Privacy pitfalls: challenges in FemTech app adoption

Shreya Mukherjee et al.

Information and Computer Security2026https://doi.org/10.1108/ics-12-2024-0319article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose FemTech, a growing field focusing on women’s health through technology, has gained popularity with applications that track menstrual cycles, fertility and overall wellness. Despite their benefits, FemTech apps face widespread concerns regarding data privacy, potentially hindering their adoption and long-term use. This study aims to explore privcy-related barriers to FemTech adoption by analyzing privacy policies and user reviews, to identify key concerns. Design/methodology/approach An in-depth analysis of the privacy policies of popular FemTech apps and an examination of user-generated reviews filtered for privacy-related keywords were conducted to identify fallacies and user pain points. Findings Results reveal issues related to data-sharing practices, location data collection, data retention, the lack of opt-in policies and lack of clarity or total absence of privacy policies, all contributing to privacy concerns that deter users. Originality/value This study provides insights that can guide app developers, policymakers and regulators in creating privacy-compliant solutions to enhance the trust, adoption and sustained use of FemTech apps, ultimately benefiting women’s health.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1108/ics-12-2024-0319

Or copy a formatted citation

@article{shreya2026,
  title        = {{Privacy pitfalls: challenges in FemTech app adoption}},
  author       = {Shreya Mukherjee et al.},
  journal      = {Information and Computer Security},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ics-12-2024-0319},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Privacy pitfalls: challenges in FemTech app adoption

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.