InVivo is now built to identify preventable healthspan transitions, assign governed low-cost pilots, adapt the UX around a chosen modality focus, measure adherence and outcomes, and report aggregate value without crossing into diagnosis, prescribing, or medication changes.
Source-grounded events, governed pilot loops, consented cohort learning, and aggregate economic endpoints.
metabolic, cardio, recovery, inflammation, function
eligibility, adherence, outcomes, safety boundaries
iOS Twin readiness and Android domain implementation
small-cell suppression before aggregate display
glucose, care-plan adherence, biosignal — secure aggregation only
InVivo starts with the device you already have. The phone can capture meals, sleep sessions, acoustic sleep context, camera-based wellness summaries, manual biomarkers, documents, and trends.
Take a photo, speak what you ate, or enter it manually. Adaptive meal logging keeps the flow clear in light or dark mode before any sensor is connected.
Use the iPhone overnight for a visible, opt-in SleepInsight session with charging readiness, microphone permission, night mode, source fidelity, correction labels, and derived summaries by default.
Run a short phone-camera wellness scan for derived heart rate, respiration, HRV, signal quality, and confidence metadata. Raw video is not retained.
Enter fingerstick values or ketone readings by hand and InVivo still computes paired GKI, trends, and timeline context.
Import blood tests, lipid panels, FHIR JSON, PDFs, and CSV files so clinical markers sit next to daily behavior.
Meals, symptoms, supplements, labs, phone sleep, and Bio Scan summaries are enough to start seeing patterns.
The population-health opportunity is not another dashboard and not a single biological-age score. It is a consented loop: source-grounded events, preventable transition models, governed low-cost interventions, CarePlan packets, Triager handoffs, measured outcomes, economic endpoints, and honest safety boundaries.
InVivo can support the hard part of population health: identifying preventable deterioration early, matching people to low-cost actions, measuring whether those actions worked, using SleepInsight to measure recovery regularity and breathing-load trends, following standardized CarePlans through consent-scoped packets, Triager handoffs, compliance and evidence, and reporting value without pretending sparse data is certainty.
source, time, confidence, consent
fidelity, caps, corrections
recency, missingness, device tier
purpose, inputs, boundaries
11 protocols, safety, outcomes
packet, handoff, compliance
aggregate-safe healthspan value
Stronger claims require stronger evidence. InVivo can begin with personal n-of-1 experiments and prospective pilots, then move toward cohort validation only when calibration, missingness, fairness, drift, correction rate, and external validation are satisfied.
Every signal can carry time, source, method, confidence, privacy class, consent scope, and source receipt metadata so population models are built from auditable evidence instead of unstructured wellness notes.
The value is not a vague health score. InVivo models transitions that can be prevented or delayed: worsening metabolic recovery, cardiovascular marker momentum, SleepInsight recovery debt, persistent inflammation context, and functional decline.
The native model layer implements measurable protocols with eligibility, required inputs, preferred inputs, burden, cost, measurement windows, prohibited-use withholding, and aggregate-safe reporting.
Standards-backed CarePlans now become consent-scoped Clinician Packets: included, withheld, stale and missing evidence; source receipts; Triager handoff context; observed effects; and reviewable signals for how a plan may need clinician review.
Sleep becomes a source-grounded nightly recovery event: source fidelity, sanity validation, user corrections, breathing-load boundaries, HRV/resting-heart-rate context, and derived-feature cohort eligibility.
Individuals get personal feedback while consented cohorts can reveal which low-cost actions work, where data is missing, and which groups need better measurement before stronger claims are made.
Population health value is measured through avoided duplicated labs, completed follow-up, lower user burden, clinician minutes saved, low-cost intervention adherence, and healthier daily function.
Population reporting is aggregate-safe, small-cell suppressed, consent-scoped, and explicit about what remains personal, what can be shared, and what is never diagnosis or medication advice.
