The data we look at first when making decisions about performance, health, fitness, and longevity.
You've never had access to more health data than right now. And there's never been less agreement about what any of it means. Open a longevity podcast, a wearable app, or a supplement newsletter, and the message underneath is always the same: measure more. More biomarkers, more scans, more continuous streams, more panels with more analytes on them. The number of things you can measure has exploded. The number worth measuring has barely budged.
For all that data, most people still don't get where they're trying to go. They lose the same fifteen pounds three times. They train hard for a decade without their aerobic engine ever getting measurably bigger. They optimize a sleep score and tinker with a supplement stack while the four or five numbers that would actually redirect the whole effort sit unmeasured. It's tempting to call that a discipline problem. But it isn't. It's that almost nothing they collected was ever attached to a decision. The data changed how they felt ("I'm on top of it!") without once changing what they did. You can't out-discipline a dashboard pointed at the wrong things.
We run a lot of tests on a lot of people at A3, and the discipline we've landed on to avoid the "measure everything" reflex is a single filter:
Measure only what changes a decision.
That sounds obvious. But in practice, it's close to the opposite of how the industry works. Most of the health-data economy is built on the pleasant feeling of knowing your numbers, whether or not those numbers ever change a single choice you make. A dashboard with sixty metrics on it feels like rigor. Usually it's the enemy of rigor, because it buries the four or five numbers that affect what you actually do under fifty-five that don't.
Hence this series: a map. Not of everything you could measure, but of what you actually should, why, and what to do with the data once you have it. Some of these you already track. Some you've probably never heard of. A few are here mainly so we can explain why we don't think they're worth measuring at all.
The Filter: What Makes a Measurement Actionable
A number earns its place on our map only if it clears three bars.
First, it has to be tied to a decision. Not "interesting." Not "predictive in a large cohort." Tied to something you actually do differently depending on the result. Your VO2max changes how you train. Your fasting insulin changes how you eat. If a measurement can't finish the sentence "because of this number I will now ___," it's useless, however scientific.
Second, the result has to be reliable enough to trust. A measurement that's off by 15–20% can put you in the wrong decision bucket entirely. Which is worse than not measuring at all, because now you're confidently wrong. That's why we're picky about how things get measured, not just what we measure. A real number off a calibrated instrument and a plausible-looking number off an algorithm are not the same input, whatever the label says. (E.g., your watch is guessing at your VO2max from heart rate and pace. Against a metabolic cart it runs off by as much as 20%, enough to make things like derived heart rate zones completely useless.)
Third, you have to be able to move it. A measurement earns its keep when it's a dial you can turn. Most of what gets sold to you is a verdict you receive. The best measures on this list are the ones where a known intervention reliably moves the number, and moving the number reliably improves the outcome. That closed loop (measure, act, re-measure, adjust) is our entire game.
Almost everything that survives all three, we collect. Almost everything else, we don't, no matter how good it looks on a chart.
What follows is the whole list. Every measure below gets its own article over the coming weeks, linked back here, so this page will eventually work as a table of contents. Some of these we can measure in a single visit. The others need a blood draw, a scanner, a sequencer, or weeks of continuous data. We start with the first group because it's fast, we can do all of it in-house at Reboot, and it points us in the right direction. We send clients out for the deeper dives when the deeper dives earn their keep.
What One Visit Can Tell You
There's a surprising amount you can learn about yourself in a single well-designed session: how big your aerobic engine is, what your metabolism actually burns, what you're (literally) made of, roughly how strong you are, how well you move. No lab, no genome, no months of data. Just the right instruments and about two hours. This is the battery we run at Reboot as our Baseline Assessment. It's normally $950, but we're currently comping it for the guinea pigs in our Founding 100 data collection project; apply here to do it free.
VO2max: the ceiling on your aerobic engine. The maximum rate your body can use oxygen, and probably the single most predictive number in all of health: the least fit carry four times the mortality risk of the fittest, an effect that dwarfs smoking, diabetes, and hypertension. Yet almost nobody has ever had theirs actually measured. (We've already written the full playbook on this one, five parts running from the research to the testing protocol: the VO2max Playbook.)
Resting Metabolic Rate: what you actually burn doing nothing. Most people manage their nutrition against a number they guessed, or pulled off an online calculator that's often wrong by 300–500 calories a day. A measured RMR swaps the guess for your real baseline, plus it tells you how efficiently you're burning fat versus carbs. It changes every decision about how much to eat, how to diet, and why the last cut stalled out at week six.
