Associations between objective measures of performance-related characteristics and perceived stress in young cross-country skiers during pre-season training.

Monitoring performance-related characteristics of athletes can reveal changes that facilitate training adaptations. Here, we examine the relationships between submaximal running, maximal jump performance (CMJ), concentrations of blood lactate, sleep duration (SD) and latency (SL), and perceived stress (PSS) in junior cross-country skiers during pre-season training. These parameters were monitored in 15 male and 14 females (17 ± 1 years) for the 12-weeks prior to the competition season, and the data was analysed using linear and mixed-effect models. An increase in SD exerted a decrease in both PSS (B = -2.79, p ≤ 0.01) and blood lactate concentrations during submaximal running (B = -0.623, p ≤ 0.05). In addition, there was a negative relationship between SL and CMJ (B = -0.09, p = 0.08). Compared to males, females exhibited higher PSS scores and little or no change in performance-related tests. A significant interaction between time and sex was present in CMJ with males displaying an effect of time on CMJ performance. For all athletes, lower PSS appeared to be associated with longer overnight sleep. Since the females experienced higher levels of stress, monitoring of their PSS might be beneficial. These findings have implications for the preparation of young athletes' competition season.

Metabolic health, menopause, and physical activity-a 4-year follow-up study.

BACKGROUND: In women, metabolic health deteriorates after menopause, and the role of physical activity (PA) in mitigating the change is not completely understood. This study investigates the changes in indicators of metabolic health around menopause and evaluates whether PA modulates these changes. METHODS: Longitudinal data of 298 women aged 48-55 years at baseline participating in the ERMA and EsmiRs studies was used. Mean follow-up time was 3.8 (SD 0.1) years. Studied indicators of metabolic health were total and android fat mass, waist circumference, waist-to-hip ratio (WHR), systolic (SBP) and diastolic (DBP) blood pressure, blood glucose, triglycerides, serum total cholesterol, and high- (HDL-C) and low-density (LDL-C) lipoprotein cholesterol. PA was assessed by accelerometers and questionnaires. The participants were categorized into three menopausal groups: PRE-PRE (pre- or perimenopausal at both timepoints, n = 56), PRE-POST (pre- or perimenopausal at baseline, postmenopausal at follow-up, n = 149), and POST-POST (postmenopausal at both timepoints, n = 93). Analyses were carried out using linear and Poisson mixed-effect models. RESULTS: At baseline, PA associated directly with HDL-C and inversely with LDL-C and all body adiposity variables. An increase was observed in total (B = 1.72, 95% CI [0.16, 3.28]) and android fat mass (0.26, [0.06, 0.46]), SBP (9.37, [3.34, 15.39]), and in all blood-based biomarkers in the PRE-POST group during the follow-up. The increase tended to be smaller in the PRE-PRE and POST-POST groups compared to the PRE-POST group, except for SBP. The change in PA associated inversely with the change in SBP (-2.40, [-4.34, -0.46]) and directly with the change in WHR (0.72, [0.05, 1.38]). CONCLUSIONS: In middle-aged women, menopause may accelerate the changes in multiple indicators of metabolic health. PA associates with healthier blood lipid profile and body composition in middle-aged women but does not seem to modulate the changes in most of the studied metabolic health indicators during the menopausal transition.

Extracting Conditionally Heteroskedastic Components using Independent Component Analysis.

In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online.

Parent-child and spousal relationships in families with a young child with end-stage renal disease.

BACKGROUND: End-stage renal disease (ESRD) leads to the need for dialysis and renal transplantation (Tx). Peritoneal dialysis (PD) of young children is normally performed at home by the parents and affects the whole family. We studied the coping of families with a young child with ESRD by interviewing the parents of 19 children. METHODS: The spousal and parent-child relationships were assessed by using the Psychosocial Assessment of Childhood Experiences (PACE) and the Brief Measure of Expressed Emotion, respectively. A control group of 22 families with a healthy child was used for the parent-child relationship evaluation. RESULTS: The spousal relationship at the start of PD was good or fairly good in most of the families and remained good in half of the families following renal Tx. Lack of support from close relatives and renal Tx were associated with a poorer relationship quality. Almost all parents expressed much or fairly much emotional warmth towards the child throughout the study, but there was a trend towards increased criticism over time. No differences in the degree of expressed warmth or criticism were noted between the index parents and controls. CONCLUSIONS: Overall, the study families appeared to cope well despite the serious illness of their child and the demands of the treatments.

