# Auxiliary data, quality assurance and quality control for wearable light loggers and optical radiation dosimeters

## TL;DR

This paper improves the reliability of wearable light data by adding contextual information and quality control methods for better health research.

## Contribution

A six-domain auxiliary data framework and tools for quality assurance in wearable light logger data.

## Key findings

- Experts rated sleep and wear-time tracking as the most essential auxiliary data.
- Implementation tools like the LightLogR R package were developed for streamlined data integration.
- Combining contextual data with QA/QC procedures enhances the reliability of field-collected light data.

## Abstract

Wearable light loggers and optical radiation dosimeters are increasingly used in chronobiology and circadian health research, yet their data often lack contextual information (e.g., sleep, activity, environmental conditions) and may be compromised by non-wear periods, compliance issues, or technical faults. To address these limitations, we conducted interviews (n = 21) and a survey (n = 16) with domain experts to distil and iteratively develop auxiliary data and quality-control strategies aimed at improving the accuracy and interpretability of wearable light measurements. From this process, we established a six-domain auxiliary data framework encompassing wear/non-wear logging, sleep monitoring, light-source context, participant behaviour, user experience, and environmental light levels. Survey responses showed strong consensus on the value of auxiliary information (importance 4.0/5), with sleep and wear-time tracking rated as the most essential additions. To support practical adoption, we provide implementation tools, including extensions to the open-source R package LightLogR, enabling streamlined integration of wearable and auxiliary data as well as systematic quality assurance and control. Experts agreed that combining contextual records with rigorous QA/QC procedures substantially improves the reliability of field-collected light-exposure data. These recommendations and tools aim to help researchers in chronobiology, wearable sensing, and health sciences maximise data quality and enhance interpretation in real-world light-exposure studies.