Time Of Day Research Articles

Sleep, Fatigue, and Recovery Profiles of the Longest Solo Unsupported One-Way Polar Ski Journey Across Antarctica.

Antarctic expeditions are exceptional physiological challenges. Sleep plays a critical role in athletic performance, recovery, and wellness, with sleep disturbances having a negative impact on health and performance. The authors investigated sleep, fatigue, and recovery profiles of the longest solo unsupported one-way polar ski journey across Antarctica. A 33-year-old woman covered 1484.53km from Hercules Inlet to the South Pole, finishing at the Ross Ice Shelf, in 70days and 16hours. Questionnaires on sleep (Pittsburgh Insomnia Rating Scale and Karolinska Sleepiness Scale), fatigue (Subjective Assessment of Fatigue), recovery (Total Quality Recovery), and wellness were completed at different time points (before, during, and after the expedition). Average daily sleep time was between 4 and 5hours, increasing to 7hours for the final part of the expedition. Satisfaction of sleep and lack of energy deteriorated as the expedition progressed, alongside signs of clinical insomnia. Fatigue and muscle soreness increased with increasing milage, with extreme levels and very poor recovery toward the end of the expedition. Despite this, the adventurer continued to perform on extremely high levels. Postexpedition scores returned to baseline, demonstrating the incredible adaptation and ability to recovery. The postexpedition interview showed that prior experience of an Antarctic expedition may have prepared the athlete and made her more resilient for this challenge. The data provide unique insights into Antarctic expeditions and may help us understand the limits of human performance when planning future expeditions of this nature. Female athletes are capable of extreme challenges, breaking established performance boundaries.

Examining Circumstances Surrounding Fatal Pediatric Heatstroke in the United States

Fatal Pediatric Vehicular Heatstroke (PVH) is a preventable tragedy that claims the lives of an average of 38 children each year. The purpose of this article is to explore the relationship between fatal Pediatric Vehicular Heatstroke incidents and various circumstantial factors. Data was collected through customized online searches of electronic media using tools such as Google News and Lexis-Nexus. A set of linear regression analyses were conducted to examine the relationship between fatal PVH and three circumstantial variables: geographic region, time of day, and outside temperature. The finding show that specific U.S. Regions, Time of Day, and Outside Temperature significantly predicted fatal Pediatric Vehicular Heatstroke (PVH). Understanding these relationships can aid in the development of more targeted prevention technologies, legislation, and public education efforts aimed at reducing the risk of fatal PVH within the United States.

Relative effectiveness of high-dose vs. standard-dose quadrivalent influenza vaccine according to time of day of vaccination: a post-hoc analysis of the DANFLU-1 randomised clinical trial

Abstract Background Circadian variations have been shown to affect antibody response following vaccination. We sought to evaluate whether time of day of vaccination (ToV) modified the relative effectiveness of high-dose (QIV-HD) vs. standard-dose (QIV-SD) quadrivalent influenza vaccination, and further whether ToV was independently associated with hospitalisations and mortality. Methods This is a post-hoc analysis of the DANFLU-1 trial which was a randomized trial of QIV-HD vs. QIV-SD in adults aged 65-79 years conducted during the 2021-2022 influenza season. Vaccination took place between 7:05 AM and 8:08 PM. For this study, we stratified participants into early and late vaccination groups according to ToV before or after 11 AM, respectively. Prespecified outcomes included hospitalisations for pneumonia or influenza, respiratory disease, cardiovascular disease, and cardio-respiratory disease as well as all-cause hospitalisations and mortality. Hospitalisations were analysed as recurrent events using negative binomial regression, while mortality was analysed using Cox proportional hazard models, both of which were also used to test for interaction. For multivariable analysis, models were adjusted for baseline risk factors including sex, age, and multiple lung diseases. ToV was furthermore assessed as a continuous variable with 2-hour increments. Results In the final study population, the early group consisted of 5,371 (43%) participants (mean age 71.6±3.9 years, male sex 53.6%, QIV-SD 50.1%), while the late group consisted of 7,106 (57%) (mean age 71.8±4.0 years, male sex 52.4%, QIV-HD 49.9%). During follow-up, we observed 43 hospitalisations for pneumonia or influenza, 76 respiratory hospitalisations, and 62 mortalities. In recurrent event analysis, QIV-HD was associated with lower incidence of hospitalisation for pneumonia or influenza compared with QIV-SD irrespective of whether administered early (IRR 0.17 (95% CI 0.03-0.86), p=0.032) or late (IRR 0.37 (95% CI 0.16-0.88), p=0.024) (p-value for interaction = 0.69) (Figure 1). Effect estimates were consistent for all outcomes excluding cardiovascular hospitalisations regardless of early or late vaccination, and no effect modification was found (Figure 1). Interestingly, late vaccination was associated with higher incidence rate of respiratory hospitalisation (IRR 3.00 (95% CI 1.13-7.98), p=0.024, Figure 2), irrespective of vaccine type. The same association was found for ToV as a continuous variable (IRR 1.28 per 2-hour increment (95% CI 1.02-1.60), p=0.035). All above associations remained significant in multivariable analysis. Conclusion QIV-HD was associated with lower incidence of hospitalisation for pneumonia or influenza regardless of time of day for vaccination. In addition, later vaccination was associated with higher incidence of respiratory hospitalisation independent of vaccine type and known risk factors. Due to low power, however, this finding should be regarded as exploratory.Figure 1:Forest plotFigure 2:Incidence rates

