The FemTech Opportunity: Why Women's Health Data Is the Biggest Gap in Digital Health

Women make up half the population and approximately 80% of healthcare spending decisions. Yet the data infrastructure underpinning digital health — the algorithms, the scoring models, the reference populations — was largely built without them.

Most health algorithms were trained on male-dominated datasets. Women were effectively excluded from drug trials until 1993. Roughly 85% of preclinical neuroscience studies used only male subjects [1]. The cascading effect: diagnostic thresholds, risk models, and health scoring systems that systematically underestimate severity in women. When sex-specific troponin thresholds were introduced for heart attack diagnosis, female detection jumped 42% — compared to just 6% for men [1]. The previous threshold wasn’t too low; it was calibrated for the wrong population.

This isn’t just a clinical problem. It’s a product problem. Every health app that computes a readiness score, a sleep quality metric, or a recovery assessment without accounting for menstrual cycle phase, hormonal variation, pregnancy, or perimenopause is delivering a less accurate experience to half its users.

The femtech market — valued at $9.12 billion in 2025 and projected to surpass $100 billion by 2030 [2] — exists because this gap is both massive and increasingly solvable.

The capital thesis behind femtech

Investment in women’s health technology has reached a scale that signals structural conviction, not trend-chasing.

Flo Health raised over $200 million from General Atlantic in 2024, becoming the first purely digital consumer women’s health app to achieve unicorn status at over $1 billion valuation [3]. The period tracking app has built one of the largest female health datasets in the world.

Midi Health closed a $100 million Series D in February 2026 at a $1 billion-plus valuation, led by Goodwater Capital with participation from Serena Ventures [4]. Midi focuses specifically on menopause and midlife women’s health — a segment historically ignored by both healthcare and technology.

Oura launched its first proprietary AI model specifically for women’s health in February 2026, covering menstrual cycles, fertility, pregnancy, and menopause [5]. For a company valued at $11 billion, building dedicated women’s health AI signals that this is a core product priority, not a feature add-on.

Future Family secured $400 million in financing for fertility services. Conceivable Life Sciences raised $50 million for reproductive technology [2]. Across the landscape, there are now 9 unicorns in women’s health globally, with over 150 VC funds actively investing in the space [2].

The thesis is consistent: women’s health is the largest underserved segment in consumer health, the data and technology to serve it properly are now available, and the products built on this foundation will capture outsized value.

What wearable data reveals about female physiology

The most significant development enabling femtech isn’t a new app or a new device — it’s the growing body of research showing that standard wearable biomarkers fluctuate predictably across the menstrual cycle.

A study published in npj Digital Medicine analyzing 11,590 participants across 45,811 menstrual cycles found consistent, measurable patterns in cardiovascular metrics [6]:

• Resting heart rate peaks around cycle day 26 and reaches its minimum around cycle day 5
• Heart rate variability (RMSSD) follows the inverse pattern — lowest around day 27, highest around day 5
• These fluctuations were consistent enough to define a “cardiovascular amplitude” metric that reflects hormonal status

Separate research found that skin temperature shifts approximately 0.5°C post-ovulation, while HRV drops roughly 12% from follicular to luteal phase and resting heart rate rises approximately 8 bpm across the same transition [5].

Machine learning research published in 2025 demonstrated that wearable physiological data — skin temperature, electrodermal activity, interbeat interval, and heart rate — can identify menstrual cycle phase with 87% accuracy for three-phase classification [7].

Why this matters for health products

These aren’t subtle variations. A resting heart rate that rises 8 bpm and HRV that drops 12% across the cycle means that a health score or readiness assessment computed without cycle awareness will systematically misread a woman’s health state for roughly half of every month.

During the luteal phase, a woman’s elevated resting heart rate and depressed HRV are normal hormonal responses — not signs of poor recovery, stress, or overtraining. But a cycle-unaware algorithm interprets them exactly that way, delivering inaccurate readiness scores, misleading recovery recommendations, and fitness advice calibrated for a physiology that isn’t hers.

The research makes the product implication clear: any health scoring system that doesn’t account for menstrual cycle phase is less accurate for women by design.

The product landscape

Cycle-aware fitness

A new category of apps adapts training recommendations to menstrual cycle phase, aligning workout intensity with hormonal rhythms.

DROP IT automatically adjusts workouts — intensity, weights, and progression — based on cycle phase [8]. During the follicular phase and ovulation, when estrogen supports strength and energy, it programs higher-intensity training. During the luteal phase and menstruation, it shifts toward recovery, lighter loads, and flexibility work.

Lively, LunaFlow, and FemVerse offer similar cycle-synced approaches, combining cycle tracking with personalized recommendations for nutrition, exercise, productivity, and recovery [8]. FemVerse specifically uses AI to unify cycle, fitness, and nutrition data into dynamic plans.

Evea integrates Apple Watch data with daily check-ins to deliver phase-aware insights on fitness, nutrition, and mood — using self-hosted AI models for privacy [8].

The common pattern: these apps treat the menstrual cycle not as a separate health concern but as foundational context that affects every other health metric. Training, nutrition, sleep, recovery, and mood all shift across the cycle — and the products that account for this deliver more accurate and more useful experiences.