Transition: Stable metabolic pattern -> poor glucose recovery or higher-risk lab band
Transition: Stable marker pattern -> unmanaged BP, lipid, glucose, or recovery momentum
Transition: Stable recovery -> SleepInsight recovery debt, lower adherence, or fewer functional days
Transition: Weak context -> persistent abnormal marker pattern needing clinician discussion
Transition: Stable function -> mobility decline, lower strength reserve, or falls context
Population Healthspan Value equals avoided risk transitions, improved functional capacity, better validated biomarkers, and healthier daily recovery minus intervention cost, unnecessary utilization, user burden, clinical burden, and governance risk.
metabolic, cardio, recovery, inflammation, function
every declared intervention has a measurable protocol
packets, handoff, compliance, outcome review
sleep/wake, acoustic, recovery, uplift, metabolic response
care-plan adherence round, consent-gated, secure aggregation
iOS Twin readiness plus Android domain parity
minimum cohort cell before aggregate display
InVivo now has a measurable pilot layer for every declared population-healthspan intervention. Post-meal walking keeps the deepest glucose-specific outcome scoring; the full suite adds structured eligibility, CarePlan activity assignment, consent-scoped Clinician Packets, stale or withheld evidence, Triager handoffs, prohibited-use withholding, follow-up assessment, and small-cell-suppressed cohort reports across all five model families.
Check source coverage, protocol-specific required inputs, missing preferred inputs, readiness, and prohibited medical-use triggers before assigning a pilot.
Select a declared protocol or standardized CarePlan activity with an action label, measurement window, required source context, and safety boundary.
Capture completion, burden, source count, SleepInsight correction labels, Clinician Packet readiness, stale or withheld evidence, and safety handoffs rather than assuming a recommendation was followed.
Measure response using predefined endpoints such as glucose recovery, BP coverage, SleepInsight sleep regularity, packet follow-up, functional days, duplicate testing avoided, or CarePlan goal movement.
Report only cohort-safe counts, adherence, missingness, response distribution, burden, economic endpoints, and plan-modification hypotheses with small-cell suppression.
meal adherence and 30/60/120-minute glucose response
overnight recovery, morning glucose or ketone context
packet review, activity minutes, weight/waist or lab follow-up
reading consistency, source provenance, follow-up readiness
clinician review, duplicated tests avoided, source-document utility
source-fidelity sleep timing, HRV/resting HR, correction rate, recovery debt, functional-day reports
accepted suggestions, correction rate, next-day recovery
repeat-lab completion, escalation preserved, duplicated explanation avoided
symptom resolution, recovery movement, lab-anchor timing
session adherence, walking consistency, soreness, functional-day report
mobility-check completion, walking trend, escalation completion
Prepare better pre-visit context, reduce duplicated history taking, and identify which standardized CarePlans produce measurable follow-through using Clinician Packets before escalation.
Run consented, source-grounded observational pilots with explicit missingness, device tier, standards evidence, subgroup calibration, and outcome-window capture.
Evaluate aggregate healthspan programs through CarePlan packet readiness, compliance, burden, avoided duplicate testing, functional days, and privacy-safe cohort summaries rather than surveillance.
Keep personal insight useful while packaging source receipts, medicine/supplement context, and clinician questions when the next step belongs in care.
Instead of pretending every signal has the same certainty, InVivo labels which evidence tier is active and keeps the original source visible.
Meals, Bio Scan, SleepInsight phone sessions, symptoms, manual readings, and journal context.
Apple Health, CGMs, watches, rings, chest straps, scales, and sync recency.
Blood tests, lipids, CMP, inflammation markers, PDFs, FHIR JSON, and CSV imports.
Symptoms, medicines, supplements, protocols, visit notes, and questions for review.
Radiology reports, DEXA, ultrasound, MRI, and third-party deep-assessment imports.
Likely contributors: shorter sleep, lower Bio Scan HRV, later meal timing, and a recent bloating symptom. This is a pattern summary from your timeline, not a diagnosis.
Every explanation can point to source type, modality, date, confidence, and whether the signal came from a phone, device, lab, clinician note, or imported report.
