What to do with your health data: 5 workflows, from fun to life-changing
The number one question we get about vitalina: “Okay, I exported my data... now what?” Fair question. A CSV file isn't an insight. So here are the five things I actually do with my own data - ordered from “party trick” to “changed how I live”. All examples below are real numbers from my own 12-year archive.
By Martin
Co-founder, vitalina · August 26, 2026
Workflow 1: The lifetime highlight reel (5 minutes, pure fun)
Export everything, upload to an AI, and ask for your all-time stats. Mine:
- •69.7 million steps - 61,600 km, or 1.5 times around the Earth
- •3.7 million active calories - roughly 6,600 Big Macs
- •125,000 flights climbed - 1,160 Eiffel Towers
- •5,368 workouts, including 900+ runs totaling 9,400 km
Is any of this useful? Not really. Did I immediately tell my wife? Absolutely. It's also the perfect warm-up: it teaches you the export-upload-ask loop on questions with zero stakes. Start with your activity data exported as CSV.
The highlight reel prompt
“Give me my lifetime totals and turn them into fun comparisons - distances around the Earth, famous buildings climbed, food equivalents burned.”
Why this works: Zero stakes, instant payoff - and it teaches you the export-upload-ask loop.
Workflow 2: Your true personal records (15 minutes, for athletes)
Your watch knows your workout times. But your real PBs hide inside workouts: the fastest 5k might be the middle of a 10k tempo. When I analyzed the splits from all my runs, my half marathon PB came out at 1:42:52 - found as the fastest 21.1 km window across every run I've ever recorded, not just races.
Bonus discovery: my 10k PB from 2015 stood unbeaten for a decade - not because I got slower, but because I stopped racing short distances. The data politely suggested it's time. Export your workout data with splits to try it.
The true-PB prompt
“From my running workouts with splits, find my fastest time for each standard distance - 1k, 1 mile, 5k, 10k, half marathon, marathon - including fast segments inside longer runs. Flag any suspicious GPS data.”
Why this works: Your real records hide inside workouts, not in the workout list.
Workflow 3: The slow-trend check (quarterly, quietly important)
Some things change too slowly to notice but too steadily to ignore. My data held three of these:
- •Diastolic blood pressure: drifted from 80 to 84 over five years
- •Breathing disturbances during sleep: 1.3 → 1.9 → 2.5 per night across three exports
- •Weight: +3 kg over two years, in 100-gram monthly steps I never felt
None of these show up in a daily glance at the Health app. All three showed up instantly in a multi-year trend line. This is, honestly, the workflow I'd recommend to anyone over 35: export a year of vitals every few months and ask one question.
The slow-trend prompt
“Compare my last 3 months against my long-term averages for every metric. What's trending in the wrong direction, even slowly?”
Why this works: One question surfaces every drift that daily glances hide.
Workflow 4: Your marker ages (once a year, motivating)
Population studies tell us what VO2 Max, HRV, resting heart rate, and heart rate recovery look like at every age. Compare your values against those references and you get “marker ages” - the age your body performs like.
At 40, my results: fitness age ~20-25 (VO2 Max 56.2), HRV age ~20 (71 ms), heart rate recovery age ~20-25 (30 bpm/min). Composite estimate including my mediocre sleep and creeping blood pressure: high 20s to low 30s.
Two honest caveats: these are estimates from population data, not lab measurements. And the same analysis will happily tell you what ages you - in my case, 6.4 hours of average sleep. The flattery comes with homework. You'll find all four metrics in your heart rate export.
The marker-age prompt
“I'm [age], [height], [weight]. Based on my VO2 Max, HRV, resting heart rate, and heart rate recovery, what are my marker ages compared to population references? Include an honest composite estimate and list what in my data is aging me. Show your reasoning.”
Why this works: Asking for the reasoning keeps the estimate honest - and the homework visible.
Workflow 5: The doctor visit upgrade (when it matters most)
Everything above is self-service. This one involves a professional.
When I noticed the blood pressure drift and the rising breathing disturbances, I didn't ask an AI what to do about it - I exported a clean PDF report with exactly those metrics and brought it to my checkup. Five years of trends on two pages beats “I think it's been higher lately” every time. My doctor's reaction: “I wish every patient brought this.”
Do: pick only the relevant metrics, set the date range to 6-12 months, export as PDF, AirDrop it before your appointment. That's it - that's the whole workflow.
Before you upload anything
A quick privacy note, because health data deserves it. When using AI tools:
- Go to ChatGPT Settings > Data controls > turn off “Improve the model for everyone”
- Or use a Temporary Chat, which is not used for training
vitalina itself never collects your data - exports stay on your device until you decide where they go.
Start with one
Don't try all five this weekend. Do the highlight reel today - it takes five minutes and it'll make you smile. The rest will follow naturally, because once you've seen what's in your data, you can't unsee it.
Your body has been writing you letters for years. Time to read your mail.
FAQ
What can I do with exported Apple Health data?
Five workflows cover most of it: a lifetime highlight reel of your all-time stats, finding your true personal records inside workout splits, a quarterly check for slow-moving trends, comparing your metrics against population references for marker ages, and exporting a clean PDF to bring to a doctor's appointment.
Which export format should I use for AI analysis?
CSV. ChatGPT, Claude, and other AI assistants read tabular data most reliably. Use PDF when a person is the audience - a doctor, a coach, or your own records.
How often should I check my health data for trends?
Quarterly works well. Export a year of vitals every few months and ask an AI to compare your last 3 months against your long-term averages. Slow drifts in blood pressure, weight, or sleep are invisible day to day but obvious in a multi-year trend line.
What are marker ages, and are they accurate?
Marker ages compare your VO2 Max, HRV, resting heart rate, and heart rate recovery against population reference data to estimate the age your body performs like. They are estimates from population studies, not lab measurements - treat them as motivation and direction, not diagnosis.
How do I prepare my health data for a doctor's appointment?
Pick only the relevant metrics, set the date range to 6-12 months, export as PDF, and AirDrop or email it before your appointment. Our sharing guide covers the details, metric by metric.
Related guides
- → 9 things hiding in your Apple Health data - what these workflows turned up in 12 years of data
- → What to ask ChatGPT about your Apple Health data - the full prompt library, all metrics
- → Analyze your Apple Health sleep data with ChatGPT - sleep-specific prompts and patterns
- → Share Apple Health data with your doctor - workflow 5 in full detail
Ready to try workflow 1?
Export your Apple Health data with vitalina, upload it to your favorite AI, and ask for your highlight reel.
vitalina is a data export tool only. It does not provide medical advice, diagnosis, or treatment.