Integration combines continuous biometrics with multi-omic testing and physician-led care, with an eye on earlier prevention.
Women’s precision health platform Xella Health has integrated with Oura, connecting longitudinal wearable data with multi-omic diagnostics and physician-guided care in an attempt to move women’s health monitoring beyond observation and towards clinical interpretation.
The integration lets Xella members share their Oura data – sleep, heart rate variability, resting heart rate, temperature trends, recovery, activity, menstrual-cycle insights – for Xella to read alongside its own diagnostics: hormonal and metabolic health, inflammation, nutritional status, reproductive health, cardiometabolic risk. Clinicians then work from the combined picture, both in building a care plan and in watching how a patient responds to it over time.
Longevity.Technology: Wearables have become remarkably good at telling us that something has changed; the considerably harder task is deciding whether that change matters. For women, that distinction is particularly important. Heart rate, HRV, temperature and sleep do not fluctuate against a static physiological backdrop, but within menstrual cycles, reproductive transitions, changing metabolic states and the ordinary noise of life – making the population ‘normal’ a potentially blunt instrument for interpreting an individual woman. The more interesting proposition behind this integration, then, may be the shift from asking whether a measurement falls inside a reference range to asking whether a woman is moving away from her own established baseline – and, crucially, whether multiple signals moving together can tell us something clinically useful before disease announces itself more conventionally.
That is an enticing longevity proposition, especially around perimenopause, when neuroendocrine change can coincide with shifts in sleep, body composition and cardiometabolic risk; a longitudinal physiological record, interpreted alongside molecular diagnostics and clinical history, could potentially turn a familiar life transition into an earlier window for prevention rather than simply another period of symptom management. But continuous measurement should not be confused with continuous understanding. More data can reveal a trajectory; it cannot, by itself, tell us why that trajectory is changing, and multi-omics does not magically confer causality simply by measuring biology at greater resolution. Xella and Oura are therefore attempting something more consequential than joining two datasets – they are trying to close the loop between detection, interpretation, intervention and response. Whether that loop can distinguish meaningful physiological change from everyday variation, and whether it can turn midlife signals into opportunities for earlier prevention, are rather more interesting questions. To explore both, we sat down with Xella Health CEO and Cofounder Kelly Lacob.
When change actually means something
For Lacob, the starting point is acknowledging an obvious but easily overlooked problem with wearable data: physiology moves.
“A woman’s heart rate, temperature, sleep, and HRV naturally fluctuate with her menstrual cycle, stress, physical activity, and countless other factors,” she says. “The challenge is distinguishing normal physiological variation from persistent changes that warrant further investigation.”
Rather than treating a single deviation as evidence of a clinical problem, Xella intends to establish individualized physiological baselines and interpret changes according to menstrual cycle, life stage, symptoms, medical history and underlying biology. “Rather than reacting to a single abnormal reading, we want to understand the magnitude, persistence, and relationships between multiple signals,” Lacob says.
This is where the company sees its diagnostics adding another layer. Xella says its platform can identify more than 130 conditions specific to female biology, with its testing intended to investigate hormonal, metabolic, inflammatory or nutritional contributors that might help explain a persistent change in wearable data.
Importantly, Lacob is cautious about what combining those datasets has yet to prove.
“The real test of this partnership isn’t whether we can generate more data or identify more correlations,” she says. “It’s whether combining these data sources produces clinically meaningful insights that neither could deliver independently.”
That will require validation showing the integrated approach can separate meaningful change from physiological variation, improve diagnostic accuracy or clinical decision-making and, ultimately, produce measurable improvements in patient outcomes, she adds.
The ambition is also to give clinicians information between appointments. Longitudinal wearable data could potentially show how physiology changes after an intervention, rather than leaving physicians dependent on periodic laboratory measurements and a patient’s recollection of how she has felt over the intervening weeks or months.
Making midlife a window
The longevity case becomes particularly interesting around perimenopause. Historically, Lacob notes, it has been treated primarily as a reproductive transition, with care centered on managing symptoms like “hot flashes, disrupted sleep, and brain fog.” That framing misses rather a lot. Beneath the symptoms sits a period of real neuroendocrine and metabolic upheaval – body composition shifting, insulin sensitivity changing, cholesterol and vascular health moving with it, as ovarian hormones decline. None of which stays politely confined to midlife. A woman’s fortysomething symptoms and her sixty-something risk factors may simply be two readings of the same underlying process.
Lacob argues against separating those two timelines.
“I don’t think we should view symptom management and long-term prevention as separate opportunities,” she says. “The real promise of continuous, integrated data is that we can connect the two: helping women feel better today while identifying opportunities to improve their health trajectory over the coming decades.”
Take worsening sleep during perimenopause. On its own, it reads as exactly that – a sleep problem, treated as one. Set alongside biomarker evidence of emerging insulin resistance or a climbing ApoB, it becomes something else: an early flag for cardiometabolic risk, worth a wider look rather than a prescription for melatonin.
Not every change will prove meaningful, of course. Nor is a wearable-derived deviation necessarily an early-warning system for future disease. Lacob says establishing whether combinations of wearable signals and biological markers can identify emerging health risks earlier or more accurately than conventional approaches remains a research question requiring longitudinal validation.
Still, perimenopause offers an unusually identifiable point in the life course at which such questions can be asked. It is a period of substantial physiological change, often accompanied by symptoms significant enough to bring women into contact with healthcare – potentially creating an opportunity to look beyond immediate symptom relief.
“Our vision is to use the menopausal transition and other critical life stages as opportunities for earlier, more personalized prevention,” Lacob says. “If we can help a woman understand what’s changing in her body at 45, address her symptoms, identify modifiable risk factors, and guide appropriate interventions, we have an opportunity to influence her health trajectory for decades to come.”
From reference range to trajectory
Precision health is sometimes presented as a matter of measuring more things more precisely. Longitudinal care poses a subtler question: whether medicine can become better at recognizing change within an individual before that change becomes pathology.
For women’s health, where physiology is patterned by cycles and life stages that population averages can smooth into invisibility, that could prove a particularly useful shift. Lacob explains her vision is to move “from isolated snapshots of health toward a continuous, clinically actionable understanding of each woman’s biology” – though realizing it, she adds, requires demonstrating not just that the technology can detect change, “but that acting on those insights actually improves care.”
The next challenge is evidence – establishing which deviations deserve attention, which are simply biology being biology and whether acting earlier actually changes what comes later.
