In chronobiology and circadian health-related fields, wearable light loggers and optical radiation dosimeters are increasingly used to capture personal light exposure, but their data often lack essential contextual information (e.g. non-wear periods, sleep, activity or environmental conditions) and can be compromised by wearer compliance and technical issues. To address these challenges, we conducted a mixed-methods study (21 expert interviews; n=16 survey respondents) to iteratively develop auxiliary data and quality-control strategies that enhance the utility and accuracy of wearable light data. We derived an auxiliary data framework spanning six domains - wear/non-wear logging, sleep monitoring, light-source context, participant behaviour, user experience, and environmental light levels - to systematically augment wearable recordings. Survey respondents showed overwhelming consensus on the value of auxiliary data (mean importance 4.0 out of 5). In particular, tracking sleep and wear time was rated as the most critical augmentation. To facilitate implementation, we provide concrete tools - notably the open-source R package LightLogR - for streamlined integration of wearable and auxiliary data and for facilitation of quality assurance. Our findings indicate that combining contextual logs with rigorous quality assurance and quality control markedly improves the reliability of field-collected light exposure data. These recommendations and tools will help researchers in chronobiology, wearable technologies, and health to maximise data quality and interpretability in real-world light-exposure studies.
Zauner et al. (Thu,) studied this question.
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