By Zahra Bhatti, founder and CEO, Véa

We are living through a measurement boom.

Wrist-worn wearables ship in the hundreds of millions IDC forecast worldwide shipments at 537.9 million units in 2024, with 136.5 million units shipped in Q2 2025 alone.

We can track steps, sleep stages, heart rate, HRV, temperature, glucose variability and recovery scores.

We have never had more physiological insight into the human body.

So why are women still burning out? Still overwhelmed? Still carrying invisible cognitive load that never appears on a single dashboard?

If the data revolution in health tech was supposed to empower women, why do so many feel more monitored than supported?

A number on your wrist can tell you what happened in your body. It rarely tells you why it happened, what it meant or what you need next.

That missing layer is emotional data. And femtech is uniquely positioned to build it.

We Built Dashboards. We Didn’t Build Interpretation.

Picture this.

It’s 6:47am. You’ve been up since 4 with a teething toddler, made packed lunches on autopilot, managed a meltdown at the school gates and arrived at your desk already running on fumes.

Your watch buzzes. Sleep score: 38. Stress: High. Recovery: Poor. Thanks. You already knew.

This is the problem no one in health tech wants to name.

Wearables are extraordinary at capturing signals but measurement without meaning stops at awareness.

Your HRV dips and a notification pings. It cannot tell you whether that dip came from the argument you didn’t finish with your partner, the guilt of missing bedtime again, the weight of being the only one who remembers the GP appointment or the hormonal crash of your luteal phase hitting while all of it lands at once.

The sensor caught the signal but it missed the entire story.

The evidence backs up what women already feel in their bones.

While activity trackers can increase step counts, a Lancet Digital Health umbrella review found their effect on broader psychological wellbeing is limited.

A 2024 systematic review went further, calling the evidence for wearables improving mental health “extremely limited”.

The sensors work but the interpretation doesn’t. That gap between data and meaning is exactly where women fall through.

Women’s Mental Health Is Not a Niche Concern. It Is a Systems Failure.

Consider the architecture of burden women navigate daily.

Depression is approximately 1.5 times more common among women than men, according to the World Health Organization.

The gender gap emerges at puberty and persists through the lifespan, driven by biological, psychological and social factors that compound over decades.

In the UK, 26.2 per cent of women reported high anxiety in the most recent ONS quarterly data, compared with 18.8 per cent of men – a gap that has remained statistically significant for over a decade.

But here is the question nobody in wellness tech seems to be asking: where does all that invisible labour live in the data?

Globally, women perform 2.5 times more unpaid care and domestic work than men.

That is time, emotional bandwidth and cognitive effort that never surfaces in economic metrics or health dashboards.

Forty-five percent of working-age women are outside the labour force because of unpaid care responsibilities, compared with just 5 per cent of men.

For those who do stay at work, the toll compounds: CIPD research found that 67 per cent of women aged 40–60 experiencing menopause symptoms report a mostly negative impact at work, with 79 per cent feeling less able to concentrate and one in six considering leaving their role entirely.

These are not isolated statistics.

They describe accumulated cognitive and emotional load across a lifetime a compounding interest of stress that no single intervention can repay.

Yet most wellness technologies still focus on optimisation metrics such as: output, recovery, movement and productivity.

Women do not simply need better tracking. They need systems that reduce the burden of self-interpretation.

When did we decide that measuring a woman’s body was more important than understanding what she’s carrying inside it?

Emotions Are Not Soft Signals. They Are Early Data.

Emotions are routinely dismissed as subjective, anecdotal and too messy to measure.

But from a systems perspective, they are high-frequency signals about safety versus threat, capacity versus overload, connection versus isolation and alignment versus self-betrayal.

They are early-warning indicators arriving long before burnout becomes clinical, long before sleep deteriorates especially long before productivity drops.

Physiology lags behind the emotional moment.

Your heart rate spikes after the confrontation. Your sleep fragments after a week of over-functioning. Your inflammation markers will never capture the micro-stresses that accumulated all day. Emotions do.

They are the body’s first responders faster than cortisol, more specific than HRV, more honest than any self-reported wellness score.

When emotional data is captured consistently, patterns emerge that no wearable can detect alone: anxiety clustering after specific meetings, energy dipping during certain cycle phases, irritability rising after relational overextension, creative clarity following solitude or movement.

This is not mood tracking for novelty. This becomes behavioural pattern recognition – the diagnostic layer women have been missing and needing.

From Self-Optimisation to Self-Understanding

We have built extraordinary tools to measure the female body.

We have not yet built infrastructure to interpret the emotional load women carry daily, the invisible labour, the relational tension, the hormonal transitions and most importantly the resulting cognitive overload.

These forces rarely appear in a recovery score rather they show up unmistakably in emotional patterns.

Imagine: a wearable detects sustained stress variability. An emotional check-in identifies relational strain. Context shows deadline pressure and reduced recovery. The system responds not with another metric, but with a small, realistic intervention that fits your life.

From dashboard to preventative mental health infrastructure. THIS is the golden opportunity femtech has to lead.

When emotions are treated as structured, longitudinal data rather than vague self-expression, they become a preventative signal.