Miracle in the evening: Explaining the boundary condition of consumer's self-control on purchase intention of near-expired products

Due to consumers' misunderstanding of product expiration dates, huge amounts of food waste and retail losses have been recognized as a worldwide problem. This research explores a solution to mitigate such waste and losses in the consumer purchase decision-making process of near-expiry products. Drawing from the literature on expiration date-based pricing (EDBP) and self-control, we propose that consumers' purchase intention for near-expiry products vary depending on the time of day (morning vs. evening). The results from mixed methodologies, including an analysis of actual sales data from an online retailer (Study 1) and randomized controlled experimental studies (Studies 2 and 3), reveal that consumers are less sensitive to near-expiry products when purchasing EDBP products in the evening compared to the morning. Furthermore, we examine how self-control mediates the interaction effects of the nearness of the expiration date and the time of day on the purchase intention of near-expiry EDBP products. A novel perspective on understanding consumers’ dynamic evaluations of sales promotions over time and managerial implications for promotional strategies to promote products approaching their expiration date are discussed.

Cost of fall injuries in traffic are for pedestrians and bicyclists - municipal respons needed

Abstract Motivation Pedestrians and bicyclists have been posting in news and social media with reflections on municipal efforts to clean bicycle paths and sidewalks in icy and wet conditions. The municipality have lowered budgets for these cleaning efforts in recent years. Methods Analysis of A&E department registration data with the Nomesco core dataset allows for separation of location, age, sex, time of day and date of injury. Injury severity is classified according to diagnosis of treatment (minor/major). Cost calculations are based on national scales from the Ministry of Transport (ambulance/police/treatment/rehabilitation). Cold weather was defined as lowest day temperature < 5 degree C (Danish Metereological service DMI). Results During 2023 all pedestrians or bicyclists (n = 2516) treated at the A&E following an incident on sidewalks, streets or bicyclepaths (population 225.000) were included. Non-serious were (total/cold weather): sidewalks (699/266), bicyclepaths (414/117), streets (762/277). Serious: sidewalks (290/130), bicyclepaths (123/32), streets (228/100). Assessment of economical costs was DKK: Non-serious were (total/cold weather): sidewalks (20.8/7.9), bicyclepaths (12.4/3.5), streets (22.7/8.3). Serious: sidewalks (42.2/18.9), bicyclepaths (17.9/4.7), streets (33.2/14.6). Discussion and conclusions The economic burden for the population and municipality far exceeds the costs for cleaning the streets, bicyclepaths and sidewalks in icy and wet conditions. With appropriate injury surveillance and communication to municipal authorities revised cleaning principles was decided at the political level Key messages • Appropriate and focused injury registration is needed for targeted political level decision. • Standardized and targeted economic calculations are persuasive in the political arena at the local level.

