Wearable devices have surged over the past decade, from fitness trackers to smartwatches to smart rings. Yet their development has often been shaped around the most easily measured parts of health: heart rate, steps, activity, sleep, and recovery. These metrics are useful, but they also reveal the category’s early assumptions. Much of wearable health has been built around performance, optimization, and short-term feedback.
For many women, that is not enough.
Women’s health is shaped by long-term physiological context: menstrual cycles, hormonal fluctuations, contraception, pregnancy, postpartum recovery, perimenopause, and menopause. These are not occasional edge cases. They are central to how sleep, energy, body temperature, mood, recovery, and daily well-being may change over time. Yet in many products, they still appear as secondary features inside systems organized around more generic metrics.
That gap matters beyond product design. A 2024 McKinsey Health Institute and World Economic Forum report estimated that closing the women’s health gap could add at least $1 trillion to the global economy annually by 2040. The report also notes that women spend 25% more of their lives in poor health than men. If wearable technology is becoming part of the front door to everyday health awareness, it needs to do more than measure. It needs to understand context.
From metrics to context
Most wearables still organize health around dashboards: scores, charts, trends, and alerts. This model works reasonably well for simple questions. Did I sleep enough? Did I move enough? Is my heart rate higher than usual? But women’s health often requires a different kind of interpretation. The same recovery score may mean different things depending on cycle phase, hormonal contraception, stress, illness, postpartum changes, or menopause symptoms.
In short, women’s health is not just a feature gap in wearables. It is an operating system problem.
The issue is not only functional. It is experiential. Many women do not need more scores. They need continuity, context, and support that feels companionable rather than corrective. A dashboard can show that something changed, but it often cannot explain why it may have changed or whether it matters. Alerts can create stress instead of insight. A low readiness score may be technically accurate, but without hormonal, emotional, and life context, it can feel like judgment rather than guidance.
The dashboard problem
This is one of the lessons the wearable industry needs to absorb. Health is not always a straight line. For women especially, it often moves through cycles, transitions, and patterns that only become meaningful over time.
Designing for women’s health means designing for time. It means building systems that can interpret patterns across weeks, months, and life stages, not just compare today against yesterday.
The next generation of wearables should move from isolated metrics toward longitudinal understanding. Sleep, temperature, HRV, recovery, symptoms, activity, and mood should not live as separate tiles on a dashboard. They should become part of a broader pattern, interpreted with enough context to be useful and enough restraint to avoid unnecessary anxiety.
The category is starting to shift
The category is beginning to move in that direction. Apple’s Women’s Health Study, conducted with the Harvard T.H. Chan School of Public Health and the National Institute of Environmental Health Sciences, is a long-term study focused on menstrual cycles and their relationship to broader health conditions. Apple has also used wrist temperature sensing on Apple Watch to support retrospective ovulation estimates and improve period predictions, while clearly noting that cycle tracking data should not be used to diagnose health conditions or as birth control.
Oura has also expanded its women’s health features. In May 2026, the company introduced hormonal birth control support within Cycle Insights, designed to help users understand how different contraceptive methods may interact with biometrics such as temperature, sleep, and recovery. Oura has also introduced menopause-related insights, reflecting a broader move from simple cycle tracking toward hormonal context across life stages.
Other companies have explored this territory from different angles. Ava focused on fertility and cycle insights through multi-sensor physiological data. Clue built its product around cycle tracking, prediction, and education. Taken together, these examples suggest a larger shift: women’s health technology is moving from single-point metrics toward pattern recognition, personalization, and longer-term interpretation.
AI may accelerate this shift, but only if it is used carefully. The opportunity is not to turn every body signal into a diagnosis or every fluctuation into a notification. The opportunity is to translate data into everyday meaning. That means explaining why a pattern may matter, when it may be worth observing, and when it may be appropriate to seek professional care. It also means knowing when to stay quiet.
What the next generation must get right
For product leaders, designers, and engineers, a few principles should guide the next generation of women’s health wearables.
First, design for context, not just metrics. A heart rate, HRV trend, or sleep score should be interpreted alongside hormonal phase, symptoms, stress, and life stage when possible.
Second, design for time, not just moments. Many meaningful changes in women’s health emerge across cycles, months, or transitions such as postpartum recovery and menopause.
Third, make the experience less judgmental. Health technology should not make users feel constantly evaluated. The best interaction may feel more like a quiet companion than a coach with a stopwatch.
Finally, treat women’s health as foundational, not peripheral. If nearly half the market has health patterns that are under-modeled, this is not a niche opportunity. It is a category-level design challenge.
The future of wearables will not be won by adding more dashboards. It will be shaped by products that can organize data into meaning, support people through change, and respect the complexity of lived physiology.
Photo: asnidamarwani, Getty Images
Gee Gu is the founder and CEO of Vilo, where he leads product direction across hardware, software, and design. His core thesis is simple: wearables should not feel like dashboards. They should feel like support. He also leads Vilo’s health and medical advisory work with clinical partners and researchers to build responsible, evidence-informed guidance. Gee is also the founder and Chairman of Vesta Sleep, where he previously served as CEO and grew the business from zero to $150 million in global revenue within five years. Earlier in his career, he was a core member of Vincross Robotics, helping develop the HEXA robot and Mind OS, a robotics operating system that was ahead of its time. He graduated from the University of Wisconsin Madison.
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