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Expand Up @@ -6,8 +6,12 @@ tags:
- light exposure
- luminous exposure
- personal light exposure
- melanopic
- ipRGC
- melanopsin
- ipRGCs (intrinsic photosensitive retinal ganglion cells)
- circadian rhythm
- chronobiology
- photoperiod
- occupational exposure
- time series analysis
- wearable devices
- wearable sensors
Expand All @@ -31,109 +35,58 @@ editor_options:
markdown:
wrap: 72
affiliations:
- name: TUM School of Medicine and Health, Department Health and Sports Sciences,
Chronobiology & Health, Technical University of Munich, Munich, Germany
- name: Technical University of Munich, TUM School of Medicine and Health, Department Health and Sports Sciences, Chronobiology & Health, Munich, Germany
index: 1
ror: 02kkvpp62
- name: Max Planck Institute for Biological Cybernetics, Max Planck Research Group
Translational Sensory & Circadian Neuroscience, Tübingen, Germany
- name: Max Planck Institute for Biological Cybernetics, Max Planck Research Group Translational Sensory & Circadian Neuroscience, Tübingen, Germany
ror: 026nmvv73
index: 2
- name: Laboratory of Integrated Performance in Design (LIPID), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- name: École Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering (ENAC), Laboratory of Integrated Performance in Design (LIPID), Lausanne, Switzerland
ror: 02s376052
index: 3
- name: "TUM Institute for Advanced Study (TUM-IAS), Technical University of Munich,
Munich, Germany"
- name: TUM Institute for Advanced Study (TUM-IAS), Technical University of Munich, Garching, Germany
index: 4
- name: TUMCREATE Ltd., Singapore, Singapore
index: 5
---

# Summary

Light plays an important role in human health and well-being, which
necessitates the study of the effects of personal light exposure in real-world
settings, measured by means of wearable devices. A growing number of studies incorporate
these kinds of data to assess associations between light and health outcomes.
Yet with few or missing standards, guidelines, and frameworks, setting up measurements, analysing the data,
and comparing outcomes between studies is challenging, especially
considering the significantly more complex time series data from
wearable sensors compared to controlled stimuli used in laboratory studies. In
this paper, we introduce a novel resource to facilitate these research efforts
in the form of an open-source, permissively licenced software package
for the statistical software R: `LightLogR`. As part of a developing
software ecosystem, `LightLogR` is built with the challenges of current
and future datasets in mind. The package standardizes many tasks for
importing and processing personal light exposure data, provides
quick as well as detailed insights into the datasets through summary and visualization
tools, and incorporates major metrics used in the relevant
literature, all while embracing an inherently hierarchical,
participant-based data structure.
Light plays an important role in human health and well-being, which necessitates the study of the effects of personal light exposure in real-world settings, measured by means of wearable devices. A growing number of studies incorporate these kinds of data to assess associations between light and health outcomes. Yet with few or missing standards, guidelines, and frameworks, setting up measurements, analysing the data, and comparing outcomes between studies is challenging, especially considering the significantly more complex time series data from wearable light loggers compared to controlled stimuli used in laboratory studies. In this paper, we introduce `LightLogR`, a novel resource to facilitate these research efforts in the form of an open-source, GPL-3.0-licenced software package for the statistical software R. As part of a developing software ecosystem, `LightLogR` is built with common challenges of current and future datasets in mind. The package standardizes many tasks for importing and processing personal light exposure data, provides quick as well as detailed insights into the datasets through summary and visualization tools, and incorporates major metrics used in the relevant literature, while embracing an inherently hierarchical, participant-based data structure.

# Statement of need

Personalized luminous exposure data is progressively gaining importance
across various domains, including research, occupational affairs, and
lifestyle tracking. Data are collected through a proliferating selection
of wearable light loggers and dosimeters, varying in size, shape,
functionality, and output format [@hartmeyer_towards_2023]. Despite or potentially because of numerous
use cases, the field still lacks a unified framework for collecting,
validating, and analyzing the accumulated data [@hartmeyer_towards_2023][@spitschan_verification_2022].
This issue increases the time and expertise necessary to handle such data and also compromises
the FAIRness (Findability, Accessibility, Interoperability, Reusability)
of the results, especially for meta-analyses [@de_vries_recommendations_2024].

