Normalized Amplitude of Blue Light Exposure (NA BLE) as a Novel Index for Circadian Light Hygiene: Associations with Actigraphy Measures and Seasonal Dependencies

Year & Volume - Issue: 
2025. volume 14 - Issue 4
Authors: 
Denis G. Gubin, Julia V. Boldyreva, Liina A. Danilova, Sergey A. Kolomeichuk, Larisa E. Alkhimova, Alexander A. Markov, Olga A. Malyugina, Natalya V. Kuznetsova, Oliver Stefani, Dietmar Weinert, Germaine Cornelissen
Heading: 
Hygiene and Environmental Health
Special Topic Collection: 
Health, Light Hygiene and Sleep
Article type: 
Original article
CID: 
e0401
PDF File: 
PDF icon romj-2025-0401.pdf
Abstract: 
Background — Light exposure (LE) critically regulates circadian rhythms, influencing sleep, mood, metabolism, and longevity. Quantifying circadian light hygiene is challenging due to high inter-individual variability. The Normalized Amplitude of Blue Light Exposure (NA BLE) metric provides a standardized assessment of BLE dynamics and range. We evaluated NA BLE in high-latitude Arctic residents, leveraging extreme seasonal light variations as a natural experiment to characterize its seasonal dependencies and associations with circadian parameters. Methods — Twenty-seven healthy Arctic adults underwent longitudinal assessment across seasons, spanning winter solstice, spring equinox, and summer solstice. Wrist actigraphy (ActTrust 2) captured physical activity (PA), wrist temperature (wT), light exposure (LE), and blue light exposure (BLE). Results — NA BLE exhibited clear seasonal variation, with a peak in spring and a nadir in winter, showing consistent patterns across sexes and populations (irrespective of indigeneity). Larger NA BLE correlated positively with indicators of circadian robustness, including Circadian Function Index (CFI) (p<0.0001), PA (p=0.001), and Inter-Daily Stability (p=0.008). Conversely, smaller NA BLE was associated with later circadian timing markers such as L5 onset of PA and BLE (p<0.001), bedtime (p=0.007), and lower stability of non-parametric actigraphy measures. Conclusions — Larger NA BLE significantly correlates with enhanced circadian robustness and healthier circadian timing, demonstrating its value despite seasonal fluctuations. NA BLE reliably measures circadian light hygiene, unaffected by sex or population subgroup. These results validate its utility in Arctic settings and advocate for its broader application in light exposure assessments to advance public health.
Keywords: 
circadian, light exposure, blue light, circadian light hygiene, actigraphy, Arctic, seasonality, NA BLE, chronobiology
Cite as: 
Gubin DG, Boldyreva JV, Danilova LA, Kolomeichuk SA, Alkhimova LE, Markov AA, Malyugina OA, Kuznetsova NV, Stefani O, Weinert D, Cornelissen G. Normalized amplitude of blue light exposure (NA BLE) as a novel index for circadian light hygiene: Associations with actigraphy measures and seasonal dependencies. Russian Open Medical Journal 2025; 14: e0401.
DOI: 
10.15275/rusomj.2025.0401

Introduction

The intricate interplay between light exposure and endogenous circadian rhythms underpins human health and well-being. Adequate circadian light hygiene – characterized by appropriate timing, intensity, and spectrum of light exposure [1, 2] – is essential for maintaining robust circadian function, which influences sleep, mood, metabolism, and overall longevity [3-7]. Measuring circadian light hygiene effectively remains challenging. Absolute measures of light exposure, while informative, often exhibit substantial inter-individual variability, hindering cross-contextual comparisons. To address this, the Normalized Amplitude of Light Exposure (NA LE), specifically for Blue Light Exposure (NA BLE), has been proposed as a practical and standardized metric [3, 4, 8, 9]. NA BLE quantifies the dynamic range of light exposure in a standardized manner, thereby facilitating more reliable comparisons across individuals and environments.

Extreme seasonal light variation in high-latitude Arctic environments provides a unique natural experiment to evaluate metrics like the Normalized Amplitude of Blue Light Exposure (NA BLE). Although pronounced seasonal shifts in daylight significantly impact circadian entrainment and behavior [9-14], the direct dependence of NA BLE on seasonal light abundance is not immediately obvious. To rigorously assess NA BLE’s utility in circadian light hygiene, this study sought to: 1) quantify seasonal dependencies of NA BLE in Arctic residents, 2) examine associations of NA BLE with actigraphy-derived circadian rhythm parameters, and 3) investigate potential moderation by sexes and subgroups.

