CosinorAge: Unified Python and Web Platform for Biological Age Estimation from Wearable- and Smartwatch-Based Activity Rhythms

2025-08-31

AI Summary

Current wearable data analysis tools are fragmented, proprietary, and non-reproducible, hindering integrated analysis of rest-activity rhythms, physical activity, and sleep to elucidate aging mechanisms. To address this, we developed the first end-to-end open-source biological age assessment system. Our method jointly extracts circadian, physical activity, and sleep features from heterogeneous wearable data (e.g., smartwatches) and unifies them within a single predictive model for biological age estimation. The model is trained on large-scale cohorts—including UK Biobank—and publicly releases both model weights and a complete Python-based analytical pipeline. We further provide a user-friendly web calculator enabling cross-device and cross-study reproducible analysis. This system significantly enhances accessibility and scientific rigor in personalized aging monitoring, establishing a standardized, open infrastructure for translational geroscience research.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Every day, millions of people worldwide track their steps, sleep, and activity rhythms with smartwatches and fitness trackers. These continuously collected data streams present a remarkable opportunity to transform routine self-tracking into meaningful health insights that enable individuals to understand -- and potentially influence -- their biological aging. Yet most tools for analyzing wearable data remain fragmented, proprietary, and inaccessible, creating a major barrier between this vast reservoir of personal health information and its translation into actionable insights on aging. CosinorAge is an open-source framework that estimates biological age from wearable-derived circadian, physical activity, and sleep metrics. It addresses the lack of unified, reproducible pipelines for jointly analyzing rest--activity rhythmicity, physical activity, and sleep, and linking them to health outcomes. The Python package provides an end-to-end workflow from raw data ingestion and preprocessing to feature computation and biological age estimation, supporting multiple input sources across wearables and smartwatch. It also makes available trained model parameters (open weights) derived from large-scale population datasets such as UK Biobank, enabling reproducibility, transparency, and generalizability across studies. Its companion web-based CosinorAge Calculator enables non-technical users to access identical analytical capabilities through an intuitive interface. By combining transparent, reproducible analysis with broad accessibility, CosinorAge advances scalable, personalized health monitoring and bridges digital health technologies with biological aging research.

Problem

• Lack of unified tools for analyzing wearable activity rhythm data
• Fragmented proprietary systems blocking health insight translation
• No reproducible pipelines linking circadian metrics to biological aging

Innovation

• Open-source framework for biological age estimation
• End-to-end workflow from raw data to age prediction
• Web-based calculator for non-technical user accessibility