LightLogR: Reproducible analysis of personal light exposure data

Area of Science:

• Chronobiology
• Environmental Health
• Wearable Technology

Background:

• Personal light exposure is crucial for human health and well-being.
• Wearable devices enable real-world measurement of light exposure, but data analysis lacks standardization.
• Complexity of time-series data from light loggers challenges research comparability.

Purpose of the Study:

• Introduce LightLogR, an open-source R package to standardize and facilitate the analysis of personal light exposure data.
• Address challenges in data processing, analysis, and outcome comparison in light and health research.
• Provide tools for efficient data handling and comprehensive metric calculation.

Main Methods:

• Developed an open-source R package (LightLogR) under an MIT license.
• Implemented standardized functions for importing and processing time-series data from wearable light loggers.
• Integrated calculation of 61 metrics across 17 families, supporting hierarchical, participant-based data structures.

Main Results:

• LightLogR standardizes common tasks for personal light exposure data.
• The package offers tools for rapid and detailed data exploration, including summary and visualization.
• It incorporates a wide range of established light exposure metrics.

Conclusions:

• LightLogR provides a robust framework for researchers studying personal light exposure and its health implications.
• The package enhances data comparability and facilitates deeper insights into light-environment-health relationships.
• It supports the growing field of wearable sensor data analysis for public health research.