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<p>In field studies using wearable light loggers, participants often need to remove the devices, resulting in non-wear intervals of varying and unknown duration. Accurate detection of these intervals is an essential step during data pre-processing. Here, we deployed a multi-modal approach to collect non-wear time during a longitudinal light exposure collection campaign and systematically compare non-wear detection strategies. Healthy participants (<i>n</i> = 26; mean age 28 ± 5 years, 14F) wore a near-corneal plane light logger for 1&#xa0;week and reported non-wear events in three ways: pressing an "event marker" button on the light logger, placing it in a black bag, and using an app-based Wear log. Wear log entries, checked twice daily, served as ground truth for non-wear detection, showing that non-wear time constituted 5.4 ± 3.8% (mean ± SD) of total participation time. Button presses at the start and end of non-wear intervals were identified in &gt;85.4% of cases when considering time windows beyond 1&#xa0;min for detection. To detect non-wear intervals based on black bag use and lack of motion, we employed an algorithm that detects clusters of low illuminance and clusters of low activity. Performance was higher for illuminance (F1 = 0.78) than for activity (F1 = 0.52). Light exposure metrics derived from the full dataset, a dataset filtered for non-wear based on self-reports, and a dataset filtered for non-wear using the low illuminance clusters detection algorithm showed minimal differences. Our results highlight that while non-wear detection may be less critical in high-compliance cohorts, systematically collecting and detecting non-wear intervals is feasible and important for ensuring robust data pre-processing.</p>

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Collecting, detecting, and handling non-wear intervals in longitudinal light exposure data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月12日

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  • Carolina Guidolin,
  • Johannes Zauner,
  • Steffen Lutz Hartmeyer,
  • Manuel Spitschan

摘要

In field studies using wearable light loggers, participants often need to remove the devices, resulting in non-wear intervals of varying and unknown duration. Accurate detection of these intervals is an essential step during data pre-processing. Here, we deployed a multi-modal approach to collect non-wear time during a longitudinal light exposure collection campaign and systematically compare non-wear detection strategies. Healthy participants (n = 26; mean age 28 ± 5 years, 14F) wore a near-corneal plane light logger for 1 week and reported non-wear events in three ways: pressing an "event marker" button on the light logger, placing it in a black bag, and using an app-based Wear log. Wear log entries, checked twice daily, served as ground truth for non-wear detection, showing that non-wear time constituted 5.4 ± 3.8% (mean ± SD) of total participation time. Button presses at the start and end of non-wear intervals were identified in >85.4% of cases when considering time windows beyond 1 min for detection. To detect non-wear intervals based on black bag use and lack of motion, we employed an algorithm that detects clusters of low illuminance and clusters of low activity. Performance was higher for illuminance (F1 = 0.78) than for activity (F1 = 0.52). Light exposure metrics derived from the full dataset, a dataset filtered for non-wear based on self-reports, and a dataset filtered for non-wear using the low illuminance clusters detection algorithm showed minimal differences. Our results highlight that while non-wear detection may be less critical in high-compliance cohorts, systematically collecting and detecting non-wear intervals is feasible and important for ensuring robust data pre-processing.