Influence of the Time of Day on Triage-to-Nerve Block Duration in Acute Hip Fracture Patients

Influence of the Time of Day on Triage-to-Nerve Block Duration in Acute Hip Fracture Patients

Visualizing patterns of factors influencing school health promotion implementation

Abstract Background The school setting offers a special opportunity for health-promotion (HP) interventions, as children spend 1/3 of their daily time there. Yet, implementing HP remains challenging due to the complexity of schools and unique contextual factors influencing implementation. While common influencing factors have been described, the aim of this study is to explore how influencing factors co-occur within and vary across schools, forming potential patterns of barriers and facilitators. Methods As part of a school HP program involving 258 institutions across Germany, nine schools were selected using maximum heterogeneity sampling for in-depth interviews on influencing factors by employing the Consolidated Framework for Implementation Research (CFIR). The interviews were transcribed verbatim and analyzed deductively using the CFIR as category system. Identified influencing factors were quantified [-5; +5] whether being a major barrier (-5) or a major facilitator (+5). To visualize potential patterns, a matrix heat map (MHM) displaying all influencing factors across all schools was created. Preliminary results Influencing factors in almost all CFIR categories were identified and all schools reported factors as barriers, such as parental engagement or (in)sufficient time. From the MHM, patterns of influencing factors, co-occurring within and differing across schools, emerged. For instance, some schools found the physical environment to be a barrier, while others considered it a facilitator, often in conjunction with financing as another facilitator. Conclusions Qualitative analysis and visualization of results with a MHM served as a way to navigate the complexity of the school setting and to highlight patterns of factors influencing implementation. Understanding these patterns can enhance tailoring implementation strategies, thereby improve implementation of school HP interventions and, ultimately, foster child health outcomes. Key messages • Influencing factors for school HP implementation manifest in varied patterns of barriers and facilitators across schools, which has implications for tailoring implementation strategies. • Using qualitative analysis guided by the CFIR and a MHM for visualization can aid in navigating the complexity of schools.

Physical activity and sedentarism among university students: an approach using a real-life survey

Abstract Background Evidence shows physical activity (PA) and sedentary behavior (SB) impact health-related outcomes. World Health Organization (WHO) provides PA guidelines for different life stages. A recent study revealed that engaging in SB > 6 hours/day increases all-cause mortality risk. Nevertheless many students spend a significant portion of their daily time on SB and PA levels are insufficient. Methods Students were selected by convenience sampling to complete a survey between October-November 2023. A validated PA questionnaire was included including PA Motivations and barriers. PA and SB were classified into 4 levels: Active but sedentary, extremely sedentary, physically active and discreetly active. Data were analyzed using Statistical Package for the Social Sciences (SPSS). Results Out of 2,216 surveys 1,648 were women, 568 men and 59.7% were in first university years. In our sample, 12.9% don’t do PA. Of those doing PA, 47.1% (n = 1043) don’t meet PA recommendations. Regarding SB, 84.7% spend >6 hours/day in SB. When grouped into PA/SB categories 40.5% were extremely sedentary in contrast to 8.8% physically active. Non-university facilities (41.1%) were preferred over university facilities (11.1%). Use of non-university and university facilities correlated with WHO compliance. Main barriers among inactive students were lack of time (48%) and lack of a workout partner (18.6%). Conclusions Most students don’t meet WHO guidelines. Less than 10% are considered active, while 40.5% are extremely sedentary. University sports facilities are underused even if linked to better compliance. To promote facilities as health-promoting spaces and investigate reasons for their underuse is necessary. Lack of company as a reason for not engaging in PA points out the need to strength university sports communities. Key messages • Although many students exercise, the majority do no meet World Health Organization recommendations. • We must intensify university programs to promote physical activity and reduce sedentary behavior.

Elemental composition and health risk assessment of PM10, PM2.5, at different microenvironments: Addis Ababa, Ethiopia.

This study was designed to evaluate the health risks faced by inhabitants living in the slum areas of Addis Ababa, Ethiopia. The levels of PM2.5 and PM10 and elemental composition of the PM10 were measured in indoors (in the kitchen and living room) and outdoors (at the roadside). A total of 75 sampling locations (45 indoor and 30 outdoor) were selected for the study. The levels of PM2.5 and PM10 were determined using an AROCET531S instrument, while an universal air pump was used for the sampling of PM10 for the determination of trace elements by inductively coupled plasma-optical emission spectroscopy (ICP‒OES). The health impacts of PMs on the inhabitants of twelve microenvironments (MEs), where they spend much of their daily time, were estimated. The total amounts of PM2.5 and PM10, and trace metals in PM10 found in the nine or twelve MEs ranged from 10.6-119, 128-185, and 0.007-0.197 μg m-3, respectively. According to the United States Environment Protection Agency (USEPA) guidelines, ten of the twelve MEs can cause significant health problems for inhabitants (HI > 1) due to PM2.5 and PM10. Thus, special attention should be given by stakeholders/inhabitants to minimize the health impacts on long-term exposure. This study assessed the risk of levels of trace elements on the inhabitants who spend most of their daily lives. The study revealed that the lifetime cancer risk values for the individual and cumulative trace elements were within the tolerable range set by the USEPA guidelines.

