Combining Wake-Up-Back-to-Bed with Cognitive Induction Techniques: Does Earlier Sleep Interruption Reduce Lucid Dream Induction Rate?

Lucid dreaming offers the chance to investigate dreams from within a dream and by real-time dialogue between experimenters and dreamers during REM sleep. This state of consciousness opens a new experimental venue for dream research. However, laboratory study in this field is limited due to the rarity of lucid dreamers. In a previous study, we were able to induce in 50% of the participants a lucid dream in a single sleep laboratory night by combining a wake-up-back-to-bed (WBTB) sleep routine and a mnemonic method (Mnemonic Induction of Lucid Dreams, MILD). In three experiments, we tried to replicate our earlier findings while we adapted our procedure in shortening (Exp1–3: 4.5 vs. 6 h of uninterrupted sleep in the first half of the night), simplifying (Exp2: time-based wakening vs. REM wakening in the second half of the night), and applying another induction technique (Exp3: reality testing vs. MILD). In the three conditions, four out of 15 (26%), zero out of 20 (0%), and three out of 15 (20%) participants reported a lucid dream. Compared to the original study, the earlier sleep interruption seems to reduce the lucid dream induction rate. Furthermore, without REM awakenings in the morning, lucid dream induction failed, whereas reality testing showed a lower success rate compared to MILD. Further systematic sleep laboratory studies are needed to develop reliable techniques for lucid dream research

Worsened Parkinson's Disease Progression: Impact of the COVID-19 Pandemic

Whilst some studies investigated the impact of viral infection or reduced access to medication during the COVID-19 pandemic in patients with Parkinson's disease (PD), data on the effects of pandemic restrictions are still scarce. We retrospectively analyzed motor symptoms of longitudinally followed PD patients (n = 264) and compared motor disease progression before and during the COVID-19 pandemic. Additionally, we performed a trend analysis of the yearly evolution of motor symptoms in 755 patients from 2016 until 2021. We observed a worsening of motor symptoms and a significantly increased motor disease progression during pandemic-related restrictions as compared to before the COVID-19 outbreak

Performance of multi-city land use regression models for nitrogen dioxide and fine particles

BACKGROUND: Land use regression (LUR) models have mostly been developed to explain intra-urban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES: To develop European and regional LUR models and to examine their transferability to areas not used for model development. METHODS: We evaluated LUR models for nitrogen dioxide (NO2) and Particulate Matter (PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE study areas across 14 European countries for PM and NO2. Models were evaluated with cross validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. RESULTS: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5 and 70% for PM2.5 absorbance. The HV R(2)s were only slightly lower than the model R(2) (NO2: 54%, PM2.5: 80%, absorbance: 70%). The European NO2, PM2.5 and PM2.5 absorbance models explained a median of 59%, 48% and 70% of within-area variability in individual areas. The transferred models predicted a modest to large fraction of variability in areas which were excluded from model building (median R(2): 59% NO2; 42% PM2.5; 67% PM2.5 absorbance). CONCLUSIONS: Using a large dataset from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted

Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects of exposure to air pollution on human health in Europe. This paper documents the spatial variation of measured NO2 and NOx concentrations between and within 36 ESCAPE study areas across Europe. In all study areas NO2 and NOx were measured using standardized methods between October 2008 and April 2011. On average, 41 sites were selected per study area, including regional and urban background as well as street sites. The measurements were conducted in three different seasons, using Ogawa badges. Average concentrations for each site were calculated after adjustment for temporal variation using data obtained from a routine monitor background site. Substantial spatial variability was found in NO2 and NOx concentrations between and within study areas; 40% of the overall NO2 variance was attributable to the variability between study areas and 60% to variability within study areas. The corresponding values for NOx were 30% and 70%. The within-area spatial variability was mostly determined by differences between street and urban background concentrations. The street/urban background concentration ratio for NO2 varied between 1.09 and 3.16 across areas. The highest median concentrations were observed in Southern Europe, the lowest in Northern Europe. In conclusion, we found significant contrasts in annual average NO2 and NOx concentrations between and especially within 36 study areas across Europe. Epidemiological long-term studies should therefore consider different approaches for better characterization of the intra-urban contrasts, either by increasing of the number of monitors or by modelling