They reveal when capacity is shrinking, when boundaries are leaking, when resilience is building. They show what no heart rate monitor ever could: the moment a woman stops prioritising herself, and the pattern that follows.

This shift is already beginning.

Platforms like Véa are building emotional operating systems that treat emotions as legitimate health data translating micro-check-ins and pattern recognition into contextual insight, reducing the invisible labour of self-analysis rather than adding to it.

Not more optimisation. Not more self-surveillance. Structured self-understanding that actually lightens the load.

In a world saturated with metrics, the competitive advantage is no longer more data. It is better meaning.

Emotions remain the most underutilised dataset in women’s health. Femtech has the infrastructure, the audience and the moment to build the missing layer.

The question is whether it will.

By Dr Melinda Rees, CEO, Psyomics

Spend enough time in the NHS and you stop flinching at the word “productivity”.

You hear it in every strategy document, every board meeting, every government announcement.

And almost every time, it means the same thing: do more with less.

It’s the wrong framing.

After 25 years working in and around clinical services – from NHS leadership to service delivery in the independent sector to where I am building technology that works with NHS mental health services – I’d argue it’s part of why progress has been so hard to achieve and sustain.

Productivity in healthcare shouldn’t mean squeezing more out of an already over stretched workforce.

It should mean something more precise: delivering greater value per pound by protecting and deploying finite clinical expertise intelligently.

That distinction sounds subtle. In practice, it changes everything about how you approach the problem.

The demand side of this equation isn’t going to get easier.

Multi-morbidity is rising. Mental health need is growing. Cases are more complex, and patient expectations – rightly – are higher.

The assumption that we can recruit our way out of this is understandable but wrong.

Training pipelines take years. Financial resources are finite. Even in an optimistic scenario, workforce expansion alone doesn’t close the gap.

So, the real question isn’t how do we get more clinicians. It’s whether we’re deploying the ones we have with maximum precision.

And honestly, in most services, the answer is no.

• Clinical time – the most valuable finite resource in the system – is routinely lost to things that have nothing to do with clinical decision-making.
• Administration.
• Repetitive documentation.
• Poor workflow.
• Systems that don’t share information across boundaries.
• Inconsistent and variable clinical decision-making.
• Referrals that shouldn’t have reached a specialist clinic in the first place.
• Reactive care models that wait for deterioration rather than anticipating it.
• Gathering baseline information that could have been collected earlier, more consistently, and without the clinician in the room.

Meanwhile, the waiting list grows.

This isn’t a motivation problem or a workforce culture problem. It’s a system design problem.

And it’s solvable – meaningfully – if we’re willing to rethink how technology fits into the picture.

The challenge with digital implementation in the NHS has rarely been the technology itself – it’s been layering new tools onto processes that were already under strain.

A new system that digitises an inefficient workflow is still an inefficient workflow.

Real productivity gains come when technology is used to redesign how work actually happens – not just record it.

In practice, that means four things.

First, automating the tasks that don’t require clinical expertise – structured data capture, digital triage, standardised assessment pathways.

Every minute saved on documentation is a minute returned to care. At scale, those minutes add up fast.

Second, bringing patients into the process earlier.

When a patient contributes structured, meaningful information before their first appointment, the clinician and patient have a great head start.

Better routing, smarter questions, faster and safer decisions, quicker access to the right treatment.

Third, monitoring caseloads intelligently.

Utilising tools that flag deterioration or signal when a care plan needs to change, rather than waiting for a crisis to trigger a review.

Finally fourth, making sure every appointment actually advances care. That sounds obvious.

In practice, without recorded structured outcome data, it’s surprisingly hard to know.

None of this requires drastic AI transformation or futuristic promises.

Some of the biggest gains come from making simple workflow tasks consistent and seamless – the kind of unglamorous operational improvement that doesn’t make headlines but compounds quietly across thousands of patient interactions and increases productivity.

A 1-2 per cent productivity gain per clinician sounds modest.

At NHS scale, across millions of appointments, it isn’t. It’s the difference between a system grinding and one with genuine headroom to breathe.

It’s the difference between your close relative being able to get an appointment when they genuinely need one or languishing on a waiting list with little hope.

I think about this a lot through the lens of mental health services specifically, where I’ve spent most of my career and where Psyomics works.

Mental health has historically been underfunded and under-prioritised – something that disproportionately affects women, both as patients and as the clinicians and carers holding those services together.

The pressure to do more with less lands hardest here. And the argument that productivity means working harder is, in this context, particularly damaging.

Burnout in mental health services isn’t a footnote. It’s a crisis within a crisis.

The better argument – the one I’d like to see shape NHS policy – is that productivity means precision.

Precision in how we route patients. Precision in how we use structured data to reduce variation and improve decisions. Precision in how we protect clinical time for the work that only a skilled clinician can do and loves to do.

That’s not a technology story, exactly. It’s a system design story, in which technology plays an enabling role.

The NHS doesn’t need to do more with less.

The goal isn’t harder-working, exhausted clinicians – it’s smarter-working, compassionate enabled clinicians, and patients who are seen sooner.