`LightLogR` was designed to be used by researchers who deal with
personal light exposure data collected from wearable devices. These data
are of interest for various disciplines, including epidemiology,
chronobiology, and sleep research, as well as for post-occupancy evaluations in
architecture and lighting design. The package is intended to streamline the process of importing,
processing, and analysing these data in a reproducible and transparent manner. Key
features include:

- a growing list of supported devices with pre-defined import
functions tailored to their data structure (17 at the time of
writing, see \autoref{tab:one})

- preprocessing functions to combine different time series, aggregate
and filter data, and find and deal with implicitly missing data

- visualization functions to quickly explore the data. These function
are based on the popular `ggplot2` [@ggplot2] plotting package
and are designed to be easily customizable to construct
publication-ready figures (see, e.g., \autoref{fig:one}).

- a large and growing set of metrics that cover most if not all major
approaches found in the literature (at the time of writing 61
metrics across 17 metric families, see \autoref{tab:two})),
accessible via a consistent function interface.

![Light logger data can powerfully convey insights into personal light
exposure and health-related outcomes. `LightLogR` facilitates the import
and combination of different data sources into one coherent data
structure, as seen here by combining environmental daylight availability
and personal light exposure with data from a sleep diary. The
visualization functions in the package further allow tweaking to produce
publication-ready results.
\label{fig:one}](Day.png){width="80%"}
Personalized luminous exposure data is progressively gaining importance across various domains, including research, occupational affairs, and lifestyle tracking. Data are collected through a proliferating selection of wearable light loggers and dosimeters, varying in size, shape, functionality, and output format [@hartmeyer2023]. Despite or potentially because of numerous use cases, the field still lacks a unified framework for collecting, validating, and analyzing the accumulated data [@hartmeyer2023][@spitschan2022]. This issue increases the time and expertise necessary to handle such data and also compromises the FAIRness (findability, accessibility, interoperability, reusability) [@wilkinson2016] of the results, especially for meta-analyses [@devries2024].

`LightLogR` was designed to be used by researchers who deal with personal light exposure data collected from wearable devices. These data are of interest for various disciplines, including epidemiology, chronobiology, and sleep research, as well as for post-occupancy evaluations in architecture and lighting design. The package is intended to streamline the process of importing, processing, and analysing these data in a reproducible and transparent manner. Key features include:

- a growing list of supported devices with pre-defined import functions tailored to their data structure (17 at the time of writing, see \autoref{tab:one}),

- preprocessing functions to combine different time series, aggregate and filter data, and find and deal with implicitly missing data,

- visualization functions to quickly explore the data. These function are based on the popular `ggplot2` [@ggplot2] plotting package and are designed to be easily customizable to construct publication-ready figures (see, e.g., \autoref{fig:one}),

- a large and growing set of metrics that cover most if not all major approaches found in the literature (at the time of writing 61 metrics across 17 metric families, see \autoref{tab:two})), accessible via a consistent function interface.

![Light logger data can powerfully convey insights into personal light exposure and health-related outcomes. `LightLogR` facilitates the import and combination of different data sources into one coherent data structure, as seen here by combining environmental daylight availability and personal light exposure with data from a sleep diary. The visualization functions in the package further allow tweaking to produce publication-ready results. \label{fig:one}](Day.png){width="80%"}

| Device Name | Manufacturer |
|----|----|
| Actiwatch Spectrum | Philips Respironics |
| ActLumus | Condor Instruments |
| ActTrust | Condor Instruments |
| melanopiQ Circadian Eye (Prototype) | Max-Planck-Institute for Biological Cybernetics |
| DeLux | Intelligent Automation Inc |
| DeLux | Intelligent Automation Inc. |
| GENEActiv (with GGIR preprocessing) | Activeinsights |
| Kronowise | Kronohealth |
| Lido | University of Lucerne |
| Lido | Lucerne University of Applied Sciences and Arts |
| LightWatcher | Object-Tracker |
| LIMO | ENTPE |
| LIMO | École nationale des travaux publics de l'État (ENTPE) |
| LYS Button | LYS Technologies |
| Motion Watch 8 | CamNtech |
| melanopiQ Circadian Eye (Prototype) | Max Planck Institute for Biological Cybernetics |
| XL-500 BLE | NanoLambda |
| OcuWEAR | Ocutune |
| Speccy | Monash University |
| Speccy | Monash University Malaysia |
| SpectraWear | University of Manchester |
| VEET | Meta Reality Labs |

