Bloodwork Goes Consumer: What the Whoop, Oura & Function Lab-Testing Boom Means for Health Apps

More than 350,000 people joined a waitlist to give a fitness company their blood.

That was Whoop, the wearable known for recovery scores and strain, previewing Advanced Labs: a clinician-reviewed blood-testing service, run through Quest Diagnostics, that shipped in September 2025 [3]. A few months later Oura, the smart-ring maker, rolled out Health Panels, letting members tie 50 blood biomarkers to their daily sleep and readiness [4]. Function Health had already turned a 160-biomarker panel into a subscription [1]; Superpower raised $30M to build a “health super-app” around bloodwork [2]. The message across the industry is the same: the wearables measured the outside of your body, and now everyone wants the inside.

For anyone building health products, this is the most consequential data shift since wearables went mainstream, and it’s easy to misread. The wearable era measured your behavior; this next one wants your biology, and it’s on track to repeat every data-fragmentation mistake wearables already made with something far more sensitive. It’s also not only a health story: as you’ll see, blood testing is the best subscription-and-retention play a hardware company has found in years. So here’s the field guide: who actually offers blood testing and what they collect, what’s really in a blood panel, why it’s happening, what the boom gets wrong, and what the collision of continuous and episodic health data means if you build on it.

Who’s offering blood testing, and what they collect

What’s emerged is a wave of subscription and add-on services, most of them riding the same two labs (Quest and Labcorp) underneath, differentiated mostly by biomarker count, collection method, and how tightly they tie results to everyday data. Here’s the landscape as it stands in 2026:

Read across the table and three patterns jump out:

• The wearable companies are converging on labs. Whoop and Oura both added blood testing within months of each other, and both frame the value the same way: periodic bloodwork gives their continuous data context. Whoop calls it a feedback loop: a blood test in the morning informing the recovery coaching that evening [3]. Oura literally links each of its 50 biomarkers to your sleep, readiness and activity trends [4].
• The labs underneath are consolidated. Function, Whoop and Oura all run their draws through Quest Diagnostics [1][3][4], and the rest lean on the same national-lab infrastructure (Quest and Labcorp). The startups are largely a software-and-membership layer on top of decades-old clinical infrastructure. That matters for builders: the data is more standardized than the dozen brands make it look, but the access is not.
• Collection is less varied than the branding suggests, and it’s the real friction. Function, Whoop and Oura all funnel to the same place: a standard venous draw by a phlebotomist at one of Quest’s roughly 2,000 sites [3][4]. InsideTracker uses a phlebotomist draw too. The genuine outlier is SiPhox, whose at-home, needle-free EasyDraw device collects from your upper arm and ships to the lab [5]. That draw, not the biomarker count, is the thing most likely to decide whether someone tests twice a year or once and never again, and everyone except the at-home kits shares the same choke point.

One caution on the headline numbers: more biomarkers is not automatically better. Past the standard panels, the marginal markers vary enormously in how actionable they are, and a bigger panel on a healthy person mostly increases the odds of an incidental, hard-to-interpret result (more on that below).

Beyond the wearable players

The six above are the ones tying blood to continuous data (the focus of this piece), but they sit inside a larger consumer-testing market worth acknowledging:

• Mail-in DTC pioneers. Everlywell (30+ kits, sold in Target, CVS and Walgreens) and LetsGetChecked have offered at-home, mail-in blood tests since the mid-2010s [9]. Higher volume and lower friction than the longevity startups, but fewer biomarkers per kit and no ongoing clinician relationship.
• Clinician-led programs. Lifeforce pairs a broad panel with doctors and coaches; Everlab (Australia) goes further, bundling 100+ biomarkers with imaging and genetics under a longevity-trained physician, from about AUD $299 to $2,700 [10]. These are programs, not data feeds.
• The scan cousins. Neko Health, Prenuvo and Ezra image your insides (full-body sensors or MRI) rather than test blood. It’s a different modality riding the same “know what’s inside” impulse.

The common thread: what used to be a once-a-year physical is fragmenting into a dozen consumer products at different price points and depths, which only sharpens the integration problem for anyone building on the data.

What’s actually in a blood panel

Under the marketing, most of these services are built on the same handful of decades-old clinical panels, then layered with a set of “advanced” longevity markers where the real differentiation (and the real debate) lives. If you’re building on this data, knowing the standard vocabulary is essential, because it’s the closest thing to a schema the space has.

The conventional annual workup is roughly five tests [8]:

That “standard health panel” (CBC + CMP + lipids + a urinalysis + TSH) is what most annual physicals order. The consumer platforms then layer on the advanced markers that the longevity movement cares about:

• ApoB (Apolipoprotein B) counts the actual number of artery-clogging particles rather than just cholesterol content. It’s increasingly considered a better cardiovascular predictor than a standard lipid panel, and up to ~17.5% of people have dangerously high ApoB despite normal cholesterol [8], exactly the kind of “hidden” finding that sells a 100-biomarker test.
• hs-CRP (high-sensitivity C-reactive protein) is a marker of systemic inflammation tied to cardiovascular risk [8].
• Plus fasting insulin, hormones (testosterone, estradiol, thyroid panel), vitamin D, ferritin, omega-3 index, and biological-age estimates.