Time junior middle school students invested to sports and their cognitive skill development: a time economics perspective

Great importance is attached to physical exercise and students’ cognitive development, yet the precise mechanism linking the time duration of physical activities or sports with adolescents’ cognitive development remains inconclusive. Based on the two-wave panel data of China Education Panel Survey (CEPS), this paper empirically examines the impact of daily physical exercise duration on junior middle school students’ cognitive abilities, by using the two-way fixed-effect estimation. The findings reveal a nonlinear relationship, specifically an inverted “U”-shaped pattern, between daily time length of physical activities or sports and students’ cognitive performance. The cognitive benefits of time investment in physical activities or sports reach the peak at approximately 0.829 h (equivalent to 50 min) per day among the teenagers in China. Furthermore, the daily time allocated to physical activities or sports affects students’ cognitive performance differentially, students attending rural schools were rewarded more progressively, students of poorer physical fitness and those in good shape did not show discernible between-group differences. This study shades light on educational governance and time management, urging school administrators, parents and students to optimize the efficiency and utility of time resource invested in sports activities.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Green means go: The effect of a visualization tool towards increased use of renewable energy in households

There is a need to further develop digital and hardware tools to support consumers in the adaptation of their energy use to, for example, integrate renewable sources into it and/or reduce expenses. This paper presents an evaluation study of a tool, including its hardware and software versions, that supports consumers in their electricity consumption optimization, most notably concerning renewable energies for specific devices. We conducted a six-week study in Oldenburg, Germany involving 43 participants divided into three groups: one hardware and one digital variant each of an energy visualization tool, as well as a control group. We furthermore collected quantitative data on energy usage patterns and qualitative insights through semi-structured interviews. Here the participants received information about the percentage of renewable energy currently in the grid, and were asked to document their washing machine and dishwasher use. This paper provides insights into the perception of the proposed visualization tools achieved through semi-structured interviews, comparing the digital and hardware versions, while outlining the challenges of and possibilities for changing energy use behavior, especially regarding the time of day of an appliance's use. Even though the measurable influences of the tool were limited due to thresholds being reached too easily as a result of e.g. high wind levels generating higher-than-anticipated amounts of renewable energy, in terms of the amount of renewable energy use, slight improvements in the two test groups were observed compared to the control group. Regarding consumer behavior, changes in the appliances' use time show slightly higher improvement compared to the control group.

Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.

Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non-paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject-specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within-day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R-package.

Multiphasic movement and step-selection patterns of dispersed tigers in the central Indian landscape.

Large carnivores play a crucial role in the ecosystem, though their conservation needs a landscape-level approach due to their wide range of habitats and dispersal events. The study of tigers in a human-dominated landscape matrix and their adaptation and adjustment of movement behaviours during the dispersal phase is essential for long-term conservation planning and management policy. We studied the dispersal event of five VHF/GPS collared individuals during 2009-2020. We investigated movement parameters (step length), and the effects of anthropogenic pressures (distance from village), distance from water and vegetation cover, on behavioural phase under a Hidden Markov Model framework. We also tested the effects of distance from village, vegetation cover, and habitat types on animal movement using an integrated Step Selection Function framework. The mean step length (405.44±10.63 m/hr) varied widely by different time of day. Displacement was high during the night (665.28±21.36 m/hr) compared to day (434.16±17.37 m/hr). Tigers moved fast (872.7m; 95% CI 839.1-906.3m) with longer step length and a directional turning angle in non-forested areas (i.e. the human-dominated landscape), likely to avoid conflict with humans. Individuals distinctly exhibited two behavioural states: encamping (~32% of the time) and travelling (~68% of the time). Further, they avoided the human-dominated landscape and mostly remained in and forested areas, especially during nighttime. Our study is the first attempt to understand behavioural transition of dispersal tigers and their habitat selection. Lesser anthropogenic disturbance and high vegetation cover positively influenced the tiger dispersal, while water availability did not affect their state transitional probability. Additionally, dispersers showed high affinity towards forested land during nighttime for travelling.The findings of this study show the importance of functional corridors and stepping stones (mostly encamping areas), and also provide baseline knowledge for integrated landscape management planning and policymaking for the long-term survival of tigers in metapopulation framework.

Unveiling the Spectrum of Glucose Variability: A Novel Perspective on FreeStyle Libre Monitoring Data.

