Project Summary Bipolar disorder (BD) is strongly associated with financial instability. Symptomatic periods in BD often manifest in poor financial decision-making. For example, 70% individuals with BD have reported impulsive spending during hypomania. Such problematic financial behaviors during symptomatic periods can lead to serious long-term financial instability, which can severely impact the quality of life for individuals with BD and their care partners. Maintaining financial stability is, thus, a critical challenge to ensure the long-term wellbeing for individuals with BD. As such, there has been an increasing focus on understanding financial behaviors of individuals with BD. However, there remains a knowledge gap regarding how idiosyncratic, context-driven, and illness-specific factors impact financial decision-making in BD. Furthermore, the lack of granular, in-situ assessment methods is a key challenge against developing just-in-time and personalized interventions focusing on financial stability for this population. Given the importance of financial stability for individuals with BD, this remains a serious knowledge gap with broad practical and societal implications. In recent years, there has been a considerable progress toward more open and accessible financial data. We argue that the granular and real-time access to financial activity data can lead to a paradigm shift in the domain of financial wellbeing and BD. That is, it can provide a unique opportunity to explore the nuanced relationship between financial behaviors and BD, uncover financial patterns indicative of early-warning signs, and develop preemptive interventions to sustain financial stability and improve long-term quality of life for this population. This project aims to advance this vision of using financial activity data as an objective behavioral marker in BD. We will develop a prototype to collect privacy-preserving, customized financial data. The prototype will also collect symptom and illness trajectory data. We will follow the Center for eHealth Research (CeHRes) roadmap for implementation. We will use the prototype to retrieve financial data from individuals with BD (N=50) for the last 24 months (i.e., 1200 months of financial data in total). We will use the data to develop and evaluate privacy-preserving machine learning models to identify early-warning signs in BD. We will also conduct focus group interviews to collect in-depth usability and acceptance data from individuals with BD. This project will establish a preliminary evidence base regarding the feasibility and acceptability of using financial activity data as an objective behavioral marker in BD. Given the association between BD and financial instability, the project will provide crucial and urgent insights into assessment and intervention methods to support financial stability and overall wellbeing in BD, including for those living in remote and rural areas.