Evaluating circadian light hygiene: Methodology and health implications

Comparative Effects of Red and Blue LED Light on Melatonin Levels During Three-Hour Exposure in Healthy Adults

Circadian rhythms, essential for regulating human physiology and behavior, are influenced by light exposure, particularly at night. This study examined the impact of red (631 nm) and blue (464 nm) LED light on melatonin secretion, a key circadian marker. Twelve participants aged 19-55 years were exposed to red and blue light for three hours (9:00 p.m.-midnight), with hourly saliva samples analyzed via ELISA to track melatonin levels. Initially, melatonin levels were comparable under both light conditions. After one hour, both lights suppressed melatonin, but differences emerged after two hours: blue light-maintained suppression, with levels at 7.5 pg/mL, while red light allowed recovery to 26.0 pg/mL (p = 0.019). This pattern persisted at the third hour. Blue light had stronger suppression effects, particularly in younger participants and men. These results underscore blue light's disruptive effects on circadian health and highlight red light as a less disruptive alternative for nighttime environments.

Wearables in Chronomedicine and Interpretation of Circadian Health

Wearable devices have gained increasing attention for use in multifunctional applications related to health monitoring, particularly in research of the circadian rhythms of cognitive functions and metabolic processes. In this comprehensive review, we encompass how wearables can be used to study circadian rhythms in health and disease. We highlight the importance of these rhythms as markers of health and well-being and as potential predictors for health outcomes. We focus on the use of wearable technologies in sleep research, circadian medicine, and chronomedicine beyond the circadian domain and emphasize actigraphy as a validated tool for monitoring sleep, activity, and light exposure. We discuss various mathematical methods currently used to analyze actigraphic data, such as parametric and non-parametric approaches, linear, non-linear, and neural network-based methods applied to quantify circadian and non-circadian variability. We also introduce novel actigraphy-derived markers, which can be used as personalized proxies of health status, assisting in discriminating between health and disease, offering insights into neurobehavioral and metabolic status. We discuss how lifestyle factors such as physical activity and light exposure can modulate brain functions and metabolic health. We emphasize the importance of establishing reference standards for actigraphic measures to further refine data interpretation and improve clinical and research outcomes. The review calls for further research to refine existing tools and methods, deepen our understanding of circadian health, and develop personalized healthcare strategies.

[Factors of depression according to actigraphy in the fall season]

OBJECTIVE

To evaluate the relationship between sleep parameters, physical activity, and illumination obtained by weekly actigraphy and depression symptoms measured by the Beck Depression Inventory (BDI-II) in young adults in the fall season.

MATERIAL AND METHODS

The crossover study, conducted during one fall month, included 122 adults (mean age 24.40 years, 76.6% females) from Tyumen (Russia). Participants were monitored for seven days using actigraphy and completed the Russian version of the BDI-II questionnaire to assess depressive symptoms. Actigraphy data were analyzed for quantitative and qualitative sleep parameters, dynamic illumination, and circadian rhythm of motor activity. Both mean values and regularity indicators were recorded. Statistical analysis was performed using linear and multiple linear regression methods, using the Benjamini-Hochberg procedure to control the incidence of false correlations associated with multiple testing.

RESULTS

The analysis showed significant correlations between various actigraphic indicators and the level of depression according to BDI-II. In particular, the BDI-II integral score was significantly associated with a decrease in the amplitude of the circadian rhythm of physical activity (PIM A: -0.258; p=0.005), a high instability of sleep efficiency (SL_EFF SD: -0.323; p=0.0003), a high standard deviation of the moment of awakening (WT SD: -0.258; p=0.005), a decrease in the inter-day stability of the activity rhythm (IS: -0.260; p=0.004), and a lower circadian light hygiene index (NA_bl: -0.193; p=0.036). After multiple comparison adjustments, low PIM A, reduced inter-day stability (IS), increased WT SD, and high SL_EFF SD remained significant predictors of depressive symptoms. In age-, sex-, and body mass index (BMI)-adjusted multiple regression, WT SD (β=-0.258; p=0.006), SL_EFF SD (β=-0.302; p=0.0006), and IS (β=-0.225; p=0.013) were significant factors.

CONCLUSION

The data obtained indicate a complex relationship between sleep dynamics and mental health in the context of BDI-II. They emphasize the critical importance of using parameters that assess sleep regularity as one of the markers of biological clock synchrony to increase informative value and improve the interpretation of data obtained using wearable devices.