Protocol for a prospective, multicentre, cross-sectional cohort study to assess personal light exposure

Guidolin, Carolina; Aerts, Sam; Agbeshie, Gabriel Kwaku; Akuffo, Kwadwo Owusu; Aydin, Sema Nur; Baeza-Moyano, David; Bolte, John; Broszio, Kai; Cantarero-García, Guadalupe; Didikoglu, Altug; González-Lezcano, Roberto Alonso; Joosten-Ma, Hongli; Melero-Tur, Sofía; Tengelin, Maria Nilsson; Pérez Gutiérrez, María Concepción; Stefani, Oliver; Svensson, Ingemar; Udovičić, Ljiljana; Zauner, Johannes; Spitschan, Manuel

doi:10.1186/s12889-024-20206-4

Cited by 3 publications

References 39 publications

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

Guidolin,

Zauner,

Hartmeyer

et al. 2024

Preprint

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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 in data pre-processing pipelines. However, the limited reporting on whether and how non-wear information is collected and detected has hindered the development of effective data pre-processing strategies and automated detection algorithms. Here, we deploy a multi-modal approach to collect non-wear time during a longitudinal light exposure 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 one 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 were checked twice a day to ensure high data quality and used as ground truth for non-wear interval detection. Participants showed high adherence to the protocol, with non-wear time constituting 5.4±3.8% (mean±SD) of total participation time. Considering button presses, our results indicated that extending time windows beyond one minute improved their detection at the start and end of non-wear intervals, achieving identification in >85.4% of cases. To detect non-wear intervals based on black bag use, we applied an algorithm detecting clusters of low illuminance to our data and compared its performance to detecting clusters of low activity. Performance was higher for illuminance (F1=0.76) than activity (F1=0.52). Transition states between wear and non-wear emerged as a major source of misclassification, and we suggest that combining illuminance and activity data could enhance detection accuracy. Lastly, we compared light exposure metrics averaged across the week derived from three datasets: 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. The differences in light exposure metrics across these datasets were minimal. Our results highlight that while non-wear detection may be less critical in high-compliance cohorts, systematically collecting and detecting non-wear intervals is both feasible and important for ensuring robust data pre-processing.

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

Guidolin,

Zauner,

Hartmeyer

et al. 2024

Preprint

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How to deal with darkness: Modelling and visualization of zero-inflated personal light exposure data on a logarithmic scale

Zauner,

Guidolin,

Spitschan

2024

Preprint

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Personal light exposure, the pattern of ocular light levels across time under free-living conditions measured with wearable devices, has become increasingly important in circadian and myopia research. Very small measurement values in light exposure patterns, especially zero, are regularly recorded in field studies. These zero-lux values are problematic for commonly applied logarithmic transformations, and should neither be dismissed nor be unduly influential in visualizations and statistical models. Common approaches used in zero-inflated data sets fail in at least one of these regards. We compare four ways to visualize such data on a linear, logarithmic, hybrid, or symlog scale and we model the light exposure patterns with a generalized additive model by removing zero-lux values, adding a very small or −1 log10 lux value to the dataset, or using the Tweedie error distribution. We show that a symlog-transformed visualization displays relevant features of light exposure across scales, including zero-lux, while at the same time reducing the emphasis on the small values (<1 lux). Symlog is well-suited to visualize differences in light exposure covering heavy-tailed negative values. The open-source software package LightLogR includes the symlog transformation for easy access. We further show that small but not negligible value additions to the light exposure data of -1 log10 lux for statistical modelling allow for acceptable models on a logarithmic scale, while very small values distort results. We also demonstrate the utility of the Tweedie distribution, which does not require prior transformations, models data on a logarithmic scale, and includes zero-lux values, capturing personal light exposure patterns satisfactorily. Data from field studies of personal light exposure requires appropriate handling of zero-lux values in a logarithmic context. Symlog scales for visualizations and an appropriate addition to input values for modelling, or the Tweedie distribution, provide a solid basis.

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Effect of Exposure to Blue Light from Electronic Devices and the Mediterranean Diet on Macular Pigment

García-Romera,

Ponce-García,

Torres-Parejo

et al. 2024

JCM

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Objective: To explore the effect of time exposure to flat screen electronic devices with LED lighting and the Mediterranean diet on macular pigment optical density (MPOD). Methods: In this cross-sectional observational study, the MPOD was measured by heterochromatic flicker photometry in 164 eyes (47 of younger women aged 20–31 and 35 of older women aged 42–70). Exclusion criteria: evidence of macular degeneration and eyes with cataracts. Data on the use of electronic devices and Mediterranean diet adherence were collected through a survey. Nonparametric analysis of variance and independent sample t-tests were used to compare subjects. Results: Significant differences (p < 0.01) were found in total time of exposure to LEDs (hours per day) between both groups (9.31 ± 3.74 younger women vs. 6.33 ± 3.64 older women). The MPOD values for the younger and adult populations were significantly different: 0.38 ± 0.16 and 0.47 ± 0.15 (p < 0.01), respectively. When comparing both groups for the same time of exposure to LEDs, differences were obtained between MPOD values of both populations: For total exposures greater than 6 h per day, the MPOD values were lower in younger women than in adult ones (0.37 ± 0.14 vs. 0.50 ± 0.14, p < 0.01). On the other hand, a significantly higher adherence was found in the older women in comparison with the younger women (OW 9.23 ± 2.50 vs. YW 7.70 ± 2.08, p < 0.01), with higher MPOD values (OW (0.52 ± 0.14) vs. (YW (0.34 ± 0.18). Conclusions: Higher MPOD values are observed with decreasing exposure time to electronic devices with LED lighting screens and higher adherence to the Mediterranean diet.

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Protocol for a prospective, multicentre, cross-sectional cohort study to assess personal light exposure

Cited by 3 publications

References 39 publications

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

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

How to deal with darkness: Modelling and visualization of zero-inflated personal light exposure data on a logarithmic scale

Effect of Exposure to Blue Light from Electronic Devices and the Mediterranean Diet on Macular Pigment

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