This is not a wellness program with better branding. It is a new category, the missing layer between continuous workforce behavior and employer healthcare cost, built to operate with the same forward looking discipline as every other financial planning function in the enterprise.
The ability to read metabolic health as a leading indicator, not a lagging one. Built on daily biometric signals, blood pressure, weight trajectory, waist circumference, fasting and sleep windows, body composition, interpreted through a proprietary model that produces a single composite risk score, the METRA Score. Updated continuously. Predictive in nature. Individual in application, aggregated and de identified at the population level.
The projection of future health states and the cost they imply, based on current behavior and metabolic trajectory. Thirty, sixty, and ninety day forecast bands, updated continuously as behavioral data accumulates. The result is a healthcare cost model that behaves like a revenue model, with confidence intervals, trend lines, and the ability to model intervention scenarios before they are committed to.
Wellness programs measure participation and engagement. Healthcare claims measure outcomes that have already been incurred. Between those two endpoints sits the layer that has never existed: the continuous, model driven translation of workforce metabolic state into a financial forecast that finance can act on.
Wellness programs, on one side, were built around step counts, engagement scores, and well intentioned but financially disconnected metrics. Healthcare claims, on the other side, are reported twelve to eighteen months after the underlying risk has formed. Metra sits between them. Metabolic signals become a risk score. The score becomes a financial forecast. The forecast becomes an intervention plan. The plan becomes a measurable shift in the trajectory before the claims arrive.
The METRA Score is a composite metabolic health index updated continuously as employee data changes. It is built from four input categories. Primary metabolic markers include fasting glucose, HbA1c, waist circumference, body mass index, and blood pressure, anchored to the NCEP ATP III metabolic syndrome criteria and ADA diagnostic thresholds. Behavioral risk inputs include physical activity frequency and intensity, dietary pattern scoring, sleep quality, and a stress index. Trajectory signals capture thirty, sixty, and ninety day change in primary markers and the directional velocity of metabolic indicators, anchored to the Tabák 2012 longitudinal trajectory model. Engagement data captures coaching session completion, milestone achievement, and platform interaction frequency.
The score is the input. The forecast is the output. Between them sits a model that has been built on the published clinical literature, calibrated to the metabolic syndrome cluster that drives the majority of employer healthcare spend, and structured to operate at the population level with strict individual privacy controls.
Individual data is never exposed to the employer. All workforce level insights are anonymized and aggregated. Employees benefit individually from the coaching, the score, and the trajectory. Employers see population level intelligence, departmental cohort views with minimum cohort thresholds, and forecast bands with confidence intervals. Privacy is not a feature on top of the platform. It is the architectural premise that makes population level analytics possible.
The fastest way to understand what the platform does is to see the forecast bands produced against a representative population. Model the exposure, download the actuarial brief, and request an enterprise briefing for a deeper walkthrough.