Methodology
Last reviewed: April 25, 2026
LongevityMate turns blood test data, optional wearable context, goals, and lifestyle answers into educational insights. This page outlines the high-level process we use so users can understand what powers each report, dashboard, and Mate response.
1. Data intake and normalization
We ingest biomarker values, units, and dates from uploaded or manually entered results. Where users choose to connect Apple Health or Health Connect, we can also use authorized wearable summaries for extra context. We normalize units where possible so values can be compared consistently over time.
2. Biomarker grouping and scoring
Core biomarkers are grouped into eight scored health categories. Each category uses Must biomarkers to unlock the score, then folds in Support biomarkers when they are available to refine the result. The Essential biomarker panel also includes every marker needed for biological age estimation.
Full marker coverage is listed on the Test Guide.
3. AI-assisted recommendation drafting
AI components use your structured biomarker context, goals, onboarding answers, available wearable signals, and progress history to draft educational suggestions and action priorities. Outputs are intended to support informed discussions with clinicians, not replace them.
See boundaries and known limitations on the AI Limitations page.
4. User controls and privacy
Users can update entries, control connected data sources, export information, and request deletion. Data handling details are documented in our Privacy Policy and usage boundaries are in the Terms of Service.