Hidden Connections: What John Muir Can Teach Us About Apple’s New Hypertension Notifications
“When we try to pick out anything by itself, we find it hitched to everything else in the universe.”
John Muir, circa 1902
When the naturalist John Muir penned that quote, he was writing about the natural world after working as a shepherd in California’s Sierra Nevada, immersed in the mutual dependence of mountains, forests, rivers, and animals.
More than a century later, his insight is proving just as true for the wilderness inside of us. We’re discovering that nothing in our physiology exists in isolation. The eye, the heart, the skin, the voice — each carries hidden connections to other systems. And increasingly, it is AI that is uncovering those links.
An Early Example: Apple Heart Study
In 2017, Apple and Stanford launched the Apple Heart Study, enrolling more than 400,000 participants. Researchers asked a simple question: could the Apple Watch’s pulse sensor — a photoplethysmograph (PPG) LED originally designed just to measure heart rate — be used to detect something far more serious?
The answer was yes. By applying machine learning, Apple showed that irregular pulse patterns captured by PPG could flag atrial fibrillation (AFib), a condition often silent until it causes a stroke. The connection between a consumer watch’s pulse sensor and a dangerous cardiac arrhythmia was a hidden connection — one not previously recognized or used at scale. And gaining the valuable additional information didn’t involve any new hardware at all.
More Hidden Connections Revealed by AI
Since the Apple study, machine learning has surfaced hidden links across medicine, and not just with watches and phones and other mobile devices:
Retinal images — long used to check eye health — can reveal sex, race, smoking status, blood pressure, and even cardiovascular risk.
Gait and voice patterns — captured casually by smartphones or microphones — can detect the earliest signs of neurological disease without a visit to a neurologist
Electrocardiograms (ECGs) — the same tracings used for decades to diagnose heart rhythm — now allow AI to screen non-invasively for anemia, electrolyte imbalance, and left ventricular dysfunction
CT scans — often ordered for one purpose — can be re-analyzed at minimal cost to estimate bone density or coronary calcium, each tied to long-term risk — without needing additional scans (or radiation)
The information was always there. What’s changed is that AI gives us the ability to see it.
The Apple Watch and Hypertension
Perhaps the end of the beginning
This is particularly true for Apple’s new hypertension notifications, because they don’t use any kind of new sensor in the Apple Watch. The same green LEDs still measure blood oxygen levels. The electrodes still capture heart rhythm. The accelerometer still tracks movement. What changed was Apple’s recognition that these existing signals already carried the imprint of something new: hypertension.
For years, millions of Apple Watch users have been streaming this data. But it’s only now — after training AI on the right relationships — that the watch can flag blood pressure risk with confidence.
Another hidden connection uncovered.
Beyond the Watch
We can expect that this pattern will repeat itself everywhere: applying AI to old data streams to produce new insights. Combined, of course, with new data streams — very likely including non-invasive blood glucose monitoring in the near future.
Credit card purchases — will allow us to understand not just spending, but nutrition and mental health
Browsing histories — can understand cognition and promote education
Medical images — will be repurposed by AI to surface cardiovascular, skeletal, or metabolic risk factors unrelated to the original reason for the scan
Muir’s observation was ecological, but it applies just as well here. Health is not a set of isolated numbers. It is a network of hidden links — and we are only beginning to trace them.
Closing Thought
Apple’s hypertension notifications are not the end of something — they are the beginning. They can help us imagine how every stream of data we collect may reveal more hidden connections between body systems than we ever imagined. And the future of health will be built on our ability to recognize those links connecting what we already measure to what we most need to know.