2021/01/25 by Kendra Albert, Maggie Delano · 9 citations
Computer Science · Health Professions · Mathematics · Medicine · Psychology · #Bioelectrical impedance analysis #Body Composition Measurement Techniques #Composition (language) #Computer science #Data science #Gender studies #Human–computer interaction #Linguistics #Mathematics #Medicine #Narrative #Obesity and Health Practices #Product (mathematics) #Psychology #Scale (ratio) #Sex and Gender in Healthcare #Sociology #Transgender #cs.CY
paper · pdf · doi:10.1145/3442188.3445898
Accepted at FAccT 2021. Both authors contributed equally to the work. v2: added citations, v3: fixed typo, minor formatting issues
arxiv created 2021/01/25 · arxiv updated 2021/01/27 · openalex publication_date 2021/03/01 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/29
Smart weight scales offer bioimpedance-based body composition analysis as a supplement to pure body weight measurement. Companies such as Withings and Fitbit tout composition analysis as providing self-knowledge and the ability to make more informed decisions. However, these aspirational statements elide the reality that these numbers are a product of proprietary regression equations that require a binary sex/gender as their input. Our paper combines transgender studies-influenced personal narrative with an analysis of the scientific basis of bioimpedance technology used as part of the Withings smart scale. Attempting to include nonbinary people reveals that bioelectrical impedance analysis has always rested on physiologically shaky ground. White nonbinary people are merely the tip of the iceberg of those who may find that their smart scale is not so intelligent when it comes to their bodies. Using body composition analysis as an example, we explore how the problem of trans and nonbinary inclusion in personal health tech goes beyond the issues of adding a third "gender" box or slapping a rainbow flag on the packaging. We also provide recommendations as to how to approach creating more inclusive technologies even while still relying on exclusionary data.