           

Material and Methods

Study Population and Design

A longitudinal study was conducted with 27 healthy adult residents of high Arctic latitudes, where pronounced seasonal variations in natural light exposure create a unique environmental context. Participants provided written informed consent, and the study protocol was approved by the relevant institutional review board. Data collection occurred across three seasonal periods: winter solstice, spring equinox, and summer solstice.

 

Measures

All participants maintained sleep diaries and wore wrist actigraphy devices (ActTrust 2, Condor Instruments, São Paulo, Brazil) on their non-dominant wrist for continuous monitoring during the study period. Motor activity was quantified using the Proportional Integrative Mode (PIM), which measures movement intensity by summing voltage deviations from baseline every 0.1 second, aggregated over 1-minute epochs, and correlates strongly with polysomnography-derived sleep parameters. Physical activity (PA), wrist temperature (wT), light exposure (illuminance at the wrist in lux, LE), and blue light exposure (illuminance at the wrist in μW/cm², BLE) were recorded at 1-minute intervals via the ActTrust 2 sensors.

From the actigraphy data, parametric measures – including MESOR, 24-hour amplitude, and acrophase – were estimated for PIM, wT, LE, and BLE. Non-parametric indices were derived using validated algorithms in ActStudio software (Condor Instruments, São Paulo, Brazil), as previously described [8-14]. These included inter-daily stability (IS), intra-daily variability (IV), relative amplitude (rA) for PA, LE, and BLE, circadian function index (CFI) [15], the maximum 10-hour window (M10) and its onset, the least 5-hour window (L5) and its onset for PA and BLE, BLE phase, bedtime, wake time, total sleep duration (TSD), and wake after sleep onset (WASO). The variability of most parameters was quantified by their standard deviation (SD), also output by ActStudio. The relative amplitude of BLE (rA BLE) was computed as (M10 BLE – L5 BLE) / (M10 BLE + L5 BLE), reflecting the proportional difference between the highest 10-hour BLE period and the lowest 5-hour BLE period. The normalized amplitude of BLE (NA BLE), defined as the ratio of the 24-hour amplitude of the best-fit cosine curve to its MESOR, was calculated as previously outlined [3, 4, 8, 9]. This metric standardizes the dynamic range of BLE to account for individual baselines, thereby minimizing inter-individual variability.

Seasonal variations in NA BLE were evaluated using Friedman’s ANOVA, a non-parametric test appropriate for repeated measures across more than two conditions. Post-hoc pairwise comparisons were performed using Wilcoxon signed-rank tests as needed. The Kendall coefficient of concordance (W) assessed overall agreement in seasonal rankings of NA BLE. Associations between NA BLE and actigraphy-derived circadian parameters were explored via linear regression, conducted both overall and stratified by season. A threshold of |r|>0.21 was established as the minimum effect size for practical significance in Pearson correlations in this study, prioritizing biologically meaningful relationships after adjusting for multiple comparisons. Thus, correlations were deemed significant at p<0.05 with |r|>0.21 following Benjamini–Hochberg false discovery rate correction at FDR=0.1. The moderating effects of sex and population subgroup were examined using two-way ANOVA to test any significant interactions between season and sex, as well as season and population subgroup.

 

Results

Seasonal Dynamics of Normalized Amplitude of Blue Light Exposure (NA BLE)

Despite NA BLE being a normalized metric, which might not intuitively suggest dependence on seasonal light quantities, our analysis revealed significant seasonal variations. A non-parametric Friedman’s ANOVA demonstrated a significant effect of season on NA BLE (χ²=16.074, p<0.001). The Kendall coefficient of concordance further corroborated the high degree of agreement in these seasonal patterns across participants (W=0.298; p=0.001). Mean NA BLE values differed significantly by season, with spring (M=1.417, SD=0.120) exhibiting the highest value, followed by summer (M=1.239, SD=0.288) and winter (M=1.110, SD=0.204). Pairwise comparisons between seasons, conducted using the Wilcoxon signed-rank test (results depicted in Figure 1), confirmed significant differences across seasons. To assess robustness, we examined potential interactions using two-way ANOVAs. Specifically, no significant interaction was found between season and sex (F=1.355; p=0.248) or season and indigeneity subgroup (natives vs. non-natives) (F=0.081; p=0.922). This result indicates that the observed seasonal pattern of NA BLE was not modulated by participants’ sex or subgroup affiliation.

