Hormones govern fertility, metabolism, sleep, mood, cardiovascular risk, bone health, and cognition. They fluctuate across the day, across the menstrual cycle, across pregnancy, and across the menopausal transition. And yet, for the vast majority of women, the only way to know what their hormones are doing is a blood draw at a clinic - a single snapshot of a constantly moving system.
That is changing fast. Over the past 2 years, we've covered a steady stream of companies building new approaches to hormone testing - from at-home urine kits to instant saliva devices to wearable biosensors. And in just the past week, Clair Health raised $11.6 million and Impli secured a £1.4 million NIHR grant - both advancing continuous hormone monitoring. What's emerging is not just a product category. It's a generational shift in how we understand female biology.
As Ida Tin, the Clue founder now leading SPRIND's €40 million Hormone Challenge, put it in our recent interview: Hormones are "medicine's biggest missing dataset."
Generation 1: Bringing Testing Home
The first shift was moving hormone testing out of the clinic and into the home. Companies like Mira, Oova, Proov, Hormona, and Hertility built this wave - at-home urine and blood test kits that measure key reproductive hormones like LH, progesterone, estrogen, and FSH.
This generation solved the access problem. Women no longer needed a doctor's order and a lab appointment to check their hormone levels. But the fundamental limitation remained: Each test is still a point-in-time snapshot. A single progesterone reading tells you what's happening right now. It doesn't tell you the trajectory, the fluctuations across the day, or how today's reading compares to a continuous pattern.
Generation 2: Instant Results, More Frequent Testing
Eli Health represents the next step. Its Hormometer uses saliva samples and a smartphone to deliver hormone results in minutes - no lab processing, no waiting days for results. The company launched with cortisol monitoring, added a progesterone test earlier this year, and has testosterone and estradiol on the roadmap. Inne, meanwhile, offers a saliva-based ovulation test that measures progesterone metabolites daily.
The shift here is frequency. When testing takes minutes instead of days, women can test more often and build a richer picture of their hormonal patterns. It's still not continuous - each measurement requires a deliberate action - but it closes the gap significantly. And for hormones like cortisol, which fluctuates dramatically across the day, the ability to potentially test multiple times in a single day is genuinely new.
Generation 3: Continuous Monitoring
The frontier is continuous hormone monitoring - a wearable device that tracks hormonal changes in real time, the way a CGM tracks glucose. This is where the most capital is now flowing right now, and where 2 fundamentally different technical philosophies are emerging.
Direct measurement is the approach most analogous to CGM. Level Zero Health ($6.9 million pre-seed) is building a wearable patch that uses DNA-based biosensors to measure hormones directly from interstitial fluid - the same fluid CGMs use for glucose. The company reports 98% accuracy across the clinical range in early validation. Persperity Health, a Caltech spin-out, uses a different medium entirely: Sweat. Its technology induces a small amount of sweat on demand via iontophoresis and measures estradiol, progesterone, and LH in real time. And Impli, backed by Bayer and now by an NIHR grant, is developing an implantable biosensor designed specifically for IVF - detecting hormone changes in real time to optimize treatment timing.
AI-based inference takes a different approach entirely. Clair Health ($11.6 million, led by Khosla Ventures with a16z) doesn't measure hormones directly. Instead, it uses a wrist-worn device that captures over 130 physiological signals - cardiovascular, thermoregulatory, autonomic, electrodermal, sleep - and uses AI models trained against clinical-grade hormone data to infer the underlying hormonal state. The company reports 94.1% accuracy for cycle phase classification and 87% sensitivity for LH surge detection in early validation across 127 cycles. It's a fundamentally different bet: That you don't need to measure the hormone itself if you can reliably read its effects on the body.
Both approaches face hard scientific challenges. Direct measurement must contend with the fact that sex steroid hormones are present at extraordinarily low concentrations. AI inference must prove that its models generalize across diverse physiologies, conditions like PMOS, anovulatory cycles, and the hormonal chaos of perimenopause. Neither has been proven at scale in clinical settings yet. But the pace of development is accelerating.
The Missing Layer: A Shared Dataset
Underlying all of this is a problem that no individual company can solve alone. There is no comprehensive reference dataset for female hormone levels across ages, ethnicities, life stages, and health conditions. Every company building in this space is working from incomplete maps.
This is what a new program the SPRIND's €40 million Hormone Challenge is designed to address. The program, which just opened applications with a July 13 deadline, will fund up to 8 teams developing continuous hormone biosensors over 3 years. But alongside the sensor work, participating teams will contribute to building a shared reference data pool - a validated dataset intended to become an open resource, eventually housed in a European biobank.
Why This Matters Beyond Fertility
Much of the early commercial focus in hormone monitoring has been on fertility - predicting ovulation, timing IVF, confirming conception. That's the most immediate market. But the potential applications extend across the full scope of women's health. Continuous hormone data could transform how perimenopause is identified and managed - replacing the current experience of years of unexplained symptoms before a diagnosis. It could enable truly personalized menopause care, adjusting HRT dosing based on real-time hormonal data rather than standard protocols. It could improve understanding of how hormones interact with metabolic health, sleep, cardiovascular risk, mood, and more. And it could begin to close the research gap that has left female physiology systematically under-studied.
We're still early. Most of the continuous monitoring companies are pre-commercial. The science is hard, the regulatory pathway is complex, and the shared dataset doesn't exist yet. But the trajectory is clear. Hormone testing is moving from occasional clinic visits, to at-home snapshots, to instant readings, to continuous real-time data. Each generation brings us closer to understanding what's actually happening inside women's bodies - not once a quarter at a blood draw, but continuously, across the lifecycle.
That's not an incremental improvement. It's a foundational shift.
