Verification, analytical validation and clinical validation (V3) of wearable dosimeters and light loggers

Selecting, implementing and evaluating control and placebo conditions in light therapy and light-based interventions

Introduction: Light profoundly influences human physiology, behaviour and cognition by affecting various functions through light-sensitive cells in the retina. Light therapy has proven effective in treating seasonal depression and other disorders. However, designing appropriate control conditions for light-based interventions remains a challenge.Materials and methods: This article presents a novel framework for selecting, implementing and evaluating control conditions in light studies, offering theoretical foundations and practical guidance. It reviews the fundamentals of photoreception and discusses control strategies such as dim light, darkness, different wavelengths, spectral composition and metameric conditions. Special cases like dynamic lighting, simulated dawn and dusk, complex interventions and studies involving blind or visually impaired patients are also considered.Results: The practical guide outlines steps for selection, implementation, evaluation and reporting, emphasizing the importance of α-opic calculations and physiological validation.Conclusion: In conclusion, constructing effective control conditions is crucial for demonstrating the efficacy of light interventions in various research scenarios.

Towards an evidence-based integrative lighting score: a proposed multi-level approach

Background: Human circadian clocks are synchronized daily with the external light-dark cycle and entrained to the 24-hour day. There is increasing evidence that a lack of synchronization and circadian entrainment can lead to adverse health effects. Beyond vision, light plays a critical role in modulating many so-called non-visual functions, including sleep-wake cycles, alertness, mood and endocrine functions. To assess (and potentially optimize) the impact of light on non-visual functions, it is necessary to know the exact 'dose' (i.e. spectral irradiance and exposure duration at eye level) of 24-hour light exposures, but also to include metadata about the lighting environment, individual needs and resources.

Problem statement: To address this problem, a new assessment tool is needed that uses existing metrics to provide metadata and information about light quality and quantity from all sources. In this commentary, we discuss the need to develop an evidence-based integrative lighting score that is tailored to specific audiences and lighting environments. We will summarize the most compelling evidence from the literature and outline a future plan for developing such a lighting score using internationally accepted metrics, stakeholder and user feedback.

Conclusion: We propose a weighting system that combines light qualities with physiological and behavioral effects, and the use of mathematical modelling for an output score. Such a scoring system will facilitate a holistic assessment of a lighting environment, integrating all available light sources.

Open-Source Python Module for the Analysis of Personalized Light Exposure Data from Wearable Light Loggers and Dosimeters

Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual's light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data.

The Ecology of Human Sleep (EcoSleep) Cohort Study: Protocol for a longitudinal repeated measurement burst design study to assess the relationship between sleep determinants and outcomes under real-world conditions across time of year

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

A Wrist-Worn Internet of Things Sensor Node for Wearable Equivalent Daylight Illuminance Monitoring

Light exposure is a vital regulator of physiology and behavior in humans. However, monitoring of light exposure is not included in current wearable Internet of Things (IoT) devices, and only recently have international standards defined [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of EDI measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured [Formula: see text]-opic EDI results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT-based lighting control.

Global research on wearable technology applications in healthcare: A data-driven bibliometric analysis

Background

In recent years, with the advancement of technological innovation and the widespread application of semiconductor materials, wearable technology has emerged as a significant branch in healthcare, demonstrating considerable potential for further development. This analysis aims to explore the global scientific trends on wearable technology applications in healthcare.

Methods

Scientific publications on wearable technology applications in healthcare from 1 January 2003 to 31 December 2022 were retrieved from the Web of Science Core Collection. A total of 19,426 publications were included in the bibliometric analysis. VOSviewer and CiteSpace were used to conduct bibliometric and visualized analysis. Key metrics such as country, institution, author co-authorships, cited references, journal citations, and keyword co-occurrences were selected for analytical emphasis.

Results

The United States of America and China emerged as the top two contributing countries, with significantly higher publication compared to other countries/regions. Chinese Acad Sci and Sensors are the institution and journal with the largest number of publications, respectively. Najafi, Bijan is the most active author. Research hotspots of wearable technology were divided into four clusters based on the co-occurrence analysis of keywords: (1) Wearable Technology for Detecting and Monitoring Human Physiological Parameters; (2) Wearable Technology for Human Chronic Disease Detection and Management; (3) Wearable Technology Exercise Health and Sports Rehabilitation Therapy under Intervention; and (4) The Technical Realization of Accuracy Enhancement in Wearable Technology.

Conclusions

The number of annual publications on wearable technology applications in healthcare has increased over the past 20 years. This analysis identified the status, trends, hot topics, and frontiers of wearable technology applications in healthcare. These findings will help researchers quickly identify emerging themes and offer new insights into the future development of wearable technology in healthcare.