Since the introduction of insulin therapy, it has become apparent that type 1 diabetes (T1D) is accompanied by long-term microvascular and macrovascular complications. In the context of the many benefits of continuous glucose monitoring (CGM), there remain opportunities to study the large amount of data now available in order to maximise its potential in the endeavour to reduce the occurrence of diabetes tissue complications in the longer term. Continuous glucose monitoring values were downloaded for 89 type 1 diabetes mellitus (T1D) individuals for up to 18 months from 2021 to 2023. Data for patient demographics was also taken from the patient record which included Sex, Date of Birth, and Date of Diagnosis. The recorded laboratory glycated haemoglobin (HbA1c) test results were also recorded. The glucose management index (GMI) was calculated from average glucose readings for 18 months using the formula GMI (%) = (0.82-(Average glucose/100)). This was then adjusted to give GMI (mmol/mol) = 10.929 * (GMI (%) - 2.15). Average Glucose Fluctuation (AGF) was calculated by adding up the total absolute change value between all recorded results over 18 months and dividing by the number of results minus one. The % Above Critical Threshold (ACT) was calculated by summing the total number of occurrences for each result value. A cumulative 95% limit was then applied to identify the glucose value that only 5% of results exceeded in the overall population. Using this value, we estimated the percentage of total tests that were above the Critical Threshold (ACT). The mean age of the participants was 42.6years, and the mean duration of T1D was 18.4years. A total of 3.22 million readings were analysed, yielding an average blood glucose level of 10.3mmol/l and a GMI of 57.2mmol/mol. There was a strong correlation between GMI and measured HbA1c (r2 = 0.82). However, there were patients who had an above-critical threshold (ACT) of 4-10% at a GMI of 60mmol/mol or less. The percentage average value at the time of day (%AVTD) was applied to all blood glucose readings at each 15-min interval throughout the day, averaged over 18months. The %AVTD of GMI (overall average 57.2mmol/mol) increased after midday, dipped at 18:00, and peaked at 22:00. The %AVTD of AGF (overall average 0.60mmol/l) showed higher change rates after 09:00 declining at the end of the day. The %AVTD of ACT peaked at 22:00, with those having the highest %ACT showing an additional peak at 15:00. We have shown here that the percentage glucose results above 18mmol/l (top 5% of distribution) increased exponentially above 54mmol/mol HbA1c. The %AVTD is introduced as a useful measure. Our data indicate that over the 24-h period, improvement in metabolic control could be focussed on the afternoon and evening, when there are higher-than-average levels of GMI, a higher-than-average degree of glucose change, and higher-than-average risks of being above the critical threshold. In conclusion, a measure of glycaemic variation based on the amplitude of glucose change to a population mean could be used to provide valuable clinical insights into glucose change over a 24-h period.

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

Correlation of screen exposure to stress, learning, cognitive and language performance in children.

The omnipresence of mobile screens and convenience to operate them has led to increased screen time for young children whereas the sequelae of prolonged exposure are not known yet. 70 refugee children (RG) and 111 children of a clinical comparison group (CG) from a help-seeking population (age: M = 5.10; SD = 1.11; range 3.00-6.97years) were assessed concerning their amount of daily screen exposure time in relation to parental education and distress. Salivary cortisol was collected as a marker for biological stress and children were tested concerning learning performance, non-verbal IQ and vocabulary with the Kaufmann Assessment Battery for Children (KABC-II). Language skills were assessed in educator rating. The amount of children's screen exposure was negatively related to parental education and positively to distress. In the CG, higher amounts of screen time were associated with elevated cortisol levels and lower learning scores. On both measures, the RG and CG only differed in the condition of screen time less than one hour/day, for higher amounts of screen time the CG approached the more problematic scores of the RG. Whereas in the whole sample the amount of screen time was negatively correlated to language performance, it was not correlated to non-verbal IQ-scores. As a higher amount of media exposure in our clinical comparison group is associated with elevated biological stress, decreased learning and lower language performance, it should be classified as a relevant environmental factor and regularly considered in clinical assessments of children and therapeutical interventions, especially in vulnerable subgroups. German clinical trials register, registration number: DRKS00025734, date: 07-23-2021.

