Log in to star items.
Accepted Paper
Paper short abstract
Menstrual cycle tracking apps show how the promises of data-driven healthcare unfold in the meantime. Ethnography reveals how researchers, designers and users navigate messy, incomplete datasets, shaping possible outcomes through everyday data practices such as cleaning, storing, and classifying.
Paper long abstract
Menstrual cycle tracking apps (MCTAs) have become everyday technologies in the lives of millions, placing private digital technoservices at the center of contemporary women’s health innovation. Promoted by developers as tools for democratizing gynecological knowledge through self-tracking and data science, MCTAs are embedded in promissory narratives of data-driven healthcare.
Our research began with a seemingly straightforward question: how do MCTAs contribute to the production of knowledge? Through an ethnography of menstrual data infrastructures, focusing on invisible forms of data work—such as cleaning, maintenance, and interpretation—this inquiry evolved into an STS-informed analysis of the co-production of apps and scientific knowledge as an ongoing process rather than a stabilized outcome. This shift highlights the heterogeneous assemblage of actors—human and non-human—through which knowledge is produced, including academic researchers, designers, algorithms, datasets, and users whose everyday practices sustain data systems that remain perpetually incomplete.
This presentation examines how MCTAs function as sociotechnical infrastructures mediating the production of knowledge about women’s health in real time. We draw on interviews with academic researchers and in-house scientists whose work relies on proprietary MCTA datasets (n = 31), as well as interviews with MCTA designers (n = 23). Rather than focusing on the outcomes of their work, we emphasize the gap between the promised futures of personalized care through data science and the unfinished realities of everyday practices, where scientists navigate data that is “useful,” “messy,” “non-existent,” “incomplete,” or “broken.”
Caring for the possible: In the meantime of healthcare’s data-driven futures
Session 2