Publications our peer-reviewed work.

Frontiers in Public Health

The relationship between financial circumstances and mental health is well-established. New financial data sources such as bank transactions and digital payments offer new opportunities to better characterise this relationship. However, the prior financial data sources and analytical approaches have not been systematically reviewed. Understanding these is essential to guide future research and inform integrated interventions that address both financial and mental health outcomes. This scoping review systematically maps the research on money and mental health, examining: (1) the financial variables and data sources used, (2) modeling methods employed, and (3) methodological gaps that novel objective data might address. We systematically searched PubMed, PsycINFO, IEEE Xplore, ACM Digital Library, and Scopus. Papers were screened against predefined inclusion/exclusion criteria, and data extracted using a standardized spreadsheet. Analysis employed deductive coding guided by our research questions, refined iteratively through engagement with the data. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed for reporting. Of the 43 included studies, most (n = 34, 79%) examined mental health in connection with financial factors such as financial difficulty or financial strain, while a small number focused on predictive modeling with financial behavioral data (n = 5, 12%), macroeconomic indicators (n = 2, 5%), or intergenerational support between parent and child (n = 2, 5%). Depression was the most common outcome (n = 24, 56%), followed by anxiety, psychological distress, and bipolar disorder. Statistical methods dominated (77%), with 19% employing machine learning or deep learning. Ground truths relied predominantly on self-reported questionnaires—only four studies used objective financial data (three gambling records, one bank transaction). This review reinforces the complex, bidirectional relationship between financial circumstances and mental health. Most studies examined how financial difficulty affects mental health, while only a few explored how mental illness influences financial behavior, indicating a clear research gap. There is substantial opportunity to use objective financial data and more diverse analytical methods, particularly machine learning, to deepen understanding of the relationship and interactions between money and mental health and inform targeted interventions.

Pervasive Health 2026

Mood and money are closely intertwined in bipolar disorder (BD), yet it remains unclear whether financial behaviour provides reliable mood-contingent signals, and it is largely absent from digital phenotyping research. Advances in open banking enable objective, passive measurement of financial behaviour at scale. We conducted semi-structured interviews with 19 (11 UK, 4 Ireland, 4 Nigeria) adults with BD to examine how mood manifests in financial behaviour, identify potential financial mood markers, and explore data-sharing preferences. Participants reported distinct mood-related changes in spending and reciprocal effects of money on mood. Three themes emerged: (i) symptomatic drivers shaped by credit access, seasonality, and social context; (ii) candidate markers such as transaction frequency, timing, category, and amount; and (iii) conditional willingness by most participants to share data influenced by privacy, anonymity, and granularity. High-mood spending reflected goal pursuit, generosity, and insomnia, while depression involved both reduced spending and retail therapy, challenging simple bidirectional assumptions. These findings provide empirical insight into financial data as a potential mood marker and inform future research and privacy-sensitive data collection.

CIBB 2026

Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification. This challenge is particularly acute in digital phenotyping, where continuous behavioural data raises concerns around consent, privacy, and fairness. We propose a computational ethical framework for AI-driven digital phenotyping systems in which ethical requirements are formalised as deontic temporal logic constraints and overseen by a conceptual ethical agent that ensures compliance with the specified constraints. Using a case study involving financial data and mental health, we model key ethical properties and verify them using the Z3 Satisfiability Modulo Theories (SMT) solver. Results demonstrate logical consistency and show that violations of the specified ethical properties are ruled out within the formal model through counterexample-based verification. This work represents an early step toward continuous, machine-verifiable ethical checking, moving beyond retrospective compliance based on static documentation. We discuss limitations, including the need for real-world validation, challenges related to subjectivity and contextual sensitivity, and the continued importance of human oversight in delivering auditable ethical guarantees for AI systems.

