an aggregation of writings.


Matthews, M., Collier, R. & Spendlove, E. (In Review). CommonHealth: multi-agent evaluation of blockchain-based patient-centred health data networks. In European Conference on Multi-Agent Systems.

Brozena, J., Blair, J., Richardson, T., Matthews, M., Mukherjee, D., Saunders, E. F., & Abdullah, S. (2024, May). Supportive Fintech for Individuals with Bipolar Disorder: Financial Data Sharing Preferences for Longitudinal Care Management. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-15).


Brozena, J., Abdullah, S., Blair, J., Richardson, T., Matthews, M., Saunders, E. F., & Mukherjee, D. (2023). Identifying and addressing privacy concerns regarding the use of financial data from individuals with BD.

Brozena, J., Blair, J., Richardson, T., Matthews, M., Mukherjee, D., Saunders, E. F., & Abdullah, S. (2023). Fintech for Psychological and Financial Resilience: Determinants of Financial Data Sharing Behavior for Individuals with Bipolar Disorder. arXiv preprint arXiv:2306.15725.


Blair, J., Brozena, J., Matthews, M., Richardson, T., & Abdullah, S. (2022). Financial technologies (FinTech) for mental health: The potential of objective financial data to better understand the relationships between financial behavior and mental health. Frontiers in Psychiatry, 13, 810057.

Frank, E., Wallace, M. L., Matthews, M. J., Kendrick, J., Leach, J., Moore, T., … & Kupfer, D. J. (2022). Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment. Frontiers in Digital Health, 4, 870522.


Gaggioni, G., Brown, J., Chan, S., Cullen, B., Farquhar, M., Gibbons, D., … & Smith, D. (2021). 46 The sleep, circadian rhythms and mental health in schools (SCRAMS) feasibility study.


Murnane, E. L., Snyder, J., Voida, S., Bietz, M. J., Matthews, M., Munson, S., & Pina, L. R. (2018, October). Social issues in personal informatics: Design, data, and infrastructure. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 471-478).


Matthews, M., Murnane, E., & Snyder, J. (2017). Quantifying the Changeable Self: The role of self-tracking in coming to terms with and managing bipolar disorder. Human–Computer Interaction, 32(5-6), 413-446.

Frank, Ellen, et al. “Sensing depression: Using smartphone sensors to predict changes in depression severity.” Neuropsychopharmacology. Vol. 43. MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND: NATURE PUBLISHING GROUP, 2017.

Matthews, Mark, et al. “The double-edged sword: A mixed methods study of the interplay between bipolar disorder and technology use.” Computers in Human Behavior 75 (2017): 288-300.

Aung, Min Hane, Mark Matthews, and Tanzeem Choudhury. “Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies.” Depression and Anxiety 34.7 (2017): 603-609.

Choe, E. K., Abdullah, S., Rabbi, M., Thomaz, E., Epstein, D. A., Cordeiro, F., … & Kientz, J. A. (2017). Semi-automated tracking: a balanced approach for self-monitoring applications. IEEE Pervasive Computing, 16(1), 74-84.

Abdullah, S., Murnane, E. L., Matthews, M., & Choudhury, T. (2017). Circadian computing: sensing, modeling, and maintaining biological rhythms. Mobile Health: Sensors, Analytic Methods, and Applications, 35-58.