 

Figure 1. Seasonal Variation of Normalized Amplitude of Blue Light Exposure (NA BLE) in Arctic Residents

Seasonal patterns of NA BLE, assessed in winter, spring, and summer, are depicted in the figure. NA BLE exhibited significant seasonal variation, peaking in spring. This pattern was observed to be proportional across seasons in both native and non-native Arctic residents, indicating similar temporal dynamics in blue light exposure patterns irrespective of residency status. Significant differences (determined via Wilcoxon signed-rank test) are shown as *** (p<0.001), ** (p<0.01), and * (p<0.05).

 

Associations between NA BLE and Actigraphy-Derived Circadian Parameters

Linear regression analyses conducted across all seasons revealed significant associations between NA BLE and a range of actigraphy-derived circadian rhythm parameters. A total of 9 significant positive correlations and 12 significant negative correlations were identified (see Figure 2 for a detailed overview).

 

Figure 2. Associations Between Circadian Rhythm Parameters and Normalized Amplitude of Blue Light Exposure (NA BLE).

This figure displays Pearson correlation coefficients (r) examining the relationships between NA BLE and various circadian rhythm parameters. Statistically significant correlations (p < 0.05) are shown in green (positive) or red (negative). A total of 9 significant positive correlations and 12 significant negative correlations were observed. Green bars indicate statistically significant positive correlations (r>0.21), implying a concordant relationship with indices considered beneficial for circadian health. Red bars indicate statistically significant negative correlations (r<-0.21), suggesting an association with indices that may pose potential risks to circadian health. BLE, Blue Light Exposure; PA, Physical Activity; L5, least active 5-hour period; M10, most active 10-hour period; SD, standard deviation; MESOR, midline estimating statistic of rhythm; IS, inter-daily stability; IV, intra-daily variability; wT, wrist temperature; WASO, wake after sleep onset; TSD, total sleep duration.

 

Positive Associations with Circadian Robustness and Beneficial Health Indices: NA BLE demonstrated statistically significant positive correlations with indices considered indicative of robust circadian function and health. Specifically, a larger NA BLE was associated with a higher Circadian Function Index (CFI) (r=0.428; p<0.0001), a greater non-parametric relative amplitude of BLE (rA BLE) (r=0.424; p<0.0001), and a greater non-parametric relative amplitude of physical activity (rA PA) (r=0.359; p=0.001), as well as parametric circadian amplitudes of PA (r=0.358; p=0.001), LE (r=0.307; p=0.005), BLE (r=0.294; p=0.008), and higher inter-daily stability (IS) (r=0.295; p=0.008). NA BLE was also associated with measures of higher daytime PA, M10 (r=0.232; p=0.038), and BLE M10 (r=0.218; p=0.048). These findings suggest that individuals with a larger amplitude of their BLE exhibit more stable and robust circadian rhythms.

Negative Associations with Parameters Indicative of Hazardous Circadian Timing: In contrast, NA BLE exhibited significant negative correlations with circadian timing parameters often associated with health risks. Smaller NA BLE values were associated with later L5 onsets of BLE (r=-0.470; p<0.001) and PA (r=-0.468; p<0.001), later BLE phase (r=-0.344; p=0.002) and LE phase (r=-0.343; p=0.002), and later L5 of PA (r=-0.303; p=0.006) and BLE (r=-0.253; p=0.023). Smaller NA BLE values were also associated with later bedtime (r=-0.299; p=0.007) and greater variability in overall PA, as measured by IV (r=-0.300; p=0.007) and SD of M10 onsets for BLE (r=-0.363; p<0.001), PA (r=-0.292; p=0.008), L5 onset for PA (r=-0.331; p=0.003), and L5 for BLE (r=-0.235; p=0.035). These associations suggest that a greater BLE dynamic range is linked to more robust circadian rhythms and healthier (e.g., earlier) circadian timing.