Perimenopause and menopause technology

Perimenopause — the multi-year hormonal transition that typically begins in a woman’s 40s — is one of the most underserved health domains in consumer technology. Symptoms include hot flashes, sleep disruption, mood changes, cognitive fog, and irregular cycles. An estimated 1.1 billion women worldwide will be post-menopausal by 2025.

Peri, a CES 2025 innovation award winner, is a wearable patch worn on the torso that uses accelerometry, electrodermal activity, heart rate, and temperature sensors to track perimenopause symptoms in real time [9]. The device’s torso placement was chosen because limb-based sensors — the location of most consumer wearables — couldn’t reliably detect perimenopause-specific symptoms like hot flashes and night sweats.

Midi Health, now valued at over $1 billion, provides a digital menopause clinic model — connecting women with specialized clinicians who can prescribe hormone therapy and other treatments informed by symptom data [4].

Oura’s new women’s health AI model extends its existing cycle tracking into pregnancy insights (tracking gestational physiology changes) and menopause monitoring (detecting and contextualizing the hormonal shifts that affect sleep, recovery, and cardiovascular metrics during perimenopause) [5].

Fertility and reproductive health

Fertility technology represents the most established femtech vertical. Natural Cycles — an FDA-cleared birth control app — uses basal body temperature from wearables to identify fertile and infertile days. Flo Health combines period tracking with health insights for its hundreds of millions of users.

What’s changing is the integration depth. Instead of standalone fertility apps, reproductive health data is being woven into broader health platforms. Oura integrates with Natural Cycles, Clue, and Flo. Wearable data that passively detects cycle phase through temperature and heart rate dynamics can feed fertility insights without requiring manual basal body temperature measurement — a significant friction reduction.

The privacy equation

Women’s reproductive health data carries uniquely elevated privacy risks. In the post-Dobbs legal landscape in the United States, period tracking data and fertility information have potential legal implications that general fitness data does not.

The privacy picture is sobering: 78% of femtech apps failed GDPR consent audits [5]. Most consumer health apps fall outside HIPAA protection entirely. The combination of sensitive reproductive data, inadequate regulatory coverage, and uncertain legal exposure creates a trust barrier that product teams must take seriously.

For product teams handling reproductive health data, this means:

• Minimization — collect only the data necessary for the feature, not everything available
• On-device processing — compute cycle insights locally when possible, reducing server-side exposure
• Transparent controls — users must be able to see, export, and delete their reproductive health data
• Legal awareness — understand jurisdiction-specific implications of storing reproductive data, especially in the U.S.

Products that solve the privacy equation will earn trust in a market where trust is the primary differentiator. Products that don’t will face both user backlash and regulatory risk.

What product teams need to build for women’s health

Cycle-aware health scoring

The minimum viable improvement: adjust health scores, readiness assessments, and recovery metrics based on menstrual cycle phase. When the algorithm knows that elevated resting heart rate and depressed HRV during the luteal phase are normal, it stops misclassifying healthy women as under-recovered.

This requires two data inputs: (1) cycle phase identification (from self-reported data, temperature tracking, or wearable-derived biomarker patterns) and (2) phase-specific baseline adjustments in the scoring model.

Multi-domain data fusion

Women’s health features that combine data across domains — activity, sleep, cycle phase, nutrition, stress markers — deliver substantially more value than single-domain tracking. A cycle-aware fitness recommendation that also accounts for last night’s sleep quality and this week’s training load is more useful than any of those signals in isolation.

Lifecycle coverage

Women’s health needs change dramatically across life stages: menstruation onset in adolescence, reproductive years, pregnancy, postpartum recovery, perimenopause, and post-menopause. Products that handle only one stage (typically fertility) miss the lifetime value of a health relationship. The infrastructure needs to support the full spectrum.

Population-specific baselines

Comparisons and trend analysis need female-specific reference populations. Telling a woman in her luteal phase that her HRV is “below average” based on a population that includes men is not just inaccurate — it’s the exact kind of gender data gap that femtech exists to close.

Where this is heading

Cycle awareness as a standard feature, not a specialty product. Within the next few years, any health app that computes a readiness score without accounting for menstrual cycle phase will be seen the same way we’d view a fitness app that doesn’t track steps. The data and research exist; the question is integration speed.

Menopause as a major product category. With Midi Health at unicorn valuation and purpose-built menopause wearables launching, the perimenopause/menopause segment is moving from underserved to competitive. The 1.1 billion women who will be post-menopausal globally represent an enormous addressable market for health technology that understands their physiology.

Reproductive health data as critical infrastructure. As direct wearable integrations expand and new data domains (including reproductive health) come online across health data platforms, the ability to passively detect and incorporate cycle phase from wearable biomarkers — without manual tracking — will become a foundational capability.

The gender data gap narrows from the bottom up. Clinical medicine’s gender data gap accumulated over decades of exclusion. Consumer wearable data — collected from millions of women daily — is building the female-specific datasets that clinical research never prioritized. The products and platforms that contribute to and leverage this data will define the next generation of women’s health technology.

Women’s health isn’t a niche. It’s half the market. The products that treat it as foundational rather than supplementary will capture the value that the $100 billion femtech projection implies.