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We’re excited to share a major platform update: Thryve’s foundational analytics are now available to all clients at no additional charge. This change reflects our belief that high-quality, standardized metrics should be an essential part of every health data integration, not an add-on.
Foundation analytics provide the essential health measurements that form the data infrastructure for advanced health insights. These metrics transform raw wearable signals into clinically relevant, standardized measurements using validated algorithms and robust processing logic. They power everything from risk scoring and segmentation to predictive models and personalized care pathways.
By making these metrics universally available, we’re eliminating a common barrier to building accurate, reliable health applications: the time, cost, and complexity of processing and harmonizing wearable data.
So today, we would like to walk you through what foundation analytics are, why they matter, which metrics are included, and how you can start using them today.
Wearable devices generate vast volumes of data, but raw signals vary significantly between devices, manufacturers, and use cases. Basically, you can end up having tons of unprocessed data that doesn’t form any reliable picture on its own. This type of data is noisy, inconsistent, and difficult to interpret, especially in clinical or research contexts.
Foundation analytics fills in between raw data and high-level indicators. We standardize, clean, and validate incoming data streams, turning them into meaningful, comparable measurements that can be used reliably across populations, products, and time.
These foundational metrics are designed to:
In short, we turn data points into data infrastructure, which is a the stable base upon which advanced features are built.
We’ve organized our foundation analytics into several key domains that reflect real health behaviors and physiological states:
While most wearables don’t measure body composition directly, Thryve uses validated anthropometric algorithms to estimate key indicators:
These metrics help you analyze population health trends such as obesity risk, metabolic status, and central adiposity patterns, even when clinical equipment isn’t available.
Activity data can come from multiple devices, apps, and manual entries. Thryve processes all of it into standardized daily summaries for key activity categories:
The logic includes:
Whether a user logs activity manually or through a connected device, Thryve consolidates and harmonizes the results.
Standardized daily outputs include:
Understanding the intensity of activity, not just duration, is vital for advanced health insights. Thryve computes Metabolic Equivalent (MET) values based on calorie expenditure and activity context:
Using these MET values, Thryve generates:
These outputs provide a continuous view of activity intensity and cardiovascular capacity, often only available in clinical exercise tests.
Sleep data is notoriously inconsistent across wearables. Thryve solves this with a harmonized sleep model that applies a consistent definition of sleep cycles, regardless of source.
Our sleep logic includes:
Standard sleep metrics produced by Thryve include:
By normalizing sleep patterns across devices, you get a consistent measure of rest and disruption, even when users switch hardware.
Making foundation analytics part of the core platform changes the development experience in profound ways:
These metrics are available to all Thryve clients automatically, meaning no configuration, no additional fees required. Simply connect wearable data sources, and foundation analytics begin delivering standardized outputs in your dashboard and API responses.
Foundation analytics are the base layer for:
If you have questions about how these metrics work, how to interpret them, or how to integrate them into your product, our support team and documentation are ready to help.
You can explore the full documentation here or reach out to your account contact for tailored guidance.