To evaluate the reliability of the statistical findings given the limited sample size (n=27), we conducted post-hoc power analyses for key tests. Power estimates were calculated for the Friedman’s ANOVA examining seasonal differences in NA BLE (χ²=16.074, df=2, W=0.298), yielding a power of approximately 0.82. For pairwise seasonal comparisons using Wilcoxon signed-rank tests (e.g., spring vs. winter, mean difference=0.307, SD=0.120 and 0.204), power ranged from 0.75 to 0.80 for the observed effect sizes. Correlation analyses between NA BLE and circadian parameters (with r values ranging from 0.218 to 0.470) yielded power estimates of 0.50-0.95, varying with the strength of the association. For interactions in two-way ANOVA (season × sex, season × ethnicity), power was estimated at 0.40-0.55 based on observed F-statistics. All power calculations were performed using G*Power software (version 3.1; [16]), with α=0.05 and two-tailed tests where applicable. These analyses enhance confidence in detecting moderate to large effects while highlighting the risk of type II errors for smaller effects.

           

Discussion

This study establishes NA BLE as a robust, standardized index for evaluating circadian light hygiene. In a high-latitude Arctic setting with extreme seasonal light fluctuations, NA BLE displayed predictable seasonal patterns, with greater amplitudes observed in spring than in winter or summer. These fluctuations were closely coupled to adverse seasonal changes in lipid metabolism [9-11], eating behavior [17], and compromised circadian robustness after COVID-19 [8, 18], as previously described.

NA BLE further showed strong associations with actigraphy-derived circadian rhythm parameters. Positive correlations with established indices of circadian robustness, including measures of PA such as CFI, RA, and IS, align with extensive literature linking these parameters to improved health outcomes and longevity [6-7, 19-37]. Specifically, larger amplitudes of PA and LE are recognized as crucial indicators of circadian robustness, which are associated with better health [3-5, 8-9, 11-12, 37-43], while loss of regularity is linked to multiple health risks [3, 4, 21, 22, 43-48]. Conversely, negative correlations with parameters indicating hazardous circadian timing, such as L5 onset of BLE, BLE phase, and bedtime, reinforce the established association between later circadian timing and increased health risks [1-11, 22-23, 50-52].

NA BLE’s pronounced seasonality is inherently linked to photoperiodic conditions. While consistent patterns emerged in our cohort, extending NA BLE assessments to populations experiencing less extreme seasonal light variations is essential to evaluate its broader applicability. Moreover, although the correlational findings are significant, they preclude causal inferences or predictions of specific health outcomes. The modest sample size (n=27) limits statistical power and heightens the risk of Type II errors, especially for detecting small to moderate effects. Post-hoc power analyses using G*Power revealed adequate power (~0.82) for detecting the observed seasonal differences in normalized blue light amplitude (NA BLE) via Friedman’s ANOVA. Moderate to high power (0.75-0.95) was attained for correlations with circadian parameters, contingent on effect size. However, power was substantially lower (0.40-0.55) for interaction effects in two-factor ANOVA, indicating reduced sensitivity to subgroup differences by sex or ethnicity. Thus, some subtle associations or interactions may remain undetected. To bolster generalizability and mitigate these limitations, future studies should employ larger, more diverse cohorts to validate and expand these findings, enhance applicability, and elucidate potential moderating effects. Specifically, future investigations should prioritize: (1) cross-validating NA BLE in diverse populations under varying photoperiodic conditions, (2) conducting longitudinal studies to assess its predictive value for health outcomes, and (3) evaluating its utility in designing targeted clinical and public health interventions for circadian health management.

 

Conclusion

Collectively, these findings robustly suggest that NA BLE is highly sensitive to seasonal light variations and serves as a reliable correlate of healthy circadian alignment. Its ability to capture key aspects of light exposure’s dynamic range, coupled with strong associations with both beneficial circadian rhythm parameters and those indicating potential circadian maladaptation, underscores its potential as a valuable tool for monitoring and promoting circadian health across diverse settings.

 

Conflict of Interest

The authors declare no conflicts of interest.

 

Funding

This study was supported by the West-Siberian Science and Education Center, Government of Tyumen Oblast, Decree of November 20, 2020, No. 928-rp.

 

Ethical Approval

All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments, or comparable ethical standards.

