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Short abstract:

We argue that Femtech, which uses medical expertise to stigmatise weight and non-normative bodies in connection with problematised reproductive health, can compound medical discrimination, highlighting the insidiousness of platform expertise, which is based on normative and idealised existences.

Long abstract:

The prevalence of digital health technologies, supported by quantified-self discourses but targeted specifically at women, is rising. Yet, as authors such as Hendl and Jansky (2022) have found, health apps and the discourse surrounding them are based on exclusionary ontologies, normative femininity, and epistemic injustice, under the guise of promoting self-discovery and autonomy. Many such technologies position themselves as the trusted advisor whose expertise is new and superior to manipulate users to rely on their technology for health advice. We draw on fat studies (Gibson, 2021), disability and queer studies (Slater & Liddiard, 2018), and STS and Feminist Technoscience scholars (Lupton, 2016; Wajcman, 2010; Benjamin, 2019), to expand the criticism of femtech to explicitly include non-normative bodies which are intersectionally targeted by idealised framing and encoded bias.

Through a content analysis of the Femtech “Evie Ring”, we highlight the idealised normative user that is encoded into the technologies. As such, we show how technologies that use medical expertise to stigmatise weight and non-normative bodies in connection with problematised reproductive health can compound medical discrimination faced by women who do not present with normative bodies, encoding non-normative body bias into technology. In doing so, we challenge the offered self-expertise as a form of exploitation framed as medical advice and highlight the insidiousness of platform expertise, which is based on normative and idealised existences. This adds to STS literature by amalgamating theories of non-normative bodies into a holistic critique of Femtech and the mechanisms through which medical expertise repeatedly marginalises difference.