CHI 2025

Symptomatic financial behavior in bipolar disorder can increase stress, worsen symptoms, and hinder recovery. While spending sprees signal high mood states, research on personal experiences remains limited. This study explores mood-spending dynamics through semi-structured interviews in Ireland and Nigeria (N=5, projected N=10), assessing geo-economic influences and data-sharing preferences for future research. Participants reported varied impulsive spending patterns and emphasized privacy, security, and transparency in financial data sharing. These findings highlight the need for protective financial technologies for vulnerable individuals

CHI 2025

Money and financial activities reflect social connections and societal norms. Collaborative financial activities and decision-making are highly common in our day-to-day activities. However, existing financial technologies (fintech) are often limited to individual-centric approaches and goals. Recent HCI work has repeatedly noted the need for creating new interaction strategies and design paradigms to better support our financial behaviors, habits, and goals. However, there has not been much concrete work yet, specifically when it comes to supporting collaborative behaviors and social norms that underpin much of our daily financial activities. In this in-person workshop, we will bring together an interdisciplinary group of researchers interested in reshaping the current landscape of digital money and fintech with a focus on social and collaborative interactions. Specifically, we will identify limitations of existing fintech approaches and potential strategies to address these limitations. We will also discuss key challenges for fintech design and development, including collaboration, privacy, agency, trust, and accessibility. The workshop will lead to identifying novel HCI research and implementation directions focusing on the future of financial technologies.

Springer Nature

Information is essential to delivering effective healthcare. Health information traditionally resides with the entity providing healthcare to the patient. However, this is just one model for how information can be situated within a healthcare context. Decentralized health networks provide the infrastructure to realize a different information and computational model that centers the locus of health information around the patients. In this paper, we present a framework for building and testing decentralized health networks that enable patients to share their data with a communal network to gain value derived from aggregated data. While decentralized networks disperse control of health information among a network of individuals, they require careful design and testing to ensure the network’s incentives promote the interests of each member, scale cost-effectively, and protect privacy. Deploying a decentralized health network without appropriate testing presents multiple risks, including the risk of data breach, erroneous machine learning model predictions, and costly platform management. To counter these risks, we demonstrate how multi-agent-based evaluation can be central to testing and refining patient-centered decentralized health networks such that the incentives are robust, sustainable, and less susceptible to exploitation. We find that agent-based approaches can help speed up development and build confidence in scalability, safety, and effective functioning prior to real-world deployment. We also provide design recommendations and suggestions for future work.

ISBD 2025

Bipolar Disorder (BD) is associated with symptoms that can significantly affect financial stability, with individuals diagnosed with BD I reported to be substantially more likely to experience severe financial difficulties such as bankruptcy compared to healthy individuals. Recent advances in financial data accessibility, including the emergence of open banking APIs, provide opportunities to objectively study BD-related financial behaviors and identify spending patterns associated with mood episodes, such as impulsive spending bursts during manic phases. In addition, collaboration with care partners, including close friends and family members, may support financial decision-making during periods of mood instability. However, sharing financial data and financial control introduces important ethical, privacy, and autonomy-related challenges that require careful consideration in system design. Developing collaborative just-in-time financial interventions may help individuals maintain financial stability while also serving as an early warning mechanism for mood episode onset.

CHI 2024

Financial stability is a key challenge for individuals living with bipolar disorder (BD). Symptomatic periods in BD are associated with poor financial decision-making, contributing to a negative cycle of worsening symptoms and an increased risk of bankruptcy. There has been an increased focus on designing supportive financial technologies (fintech) to address varying and intermittent needs across different stages of BD. However, little is known about this population’s expectations and privacy preferences related to financial data sharing for longitudinal care management. To address this knowledge gap, we have deployed a factorial vignette survey using the Contextual Integrity framework. Our data from individuals with BD (N=480) shows that they are open to sharing financial data for long term care management. We have also identified significant differences in sharing preferences across age, gender, and diagnostic subtype. We discuss the implications of these findings in designing equitable fintech to support this marginalized community.

Frontiers in Psychiatry

Financial stability is a key challenge for individuals with mental illnesses. Symptomatic periods often manifest in poor financial decision-making including compulsive spending and risky behaviors. This article explores research opportunities and challenges in developing financial technologies (FinTech) to support individuals with mental health. Specifically, we focus on how objective financial data might lead to novel mental health assessment and intervention methods. We have used data from one individual with bipolar disorder (BD) (i.e., an N = 1 case study) to illustrate feasibility of collecting and analyzing objective financial data alongside mental health factors. While we have not found statistically significant trends nor our findings are generalizable beyond this case, our approach provides an insight into the potential of using objective financial data to identify early warning signs and thereby, enable preemptive care for individuals with serious mental illnesses. We have also identified challenges of accessing objective financial data. The paper outlines what data is currently available, what can be done with it, and what factors to consider when working with financial data. We have also explored future directions for developing interventions to support financial well-being and stability. Furthermore, we have described the technical, ethical, and equity challenges for financial data-driven assessments and intervention methods, as well as provided a broad research agenda to address these challenges.