Background and Objectives: Digital health and personalized medicine are advancing at an unprecedented pace. Users can document their menstrual cycle data in a variety of ways, including smartphone applications (apps), temperature tracking devices, and at-home urine hormone tests. Understanding the needs and goals of women using menstrual cycle tracking technologies is the first step to making these technologies more evidence based. The purpose of this study was to examine the current use of these technologies and explore how they are being used within the context of common hormonal and reproductive disorders, like polycystic ovary syndrome (PCOS), endometriosis, and infertility. Materials and Methods: This was a cross-sectional study evaluating menstrual cycle tracking technology use. Participants were recruited in January–March 2023 using social media groups and a Marquette Method instructor email listserv. Data were collected using an electronic survey with Qualtrics. Data collected included participant demographics, menstrual cycle characteristics, reproductive health history, and menstrual cycle tracking behavior. Results: Three-hundred and sixty-eight participants were included in the analysis. Women had various motivations for tracking their menstrual cycles. Most participants (72.8%) selected “to avoid getting pregnant” as the primary motivation. Three hundred and fifty-six participants (96.7%) reported using a fertility awareness-based method to track and interpret their menstrual cycle data. The Marquette Method, which utilizes urine hormone tracking, was the most frequently used method (n = 274, 68.2%). The most frequently used cycle technology was a urine hormone test or monitor (n = 299, 81.3%), followed by a smartphone app (n = 253, 68.8%), and a temperature tracking device (n = 116, 31.5%). Women with PCOS (63.6%), endometriosis (61.8%), and infertility (75%) in our study reported that the use of tracking technologies aided in the diagnosis. Most participants (87.2%) reported a high degree of satisfaction with their use and that they contributed to their reproductive health knowledge (73.9%). Conclusions: Women in our study reported avoiding pregnancy as their primary motivation for using menstrual cycle tracking technologies, with the most frequently used being a urine hormone test or monitor. Our study results emphasize the need to validate these technologies to support their use for family planning. Given that most women in this study reported using a fertility awareness-based method, the results cannot be generalized to all users of menstrual cycle tracking technologies.

Background: Recent developments in menstrual cycle tracking technologies that record physiological biomarkers of ovulation allow women to conveniently track their menstrual health data. The rapid pace of development of these technologies has made it challenging for health care professionals to evaluate their clinical validity. Methods: The purpose of this scoping review was to synthesize the current evidence regarding menstrual cycle tracking technologies that record physiological biomarkers of ovulation. The PRISMA extension for scoping reviews (PRISMA-ScR) was used to guide this review. Embase, PubMed, and Scopus databases were searched for publications regarding menstrual cycle tracking technologies that record physiological biomarkers of ovulation. The authors performed a thorough search of the literature using several keywords and database subject headings. Inclusion criteria were primary research articles available in English, assessed humans, and assessed menstrual cycle tracking technologies available to US consumers. Results: Title and abstract screening of all potentially relevant populations (n=2845) resulted in a full-text review of 139 publications. Twenty-three publications met the inclusion criteria, and 16 menstrual cycle tracking technologies that record physiological biomarkers of ovulation were identified, including temperature devices, urine hormone devices, and others. Discussion: The existing evidence for menstrual cycle tracking technologies relies on observational study designs and lacks diverse study populations, limiting the broader clinical applicability of these technologies. With further research, these technologies hold significant potential to improve menstrual health monitoring.

The recent availability of at-home menstrual cycle tracking technology has created opportunities for personalized assessment of reproductive health, alongside improved characterization of hormone patterns in women with and without reproductive disorders such as polyendocrine metabolic ovarian syndrome (PMOS), which affects approximately 10% of reproductive-age women. In this study, we leverage self-tracked urinary hormone data to develop an autoregressive Hidden Markov model (arHMM) that maps cycle days to physiologically meaningful phases based on hormone trajectories. By modeling day-to-day hormonal dynamics rather than absolute hormone levels, and allowing variable phase durations, this approach accommodates substantial variability in menstrual cycles, thereby enabling meaningful comparisons within and between individuals. Across more than 3800 cycles from over 1100 individuals, we find that arHMM-derived phases reproduce expected hormonal patterns within follicular, periovulatory, and luteal phases, and that phase-based timing for hormone testing outperforms conventional cycle day–based testing in capturing the luteinizing hormone surge and post-ovulatory progesterone rise, highlighting limitations of fixed-day clinical protocols. We identify phase-specific differences between healthy controls and individuals with self-reported PMOS, including lower luteinizing hormone in the periovulatory phase, and reduced luteal-phase progesterone levels in PMOS. Furthermore, features derived from arHMM phase assignments enable classification of PMOS status with ∼78% accuracy, demonstrating the potential of this approach for non-invasive PMOS screening. Author Summary: Tracking menstrual cycle information has become increasingly common, thanks in part to the availability of at-home urinary hormone data tracking technologies. In this study, we leveraged data from more than 3,800 menstrual cycles contributed by over 1,100 individuals in order to develop a model that predicts physiologically meaningful cycle phases based on day-to-day hormone patterns, and compared these patterns between individuals with and without polyendocrine metabolic ovarian syndrome (PMOS), a common endocrine disorder affecting approximately 1 in 10 reproductive age women. Our model successfully captures expected hormonal dynamics across six menstrual cycle phases and can be used to identify distinct phase-specific hormone patterns among individuals with PMOS. This study demonstrates the power of physiologically informed probabilistic modeling for leveraging data from at-home hormone monitoring towards a better characterization of menstrual cycle physiology and the development of noninvasive approaches for identifying reproductive health conditions such as PMOS.