An inventory of human light exposure behaviour

Light exposure is an essential driver of health and well-being, and individual behaviours during rest and activity modulate physiologically relevant aspects of light exposure. Further understanding the behaviours that influence individual photic exposure patterns may provide insight into the volitional contributions to the physiological effects of light and guide behavioural points of intervention. Here, we present a novel, self-reported and psychometrically validated inventory to capture light exposure-related behaviour, the Light Exposure Behaviour Assessment (LEBA). An expert panel prepared the initial 48-item pool spanning different light exposure-related behaviours. Responses, consisting of rating the frequency of engaging in the per-item behaviour on a five-point Likert-type scale, were collected in an online survey yielding responses from a geographically unconstrained sample (690 completed responses, 74 countries, 28 time zones). The exploratory factor analysis (EFA) on an initial subsample (n = 428) rendered a five-factor solution with 25 items (wearing blue light filters, spending time outdoors, using a phone and smartwatch in bed, using light before bedtime, using light in the morning and during daytime). In a confirmatory factor analysis (CFA) performed on an independent subset of participants (n = 262), we removed two additional items to attain the best fit for the five-factor solution (CFI = 0.95, TLI = 0.95, RMSEA = 0.06). The internal consistency reliability coefficient for the total instrument yielded McDonald's Omega = 0.68. Measurement model invariance analysis between native and non-native English speakers showed our model attained the highest level of invariance (residual invariance CFI = 0.95, TLI = 0.95, RMSEA = 0.05). Lastly, a short form of the LEBA (n = 18 items) was developed using Item Response Theory on the complete sample (n = 690). The psychometric properties of the LEBA indicate the usability for measuring light exposure-related behaviours. The instrument may offer a scalable solution to characterise behaviours that influence individual photic exposure patterns in remote samples. The LEBA inventory is available under the open-access CC-BY license. Instrument webpage: https://leba-instrument.org/ GitHub repository containing this manuscript: https://github.com/leba-instrument/leba-manuscript .

Human-Centric Lighting Research and Policy in the Melanopsin Age

Beyond visual function, specialized light-sensitive retinal circuits involving the photopigment melanopsin drive critical aspects of human physiology and behavior, including sleep-wake rhythms, hormone production, mood, and cognition. Fundamental discoveries of visual neurobiology dating back to the 1990s have given rise to strong interest from the lighting industry in optimizing lighting to benefit health. Consequently, evidence-based recommendations, regulations, and policies need to translate current knowledge of neurobiology into practice. Here, reviewing recent advances in understanding of NIF circuits in humans leads to proposed strategies to optimize electric lighting. Highlighted knowledge gaps must be addressed urgently, as well as the challenge of developing personalized, adaptive NIF lighting interventions accounting for complex individual differences in physiology, behavior, and environment. Finally, lighting equity issues appear in the context of marginalized groups, who have traditionally been underserved in research on both fundamental visual processes and applied lighting. Biologically optimal light is a fundamental environmental right.

Usability and Acceptability of a Corneal-Plane α-Opic Light Logger in a 24-h Field Trial

Introduction

Exposure to light fundamentally influences human physiology and behaviour by synchronising our biological clock to the external light-dark cycle and controlling melatonin production. In addition to well-controlled laboratory studies, more naturalistic approaches to examining these "non-visual" effects of light have been developed in recent years. As naturalistic light exposure is quite unlike well-controlled stimulus conditions in the laboratory, it is critical to measure light exposure in a person-referenced way, the "spectral diet." To this end, light loggers have been developed to capture personalised light exposure. As an alternative to light sensors integrated into wrist-worn actimeters, pendants, or brooch-based light loggers, a recently developed wearable light logger laterally attached to spectacle frames enables the measurement of biologically relevant quantities in the corneal plane.

Methods

Here, we examine the usability and acceptability of using the light logger in an undergraduate student sample (n = 18, mean±1SD: 20.1 ± 1.7 years; 9 female; Oxford, UK) in real-world conditions during a 24-h measurement period. We probed the acceptability of the light logger using rating questionnaires and open-ended questions.

Results

Our quantitative results show a modest acceptability of the light logger. A thematic analysis of the open-ended questions reveals that the form factor of the device, in particular, size, weight, and stability, and reactions from other people to the wearer of the light logger, were commonly mentioned aspects.

Conclusion

In sum, the results indicate the miniaturisation of light loggers and "invisible" integration into extant everyday objects as key areas for future technological development, facilitating the availability of light exposure data for developing personalised intervention strategies in both research, clinical and consumer contexts.