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Background: Behaviour-based physical intensity evaluation requires rigorous calibration before application in long-term recordings of children's sleep/activity patterns. This study aimed at (i) calibrating activity counts of motor behaviour measured simultaneously with MotionWatch 8 (MW8) and ActiGraph (GT3X) in 3-year-old children, (ii) documenting movement intensities in 30s-epochs at wrist/hip positions, and (iii) evaluating the accuracy of cut-off agreements between different behavioural activities.

Methods: Thirty 3-year-old children of the NorthPop cohort performed six directed behavioural activities individually, each for 8-10 minutes while wearing two pairs of devices at hip and wrist position. These naturally-occurring behaviours were aligned to movement intensities from 'motionless' (watching cartoons) and 'sedentary' (recumbent story listening, sit and handcraft) to 'light activity' (floor play with toys), 'moderate activity' (engaging in a brisk walk) and 'vigorous activity (a sprinting game). Time-keeping was ensured using direct observation by an observer. Receiver-Operating-Curve classification was applied to determine activity thresholds and to assign two composite movement classes.

Results: Activity counts of MW8 and GT3X pairs of wrist-worn (rho = 0.94) and hip-worn (rho = 0.90) devices correlated significantly (p < 0.001). Activity counts at hip position were significantly lower compared to those at the wrist position (p < 0.001), irrespective of device type. Sprinting, floorball/walk and floorplay assigned as 'physically mobile' classes achieved outstanding accuracy (AUC > 0.9) and two sedentary and a motionless activities assigned into 'physically stationary' classes achieved excellent accuracy (AUC > 0.8).

Conclusion: This calibration provides useful cut-offs for physical activity levels of preschool children. Contextual information of behaviour is advantageous over intensity classifications only, because interventions will focus on behaviour-allocated time to reduce a sedentary lifestyle. Our comparative calibration is one step forward to behaviour-based movement guidelines for 3-year-old children.

The incidence of myopia among school children has risen markedly over the last three decades. In urban areas of South and East Asia, as many as 80–90% of young adults are now myopic. This trend is occurring elsewhere around the world. During the COVID-19 lockdowns, children in many countries were confined indoors and spent an undue amount of time exposed to television screens, computers, and mobile devices. This resulted in an acceleration in the incidence and progression of the condition. Myopia is a significant public health issue as it is a leading cause of blindness and other vision problems. Yet the underlying mechanisms that produce the condition remain elusive. Pseudomyopia has recently been proposed as an independent risk factor for myopia. We hypothesize that pseudomyopia induced by prolonged close work, stress, and anxiety combines and is further amplified by chronically low ambient light levels. If time spent outdoors in daylight is restricted, the effects worsen and together may play a significant part in myopia epidemics.

Background: Non-communicable diseases (NCDs) are a global health issue, posing a substantial burden on the individual, community, and public health. The risk of developing NCDs is influenced by a complex interplay between genetic, epigenetic, and environmental factors.

Methods: The NorthPop Birth Cohort Study (NorthPop) constitutes an infrastructure enabling cutting-edge research on the foundational pathways to NCDs in childhood, including allergic diseases and asthma, overweight/obesity, cognitive and neurodevelopmental dysfunction, gastrointestinal disorders, and caries. NorthPop aims at recruiting 10,000 families. Pregnant women and their partners residing in Västerbotten County, Sweden are eligible. Recruitment started in 2016 and is anticipated to end in 2025. Extensive data on parental, fetal and child health outcomes, lifestyle, diet, and environmental exposures are prospectively collected using web-based questionnaires in pregnancy and childhood until the children turn 7 years old. Urine samples are collected from the pregnant woman at gestational age 14–24 weeks. Blood samples are collected at gestational age 28 weeks. Placenta and cord blood are collected at birth. A breast milk sample is collected 1 month postpartum. Blood samples from the children are collected at 18 months and 7 years of age. Oral swabs and fecal samples are collected from the children within 48 h of birth, at 1, 9 and 18 months, 3 and 7 years of age. At age 7 years, children are invited to a follow-up visit, including measurements of weight, height, blood pressure, pulse, hand grip strength, working memory, skin prick test and saliva sampling. Additional measurements, such as sleep–wake and light exposure, and additional biological samples are collected in sub-cohorts. Permission for linkage to medical records and national registers e.g., the Swedish Pregnancy Register, the National Patient Register, the Longitudinal Integration Database for Health insurance and Labor market studies and the Swedish Prescribed Drug Register has been granted.

