How Do Fertility-Tracking Technologies Define Ovulation and Anovulation? A Structured Landscape Analysis.

OBJECTIVES: To characterize how commercially available fertility-tracking devices and wearables define ovulation and anovulation, evaluate available comparator evidence, and assess user burden across 19 selected technologies. STUDY DESIGN: We conducted a structured landscape analysis of 19 fertility-tracking devices and wearables available in the United States. Technologies were evaluated for biomarker type, operational definitions of ovulation and anovulation, comparator evidence, intended use, regulatory status, and user burden, among other variables. RESULTS: Technologies clustered into four major categories: luteinizing hormone (LH)-only devices, multi-hormone devices, basal body temperature (BBT)-based wearables, and BBT-based thermometers. LH-only devices generally defined ovulation by detecting an LH surge or peak; multi-hormone devices incorporated LH and/or pregnanediol-3-glucuronide (PdG) measurements; and BBT-based technologies relied on thermal shifts. Explicit definitions of anovulation were uncommon and were frequently inferred from the absence of ovulation-associated biomarkers. The existence and design of comparator evidence varied substantially across technologies. Most comparator studies relied on surrogate measures such as urinary or serum hormones, whereas few technologies had comparator evidence against physiological reference standards such as transvaginal ultrasound (TVUS). Comparator studies in irregular-cycle populations were uncommon despite frequent marketing of these devices as appropriate for users with irregular cycles. User burden varied substantially across technologies. CONCLUSIONS: Fertility-tracking technologies demonstrate substantial heterogeneity in operational definitions, comparator evidence, and user burden. Greater transparency regarding ovulation and anovulation definitions, clearer reporting of comparator evidence, and more representative evaluation in irregular-cycle populations are needed to support accurate interpretation and integration into reproductive healthcare.