Development and validation of a nomogram for sleep disorders among stroke patients

BackgroundPrecisely identifying high-risk sleep disorder patients and implementing suitable measures are important for decreasing the incidence of sleep disorders. In this study, a nomogram method was adopted to construct a tool to predict sleep disorders in stroke based on four factors: individual characteristics, treatment-related factors, psychological factors, and family-related factors. MethodsA total of 450 stroke patients were continuously diagnosed at the Affiliated Hospital of Nantong University, and the data on participants were randomly distributed into a training set (n = 315) and a validation set (n = 135). Within the training set, using LASSO regression and random forest methods, five optimal predictors of sleep disorders were identified. Five optimal predictors were used to develop a model. The calibration, discrimination, generalization, and clinical applicability of the model were evaluated using calibration curves, receiver operating characteristic (ROC) curves, internal validation, and decision curve analysis (DCA). ResultsWe found that the place of residence, average daily infusion time, the Hospital Anxiety and Depression Scale (HADS), the Type D Personality Scale-14 (DS14), and the Fatigue Severity Scale (FSS) were crucial factors associated with sleep disorders. The validation data showed an area under the curve (AUC) of 0.903 compared to 0.899 in the training set. There was an approach to the diagonal in the calibration curve of this model, and the results of DCA noted that it is clinically beneficial across a range of thresholds from 5 % to 99 %. ConclusionA model was developed to predict sleep disorders among stroke patients to help hospital staff evaluate the risk among patients and screen high-risk patients.

Early Childhood Education and Care Attendance and Its Association With Outdoor Play, Screen Time and Sleep Duration of Young Children: Findings From the CHAT Trial in Australia.

Effects of attending early childhood education and care (ECEC) on health behaviours of young children remain unclear. This study aimed to investigate whether ECEC attendance was associated with outdoor play, screen time, sleep duration and family demographics of children aged 2 and 3 years. Secondary analysis was conducted using data extracted from two linked trials conducted in Australia, 2017-2020. Telephone surveys were conducted with participating mothers for data collection. Multiple logistic and linear regression models were built to investigate the associations of ECEC attendance with outdoor play, screen time, sleep duration and family demographics among young children. At ages 2 and 3 years, 797 and 537 mothers completed surveys, respectively. Of respondents, 65% and 72% of children attended ECEC, respectively. Children who attended ECEC had 17 min (95% CI 3.8-30.5) and 28 min (95% CI 14.1-41.9) more daily outdoor playtime and had 13 min (95% CI 4.0-21.5) and 19 min (95% CI 6.4-30.7) less daily screen time at home at 2 and 3 years, respectively. Although ECEC attendance was not associated with sleep duration, children who attended ECEC were more likely to meet all three recommendations (outdoor play, screen time and sleep) with adjusted odds ratio (AOR) 1.84 (95% CI 1.24-2.72) at age 2 and AOR 2.34 (95% CI 1.28-4.28) at age 3. Mothers who were employed, first-time mothers, spoke English at home or had a high household income were more likely to use ECEC services. ECEC services may hold promise to influence outdoor play and screen time among young children. Children from lower socio-demographic background had a lower rate of ECEC attendance. Future health promotion programmes for young children need to also consider children who do not attend ECEC.

Dynamic Spillovers from US (Un)Conventional Monetary Policy to African Equity Markets: A Time-Varying Parameter Frequency Connectedness and Wavelet Coherence Analysis

Since the implementation of unconventional monetary policies (UMPs) by the US in response to the global financial crisis (GFC) and the COVID-19 pandemic, there have been increasing concerns that these forward guidance and quantitative easing programmes have had spillover effects on global equity markets. We specifically question whether the implementation of these UMPs have had spillovers to African equities, which have been previously speculated to be decoupled from global markets and shocks. Time-varying-parameter (TVP) frequency connectedness and wavelet coherency methods were used to examine the dynamic time-frequency spillovers between daily time series of the US shadow short rate and African equities returns/volatility between 1 January 2007 and 31 March 2023. On one hand, the TVP frequency connectedness analysis reveals robust long-run spillovers from US monetary policy to African equity markets during specific periods: 2009, 2013, 2020, and 2021. These coincide with instances when the Federal Reserve announced their transition from conventional to unconventional monetary practices and vice versa. On the other hand, the wavelet analysis provides insights into the ‘sign’ of the spillovers, indicating mixed phase dynamics during UMPs responding to the GFC. In contrast, anti-phase or negative co-movements characterize UMPs implemented during the COVID-19 pandemic, implying that these policies increased both returns and volatilities to African equities. Altogether, we conclude that US UMP has increasing deteriorated market efficiency and amplified portfolio risk in African equities whilst during ‘normalization’ periods US monetary policy has little transmission effect.