**Background:** Women's menstrual cycles are important indicators of overall health, and mobile health (mHealth) apps offer accessible tools for continuous tracking. Despite the proliferation of menstrual apps, no prior study had evaluated their quality from both consumer and health care provider perspectives. This study aimed to investigate the key content and quality of menstrual apps, analyze correlations between provider and consumer evaluations, and provide recommendations for future app development.
**Methods:** The researchers searched the Google Play Store and iOS App Store from April 8 to April 15, 2021, using the keywords 'period' and 'menstrual cycle' in English and Korean. Inclusion criteria were: nonduplicate app, >10,000 reviews, last updated ≤180 days ago, relevant to the topic, written in Korean or English, free of charge, and currently operational. From 1127 initial apps, 34 met all criteria (28 Android, 6 iPhone). App content was analyzed for menstrual cycle management, education, sharing information, and notifications. Quality was evaluated by 6 consumers using the user version of the Mobile Application Rating Scale (uMARS) and by 4 health care providers (nurses) using the Mobile Application Rating Scale (MARS). Each evaluator used the app for >10 minutes daily in a blind test, with each app cross-evaluated by at least two evaluators. Pearson correlation was used to analyze relationships between MARS scores, uMARS scores, star ratings, number of reviews, and app content.
**Key Results:** Most apps (31/34, 91%) offered menstrual cycle prediction, 14 (41%) provided menstruation and fertility notifications, and only 2 (6%) provided health screening information. The average MARS score (health care providers) was 3.06 (SD 0.62), and the average uMARS score (consumers) was 3.33 (SD 0.57). The iPhone 'Bom Calendar' app had the highest MARS score (4.51, SD 0.22) and highest uMARS score (4.23, SD 0.27). The Android 'Period calendar—Women's menstrual calendar❤' had the second lowest MARS (2.05, SD 0.45) and uMARS (2.09, SD 0.05) despite a high star rating of 4.8. Notably, the Android 'Menstrual calendar—ovulation & pregnancy calendar' showed contrasting scores: MARS 2.22 (SD 0.07) — third lowest — but uMARS 4.15 (SD 0.46) — third highest. Correlation analysis revealed no significant relationship between MARS and uMARS scores (r=0.32, p=0.06). uMARS scores and star ratings (r=0.11, p=0.54) and uMARS scores and number of reviews (r=0.07, p=0.67) also showed no significant correlations. The number of reviews and star rating had a very low correlation (r=0.39, p=0.02). Among app contents, the highest correlation with MARS was ovulation date management (r=0.49, p=0.003), and with uMARS was notification (r=0.39, p=0.02). Star ratings correlated most with menstrual cycle management (r=0.53, p=0.001) and visualization (r=0.51, p=0.002). All top-five scoring apps in both evaluations offered personalized alarms and symptom logging functions, while bottom-five apps lacked menstrual cycle management or prediction functions.
**Clinical Implications:** The study reveals a significant disconnect between consumer and health care provider evaluations of menstrual apps, with no correlation between MARS and uMARS scores. Consumers prioritized aesthetics and information provision, while health care providers valued engagement, functionality, and aesthetics. The lack of correlation between uMARS scores and star ratings suggests that popular star ratings may not reflect true app quality. Clinically, the finding that only 6% of apps provide health screening information is concerning, as menstrual apps could serve as platforms for early detection of health issues. The study recommends that app developers prioritize personalized monitoring, symptom logging, notifications, and evidence-based health information. Additionally, data protection features (present in 85% of apps) should be standard. The authors call for continuous quality monitoring from both perspectives and the development of a menstrual-app-specific evaluation scale. Limitations include a small number of evaluators, focus on popular apps, and potential bias from App Store search algorithms.