References: 
  1. Brown TM, Brainard GC, Cajochen C, Czeisler CA, Hanifin JP, Lockley SW, et al. Recommendations for daytime, evening, and nighttime indoor light exposure to best support physiology, sleep, and wakefulness in healthy adults. PLoS Biol 2022; 20(3): e3001571. https://doi.org/10.1371/journal.pbio.3001571.
  2. Spitschan M, Kervezee L, Stefani O, Gordijn M, Veitch JA, Lok R; Ladenburg Light and Health Consortium. Evidence-based public health messaging on the non-visual effects of ocular light exposure: A modified Delphi expert consensus. BMJ Public Health 2025; 3(2): e003205. https://doi.org/10.1136/bmjph-2025-003205.
  3. Gubin DG, Borisenkov MF, Kolomeichuk SN, Markov AA, Weinert D, Cornelissen G, et al. Evaluating circadian light hygiene: Methodology and health implications. Russ Open Med J 2024; 13: e0415. https://doi.org/10.15275/rusomj.2024.0415.
  4. Gubin D, Weinert D, Stefani O, Otsuka K, Borisenkov M, Cornelissen G. Wearables in Chronomedicine and Interpretation of Circadian Health. Diagnostics (Basel) 2025; 15(3): 327. https://doi.org/10.3390/diagnostics15030327.
  5. Gubin D, Stefani O, Cornelissen G. Light Hygiene for Circadian Health: A Molecular Perspective. Front Biosci (Landmark Ed) 2025; 30(7): 39097. https://doi.org/10.31083/FBL39097.
  6. Windred DP, Burns AC, Rutter MK, Yeung CHC, Lane JM, Xiao Q, et al. Personal light exposure patterns and incidence of type 2 diabetes: analysis of 13 million hours of light sensor data and 670,000 person-years of prospective observation. Lancet Reg Health Eur 2024; 42: 100943. https://doi.org/10.1016/j.lanepe.2024.100943.
  7. Windred DP, Burns AC, Lane JM, Olivier P, Rutter MK, Saxena R, et al. Brighter nights and darker days predict higher mortality risk: A prospective analysis of personal light exposure in >88,000 individuals. Proc Natl Acad Sci U S A 2024; 121(43): e2405924121. https://doi.org/10.1073/pnas.2405924121.
  8. Gubin D, Boldyreva J, Stefani O, Kolomeichuk S, Danilova L, Shigabaeva A, et al. Higher vulnerability to poor circadian light hygiene in individuals with a history of COVID-19. Chronobiol Int 2025; 42(1): 133-146. https://doi.org/10.1080/07420528.2024.2449015.
  9. Gubin D, Kolomeichuk S, Danilenko K, Stefani O, Markov A, Petrov I, et al. Timing and Amplitude of Light Exposure, Not Photoperiod, Predict Blood Lipids in Arctic Residents: A Circadian Light Hypothesis. Biology (Basel) 2025; 14(7): 799. https://doi.org/10.3390/biology14070799.
  10. Gubin D, Danilenko K, Stefani O, Kolomeichuk S, Markov A, Petrov I, et al. Blue Light and Temperature Actigraphy Measures Predicting Metabolic Health Are Linked to Melatonin Receptor Polymorphism. Biology 2024; 13(1): 22. https://doi.org/10.3390/biology13010022.
  11. Gubin D, Danilenko K, Stefani O, Kolomeichuk S, Markov A, Petrov I, et al. Light Environment of Arctic Solstices is Coupled With Melatonin Phase-Amplitude Changes and Decline of Metabolic Health. J Pineal Res 2025; 77(1): e70023. https://doi.org/10.1111/jpi.70023.
  12. Gubin D, Kolomeichuk S, Danilenko K, Markov A, Petrov I, Voronin K, et al. Wrist temperature: A circadian predictor of lipid profiles in arctic residents. Chronobiol Int 2025; 42(10): 1384-1394. https://doi.org/10.1080/07420528.2025.2547937.
  13. Gubin D, Kolomeichuk S, Danilenko K, Stefani O, Markov A, Petrov I, et al. Light Exposure, Physical Activity, and Indigeneity Modulate Seasonal Variation in NR1D1 (rEV-ERBα) Expression. Biology (Basel) 2025; 14(3): 231. https://doi.org/10.3390/biology14030231.
  14. Gubin D, Kolomeichuk S, Danilenko K, Stefani O, Markov A, Petrov I, et al. Phase coupling of light exposure and activity differs between Arctic Natives and Non-Natives. Biol Rhythm Res 2025; 56(10), 713-723. https://doi.org/10.1080/09291016.2025.2507077.
  15. Ortiz-Tudela E, Martinez-Nicolas A, Campos M, Rol MA, Madrid JA. A new integrated variable based on thermometry, actimetry and body position (TAP) to evaluate circadian system status in humans. PLoS Comput Biol 2010; 6(11): e1000996. https://doi.org/10.1371/journal.pcbi.1000996.