Expand All @@ -151,61 +104,37 @@ publication-ready results.
| Intradaily Variance (IV) | 1 | | `intradaily_variability()` |
| Interdaily Stability (IS) | 1 | | `interdaily_stability()` |
| Midpoint CE (Cumulative Exposure) | 1 | | `midpointCE()` |
| nvRC (Non-visual circadian response) | 4 | | `nvRC()`, `nvRC_circadianDisturbance()`, `nvRC_circadianBias()`, `nvRC_relativeAmplitudeError()` |
| nvRD (Non-visual direct response) | 2 | | `nvRD()`, `nvRD_cumulative_response()` |
| nvRC (non-visual circadian response) | 4 | | `nvRC()`, `nvRC_circadianDisturbance()`, `nvRC_circadianBias()`, `nvRC_relativeAmplitudeError()` |
| nvRD (non-visual direct response) | 2 | | `nvRD()`, `nvRD_cumulative_response()` |
| Period above threshold | 3 | above, below, within | `period_above_threshold()` |
| Pulses above threshold | 7x3 | above, below, within | `pulses_above_threshold()` |
| Threshold for duration | 2 | above, below | `threshold_for_duration()` |
| Timing above threshold | 3 | above, below, within | `timing_above_threshold()` |
| Timing above threshold (TAT) | 3 | above, below, within | `timing_above_threshold()` |
| **Total:** | | | |
| **17 families** | **61 metrics** | | |

: metrics available in version 0.4.1 \label{tab:two}

LightLogR has and is already being used in several research projects across scientific domains, such as:

- cohort study to collect light exposure data across different
geolocations [@Guidolin2024]
- cohort study to collect year-long datasets of various types of
environmental and behavioral data [@biller2024]
- power analysis method for personal light exposure
[@zauner2023power],
- intervention study on the effects of light on bipolar disorder (data
collection in progress),
- intervention study on sex and seasonal changes in human melatonin
suppression and alerting response to moderate light (publication in
progress),
- intervention study on exposure to bright light during afternoon to
early evening on later evening melatonin release in adolescents
(**note: Preprint this week, Rafael Lazar will send DOI**),
- observational study on the wearing compliance of personal light
exposure [@stefani2024],
- observational study on the differences in light exposure and light
exposure related behavior between Malaysia and Switzerland
(preregistration in progress).
- observational study on light exposure, sleep, and circadian rhythms in hospital shift workers (publication in progress)
LightLogR is already being used in several research projects across scientific domains, including:

- an ongoing cohort study to collect light exposure data across different geolocations [@guidolin2024],
- an ongoing cohort study to collect year-long datasets of various types of environmental and behavioral data [@biller2024],
- power analysis method for personal light exposure [@zauner2023],
- an intervention study on the effects of light on bipolar disorder [@roguski2024],
- an intervention study on exposure to bright light during afternoon to early evening on later evening melatonin release in adolescents [@lazar2024],
- an observational study on the wearing compliance of personal light exposure [@stefani2024],
- an observational study on the differences in light exposure and light exposure related behavior between Malaysia and Switzerland (preregistration in progress),
- an intervention study on sex and seasonal changes in human melatonin suppression and alerting response to moderate light (publication in progress),
- an observational study on light exposure, sleep, and circadian rhythms in hospital shift workers (publication in progress).

# Funding Statement

The develoment of `LightLogR` is funded through MeLiDos, a joint,
EURAMET-funded project involving sixteen partners across Europe, aimed
at developing a metrology and a standard workflow for wearable light
logger data and optical radiation dosimeters. Its primary contributions
towards fostering FAIR data include the development of a common file
format, robust metadata descriptors, and an accompanying open-source
software ecosystem.

The project (22NRM05 MeLiDos) [@SPITSCHAN2024114909] has received
funding from the European Partnership on Metrology, co-financed from the
European Union’s Horizon Europe Research and Innovation Programme and by
the Participating States. Views and opinions expressed are however those
of the author(s) only and do not necessarily reflect those of the
European Union or EURAMET. Neither the European Union nor the granting
authority can be held responsible for them.
The development of `LightLogR` is funded by MeLiDos, a joint, EURAMET-funded project involving sixteen partners across Europe, aimed at developing a metrology and a standard workflow for wearable light logger data and optical radiation dosimeters. Its primary contributions towards fostering FAIR data include the development of a common file format, robust metadata descriptors, and an accompanying open-source software ecosystem.

The project (22NRM05 MeLiDos) [@spitschan2024] has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EURAMET. Neither the European Union nor the granting authority can be held responsible for them.

# Acknowledgements

We thank Carolina Guidolin and Anna Biller from the TSCN unit for
testing the software during development and providing feature ideas.
We thank Carolina Guidolin (Max Planck Institute for Biological Cybernetics) and Dr. Anna Magdalena Biller (Technical University of Munich) for testing the software during development and providing feature ideas, and the entire Translational Sensory & Circadian Neuroscience Unit (MPS/TUM/TUMCREATE) for its support.

# References

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