Discussion: Our multidisciplinary approach allows us to study how early life exposures, as well as parental health and lifestyle, influence future health in the offspring. Our results are anticipated to contribute to the understanding of disease risk and may inform future strategies aimed at risk reduction, highly significant for public health.

Trial registration: Retrospectively registered at Researchweb 11 November 2024 (project number 279272).

Light regulates both image- and various non-image forming responses in humans, including acute effects on attention and affect. To advance the understanding of light's immediate effects, this systematic review describes the acute effects of monochromatic/narrow bandwidth and polychromatic white light during daytime on distinct aspects of attention (alertness, sustained attention, working memory, attentional control and flexibility), and measures of affect (self-report measures, performance-based tests, psychophysiological measures) in healthy, adult human subjects. Original, peer-reviewed (quasi-) experimental studies published between 2000 and May 2024 were included according to predefined inclusion and exclusion criteria. Study quality was assessed, and results were synthesized across aspects of attention and affect and grouped according to light interventions; monochromatic/narrowband-width or polychromatic white light (regular white, bright white, and white with high correlated color temperature (CCT)). Results from included studies (n = 62) showed that alertness and working memory were most affected by light. Electroencephalographic markers of alertness improved the most with exposure to narrow bandwidth long-wavelength light, regular white, and white light with high CCT. Self-reported alertness and measures of working memory improved the most with bright white light. Results from studies testing the acute effects on sustained attention and attentional control and flexibility were inconclusive. Performance-based and psychophysiological measures of affect were only influenced by narrow bandwidth long-wavelength light. Polychromatic white light exerted mixed effects on self-reported affect. Studies were strongly heterogeneous in terms of light stimuli characteristics and reporting of light stimuli and control of variables influencing light's acute effects.

Background: Light exposure significantly impacts human health, regulating our circadian clock, sleep-wake cycle and other physiological processes. With the emergence of wearable light loggers and dosimeters, research on real-world light exposure effects is growing. There is a critical need to standardize data collection and documentation across studies.

Results: This article proposes a new metadata descriptor designed to capture crucial information within personalized light exposure datasets collected with wearable light loggers and dosimeters. The descriptor, developed collaboratively by international experts, has a modular structure for future expansion and customization. It covers four key domains: study design, participant characteristics, dataset details, and device specifications. Each domain includes specific metadata fields for comprehensive documentation. The user-friendly descriptor is available in JSON format. A web interface simplifies generating compliant JSON files for broad accessibility. Version control allows for future improvements.

Conclusions: Our metadata descriptor empowers researchers to enhance the quality and value of their light dosimetry datasets by making them FAIR (findable, accessible, interoperable and reusable). Ultimately, its adoption will advance our understanding of how light exposure affects human physiology and behaviour in real-world settings.

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.

Daylight is essential for ecosystems and for the physical and mental well-being of people. In densely populated cities, only a small proportion of total daylight is available to support urban greenery and most people have little daily exposure to natural daylight. Despite this, many cities have followed a strategy of densification as a way of preventing urban sprawl and reducing energy consumption. In this article, we review the biological importance of daylight and show that urban densification leads to a reduction in the daylight available for both people and nature. We conclude that daylight in cities should be treated as a limiting resource that needs to be planned and managed carefully, much like water or energy. We suggest elements for a policy framework aimed at optimizing urban daylight, including how to determine daylight needs, how to determine the maximum viable urban density, and policy options for built and unbuilt areas.

Study Objectives: Environmental cues influence circadian rhythm timing and neurochemicals involved in the regulation of affective behavior. How this interplay makes them a probable nonspecific risk factor for psychosis is unclear. We aimed to identify the relationship between environmental risk for psychosis and circadian timing phenotypes sampled from the general population.

