Idiopathic pulmonary arterial hypertension (IPAH) is a mouthful to say, but it’s worth talking about because we diagnose it late.

Briefly, here’s idiopathic pulmonary arterial hypertension in plain terms:

There is no cure. Treatments can slow the disease, but they work best when started early.

As hinted at above, most patients do not get diagnosed until they are already sick. The average time from first symptoms to formal diagnosis is about three years. By then, the disease has often progressed significantly. Around 75% or more are in advanced functional decline (NYHA functional class III or IV) when first diagnosed, meaning they struggle to walk across a room.

We are not diagnosing IPAH slowly because it’s invisible. The initial symptoms of shortness of breath, fatigue, and slowing down overlap with a hundred other explanations, like deconditioning, heart failure, COPD/asthma. So the disease hides in plain sight while we chase other diagnoses.

That inefficiency costs lives. Below I do a root cause and impact analysis, then highlight how Apple Watch, and the broader “guardian angel tech” can help detect this disease earlier.

The problem: IPAH is routinely diagnosed 2–3 years after symptom onset, by which point many patients have advanced disease and worse outcomes.

A research team from Imperial College London, Stanford, and several UK specialty centers asked a simple question:

This was a pilot study of 109 participants across the UK, including 33 patients with confirmed IPAH, a disease control group (people with other serious conditions), and healthy volunteers. They used an app called My Heart Counts, originally developed at Stanford, paired with an Apple Watch Series 4.

What makes this study unusual is the timeline. Some participants had up to eight years of retrospective HealthKit data (step counts, walking pace, flights of stairs climbed, heart rate, heart rate variability, sleep) collected passively through their iPhones and watches before they were ever formally diagnosed.

Here were the key findings of the study:

These differences were present before diagnosis.

A machine learning model trained on just the pre-diagnosis watch data distinguished IPAH patients from controls with an AUC of 0.87. This means the algorithm could correctly classify about 87% of cases using nothing but passive activity data collected before anyone knew the diagnosis.

Add in a simple lifestyle questionnaire, and that number climbs to 0.94.

Even using just an iPhone (no watch required) the model hit 0.91 with lifestyle questionnaire data. Phones are more accessible, more universally adopted, and still captured enough signal to do meaningful classification.

Interestingly enough, once patients were diagnosed and started treatment, their activity metrics improved. Step counts went up, heart rates came down toward normal.

IPAH is rare, and the framework this study demonstrates is broadly applicable.

What we're talking about is passive digital phenotyping: using data you're already generating every day to detect early signals of serious disease. Your phone is already in your pocket, counting steps and logging walking pace, building a longitudinal record of physical function.

I've written before about what I call guardian angel technology. This is the idea that wearables, powered by machine learning, will eventually act as a silent monitor running in the background of your life. Always watching. Catching things that would otherwise go unnoticed. Not requiring you to do anything differently. Just generating signal while you go about your day.

This study is a proof of concept for that vision. The Apple Watch wasn't doing anything exotic. It was just counting steps, measuring heart rate, and tracking how fast someone climbed a flight of stairs. Ordinary data, analyzed longitudinally, drew a picture of disease developing in someone who did not yet have a diagnosis.

Guardian angel technology, applied to rare disease, could build a baseline, detect drift, and flag a pattern that no clinician could catch from a quarterly visit.

The three-year diagnostic delay in IPAH is not unique. We see it in pulmonary fibrosis, in heart failure, and in early-stage COPD. The common thread is slow, progressive functional decline that happens between clinic visits, invisible to the healthcare system until it's advanced. Guardian angel technology can close that gap, if we build the tools to use it.

There are limitations to the study, of course. This was a small pilot. The model trained on UK patients performed poorly when applied directly to US patients, because activity patterns and lifestyle differ across populations. The researchers had to retrain with some US data to get acceptable performance. That is a legitimate challenge for real-world deployment.

The signal also isn't perfectly specific. Slower walking pace and lower heart rate variability can show up in atrial fibrillation, heart failure, and musculoskeletal disease too. A clinically deployable screening tool would require bigger, more diverse datasets, plus integration with electronic health records.

Still, the direction is right. The foundational question—can a smartwatch catch a fatal disease before your doctor does?—has a preliminary answer: yes, it looks like it can.

The technology exists and the data is already being collected. The remaining work is building the infrastructure to use what's already in people's pockets.

In summary, IPAH is a progressive, fatal disease that we consistently diagnose too late. This pilot study shows that passive data from iPhones and Apple Watches (step counts, walking pace, heart rate patterns) can identify patients before formal diagnosis with impressive accuracy. This is a proof of concept, not a clinical tool yet. But it's a meaningful signal that continuous, passive monitoring could fundamentally change how we catch diseases that hide in the slow erosion of daily function.