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We study the effective scaling behavior of high-resolution accelerometric time series recorded at the wrists and hips of 100 subjects during sleep and wake. Using spectral analysis and detrended fluctuation analysis we find long-term correlated fluctuations with a spectral exponent β ≈ 1.0 (1/f noise). On short time scales, β is larger during wake (≈ 1.4) and smaller during sleep (≈ 0.6). In addition, characteristic peaks at 0.2-0.3 Hz (due to respiration) and 4-10 Hz (probably due to physiological tremor) are observed in periods of weak activity. Because of these peaks, spectral analysis is superior in characterizing effective scaling during sleep, while detrending analysis performs well during wake. Our findings can be exploited to detect sleep-wake transitions.

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To investigate factors associated with time in physical activity intensities, we assessed physical activity of 249 men and women (mean age 51.3 years) by 7-day 24h-accelerometry (ActiGraph GT3X+). Triaxial vector magnitude counts/minute were extracted to determine time in inactivity, in low-intensity, moderate, and vigorous-to-very-vigorous activity. Cross-sectional associations with sex, age, body mass index, waist circumference, smoking, alcohol consumption, education, employment, income, marital status, diabetes, and dyslipidaemia were investigated in multivariable regression analyses. Higher age was associated with more time in low-intensity (mean difference, 7.3 min/d per 5 years; 95% confidence interval 2.0,12.7) and less time in vigorous-to-very-vigorous activity (−0.8 min/d; −1.4, −0.2), while higher BMI was related to less time in low-intensity activity (−3.7 min/d; −6.3, −1.2). Current versus never smoking was associated with more time in low-intensity (29.2 min/d; 7.5, 50.9) and less time in vigorous-to-very-vigorous activity (−3.9 min/d; −6.3, −1.5). Finally, having versus not having a university entrance qualification and being not versus full time employed were associated with more inactivity time (35.9 min/d; 13.0, 58.8, and 66.2 min/d; 34.7, 97.7, respectively) and less time in low-intensity activity (−31.7 min/d; −49.9, −13.4, and −50.7; −76.6, −24.8, respectively). The assessed factors show distinct associations with activity intensities, providing targets for public health measures aiming to increase activity.

The observed association between lower socioeconomic level and increased odds for uveal melanoma possibly represents a higher occupational uveal melanoma risk for occupational categories that are usually associated with lower socioeconomic status. Concerning having children and uveal melanoma development, we hypothesize that the observed association is mediated through alpha-melanocyte-stimulating hormone, a hormone that increases during pregnancy and is linked to pigmentation alterations in pregnant women.

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.

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