Building With Ultrahuman Data: Recovery, Sleep and Glucose

Open Wearables Team · May 5, 2026 · 7 min read

Introduction

Ultrahuman's data is interesting not because any single metric is uniquely novel, but because of the combination: ring-quality sleep and recovery biometrics alongside optional continuous glucose monitoring in a single API. No other consumer wearable integration provides both streams. Building features that use them together opens a category of health insights that is not possible with any other platform.

This guide covers how to build productively with Ultrahuman data. It addresses the OAuth and token management specifics, how to work with glucose time series data, how to build recovery coaching features on top of the ring biometrics, how to construct the glucose-sleep correlation features that make Ultrahuman distinctively valuable, and how to structure your architecture for users who may or may not have the CGM patch active.

OAuth and Token Management

Ultrahuman uses OAuth 2.0 with the authorization code flow and rotating refresh tokens. The rotating refresh token pattern means every time you exchange a refresh token for a new access token, Ultrahuman returns a new refresh token in the response. Your previous refresh token is immediately invalidated.

This creates a race condition risk in distributed systems where multiple processes might attempt to refresh the same token simultaneously. Implement a per-user mutex or advisory lock around token refresh operations so only one process refreshes a given user's token at a time. Store the new refresh token atomically with the new access token. If a token exchange returns a new refresh token but your storage write fails, you have lost the valid refresh token and the user must re-authenticate.

A practical implementation stores access token, refresh token, access token expiry timestamp and a last-refreshed timestamp per user. On each API call, check whether the access token is within five minutes of expiry and refresh proactively rather than waiting for a 401 response. Log all token refresh operations for debugging.

Building Recovery Coaching Features

Ultrahuman's Recovery Score combines HRV, resting heart rate and sleep quality into a 0-100 score. For coaching applications, the score itself is less useful than the components. A user with a low Recovery Score caused by poor HRV needs different guidance than a user with the same score caused by short sleep duration.

Fetch the component data alongside the composite score: HRV (rMSSD), resting heart rate and sleep quality score separately. Build your coaching logic on the components rather than the composite. An HRV value more than one standard deviation below the user's 30-day personal HRV baseline suggests nervous system stress. A resting heart rate elevated more than five beats per minute above the user's resting baseline suggests physiological strain.

Skin temperature deviation adds a third signal. Combine HRV deviation, resting heart rate deviation and temperature deviation into a three-dimensional recovery picture. When all three are in the expected direction, your training or lifestyle recommendation can be confident. When only one or two are off, the guidance should be more cautious and conditional.

Establish personal baselines before surfacing recovery-based recommendations. A new user's first week of data is insufficient for reliable personal baselines. Use a soft launch for coaching features: during the first 14 days, show users their raw data with minimal coaching interpretation. After 14 days of consistent sleep data, begin showing trend-based guidance. After 30 days, personal baselines for HRV and resting heart rate are sufficiently stable for confident recommendations.

Working With Glucose Time Series

CGM data from the Ultrahuman M1 patch arrives as a time series with readings at approximately five-minute intervals. Each reading has a timestamp (UTC) and a glucose value in either mg/dL or mmol/L depending on the user's account locale setting. Standardize to one unit in your storage layer and convert on display as needed.

Common gaps in CGM time series occur when the patch temporarily loses sensor contact, the user's phone is out of range for sync, or during patch change transitions. Fill gaps shorter than 20 minutes with linear interpolation when calculating variability metrics. Mark longer gaps as missing in your data model rather than interpolating.

Key glucose metrics to compute from the time series: mean glucose for a defined period (day, night, post-meal window), time in range (percentage of readings within 70-140 mg/dL is the standard healthy range for non-diabetic adults), glucose variability measured as coefficient of variation (standard deviation divided by mean), and meal response peaks (local maxima in the glucose time series that occur 30-90 minutes after a tagged meal).

Ultrahuman provides a metabolic score in their API that abstracts some of this analysis. However, if you are building your own metabolic health features, working with the raw time series gives you more flexibility in how you define and surface metabolic insights.

Glucose-Sleep Correlation Features

The most distinctive product opportunity in the Ultrahuman dataset is correlating overnight glucose patterns with sleep quality metrics from the ring. Research suggests that nocturnal glucose variability (frequent dips and rises during the night) is associated with fragmented sleep, lower HRV and worse recovery scores the following morning.

To build this feature, define an overnight window for each date (for example, 10pm to 6am based on the user's typical sleep timing, or derived from the actual sleep start and end times in the ring data). Compute mean glucose, glucose variability and any nocturnal dip events (readings below 70 mg/dL) within the overnight window. Compare these metrics against the same night's Recovery Score, HRV and sleep stages from the ring.

Over 14-30 days of data, you can identify whether a given user shows the glucose-sleep correlation pattern. Not all users will. Those who do have a medically and practically significant insight available: improving their nocturnal glucose stability (typically through adjusting evening meal timing and composition) may improve their sleep quality and recovery.

Surface this insight carefully. You are not making a medical claim; you are showing the user their own data patterns. A statement like on nights when your overnight glucose was more stable, your Recovery Score averaged 12 points higher is a data observation, not a diagnosis. This framing is both accurate and appropriate for a consumer health app.

Architecture for CGM-Optional Users

Not all Ultrahuman users have active CGM data. The ring works independently; the CGM is a separate purchase. Your architecture must handle three states: user has ring data only (no CGM history), user has ring data and historical CGM data from a previous patch (data available but not current), and user has ring data and active CGM data (both streams available and current).

Design your feature set accordingly. Core recovery features based on ring data are always available. Glucose features are gated on the presence of active CGM data. Correlation features require at least 14 days of overlapping ring and glucose data. Use progressive disclosure in your UI: show core features to all users and reveal glucose-dependent features only when the data is available.

Open Wearables

Open Wearables handles Ultrahuman OAuth with rotating refresh token management, developer credential storage and data normalization. Ring biometric data maps to the common schema. CGM glucose time series is preserved as a provider-specific extension alongside the normalized fields.

For teams building metabolic health applications that need both recovery biometrics and glucose data, Open Wearables provides the Ultrahuman integration as part of a multi-provider pipeline. Self-hosted, MIT licensed, no per-user fees.

FAQ

How do I handle the CGM patch expiry in my product UI?

When CGM data stops appearing for a user (detectable as a gap in glucose readings that exceeds 24 hours), update your UI to show that metabolic monitoring is paused rather than broken. Do not display glucose-based features with stale data. Show a status indicator that the CGM is inactive and, if appropriate, a link to purchase or activate a new patch.

Should I use Ultrahuman's metabolic score or build my own glucose analysis?

Ultrahuman's metabolic score is useful for a quick summary metric. If you want to build your own glucose-derived features or correlations, use the raw time series. The two approaches are not mutually exclusive: you can show the metabolic score as a simple summary and use raw data for deeper analysis features.

Can I access Ultrahuman data for users outside India?

Ultrahuman Ring AIR and the M1 CGM are available in multiple markets including the United States, United Kingdom, European Union countries and several other regions. API availability for developers in these regions should be confirmed with Ultrahuman directly, as their developer program terms and data residency requirements may vary by geography.