Methods: Using an online survey, we devised a cumulative risk exposure score for each of the 1898 survey respondents based on 23 empirically verified transdiagnostic risks for psychosis, three dimensions of affect severity, psychotic-like experiences, and help-seeking behavior. Quantitative phenotyping of sleep and circadian rhythms was undertaken using at-home polysomnography, melatonin and cortisol profiles, and 3-week rest-activity behavior in individuals with a high-risk exposure load (top 15% of survey respondents, n = 22) and low-risk exposure load (bottom 15% of respondents, n = 22).

Results: Psychiatric symptoms were present in 100% of the high-load participants and 14% of the low-load participants. Compared to those with a low-load, high-load participants showed a later melatonin phase which was reflected by a greater degree of dispersion in circadian timing. Phase relationships between later circadian melatonin phase and later actigraphic sleep onsets were maintained and these were strongly correlated with self-reported sleep mid-points. No differences were identified from polysomnography during sleep between groups.

Conclusion: Distinguishing circadian timing from other sleep phenotypes will allow adaptation for dosage of time-directed intervention, useful in stabilizing circadian timekeeping physiology and potentially reducing the multisystemic disruption in mental health disorders.

Psychotic experiences (PE) are associated with poorer functioning, higher distress and the onset of serious mental illness. Environmental exposures (e.g. childhood abuse) are associated with the development of PE. However, which specific exposures convey risk for each type or dimension of PE has rarely been explored. The Oxford Wellbeing Life and Sleep (OWLS) survey includes 22 environmental risk factors for psychosis and was designed to examine how environmental risks are associated with specific dimensions of PE. Multivariate logistic regression models were fit using these risk factors to predict six dimensions of PE (perceptual abnormalities, persecutory ideation, bizarre ideas, cognitive disorganisation, delusional mood and negative symptoms). Models were built using only 70% of the data, and then fit to the remaining data to assess their generalisability and quality. 1789 (27.2% men; mean age = 27.6; SD = 10.9) survey responses were analysed. The risk factors predictive of the most PE were anxiety, social withdrawal during childhood and trauma. Cannabis and depression predicted three dimensions with both predicting bizarre ideas and persecutory ideation. Psychological abuse and sleep quality each predicted two dimensions (persecutory ideation and delusional mood). Risk factors predicting one PE dimension were age (predicting cognitive disorganisation), physical abuse (bizarre ideas), bullying and gender (persecutory ideation); and circadian phase (delusional mood). These results lend support for a continuum of psychosis, suggesting environmental risks for psychotic disorders also increase the risk of assorted dimensions of PE. Furthermore, it advocates the use of dimensional approaches when examining environmental exposures for PE given that environmental risks distribute differently across dimensions.

Primary insomnia is often considered a disorder of 24-hr hyperarousal. Numerous attempts have been made to investigate nocturnal heart rate (HR) and its variability (HRV) as potential pathophysiological hallmarks of altered arousal levels in insomnia, with mixed results. We have aimed to overcome some of the pitfalls of previous studies by using a young, medication-free, age- and gender-matched population consisting of 43 students aged 18–30 years half with a subthreshold insomnia complaint. We employed at-home ambulatory polysomnography and compared this attenuated insomnia group to a good sleeping group. The poor sleepers had significantly higher wake after sleep onset, arousal count, mean HR in all sleep stages (with the exception of Stage 1) and lower sleep efficiency. Consistent with previous research, we also found a significant group-by-sleep stage interaction in the prediction of nocturnal HR, highlighting the insomnia group to have a lower wake–sleep HR reduction compared to good sleepers. When restricting our analyses to insomnia with objectively determined short sleep duration, we found significantly lower standard deviation of RR intervals (SDNN; a measure of HRV) compared to good sleepers. Taken together, this lends credence to the hyperarousal model of insomnia and may at least partially explain the increased prevalence of cardiovascular morbidity and mortality observed in patients with insomnia.