A run of research in late January and February of this year has done something quietly significant. It has moved midlife symptom data from the wellness category into the neurological one. Almost no one is talking about what that means for the data women have already generated, or for where they generate it next.
In January, researchers at the University of Cambridge published an analysis of nearly 125,000 women from the UK Biobank, including around 11,000 with MRI scans. They found significant grey matter reductions after menopause in the hippocampus, entorhinal cortex, and anterior cingulate cortex. These regions are central to memory, emotional regulation, and the earliest signs of Alzheimer’s disease.1 Two weeks later, Dr. Karen Horst, a reproductive psychiatrist at Baylor College of Medicine, made the framing institutional. She argued for understanding menopause as “a predictable neuroendocrine transition,” pointing to evidence from SWAN, the Penn Ovarian Aging Study, and Mayo Clinic data showing that psychological symptoms often begin up to a decade before periods stop.2 Both build on Lisa Mosconi’s 2021 brain imaging study in Scientific Reports, which established that menopause changes brain structure, connectivity, energy metabolism, and amyloid plaque deposition. Those changes come from endocrine aging, not just from getting older.3
Taken together, this body of work does something that has consequences well outside neurology. It reclassifies midlife tracking data.
The cycle log you kept for a decade is not just a cycle log anymore.
It is a longitudinal record of the neurological transition that comes with menopause, however and whenever it arrives. For most women that is in their late thirties or forties. For others it comes earlier, through surgery, medication, or premature ovarian insufficiency. Sleep onset times, mood shifts, cognitive complaints, hot flash frequency, energy patterns: the things women have been writing into apps for years are now markers of what the science is calling a neuroendocrine transition with measurable downstream effects on cognitive trajectory.
That changes who wants the data.
A quick word about how we got here
Before going further: the apps women have been using for years made this scientific shift possible. A meaningful share of the longitudinal research on cycle health, menopause symptoms, and midlife endocrine patterns has been done in partnership with the tracking apps that hold centralised data. Without those datasets, the clinical evidence base for midlife as a neurological event would not exist in the form it now does. Researchers needed scale. Apps had scale. The partnerships worked, and they should be acknowledged for what they made possible.
This post looks at what comes next, now that the data has been recategorised.
What has actually changed
When midlife symptom data was framed as wellness data, the commercial appetite for it was bounded. Advertisers wanted hot flash relief shoppers. Wellness brands wanted intent signals around supplements, sleep aids, hormone replacement curiosity. That market existed and still exists. By today’s standards, it is a narrow one.
When the same data is framed as neurological data, the appetite expands.
A record of cognitive complaints stretching from your late thirties into your sixties is interesting to anyone modelling cognitive trajectories. Documented sleep disruption through perimenopause is interesting to anyone modelling cardiovascular and dementia risk. A decade of mood and energy logs across the menopausal transition is interesting to anyone building AI that learns to read emotional patterns, including voice agents, health chatbots, recommendation systems, and conversational triage tools aimed at the same demographic.
The data did not change. Its market category did.
Who, specifically
It is worth being concrete about who has commercial reason to want this data now that it has been recategorised. Some of what follows is documented user concern. A recent Royal Holloway study, published in ACM Transactions on Social Computing and surveying 310 UK women, found that respondents explicitly named workplace discrimination, targeted financial scams, and exploitation of sensitive data among their worries about menopause technology. The research team flagged that current platforms often do not meet GDPR standards for this category of data.4 What I’m doing here is mapping where those concerns actually land.
Insurers and reinsurers. Long-term care insurance, life insurance, disability insurance, and increasingly health insurance all price on cognitive and neurological trajectory. Women in their late thirties through fifties are the demographic most of those policies are sold to. They have also, historically, been the cheaper demographic to insure, because men die younger and carry more cardiovascular risk through midlife. That asymmetry has worked in women’s favour for a long time. A cognitive-trajectory dataset is exactly the kind of new variable that reverses it. Actuarial models will incorporate the variable; that part is not in question. What is open is whether women keep generating the dataset that lets them.
Pension and workforce planning models. Employers and pension administrators are increasingly modelling cognitive workload capacity through midlife and into early old age. Self-reported brain fog data is exactly what those models want, especially when it can be correlated with productivity tracking from elsewhere in someone’s digital footprint. The Royal Holloway study notes that nearly one million women in the UK have already left jobs because of menopausal symptoms.4 Alongside that, something quieter is happening in the conversations women are having with each other. They are deciding to stay silent at work, to keep what they are experiencing off the record, because they are afraid of how it will be recorded and used against them. The women who have left are countable. The silence of the ones still in their seats is not, and it is the larger story.
AI training pipelines. The next generation of consumer AI is being trained on patterns of how people describe their bodies and their cognition over time. Midlife women are underrepresented in those training sets. A body of self-tracking data from this population is therefore disproportionately valuable to anyone building AI for that segment, including voice agents, health chatbots, recommendation systems, and conversational triage tools.