Body Composition: because the scale is lying to you. Bodyweight is one number hiding two that matter: how much lean mass you carry, and how much fat, ideally with both broken out limb by limb. Fat then splits again into what sits under your skin (subcutaneous, the less consequential of the two) versus what wraps your internal organs (visceral, which carries real consequences for longevity and health). This one's the difference between "I lost five pounds" (of what?) and knowing whether a program is building the tissue that protects your metabolism and your independence, or quietly costing it.
Grip Strength: thirty seconds, and one of the best mortality signals we have. Simple as it sounds, your hardest squeeze of a dynamometer is among the best-validated cheap proxies for whole-body strength and neuromuscular reserve, and it tracks all-cause mortality surprisingly well. (E.g., a 5kg drop in grip strength associates with a 16% rise in all-cause mortality.) Takes half a minute, but almost nobody measures it.
Functional Movement and Motor Control Screen: a window into where you'll break down, before you break down. A structured look at how you actually move: the asymmetries, restrictions, and compensations that predict injury, and that cap what your training can safely ask of you. You can't intelligently load a movement pattern you haven't looked at.
3D Body Scan: the structural baseline you can see but can't feel. A 3D scan catches posture, symmetry, and circumferences in a way a mirror and a body composition readout can't. Change here is slow and invisible day to day, which is exactly why it's worth having an objective snapshot to compare against later.
There's a short list of additions we think belong in a complete single-visit picture: the genuinely basic vitals almost everyone skips (blood pressure, resting heart rate), and a handful of sixty-second movement tests (balance, gait, sit-to-stand) that say more about how your next decade goes than most of what's in your medical chart. We'll cover those too, and where they fit.
What Labs and Time Can Tell You
The second group needs a blood draw, a scanner, a sequencer, or a stream of data collected over weeks. All of it is powerful, and all of it shares a catch: the result is rarely the point. The interpretation is. A CGM trace or a hundred-line blood panel is noise until somebody who knows your context turns it into a decision, which is exactly what our Outperform coaching is built to do.
The Blood Panel: your annual physical draws blood and mostly wastes it. The markers that move decisions most often aren't even on the standard order, and the useful signal usually lives in the granular numbers underneath the top-line ones, or in the relationships between them. (For example, almost everyone gets their LDL-C number, yet less than 5% get the more predictive ApoB alongside it.)
Continuous Glucose Monitoring: n=1 data on the food you actually eat. No population dietary guideline can tell you how your body handles your oatmeal. A CGM can. Worn for a couple of weeks, it turns generic advice into a personal map of what spikes your blood sugar and what doesn't. It's the rare consumer gadget that produces data about you specifically, and that you can do something with. But because it's a literal stream of data, it also takes serious analysis to turn it from an interesting graph into actionable insight.
Wearables: the weather report you keep mistaking for the measurement. Sleep stages, HRV, resting heart rate trends: useful for one thing (is my trend pointing the right way?) and routinely over-trusted for another (what's my exact number?). The value is in the direction of the line over weeks, not the decimal on any given morning. (Ties to our Stress Resilience Stack series.)
Genetic Sequencing: signal, and a lot of expensive horoscope-like noise. Your genome contains a handful of genuinely actionable findings and a much larger pile of low-effect associations that feel meaningful and change nothing. The skill is telling those apart, which is most of what that post will be about.
Coronary Artery Calcium: the one scan that sees the problem coming. A CT catches arterial plaque years before it announces itself, and it's one of the most decision-changing single numbers on this entire list.
The Microbiome: the honest frontier. Here's where we hold ourselves to our own filter. We test gut microbiome, and there are real insights to be had, but it's the diciest item on our list: for most consumer testing today it isn't actionable enough to clear the "should I really do this" bar. We'll explain why, what we test for anyway, and what we're watching for over the next few years.
The Short List Is the Point
You'll notice what this list is not. It's not sixty analytes. It's not every wearable metric with a trademark. It's a deliberately short set of measurements, each one picked because, done right, it changes a decision you're otherwise making blind.
That restraint is the whole idea. The longevity conversation doesn't have a scarcity problem. It has the opposite one. What's missing isn't more things to measure, it's the judgment to know which handful is worth the needle, the scan, or the two hours. Getting a result is easy. Turning it into a plan is the actual work, and keeping the list short is what leaves you room to do it.
What's Next
Over the coming posts we'll take each measure here and give it the full treatment: what it measures, what the research actually says, how to read your result, and what to do about it.
If you'd rather not wait to find out where you stand, don't.
To mark the launch of Reboot, A3's new performance lab at 515 Madison at 53rd, we're comping our Baseline Assessment to participants in our Founding 100 data collection project. Apply here to get the full battery: your real numbers, and a conversation about what to do with them. Not estimates from an app. The real measurements, and a plan.
Your doctor probably hasn't measured most of this. We will.