Factors associated with habitual time spent in different physical activity intensities using multiday accelerometry.

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.

A Biased Diffusion Approach to Sleep Dynamics Reveals Neuronal Characteristics.

We propose a biased diffusion model of accumulated subthreshold voltage fluctuations in wake-promoting neurons to account for stochasticity in sleep dynamics and to explain the occurrence of brief arousals during sleep. Utilizing this model, we derive four neurophysiological parameters related to neuronal noise level, excitability threshold, deep-sleep threshold, and sleep inertia. We provide the first analytic expressions for these parameters, and we show that there is good agreement between empirical findings from sleep recordings and our model simulation results. Our work suggests that these four parameters can be of clinical importance because we find them to be significantly altered in elderly subjects and in children with autism.

Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers.

Accelerometry can be a practical replacement for polysomnography in large observational studies. This review discusses the need for sleep characterization in large observational studies, exemplified by the practices of the ongoing German National Cohort study. After brief descriptions of the physical principles and state-of-the-art accelerometer devices and an overview of public data analysis algorithms for sleep-wake differentiation, we demonstrate that the spectral properties of acceleration data provide additional features that can be exploited. This leads to a periodogram-based sleep detection algorithm. Finally, we address issues of data handling and quality assurance in large cohort studies.

Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography.

OBJECTIVE: To assess the agreement of sleep parameters measured by two actigraphs (SOMNOwatch plus, ActiGraph GT3X+) at two different placements (wrist, hip) and of self-reported sleep with polysomnography (PSG). METHODS: We estimated agreement with PSG for total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), number of awakenings after sleep onset (NASO), and sleep efficiency (SE%) for 100 participants of the general population, aged 18-75 years by judging mean differences to PSG and intervals of agreement using Bland-Altman plots. RESULTS: Mean difference to PSG for TST was 8.3 min (95% confidence intervals [CI] -7.4; 24.1) for SOMNOwatch plus (wrist), 39.8 min (95% CI 24.3; 55.3) for self-report, -79.0 min (95% CI -89.0; -68.9) for SOMNOwatch plus (hip), and -81.1 min (95% CI -91.9; -70.4) for GT3X+ (hip), respectively. The width of intervals of agreement differed with the placement of the devices. Mean differences to PSG were higher for hip-based measurements compared with wrist placement for most parameters. CONCLUSIONS: Agreement of sleep parameters assessed by actigraphy with PSG differs with the placement of the device and is limited for hip-based measurements. Agreement of self-report with PSG is comparable to that of actigraphy for some parameters.

Having children, social characteristics, smoking and the risk of uveal melanoma: a case-control study.

PURPOSE: We analyzed data from the Risk Factors for Uveal Melanoma (RIFA) study to evaluate possible associations between uveal melanoma risk and having children, socioeconomic level and smoking. METHODS: The RIFA study was a German case-control study conducted from September 2002 to March 2005. The study population consisted of 455 incident uveal melanoma patients (20-74 years of age) and 827 matched (age, sex, region of residence) population controls. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using conditional logistic regression. RESULTS: Women with children showed an increased OR (1.59, 95% CI 0.95-2.66) for uveal melanoma development compared to women without children. We estimated decreased ORs for subjects with higher socioeconomic level compared to the lowest status (upper secondary school leaving certificate: OR 0.68, 95% CI 0.49-0.94; higher education: OR 0.60, 95% CI 0.38-0.96). Ever smokers showed an OR of 1.19 (95% CI 0.92-1.55) compared to never smokers. CONCLUSION: 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.