Member Health Data Unified Schema
Member Health Data Unified Schema
Introduced in v1.0.72
Overview
The Member Health Data Unified Schema is the normalised data model that sits at the core of the platform's health and readiness layer. It provides a single, consistent structure for ingesting, storing, and querying member health data regardless of which wearable device or integration it originates from.
This schema is the foundation for all readiness scoring logic and AI-driven training personalisation.
Why a Unified Schema?
Different wearable devices and health platforms expose data in different formats, units, and cadences. Without normalisation, consuming data across integrations would require bespoke handling at every layer of the application. The unified schema solves this by:
- Abstracting device-specific formats — all integrations map their raw payloads into the same structure before persistence.
- Enabling consistent querying — readiness scoring, trend analysis, and AI models operate against a single predictable shape.
- Supporting future integrations — adding a new wearable requires only a mapping adapter; the rest of the pipeline remains unchanged.
Health Data Dimensions
The unified schema captures the following categories of member health data:
| Dimension | Description |
|---|---|
| HRV | Heart Rate Variability — inter-beat interval data used to assess autonomic nervous system recovery state |
| Sleep | Sleep duration, stage breakdown (light, deep, REM), and device-provided sleep quality scores |
| Resting Heart Rate | Baseline cardiovascular metric captured at rest, typically overnight |
| Readiness Score | Composite recovery and readiness indicator as reported by the source device or integration |
| Strain | Exertion and training load accumulated over a given period |
| Nutrition Macros | Daily macronutrient intake — protein, carbohydrates, and fat |
Role in the Platform
Readiness Scoring
The platform's readiness engine reads normalised health records from this schema to compute a daily readiness score for each member. By working from a unified model, the scoring algorithm is device-agnostic — a member switching from one wearable to another does not disrupt their readiness history or trend data.
AI Personalisation
The AI programming and recommendation layer uses the health data captured in this schema to adapt training to each athlete's current state. For example:
- A low HRV reading combined with poor sleep may trigger a deload or active recovery recommendation.
- High readiness scores unlock higher-intensity programming options.
- Nutrition macro trends inform fuelling recommendations alongside training load.
Integration Notes
- All wearable integrations that send health data to the platform must map their payloads to the unified schema via a dedicated adapter.
- The schema is designed to be append-only at the record level — each sync from a device produces a new timestamped health record rather than overwriting existing data.
- This release establishes the schema. Member-facing readiness dashboards and AI personalisation features that consume it will be delivered in subsequent releases.