Real AHA PREVENT science (Khan et al., 2023), computed entirely on this page — move a slider and the number updates, and nothing is ever sent anywhere. The same on-device engine the app ships.
Educational cardiovascular-risk awareness (AHA PREVENT, Khan et al. 2023). Not a diagnosis — discuss prevention with a clinician.
Track this continuously with InVivoDozens of health models work together on your device, across every major system — so your insight is broad, personal, and private by design.
Cardiovascular risk, ECG insight, and early-warning triage.
Glucose patterns, forecasting, and metabolic balance.
Sleep quality, snoring, and breathing-load screening.
Nervous-system balance and recovery capacity.
Wellness-grade biological- and functional-age context.
Educational genetic context from your own file.
Digestive patterns and microbiome report context.
Validated mental-wellness screeners and resets.
Hands-free wellness signals from your phone camera.
Effortless meal capture and nutrition estimates.
How your surroundings shape your health.
Make sense of labs and prepare for your clinician.
A source-grounded twin that cites or stays silent.
Wellness, education, and clinician-discussion tools — not a diagnosis. Stronger outputs stay gated until they pass independent validation.
Most health apps send your data to their servers. InVivo flips it: the models come to your data, not the other way around.
Every model analyzes your raw signals locally. Your ECG, glucose, photos, labs, and genome never leave the device.
If you opt in, your phone shares only tiny, math-masked model updates — never your data. They’re encrypted and combined with thousands of others, so your contribution can’t be singled out. Even we can’t see it.
The smarter model comes back to your device. Your data stayed with you the whole time.
Nothing is shared. Everything stays on your device.
Plot each heartbeat interval against the next and a healthy heart traces a broad “comet” — and that breadth is complexity. Much of cardiac disease is a loss of complexity: the dynamics becoming too regular, or dissolving into turbulence. It's the nonlinear-dynamics story behind InVivo's chaos and Poincaré biomarkers.
Phase space · stable limit cycle
A stable limit cycle — the orbit the heart snaps back to after a perturbation.
Return map · fat comet (high complexity)
A broad, fractal “comet.” A healthy heart is not a metronome — complexity is health.
Return map · diffuse cloud (irregularly irregular)
The comet collapses into a round cloud — almost no correlation between beats.
Phase space · period-2 (period-doubling onset)
A period-2 orbit (long-short-long-short) — the first whisper of chaos.
Phase space · fast monomorphic loop
A single fast re-entrant circuit — still periodic, just dangerously fast.
Phase space · strange attractor (deterministic chaos)
A strange attractor — bounded chaos that never repeats, exquisitely sensitive.
Illustrations of cardiac dynamics, not readings of a heart. The two return maps are stochastic surrogates and the fibrillation panel is a low-dimensional stand-in for high-dimensional tissue chaos — the shapes (comet vs. cloud, limit cycle vs. strange attractor) are the genuine signatures. InVivo plots your real Poincaré comet on-device.
The chaos, Poincaré, and Cardiac Dynamical Deviation biomarkers read the beat-to-beat geometry of your heartbeat intervals — so they need continuous, electrode-grade R-R. Optical wrist sensors sample intermittently and drop beats under motion: excellent for trends, but not for the beat-to-beat structure these features depend on. A Polar H10 streams near-ECG interbeat timing continuously, and InVivo reserves its heart-dynamics readouts for that class of signal.
Any continuous chest-strap source works — we simply find the Polar H10 the most reliable. Wrist and finger sensors still power the rest of InVivo.
InVivo Experiments turns wellness folklore into small, safe, falsifiable personal experiments — then aggregates the results through federated evidence. A truth engine, not a recommendation engine.
“Magnesium improves my sleep.”
Does magnesium glycinate nightly for 14–28 days improve your sleep latency by at least 15 minutes versus control — beyond your normal week-to-week variation?
InVivo is built for people who want a serious personal health system without being forced to buy a stack of hardware first. Add devices, labs, imaging reports, and clinician context when you want higher fidelity and better source-grounded answers.
Preview intake is handled directly until a public signup endpoint is live.