Privacy Analysis of Period Tracking Mobile Apps in the Post-Roe v. Wade Era

Dong, Zikan; Wang, Liu; Xie, Hui; Xu, Guanghua; Wang, Haoyu

doi:10.1145/3551349.3561343

Cited by 17 publications

(3 citation statements)

References 2 publications

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“…Prior work has noted the ethical ramifications of data collection needed to create AI models potentially leading to feelings of inappropriate surveillance and ultimately mistrust, and disproportionate benefits distributed to White and higher socioeconomic status individuals. 27 Additionally, the possibility that personal health data may be used for surveilling women of childbearing age in the post-Roe v. Wade era has caused alarm among many communities, 28 highlighting the need to identify effective strategies to anticipate and mitigate ethical issues is urgently needed.…”

Section: Discussionmentioning

confidence: 99%

Women’s perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare

Reading Turchioe,

Harkins,

Desai

et al. 2023

JAMIA Open

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This study aimed to evaluate women’s attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was “very important” for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was “very important” versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

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Section: Discussionmentioning

confidence: 99%

Women’s perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare

Reading Turchioe,

Harkins,

Desai

et al. 2023

JAMIA Open

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“…Flo, a popular period- and fertility-tracking app, for example, created an anonymous mode to make it more difficult to connect user information with health data. 15 FemTech companies must also further scrutinise the security surrounding user data that is collected, stored, and shared with third parties. Users should be given options regarding how their data are stored and utilised.…”

Section: Empowering Users To Have Control Over Their Own Personal Datamentioning

confidence: 99%

“… 14 Some period-tracking apps even explicitly state that they may disclose users’ personal data at the request of law enforcement or government agencies. 15 Not only does this cause alarm for users who choose to have an abortion, but miscarriages, irregularities in menstrual cycles, and/or imperfect engagement with a period-tracking app have the potential to be mischaracterised as abortions.…”

mentioning

confidence: 99%

Missed period? The significance of period-tracking applications in a post-Roe America

Kelly,

Habib

2023

Sexual and Reproductive Health Matters

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Understanding wrist skin temperature changes to hormone variations across the menstrual cycle

Lin,

Li,

Christofferson

et al. 2024

npj Womens Health

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Consumer devices are increasingly used to monitor peripheral body temperature (PBT) for menstrual cycle tracking, but the link between PBT and hormone variations remains underexplored. This study examines the relationship between these variables with a focus on nightly wrist skin temperature (WST). Fifty participants provided physiological and self-reported data, including WST, daily step counts, glucose levels, hormone levels (E3G, LH), and diary entries. Results show a negative correlation between WST and hormone levels when E3G and LH are below average, and this trend was robust to demographics and self-reported stress. Increased variance between mid-cycle hormonal peaks and WST fluctuations may stem from differences between basal body temperature (BBT) and WST. This research suggests that algorithms reliant on body temperature for tracking hormonal changes or other aspects of the menstrual cycle may need to account for increased variance in WST trends if they are meant to be deployed on wearable devices.

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Privacy Analysis of Period Tracking Mobile Apps in the Post-Roe v. Wade Era

Cited by 17 publications

References 2 publications

Women’s perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare

Women’s perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare

Missed period? The significance of period-tracking applications in a post-Roe America

Understanding wrist skin temperature changes to hormone variations across the menstrual cycle

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