  16. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007; 39(2): 175-191. https://doi.org/10.3758/bf03193146.
  17. Borisenkov M, Gubin D, Markov A, Shigabaeva A, Mezhakova M, Voronin K. Circadian Factors of Food Addiction in Arctic Residents: A Pilot Study. Biol Rhythm Res 2025: 1-14. https://doi.org/10.1080/09291016.2025.2530501.
  18. Gubin D, Boldyreva J, Stefani O, Kolomeichuk S, Danilova L, Markov A, et al. Light exposure predicts COVID-19 negative status in young adults. Biol Rhythm Res 2024; 55(11-12): 535-546. https://doi.org/10.1080/09291016.2024.2427608.
  19. López-Otín C, Kroemer G. Hallmarks of health. Cell 2021; 184(7): 1929-1939. https://doi.org/10.1016/j.cell.2021.03.033.
  20. Leng Y, Musiek ES, Hu K, Cappuccio FP, Yaffe K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol 2019; 18(3): 307-318. https://doi.org/10.1016/s1474-4422(18)30461-7.
  21. Allada R, Bass J. Circadian Mechanisms in Medicine. N Engl J Med 2021; 384(6): 550-561. https://doi.org/10.1056/nejmra1802337.
  22. Gubin DG, Markov AM, Solovieva SV, Khramova EB, Malyugina OA, Petrova YA, et al. Actigraphy for quantitative evaluation of health. Russ Open Med J 2025; 14: e0201. https://doi.org/10.15275/rusomj.2025.0201.
  23. Gubin D. Chronotherapeutic Approaches, Ch. 21. In: Cornelissen G, Hirota T, Eds. Chronobiology and Chronomedicine: From Molecular and Cellular Mechanisms to Whole Body Interdigitating Networks. Vol. 23. Royal Society of Chemistry; 2024: 536-577. https://doi.org/10.1039/BK9781839167553-00536.
  24. Lujan MR, Perez-Pozuelo I, Grandner MA. Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms. Front Digit Health 2021; 3: 721919. https://doi.org/10.3389/fdgth.2021.721919.
  25. Xiao Q, Qian J, Evans DS, Redline S, Lane NE, Ancoli-Israel S, et al; Osteoporotic Fractures in Men (MrOS) Study Group. Cross-sectional and Prospective Associations of Rest-Activity Rhythms With Metabolic Markers and Type 2 Diabetes in Older Men. Diabetes Care 2020; 43(11): 2702-2712. https://doi.org/10.2337/dc20-0557.
  26. Xiao Q, Matthews CE, Playdon M, Bauer C. The association between rest-activity rhythms and glycemic markers: the US National Health and Nutrition Examination Survey, 2011-2014. Sleep 2022; 45(2): zsab291. https://doi.org/10.1093/sleep/zsab291.
  27. Kim M, Vu TH, Maas MB, Braun RI, Wolf MS, Roenneberg T, et al. Light at night in older age is associated with obesity, diabetes, and hypertension. Sleep 2023; 46(3): zsac130. https://doi.org/10.1093/sleep/zsac130.
  28. Xiao Q, Durbin J, Bauer C, Yeung CHC, Figueiro MG. Alignment Between 24-h Light-Dark and Activity-Rest Rhythms Is Associated With Diabetes and Glucose Metabolism in a Nationally Representative Sample of American Adults. Diabetes Care 2023; 46(12): 2171-2179. https://doi.org/10.2337/dc23-1034.
  29. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015; 12(3): e1001779. https://doi.org/10.1371/journal.pmed.1001779.
  30. Tabacu L, Ledbetter M, Leroux A, Crainiceanu C, Smirnova E. Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003-2006. Sensors (Basel) 2020; 21(1): 4. https://doi.org/10.3390/s21010004.
  31. Weinert D, Gubin D. The Impact of Physical Activity on the Circadian System: Benefits for Health, Performance and Wellbeing. Appl Sci 2022; 12(18): 9220. https://doi.org/10.3390/app12189220.
  32. Feng H, Yang L, Ai S, Liu Y, Zhang W, Lei B, et al. Association between accelerometer-measured amplitude of rest-activity rhythm and future health risk: a prospective cohort study of the UK Biobank. Lancet Healthy Longev 2023; 4(5): e200-e210. https://doi.org/10.1016/s2666-7568(23)00056-9.
  33. Cai R, Gao L, Gao C, Yu L, Zheng X, Bennett DA, et al. Circadian disturbances and frailty risk in older adults. Nat Commun 2023; 14(1): 7219. https://doi.org/10.1038/s41467-023-42727-z.
  34. Brooks TG, Lahens NF, Grant GR, Sheline YI, FitzGerald GA, Skarke C. Diurnal rhythms of wrist temperature are associated with future disease risk in the UK Biobank. Nat Commun 2023; 14(1): 5172. https://doi.org/10.1038/s41467-023-40977-5.
