Metabolic Health
Heart rate is total metabolic flux. We split it into the parts that matter.
Indirect calorimetry sees only the aerobic share. A blood-lactate test sees one moment's glycolytic state. Heart rate sees both, integrated and continuous — but only if you can read it. TrueZone partitions the HR signal into aerobic and glycolytic flux from any wearable, no blood draws or lab visits required.
From that partition come the metrics metabolic-health applications actually need: MFI as the headline screening number, Gmax as the paired capacity to VO₂max, RZI as the at-rest fingerprint of substrate state, and endurance-aware energy expenditure that holds up against indirect calorimetry to 5.8% MAE.
Validation
R² = 0.99
MFI vs glycemic response
The Metabolic Fitness Index correlates with glycemic-control markers at clinical-grade strength — derived from heart rate alone, no blood draws required.
The Framework
Total metabolic flux = aerobic + glycolytic.
During exercise, ATP is supplied by two parallel systems: oxidative phosphorylation in the mitochondria (aerobic) and substrate-level phosphorylation in the cytosol (glycolytic). Indirect calorimetry sees the first directly — oxygen uptake reflects oxidative ATP production — but the glycolytic component, which produces lactate without consuming O₂, is invisible to it.
Heart rate is different. HR responds to the integrated cardiovascular demand: oxygen delivery, metabolite clearance, sympathetic activation, and acid buffering. It tracks total metabolic flux, not just the aerobic share. That makes HR the only continuous, wearable-grade window into both metabolic engines — and the central insight TrueZone is built around.
Steady-speed run · constant external power
External power is constant; muscular efficiency declines slowly as fatigue forces the recruitment of less-economical fibers. The aerobic component drifts up (the well-known VO₂ slow component); the glycolytic component drifts up faster (the lactate slow component). HR drifts because it tracks the total — this is the mechanism of cardiac drift.
Indirect calorimetry
Measures EE_aer via VO₂. Glycolytic ATP production is invisible; H⁺ buffering produces extra CO₂ that partially cancels the error in total EE but not in the partition.
Blood lactate
Single-point estimate of net accumulation in the blood pool. Confounded by production, clearance, and shuttle dynamics. Invasive; not continuous.
Heart rate (TrueZone)
Tracks EE_aer + EE_gly via integrated cardiovascular demand. TrueZone partitions the signal back into the two components without blood draws or gas-exchange equipment.
Two Paired Capacities
VO₂max + Gmax = Mmax.
VO₂max is the canonical aerobic-capacity number, in ml O₂·kg⁻¹·min⁻¹. Glycolytic capacity has historically been measured as a blood-lactate accumulation rate (e.g. VLamax), in different units, on a different scale — making the two impossible to compare. TrueZone's framework expresses glycolytic flux in oxygen-equivalent units, so VO₂max and Gmax live on the same axis. Their sum is total maximal metabolic capacity.
Endurance becomes a clean systems-level ratio: E = VO₂max / Mmax. High-E athletes carry most of their capacity aerobically; sprinters carry a much larger fraction glycolytically. The same total capacity can sit on either side of the partition — and the partition is the trainable axis.
Provisional athlete-type bands · ml O₂-eq · kg⁻¹ · min⁻¹
Athletes vary dramatically along this axis. Marathoners carry capacity almost entirely aerobically (E ≈ 0.95). Sprinters live at the other extreme, with a much larger glycolytic share (E ≈ 0.25). Triathletes (E ≈ 0.85), middle-distance runners (E ≈ 0.75), and 800 m / team-sport athletes (E ≈ 0.65) fill the range between. Bands are illustrative, not normative cut-offs.
MFI · Metabolic Fitness Index
A single 0–5 score that tracks like A1c.
MFI fuses four submaximal quantities TrueZone already fits per user: threshold speed, Endurance, cardiovascular range, and an allometric BMI term to keep size-bias low. The result is a single dimensionless number on a 0–5 scale, higher is better.
In the METFIT controlled-feeding study, MFI correlated with the Glycemic Response Index at R² = 0.99, supporting its use as a wearable-native proxy for glycemic control and metabolic flexibility. It is a screening / trending metric — not a diagnosis — but at population scale it does what step counts cannot.
Lactate threshold
Speed at the lactate threshold. Reflects oxidative capacity and metabolic economy. Higher threshold speed at the same cardiac cost = greater metabolic efficiency.
Aerobic share
The aerobic share of total maximal capacity. Captures fat-oxidation capacity, oxidative-fibre share, and threshold alignment in a single number — independent of VO₂max.
Aerobic range
Wider cardiac range = better cardiovascular reserve and autonomic function. Compresses with aging, obesity, diabetes; expands with training.
Allometric BMI
kg/m^2.5 reduces bias across short/tall and light/heavy individuals so MFI is comparable across populations.
Metabolic Zones · Slow Domain
Where daily life sits on the metabolic scale.
The slow domain (V0 → V1) is where most non-athletes spend the day. TrueZone splits it into three personalised bands anchored to the user's thresholds:
- Z0 · Fat zone — below P₀. Predominantly fat-oxidative, basal autonomic state.
- Z1 · Glucose zone — P₀ → P₀.₅. Mixed oxidative metabolism with rising glucose flux.
- Z2 · Lactate zone — P₀.₅ → P₁. Upper slow domain; fast-oxidative onset begins.
Resting Zone Index (RZI) is the Metabolic Gradient evaluated at the user's resting heart rate, on a 0–3 scale. Lower RZI = greater fat-dominant reserve and better autonomic efficiency. Two people with the same absolute RHR can land in entirely different metabolic zones, because thresholds are individualised.