This layering is the whole game. The standard panels are commodity: every lab runs them identically. The advanced markers are where Function’s 160 and Superpower’s 100+ justify their price, and where the “optimize your biology” narrative lives. It’s also, not coincidentally, where interpretation gets hardest.

Why blood is going consumer now

Three forces converged. The longevity movement created demand, direct-to-consumer labs and cheaper assays created supply, and the wearable companies needed blood to complete a picture their sensors can’t finish.

• The longevity wave created the demand. The “optimize your biology” culture (biological-age tracking, the Bryan Johnson effect, longevity apps chasing biological age) primed millions of health-conscious consumers to want their ApoB and hs-CRP, not just their steps. The GLP-1 era added a metabolic-health audience watching glucose and lipids closely.
• DTC labs and lower costs created the supply. Quest and Labcorp built consumer-facing arms; assay costs fell; and telehealth normalized clinician oversight without a clinic visit. A subscription wrapping a Quest draw became viable in a way it wasn’t a decade ago.
• Wearables hit a sensor ceiling. A ring or strap can infer heart rate, sleep, and strain, but it can’t tell you your cholesterol, your thyroid, or your hormones. To keep expanding “what we know about your health,” the wearable companies had to reach past the sensor, into the bloodstream.

Underneath all three is a single thesis the whole industry now shares: continuous data (what your body is doing, minute to minute) plus episodic data (what’s inside your body, a few times a year) is a more complete picture than either alone. Whoop’s feedback loop, Oura’s biomarker-to-readiness linking, and Superpower’s data-fusion pitch are all bets on the same convergence.

Why the wearable companies are really doing it

Blood testing solves a hardware company’s two hardest problems at once, recurring revenue and retention. This is the part the “complete picture of your health” marketing leaves out, and it’s worth stating plainly.

Selling devices is a brutal business: the purchase happens once, and then the customer can drift away. A subscription blood panel changes both sides of that equation. It converts a one-time buyer into a recurring payer, and it builds a moat out of your own history. The longer Oura or Whoop holds your longitudinal biomarker record alongside your daily data, the more it costs you to leave and start over somewhere with none of your baseline.

Read Dexcom’s $75M investment in Oura in that light [4]. It isn’t a marketing partnership; it’s vertical integration, a continuous-glucose-sensor maker buying its way onto the most popular health-tracking surface so its data and Oura’s flow both directions. The strategic pattern across the industry is identical: whoever holds the most complete, longest-running picture of a person owns the relationship, and blood is the fastest way to make that picture more complete.

None of this makes the health value fake. Better data genuinely can produce better guidance. But “we added blood testing” and “we found a durable, defensible business model” are, for these companies, the same sentence, and reading the trend only as a health story misses why the money is moving.

The catch: what the bloodwork boom gets wrong

The tests are run by accredited labs, so the assays are sound, but broad panels sold to healthy people create real problems of interpretation, incidental findings, and accountability that the marketing glosses over. This is the part a responsible builder has to internalize before wiring blood data into a product.

An NPR investigation in April 2026 put it bluntly: wearables now offer blood tests, and the results may confuse patients [6]. The specific failure modes:

• Incidental findings and false positives. Reference ranges are typically defined to cover about 95% of a healthy population, which means each marker has roughly a one-in-twenty chance of flagging a perfectly healthy person. Run 160 of them and a healthy customer should expect several out-of-range results from chance alone (correlated markers soften the arithmetic, but the direction holds). Each flag can trigger anxiety, unnecessary follow-up testing, and cost: the classic overtesting problem, now sold as a subscription.
• Reference range vs. “optimal.” The longevity platforms often flag results against aggressive “optimal” targets rather than standard clinical reference ranges, which makes far more results look actionable than a physician would treat.
• Missing medical context. A biomarker without the person’s history, medications, and symptoms is a number, not a diagnosis. The platforms lean on third-party physician networks to review results, but critics note those arrangements can blur who’s actually accountable for a finding, and the companies position the product as a complement to ongoing care, not a replacement [6].
• It’s an affluent-tier product, for now. A $500-a-year optimization panel is not evenly distributed. The boom is building the richest health datasets for the people who arguably need the screening least, a two-speed reality worth naming honestly even as costs fall.
• Consent and sensitivity. This is among the most sensitive data a person owns. Ingesting it raises the compliance bar (PHI handling, consent scope, storage) well above activity or sleep data.

None of this means the trend is hype. It means the honest version of “blood data in your app” comes with guardrails: clinical context, conservative interpretation, and a clear line that you’re informing, not diagnosing.

Wellness or medicine? The line that decides the market

Every one of these products lives on an unresolved boundary between wellness and medicine, and how regulators and clinicians draw that line will shape the market more than any feature.

