Four years of Apple Watch data, two blood panels, and a personal annotation layer — connected into something no existing health app could tell me.
I've worn an Apple Watch for four years. I knew my HRV was 38ms. I had no idea what that meant for my life — until I built something that showed it alongside everything else that was happening.
The data to predict your next illness, explain your worst month, and find your most effective recovery activity already exists in your pocket. The missing piece isn't the data. It's the interpretive layer that speaks human.
Apple Health gives you measurements. It has never given you self-knowledge. There's a meaningful gap between "your resting heart rate was 62 bpm" and "you were chronically under-recovered for three weeks before you got sick."
The first is a measurement. The second is a story about your body that you could actually act on.
Passive wearable data contained a 13-day warning before a fever peaked at 38.2°C. Four signals — resting HR, HRV, respiratory rate, and wrist temperature — converged simultaneously on February 24. The watch knew on February 11. I felt fine.
A second illness event in March 2025 produced a completely different signature — sudden onset within 24 hours of exposure to a sick travel companion. Two illness events, two distinguishable causal signatures, both readable from wrist sensors alone.
Across 1,621 logged workouts spanning 9 sport types, tennis produced the strongest positive HRV signal of any activity — +4.8ms above personal baseline the day after, sustained for three days. This is stronger than cycling, running, HIIT, or strength training.
I had 12 tennis sessions in the dataset. I had no idea. No existing health app would have surfaced this finding.
Resting HR dropped 4.5 bpm over four years. HRV improved 23.8%. VO2 max reached 40.2 from cross-country skiing with a dog, collapsed to 29.6 as training began, then climbed back to 38.4 through half marathon training. The body gets worse before it gets better.
On May 25, 2026 — before a midyear blood draw — nine predictions were written about how six months of half marathon training would shift blood biomarkers. The predictions were based on wearable data, physiological mechanisms, and the November 2025 baseline panel.
Results pending. This section updates when they arrive.
Every metric is computed relative to a rolling 90-day personal norm — not population averages. A 35ms HRV means something different for you specifically than for anyone else.
AI generates a weekly narrative in plain language — warm, curious, specific. It names ambiguity explicitly rather than picking the most likely interpretation and stating it as fact.
"I can't tell from sensors alone" appears when multiple explanations are equally plausible. The system asks rather than guesses. The user is the expert on their own experience.
Toggle any sport on or off to see how it shaped your cardiovascular metrics over time. Activity bars underneath show when each sport was active relative to the signal.
PDF extraction pipeline parses 100+ biomarkers from Function Health reports, tagged with cycle phase context. Cross-referenced with wearable data by date.
Annual year-in-review format that removes the introspection requirement entirely. The data speaks; the user recognizes their own life in it.
Personal baseline computation sidesteps population-level confounders entirely. The annotation system — connecting life events to physiological signals — is what transformed data into narrative. The weekly letter format is naturally resistant to gamification.
The n=1 limitation is real but also the point. This kind of longitudinal personal health intelligence only works because it's deeply individual.
Medications, chronic conditions, and hormonal variation create interpretation confounders the system can't see. Memory degrades — contextual recall two weeks later is less accurate than in-the-moment prompting. Motivated reasoning means people sometimes hear what they want to hear.
The design response to all three is the same: ask better questions rather than claiming certainty the system doesn't have.
The data to understand your own body already exists. The design work is building the interpretive layer that makes it legible.