Legal discovery. Tracking data is increasingly being requested in custody, employment, and disability disputes. Data logged as wellness gets read as evidence.
Identity and targeted fraud. A documented record of cognitive complaints, combined with biometric and behavioural data, is exactly the kind of background that voice cloning and impersonation fraud builds on. The Royal Holloway respondents named targeted financial scams as a specific concern.4 Fraud economies already target older women heavily. Adding long-term cognitive records to a target profile makes that targeting more precise.
None of these markets existed for cycle-tracking data ten years ago in any serious form. They exist for neurological data now.
What tracking forward looks like
This is the piece worth thinking about if you track, or if you are considering starting. The apps women have been using were not a mistake. The research that has come out of them is part of why your symptoms are taken more seriously today. The question is whether the data you generate from here onward sits somewhere that matches the new category.
A few questions worth asking, if you want to keep tracking and are reconsidering where:
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Where does the data physically live? On the device, or in a corporate database somewhere? Data the company never holds cannot be subpoenaed, breached, or sold.
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What is the company structurally able to do with the data? A privacy policy can promise responsible behaviour. An architecture can make some behaviours impossible. Architectures survive a change of ownership. Privacy policies don’t always.
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What happens at acquisition? Most companies are eventually acquired. The acquiring company inherits the data and the privacy policy, and is usually free to rewrite the policy. Architecture survives acquisition. Policy does not.
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Is there third-party tracking embedded in the app? Even when an app itself does not sell data, the analytics tools embedded inside it (things like crash reporters and usage trackers from big platforms) can route metadata and behavioural patterns elsewhere by default. There is already precedent for data classed as wellness moving through analytics tools into ad networks without users understanding the path.
These are the questions any midlife tracker is in a strong position to ask. The category of the data has moved, and a question that was reasonable to defer five years ago is harder to defer now.
Where I’m writing from
I should say where I sit. I’ve been building privacy-architected wellness software for women in midlife, and the architecture is the product, not a wrapper around it.
What that looks like in practice: pattern intelligence runs in encrypted, temporary computing environments. Each environment spins up to compute against one woman’s data, then destroys itself. Nothing is retained. No aggregated body-data warehouse, no per-user record sitting in a database to be queried, breached, subpoenaed, or sold.
That choice was made before the brain imaging research recategorised midlife data. There are honest trade-offs in any architectural choice. The question of where this data sits, and who gets to decide what it means later, is one worth asking now, while there is still time to choose.
The underlying point
The brain imaging work being published right now is good news. It validates what midlife women have been describing for decades, and it gives clinicians a real evidence base to work from.5 The research depended on data that has been collected at scale, often through commercial apps, often in good partnership.
What the research also does, almost as a byproduct, is reclassify the data. It is no longer wellness signal. It is neurological baseline.
The question is no longer whether your cycle data is private enough. It is whether you keep generating the dataset that reprices your insurance, shapes how your employer manages around you, and trains the AI models being built for women your age.
Footnotes
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Zühlsdorff K, Langley C, Bethlehem R, Warrier V, Romero Garcia R, Sahakian BJ. Emotional and cognitive effects of menopause and hormone replacement therapy. Psychological Medicine. 27 January 2026. DOI: 10.1017/S0033291725102845. University of Cambridge announcement: https://www.cam.ac.uk/research/news/menopause-linked-to-loss-of-grey-matter-in-the-brain-poorer-mental-health-and-sleep-disturbance. ↩
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Horst K. Menopause is a brain transition, not just a reproductive one. Baylor College of Medicine Blog Network. 13 February 2026. https://blogs.bcm.edu/2026/02/13/menopause-is-a-brain-transition-not-just-a-reproductive-one/. ↩
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Mosconi L, Berti V, Dyke J, et al. Menopause impacts human brain structure, connectivity, energy metabolism, and amyloid-beta deposition. Scientific Reports. 2021;11(1):10867. DOI: 10.1038/s41598-021-90084-y. ↩
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Robinson T, et al. User Risk Perceptions and Privacy Attitudes towards Menopause Data Collection and Use. ACM Transactions on Social Computing. 18 February 2026. DOI: 10.1145/3797822. Royal Holloway University of London summary: https://medicalxpress.com/news/2026-03-hidden-privacy-menopause-tech.html. ↩ ↩2 ↩3
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For a clinician-facing synthesis of menopause and brain function research, see Peter Attia, Menopause and brain function: https://peterattiamd.com/menopause-and-brain-function/. ↩
Continue reading
- Start hereA Different Kind of VesselWhat if there were a privacy-first federated AI model for women's wellness — and what if we, collectively in femtech, owned it?
- Privacy-Led UX Is the SurfaceA reaction to MIT Technology Review and Usercentrics's Privacy-Led UX in the AI Era. The report draws the surface beautifully. The architecture is the thing the surface is reflecting back.
- There Is No Privacy Paradox.A reaction to recent ChatGPT-influencer research. The privacy paradox isn't a paradox — it's the sound of an architecture working exactly as designed.