The platforms are careful to describe themselves as a complement to care rather than a replacement, and they route results through third-party physician networks so a licensed clinician technically stands behind each order [6]. That structure is a regulatory accommodation as much as a clinical one. The FDA polices health claims, not the underlying tests, so a product stays on the comfortable side of the line by informing rather than diagnosing.

But the boundary is genuinely contested, and that’s the real risk to the category, larger than any single product. Primary-care physicians inherit the downstream work when a patient walks in with 160 markers and a spreadsheet of “optimal” ranges. Accountability for a flagged result is diffuse when the ordering clinician is a contracted network the member never actually meets [6]. If that wellness framing frays, or a high-profile harm pushes regulators to redraw the line, the compliance burden on everyone building in this space steps up at once.

There’s also a recent precedent for how fast a consumer-biology category can unwind. Consumer genetics ran this same arc a decade ahead: explosive growth, commoditization, and then 23andMe’s 2025 bankruptcy, which put the genetic data of millions of customers into a sale process and forced regulators and users to confront what happens to the most personal data a company holds when the company itself fails. Blood-panel startups are accumulating a similarly sensitive asset on similarly untested business models. It would be lazy to predict the same ending; it would be careless not to notice the same shape.

So treat the wellness/medicine boundary as the load-bearing assumption of the whole category. If you build on this data, design as if that line could move, because eventually it will be tested.

What this means for builders

The most useful way to read the bloodwork boom isn’t as a gold rush or a bubble. It’s a new data stream maturing, with all the fragmentation that implies. Three things follow if you’re building on health data.

1. Labs are becoming a source you can build on, alongside wearables, not instead of them. The value the whole industry is racing toward is the fusion: continuous wearable signals given context by episodic bloodwork. What that unlocks depends on what you’re building, and it’s genuinely useful only where physiology drives the experience:

• Fitness and performance: a recovery or readiness score that factors in ferritin, testosterone, and inflammation, not just last night’s sleep.
• Nutrition and metabolic: meal and habit guidance anchored to real HbA1c, lipids, and continuous glucose, the backbone of the GLP-1 wave.
• Longevity and wellness: biological-age and optimization features that need the bloodwork to mean anything.
• Supplements: deficiency-driven recommendations (vitamin D, ferritin, omega-3) that land far harder when they’re measured, not guessed.
• Telehealth and care programs: chronic-condition monitoring where labs plus daily signals beat either one alone.

Note what’s not on that list. A product with no physiological hook doesn’t get more useful because it can read your cholesterol. Blood data is powerful where the body is the product, and noise everywhere else, so the honest question isn’t “could we add it” but “does biology actually change what our product does.” Where it does, the opportunity is the combination, which is exactly where most apps in those verticals have nothing today.

2. The data is fragmented, and none of it is portable. Picture one real user a year from now: their annual physical lives in their doctor’s Quest portal, their Function results in Function’s app, and their Whoop panel inside Whoop, with the same marker measured three times, displayed against three different “optimal” ranges, and none of the three systems aware the others exist. Results arrive as PDF uploads as often as structured feeds; the standard panels map to clinical codes but the advanced markers drift brand to brand; units and reference ranges differ. Worse, a member’s blood history is effectively locked inside whichever app ordered it, with no standard for moving it out. Combining a Function panel, a Whoop upload, and an Oura Health Panel into one coherent record is the same normalization problem you already face across wearables, now with a second, episodic data type on top and a higher compliance bar. This isn’t a temporary rough edge of an early market; it’s the structural default, and it compounds every time a new vendor or data type appears.

3. Whoever unifies continuous and episodic wins. Whoop and Oura are building that fusion inside their own walls, for their own members, the same platform pattern as every other health feature. For anyone building across iOS, Android, and multiple data sources, the durable need is a layer that ingests wearable and lab data, reconciles the panels and units, and turns both into biomarkers and scores your product can read the same way everywhere. That’s the layer we built Sahha to be: phones, wearables, and labs, normalized into one stream, so when the next Function or Whoop ships, it’s a new input you adopt, not a new integration you rebuild.

Where this goes next

Two arcs are worth watching, because they change what you should build toward.

The first is the move from episodic to continuous. A blood draw twice a year is a snapshot, and the history of health data is snapshots turning into streams. Continuous glucose was the first blood biomarker to make that jump into consumer hands; continuous lactate, cortisol and other analytes are in development. The moment more of them go consumer, “a panel twice a year” starts to look like a stopgap, and the episodic and continuous worlds this article treats as separate begin to merge.

The second is that as the testing itself commoditizes (the same Quest assay sits under every brand), the value migrates from collecting the data to interpreting it: turning a wall of markers into something a person, or an app, can actually act on. Interpretation, cross-referenced against continuous signals and a personal baseline, is where the durable advantage moves once the raw test is a commodity.

Both arcs point the same direction: more signals, arriving more often, from more sources, all needing reconciliation.

The bloodwork boom is real, and it’s early. The companies turning lab panels into subscriptions have proven the demand; what they haven’t solved (and won’t, because it’s not in their interest to) is making all of that data interoperable for the products built on top. That gap is the opportunity. Blood is just the newest signal; the hard part, as always, is making every signal speak the same language.