Exploring Privacy Practices of Female mHealth Apps in a Post-Roe World

Femtech, a growing sector in mobile healthcare technology, caters to women's needs across various life stages with digital solutions like period tracking and pregnancy management apps. Maintaining robust data privacy is crucial due to the sensitive nature of the information involved, such as menstrual cycles and pregnancy status. Our research analyzes user feedback from platforms like the Apple App Store and Google Play Store to understand perceptions of femtech apps, covering accessibility, interface, features, and privacy concerns. Understanding user perspectives helps developers enhance usability and trust, driving further adoption. Prioritizing privacy also fosters industry advancement. This paper stresses the importance of dialogue among developers, users, and policymakers in femtech. Our findings aim to facilitate positive change within the femtech sector, leading to more inclusive, user-centric, and ethically driven advancements, benefiting both the industry and its users.

show abstract

Shadow Health-Related Data: Definition, Categorization, and User Perspectives

El Zein,

Salehzadeh Niksirat,

Zufferey

et al. 2024

Proceedings of the 2024 European Symposium on Usable Security

View full text Add to dashboard Cite

Health-related data (HRD) about individuals are increasingly generated and processed. The sources and volume of such data have grown larger over the past years, they include wearable devices, health-related mobile apps, and electronic health records. HRD are sensitive, have important privacy implications, hence hold a special status under existing privacy laws and regulations. In this work, we focus on shadow HRD: these HRD are generated and/or processed by individuals by using general-purpose digital tools outside of a professional healthcare information system. Some examples are health-related queries made by individuals on general-purpose search engines and LLM-based chatbots, or medical appointments and contact information of health professionals synced to the cloud. Such data, and the privacy risks stemming from them, are often overlooked when studying digital health. Using information from two focus group sessions (23 participants in total), we identified and categorized a broad variety of user behaviors that, including the aforementioned examples, lead to the creation of shadow HRD. Then, informed by this categorization, we designed a questionnaire and deployed it through an online survey (300 respondents) to assess the prevalence of such behaviors among the general public, as well as user awareness of (and concerns about) the privacy risks stemming from their shadow HRD. Our findings show that most respondents adopt numerous and diverse behaviors that create shadow HRD, and that very few resort to mechanisms to protect their privacy. CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • Applied computing → Health informatics; • Human-centered computing → Empirical studies in HCI.

show abstract

Exploring Privacy Practices of Female mHealth Apps in a Post-Roe World

Cited by 3 publications

References 64 publications

A Qualitative Analysis Framework for mHealth Privacy Practices

A Qualitative Analysis Framework for mHealth Privacy Practices

Unveiling User Perspectives: Exploring Themes in Femtech Mobile App Reviews for Enhanced Usability and Privacy

Shadow Health-Related Data: Definition, Categorization, and User Perspectives

Contact Info

Product

Resources

About