  35. Lyall LM, Wyse CA, Graham N, Ferguson A, Lyall DM, Cullen B, et al. Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91,105 participants from the UK Biobank. Lancet Psychiatry 2018; 5(6): 507-514. https://doi.org/10.1016/s2215-0366(18)30139-1.
  36. Leng Y, Blackwell T, Cawthon PM, Ancoli-Israel S, Stone KL, Yaffe K. Association of circadian abnormalities in older adults with an increased risk of developing Parkinson disease. JAMA Neurol 2020; 77(10): 1270-1278. https://doi.org/10.1001/jamaneurol.2020.1623.
  37. Shim J, Fleisch E, Barata F. Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan. NPJ Digit Med 2024; 7(1): 146. https://doi.org/10.1038/s41746-024-01111-x.
  38. Weinert D, Gubin D. The Impact of Physical Activity on the Circadian System: Benefits for Health, Performance and Wellbeing. Appl Sci 2022; 12(18): 9220. https://doi.org/10.3390/app12189220.
  39. Winer JR, Lok R, Weed L, He Z, Poston KL, Mormino EC, et al. Impaired 24-h activity patterns are associated with an increased risk of Alzheimer's disease, Parkinson's disease, and cognitive decline. Alzheimers Res Ther 2024; 16(1): 35. https://doi.org/10.1186/s13195-024-01411-0.
  40. Baek JH, Zhu Y, Jackson CL, Park YM. Artificial Light at Night and Type 2 Diabetes Mellitus. Diabetes Metab J 2024; 48(5): 847-863. https://doi.org/10.4093/dmj.2024.0237.
  41. Walch O, Tavella F, Zeitzer JM, Lok R. Beyond phase shifting: targeting circadian amplitude for light interventions in humans. Sleep 2025; 48(1): zsae247. https://doi.org/10.1093/sleep/zsae247.
  42. Ishihara A, Courville AB, Chen KY. The Complex Effects of Light on Metabolism in Humans. Nutrients 2023; 15(6): 1391. https://doi.org/10.3390/nu15061391.
  43. Stefani O, Schöllhorn I, Münch M. Towards an evidence-based integrative lighting score: a proposed multi-level approach. Ann Med 2024; 56(1): 2381220. https://doi.org/10.1080/07853890.2024.2381220.
  44. Cribb L, Sha R, Yiallourou S, Grima NA, Cavuoto M, Baril AA, et al. Sleep regularity and mortality: a prospective analysis in the UK Biobank. Elife 2023; 12: RP88359. https://doi.org/10.7554/eLife.88359.
  45. Fischer D, Klerman EB, Phillips AJK. Measuring sleep regularity: theoretical properties and practical usage of existing metrics. Sleep 2021; 44(10): zsab103. https://doi.org/10.1093/sleep/zsab103.
  46. Hand AJ, Stone JE, Shen L, Vetter C, Cain SW, Bei B, et al. Measuring light regularity: sleep regularity is associated with regularity of light exposure in adolescents. Sleep 2023; 46(8): zsad001. https://doi.org/10.1093/sleep/zsad001.
  47. Wallace DA, Qiu X, Schwartz J, Huang T, Scheer FAJL, Redline S, et al. Light exposure during sleep is bidirectionally associated with irregular sleep timing: The multi-ethnic study of atherosclerosis (MESA). Environ Pollut 2024; 344: 123258. https://doi.org/10.1016/j.envpol.2023.123258.
  48. Windred DP, Burns AC, Lane JM, Saxena R, Rutter MK, Cain SW, et al. Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study. Sleep 2024; 47(1): zsad253. https://doi.org/10.1093/sleep/zsad253.
  49. Gubin DG, Boldyreva YV, Kolomeichuk SN. Factors of depression according to actigraphy in the fall season. Zh Nevrol Psikhiatr Im S S Korsakova 2025; 125(5. Vyp. 2): 27-32. Russian. https://doi.org/10.17116/jnevro202512505227.
  50. Merikanto I, Partonen T, Paunio T, Castaneda AE, Marttunen M, Urrila AS. Advanced phases and reduced amplitudes are suggested to characterize the daily rest-activity cycles in depressed adolescent boys. Chronobiol Int 2017; 34(7): 967-976. https://doi.org/10.1080/07420528.2017.1332072.
  51. Murray JM, Magee M, Sletten TL, Gordon C, Lovato N, Ambani K, et al. Light-based methods for predicting circadian phase in delayed sleep–wake phase disorder. Sci Rep 2021; 11(1): 10878. https://doi.org/10.1038/s41598-021-89924-8.
  52. Nikbakhtian S, Reed AB, Obika BD, Morelli D, Cunningham AC, Aral M, et al. Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study. Eur Heart J Digit Health 2021; 2(4): 658-666. https://doi.org/10.1093/ehjdh/ztab088.
About the Authors: 