Glycolytic Blunting
The metabolic-decline signature, trackable from a walking HR stream.
Aging, obesity, and Type-2 diabetes arise from different primary drivers, but they converge on the same phenotype: reduced capacity to generate and sustain high glycolytic flux, accompanied by a blunted upper-range heart rate response. Fast-twitch fibers are lost or denervated; glycolytic enzyme activity declines; intramuscular fat disrupts insulin signalling; sympathetic drive falls. The result is a compressed cardiovascular range and a shifted slow-domain zoning.
The same easy walking pace produces a different internal state across phenotypes. A young athletic individual rests deep in Z0 with RZI ≈ 0.65 and MFI ≈ 4.3. A middle-aged overweight individual's RHR sits in Z1 (RZI ≈ 1.5, MFI ≈ 1.5). An elderly frail individual's RHR sits in Z2 (RZI ≈ 2.1, MFI ≈ 1.7) — the cardiovascular cost of stillness has risen, before fasting glucose or HbA1c move.
Slow-domain zones (Z0 / Z1 / Z2) shift down with declining metabolic fitness. RHR rises into higher zones; thresholds fall; RZI moves up; MFI moves down. The same external world feels different internally — and the difference is measurable from a passive HR stream.
Why the upper HR range collapses
HRmax is not purely cardiac. The upper range of the HR response is constrained by glycolytic capacity: when ATP turnover cannot rise sufficiently, oxygen demand plateaus earlier and the cardiac output required to service it never materialises. β-adrenergic responsiveness also falls with age and metabolic disease, lowering the autonomic ceiling. The decline in HRmax reflects both reduced central drive and reduced peripheral metabolic demand.
What this enables clinically
Continuous, individualised, passive monitoring of glycolytic-blunting trajectory — from a wearable the user already owns. The signal moves before fasting glucose and HbA1c. For aging studies, GLP-1 trials, pre-diabetes screening, and lifestyle-intervention programs, this is a continuous metabolic-flexibility endpoint that periodic blood draws cannot reach.
Energy Expenditure
Endurance-aware calories, lab-grade from HR alone.
Generic HR-to-calorie regressions assume a fixed HR–VO₂ slope that doesn't exist individually. TrueZone classifies each beat as Base / Activity / Food and applies user-specific factors (BF, AF, FF) calibrated from a brief submaximal protocol. Because HR tracks total flux, the EE estimate stays accurate even when glycolytic metabolism contributes substantially — an intensity range where lab calorimetry under-counts.
In treadmill validation against indirect calorimetry, the model tracked rest → walk → run → recovery with 0.25% MAE (METFIT). Across mixed field activities (FITSILVER Eurostars with CSEM, Switzerland), MAE was 5.8% (±2.6%) — outperforming accelerometry baselines.
Energy Intake (Pilot)
From HR to EI — no food log.
After eating, HR rises in a thermogenic wave that peaks ~45 min post-meal and decays over ~5–7 h — the cardiovascular signature of the thermic effect of food. TrueZone fits gamma/Weibull-like waves to those minima, allocates beats as Food / Activity / Base, and converts to kcal via a personalised Food Factor (FF).
In a controlled feeding trial (n=8, METFIT), measured intake was reproduced with −4.8% mean deviation (range 0.7–9.8%), with average TEF ≈ 12.6%. The signal exists; broad validation is in progress. Combined with EE, this opens a closed-loop, food-log-free energy balance from one wearable.
EI estimation is research-grade today — the partition framework above (Mmax, Gmax, EE, MFI, RZI) is exposed in the production SDK; EI follows after broader validation. The production SDK already returns the Food Factor (FF) coefficient that the EI fit needs.
Validation
Two clinical studies. Three papers submitted.
Metabolic claims have been validated in METFIT (PhD, University of Iceland) and FITSILVER (Eurostars, Driftline + CSEM Switzerland) — 70 participants, three papers submitted.
MFI vs glycemic response (METFIT)
In the controlled-feeding trial, MFI correlated with the Glycemic Response Index at clinical-grade strength.
EE MAE — field / treadmill
Versus indirect calorimetry. Treadmill validation (METFIT) and mixed-activity validation (FITSILVER with CSEM, Switzerland).
EI deviation, feeding trial (n=8)
Pilot: HR-only EI estimation reproduced standardised meals at -4.8% mean deviation. TEF ≈ 12.6%. Broader validation in progress.
Where it lands
Continuous metabolic-health intelligence from a wearable the user already owns.
Digital health platforms
MFI as a single screening number replacing step counts. RZI as a passive metabolic-strain signal computed from rest windows.
Clinical research
Continuous glycemic-correlated metric (R²=0.99 vs GRI) for trial endpoints — no blood draws, no lab visits, no per-protocol burden.
Aging and obesity
Glycolytic-blunting trajectory tracked from a walking HR stream. RZI/MFI shift before fasting glucose or HbA1c move.
Pharma / weight management
GLP-1 trials, lifestyle interventions: continuous EE, EI (pilot), and metabolic flexibility from one device. Closed-loop coaching.
Corporate wellness
Workforce-scale screening from existing wearables. Identify metabolic-risk drift months before clinical symptoms appear.
Insurance / risk stratification
Wearable-native metabolic-risk proxy. Continuous, individualized, validated against clinical references. Compliant with passive-monitoring designs.
Heart rate is total metabolic flux. The partition is what makes it actionable.
MFI · RZI · Gmax · EE · EI — one validated SDK, trackable continuously from any HR-capable wearable. Patent runway to 2045.