Association between gut health and gut microbiota in a polluted environment.

Animals host complex bacterial communities in their gastrointestinal tracts, with which they share a mutualistic interaction. The numerous effects these interactions grant to the host include regulation of the immune system, defense against pathogen invasion, digestion of otherwise undigestible foodstuffs, and impacts on host behaviour. Exposure to stressors, such as environmental pollution, parasites, and/or predators, can alter the composition of the gut microbiome, potentially affecting host-microbiome interactions that can be manifest in the host as, for example, metabolic dysfunction or inflammation. However, whether a change in gut microbiota in wild animals associates with a change in host condition is seldom examined. Thus, we quantified whether wild bank voles inhabiting a polluted environment, areas where there are environmental radionuclides, exhibited a change in gut microbiota (using 16S amplicon sequencing) and concomitant change in host health using a combined approach of transcriptomics, histological staining analyses of colon tissue, and quantification of short-chain fatty acids in faeces and blood. Concomitant with a change in gut microbiota in animals inhabiting contaminated areas, we found evidence of poor gut health in the host, such as hypotrophy of goblet cells and likely weakened mucus layer and related changes in Clca1 and Agr2 gene expression, but no visible inflammation in colon tissue. Through this case study we show that inhabiting a polluted environment can have wide reaching effects on the gut health of affected animals, and that gut health and other host health parameters should be examined together with gut microbiota in ecotoxicological studies.

A review of second-order blind identification methods.

Second-order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modeling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source signals from an observed signal mixture. The SOS model assumes that the observed time series (signals) is a linear mixture of latent time series (sources) with uncorrelated components. The methods make use of the second-order statistics-hence the name "second-order source separation." In this review, we discuss the classical SOS methods and their extensions to more complex settings. An example illustrates how SOS can be performed. This article is categorized under:Statistical Models > Time Series ModelsStatistical and Graphical Methods of Data Analysis > Dimension ReductionData: Types and Structure > Time Series, Stochastic Processes, and Functional Data.

Robust estimation and inference for bivariate line-fitting in allometry.

In allometry, bivariate techniques related to principal component analysis are often used in place of linear regression, and primary interest is in making inferences about the slope. We demonstrate that the current inferential methods are not robust to bivariate contamination, and consider four robust alternatives to the current methods -- a novel sandwich estimator approach, using robust covariance matrices derived via an influence function approach, Huber's M-estimator and the fast-and-robust bootstrap. Simulations demonstrate that Huber's M-estimators are highly efficient and robust against bivariate contamination, and when combined with the fast-and-robust bootstrap, we can make accurate inferences even from small samples.

Efficient estimation of generalized linear latent variable models.

Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estimation algorithms based on a combination of either the Laplace approximation method or variational approximation method, and automatic optimization techniques implemented in R software. An extensive set of simulation studies is used to assess the performances of different methods, from which it is shown that the variational approximation method used in conjunction with automatic optimization offers a powerful tool for estimation.

So Many Variables: Joint Modeling in Community Ecology.

Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions.

Fall incidence in frail older women after individualized visual feedback-based balance training.

BACKGROUND: The knowledge concerning balance training actually lowering fall rates among frail older persons is limited. OBJECTIVE: The aim of this study was to examine the effects of a 4-week individualized visual feedback-based balance training on the fall incidence during 1-year follow-up among frail older women living in residential care. METHODS: Twenty-seven older women from 2 residential care homes were randomized into exercise (n = 20) and control (n = 7) groups. Balance measurements were carried out before and after a 4-week training period and falls were monitored by monthly diaries for 1 year. An interview about fear of falling and physical activity was completed before and after the intervention and after the 1-year follow-up. RESULTS: A positive effect of balance training on fall incidence was found. A dynamic Poisson regression model showed that during the follow-up the monthly risk of falling was decreased in the exercise group compared to controls (risk ratio 0.398, 95% CI 0.174-0.911, p = 0.029). In addition, the exercise group reported a reduced fear of falling and increased physical activity after a training period but these changes declined during the follow-up period. CONCLUSION: Individualized visual feedback-based balance training was shown to be a promising method for fall prevention among frail older women. High compliance (97.5%) with the training program showed that carefully targeted training programs can be carried out among older people with health limitations.