Cultural Fit, Privacy Concerns, and GCC Women’s Openness to AI-Enabled Menstrual Tracking: A Cross-Sectional Study - Zeina M. Alkhalaf; Abdullah Alhauli (International Journal of Environmental Research and Public Health, 2026)
Background/Objectives: Artificial intelligence (AI) is increasingly embedded in menstrual and reproductive health applications, yet little is known about how women in conservative, rapidly digitizing regions evaluate AI-enabled menstrual tracking tools. This study investigates how perceived cultural fit and data privacy concerns are associated with women’s openness to AI-enabled menstrual tracking in Gulf Cooperation Council (GCC) countries. Methods: We conducted an online cross-sectional survey using a convenience sample of adult women residing in GCC countries (n = 273), measuring openness to AI menstrual tools, perceptions of cultural fit (e.g., respect for religious and cultural norms, endorsement by trusted institutions, Arabic language, GCC-specific design), privacy concerns (e.g., worries about who can access menstrual health data), and key sociodemographic characteristics and smartphone use. Results: Higher perceived cultural-contextual fit was strongly and positively associated with openness to AI-enabled menstrual tracking, whereas composite privacy concerns showed no statistically significant association once cultural fit and sociodemographic factors were controlled. Item-level analyses indicated that beliefs about cultural sensitivity and GCC-specific design were the most robust predictors of openness, while concerns about data access, authority endorsement, and language accessibility played a limited role. Homemakers reported higher openness than employed women. Conclusions: Overall, the findings suggest that, in this sample and model, GCC women’s openness to AI-enabled menstrual tracking was more strongly associated with perceived cultural alignment and local design than with the measured privacy-concern construct, highlighting the importance of culturally grounded, trust-building design and communication strategies for AI-based women’s health applications.