RESUMO
Light exposure affects the circadian system and consequently can affect sleep quality. Only few studies examined this relationship in children. We evaluated associations between light exposure patterns and sleep metrics in children. We measured the sleep parameters of 247 Dutch children, aged between 11 and 13 years and recruited from the ABCD cohort, using actigraphy and sleep records for 7 consecutive nights. Personal light exposures were measured with a light meter during the whole day and night. We applied generalized mixed-effects regression models, adjusted for possible confounders, to evaluate the associations of light exposure patterns on sleep duration, sleep efficiency and sleep-onset delay. In the models mutually adjusted for potential confounders, we found the amount of hours between the first time of bright light in the morning and going to sleep and the duration of bright light to be significantly associated with decreased sleep duration (in min; ß: -2.02 [95% confidence interval: -3.84, -0.25], ß: -8.39 [95% confidence interval: -16.70, -0.07], respectively) and with shorter sleep-onset delay (odds ratio: 0.88 [95% confidence interval: 0.80, 0.97], odds ratio: 0.40 [95% confidence interval: 0.19, 0.87], respectively). Increased light intensities at night were associated with decreased sleep duration (T2 ß: -8.54 [95% confidence interval: -16.88, -0.20], T3 ß: -14.83 [95% confidence interval: -28.04, -1.62]), while increased light intensities before going to bed were associated with prolonged sleep onset (odds ratio: 4.02 [95% confidence interval: 2.09, 7.73]). These findings further suggest that children may be able to influence their sleep quality by influencing the light exposure patterns during day and night.
RESUMO
Background: Environmental factors such as air pollution have been associated with Parkinson's disease (PD), but findings have been inconsistent. We investigated the association between exposure to several air pollutants, road traffic noise, and PD risk in two Dutch cohorts. Methods: Data from 50,087 participants from two Dutch population-based cohort studies, European Prospective Investigation into Cancer and Nutrition in the Netherlands and Arbeid, Milieu en Gezondheid Onderzoek were analyzed. In these cohorts, 235 PD cases were ascertained based on a previously validated algorithm combining self-reported information (diagnosis, medication, and symptoms) and registry data. We assigned the following traffic-related exposures to residential addresses at baseline: NO2, NOx, particulate matter (PM)2.5absorbance (as a marker for black carbon exposure), PM with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), PMcoarse (size fraction 2.5-10 µm), ultrafine particles <0.1 µm (UFP), and road traffic noise (Lden). Logistic regression models were applied to investigate the associations with PD, adjusted for possible confounders. Results: Both single- and two-pollutant models indicated associations between exposure to NOx, road traffic noise, and increasing odds of developing PD. Odds ratios of fully adjusted two-pollutant models in the highest compared with the lowest exposure quartile were 1.62 (95% CI = 1.02, 2.62) for NOx and 1.47 (95% CI = 0.97, 2.25) for road traffic noise, with clear trends across exposure categories. Conclusions: Our findings suggest that NOx and road traffic noise are associated with an increased risk of PD. While the association with NOx has been shown before, further investigation into the possible role of environmental noise on PD is warranted.
