to depression, such as sleep, physical activity, cardiovascular function, and social engagement. Studies have demonstrated associations of single measures from these domains with depression. However, because most prior wearable studies have had limited sample sizes, they have not been able to synthesize actionable information across multiple domains of mobile technology data and effectively guide treatment. Our long-term goal is to substantially increase the effectiveness of depression treatments and the capacity of our mental health care system. Our objective in this application is to identify factors that can be used to effectively match patients to treatments and track their recovery. Through the PROviding Mental health Precision Treatment (PROMPT) study, we will complete the following specific aims: Aim 1) Identify factors that predict which treatment is most likely to reduce depression symptoms for a specific patient; and Aim 2) Identify passive mobile technology-based measures that serve as signals of treatment response. To achieve these aims, we will recruit 2,200 subjects from waitlist for outpatient depression treatment. We will then track patients for six months through wearable sensors, smartphones, and repeated surveys. For both aims, we will use machine learning approaches to develop comprehensive prediction models. Our approach is innovative because it applies technology and analytic tools to a large and diverse sample of subjects receiving treatment under real world conditions. Further, the project is designed to lead directly to an organization-level intervention that matches patients to treatments and continuously monitors their response to treatment. Finally, this project is significant because it has the potential to greatly accelerate recovery by identifying the treatment from which each person is likely to derive the most benefit, ultimately helping to address the high population burden of depression.