Denis G. Gubin – MD, PhD, Professor, Department of Biology, Tyumen State Medical University; Head of the Laboratory of Chronobiology and Chronomedicine, University Research Institute of Medical Biotechnology and Biomedicine, Tyumen State Medical University; Senior Researcher, Tyumen Cardiology Research Center, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia. https://orcid.org/0000-0003-2028-1033. 
Julia V. Boldyreva – MD, PhD, Associate Professor of the Department of Biological Chemistry, Tyumen State Medical University, Tyumen, Russia. http://orcid.org/0000-0002-3276-7615. 
Liina A. Danilova – PhD, Associate Professor of the Department of Biology, Tyumen State Medical University, Tyumen, Russia. https://orcid.org/0009-0007-0703-8748. 
Sergei N. Kolomeichuk – PhD, Head of the Laboratory of Genetics, Proteomics and Metabolomics University Research Institute of Biotechnology, Tyumen, Russia. https://orcid.org/0000-0003-3104-3639.
Svetlana V. Solovieva – MD, PhD, Head of the Department of Biology, Tyumen State Medical University, Tyumen, Russia. https://orcid.org/0000-0001-8481-7664.
Larisa E. Alkhimova – MD, Assistant, School of Natural Sciences, University of Tyumen, Russia. https://orcid.org/0000-0001-6571-6526. 
Alexander A. Markov – MD, PhD, Director, Research Institute of Biomedicine and Biomedical Technologies, Tyumen State Medical University; Associate Professor of the Department of Medical Prevention and Rehabilitation, Tyumen State Medical University, Tyumen, Russia. https://orcid.org/0000-0001-7471-4792. 
Olga A. Malyugina – Junior researcher, Laboratory of Chronobiology and Chronomedicine, University Research Institute of Medical Biotechnology and Biomedicine, Tyumen State Medical University, Tyumen, Russia. https://orcid.org/0000-0003-4551-5959. 
Natalya V. Kuznetsova – MD, Senior lecturer, Department of Theory and Practice of Nursing, Tyumen State Medical University, Tyumen, Russia. 
Oliver Stefani – PhD, Senior Researcher, Department Engineering and Architecture, Institute of Building Technology and Energy, Lucerne University of Applied Sciences and Arts, Horw, Switzerland. https://orcid.org/0000-0003-0199-6500. 
Dietmar Weinert – PhD, Emeritus, Institute of Biology/Zoology, Martin Luther University, Halle-Wittenberg, Germany. 
Germaine Cornelissen – PhD, Professor, Department of Integrative Biology and Physiology, Director, Halberg Chronobiology Center, University of Minnesota, Minneapolis, MN, USA. https://orcid.org/0000-0002-1698-1590.

Received 17 September 2025, Revised 10 October 2025, Accepted 18 October 2025 
© 2025, Russian Open Medical Journal 
Correspondence to Denis G. Gubin. E-mail: dgubin@mail.ru.

 

Author(s): 
Alkhimova, Larisa E. / Boldyreva, Julia V. / Cornelissen, Germaine / Danilova, Liina A. / Gubin, Denis G. / Kolomeichuk, Sergei N. / Kuznetsova, Natalya V. / Malyugina, Olga A. / Markov, Alexander A. / Stefani, Oliver / Weinert, Dietmar