# Behavioromics: A New Paradigm of Big Data-Powered Insights for Proactive Health

## TL;DR

Behavioromics uses big data from digital tools to better understand how behaviors affect health, enabling early disease prevention and personalized interventions.

## Contribution

Introduces behavioromics as a novel paradigm integrating multimodal behavioral data with biological and environmental information for proactive health management.

## Key findings

- Behavioromics integrates continuous behavioral data with molecular and environmental data for dynamic pattern analysis.
- It supports mechanism-informed inference and identifies actionable targets for early health interventions.
- The approach promises to advance proactive health through behavior-centered prevention.

## Abstract

Behavior plays a critical role in health and disease. Although molecular omics have advanced the understanding of biological mechanisms, they alone cannot explain the role of behavior in health, and traditional behavioral measurement technologies have struggled to capture the dynamic, multidimensional, and heterogeneous nature of real-world behaviors. With the development of digital technologies such as wearable devices, smartphone sensors, and ecological momentary assessment, behavioromics has emerged as a new research paradigm with a holistic systems perspective, integrating continuous, multimodal behavioral data with molecular, physiological, and environmental information. By organizing behavioral measurement, modeling, and interpretation within a unified analytical framework, behavioromics reveals dynamic behavioral patterns, supports mechanism-informed inference, and identifies actionable targets for early intervention. Despite ongoing challenges in data heterogeneity, causal interpretation, and ethical governance, behavioromics holds promise for reshaping disease prediction, early detection, and precision intervention, and for advancing proactive health through earlier, behavior-centered prevention.