Influence of a home-based exercise program on the urine pH in elderly female subjects: a secondary analysis of a randomized controlled trial

Background

A low urine pH is a characteristic metabolic feature of metabolic syndrome and type 2 diabetes. The purpose of the current study was to investigate the effects of a 12-week home-based bench step exercise on the urine pH status of elderly female subjects.

Methods

The current study is a secondary analysis of a randomized controlled trial (RCT) in which 59 postmenopausal female subjects were randomized to either the exercise group (n?=?29) or the control group (n?=?30). The subjects in the exercise group were instructed to perform home-based exercises using a bench step at the anaerobic threshold (AT), with a goal of performing ≥140 min/week at home for 12 weeks. The subjects in the control group were instructed to not change their normal lifestyle. Urine was collected after overnight fasting, and the urine pH was measured using a urinary test strip. The inter-group-differences at baseline and the pre-post changes within groups were assessed using the Mann-Whitney U test and Wilcoxon’s signed-rank test, respectively. Additionally, the difference in the post-intervention urine pH levels of the two groups, adjusted for the pre-intervention values (the estimated effect size) and the precision (95% confidence intervals) were investigated using an analysis of covariance.

Results

The pre-post comparison of the urine pH data using Wilcoxon’s signed-rank test showed a significant increase in the urine pH levels of the exercise group (p?<?0.05); there was no significant change in the urine pH levels of the control group. However, the estimated effect size (0.15) was small and the confidence interval straddled 0 (?0.25–0.55).

Conclusions

Based on the results of the current secondary analysis of an RCT, we could not clearly conclude that exercise has a beneficial effect on the urine pH. Further well-designed RCTs should be conducted to determine whether aerobic exercise is truly able to ameliorate urine acidification.

Trial registration

The study was retrospectively registered in the University Hospital Medical Information Network (UMIN) as “Effect of step exercise on aerobic fitness and progression of atherosclerosis in the elderly” under the registration number UMIN 000026743 (the date of registration: March 28, 2017).

     

All Data are Wrong,but Some are Useful? Advocating the Need for Data Auditing

In a recent article from the Annals of Applied Statistics, Cox discussed the main phases of applied statistical research ranging from clarifying study objectives to final data analysis and interpreting results. As an incidental remark to these main phases, we advocate that beyond cleaning and preprocessing the data, it is a good practice to audit the data to determine if they can be trusted at all. A case study based on Ghanaian Official Fishery Statistics is used to illustrate this need, with Benford's law being the tool used to carrying out the data audit. Supplementary materials for this article are available online.     

Adaptive multiple importance sampling for Gaussian processes

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.     

Copulas related to piecewise monotone functions of the interval and associated processes

In this work, we derive the copulas related to vectors obtained from the so-called chaotic stochastic processes. These are defined by the iteration of certain piecewise monotone functions of the interval [0, 1] to some initial random variable. We study some of its properties and present some examples. Since often these types of copulas do not have closed formulas, we provide a general approximation method which converges uniformly to the true copula. Our results cover a wide class of processes, including the so-called Manneville–Pomeau processes. The general theory is applied to the parametric estimation in certain chaotic processes. A Monte Carlo simulation study is also presented.     

Multivariate log-Birnbaum–Saunders regression models

In this paper, we present a multivariate version of the skewed log-Birnbaum–Saunders regression model. This new family of distributions holds good properties such as marginal variables following univariate skewed log-Birnbaum–Saunders distributions, besides presenting the usual log-Birnbaum–Saunders distribution as a particular case. Furthermore, the model parameters are estimated through maximum-likelihood methods, a closed-form expression for the Fisher’s information matrix is presented, and testing hypothesis for model parameters is performed. Two real datasets are analyzed and results are discussed.     

Penalised spline estimation for generalised partially linear single-index models

Generalised linear models are frequently used in modeling the relationship of the response variable from the general exponential family with a set of predictor variables, where a linear combination of predictors is linked to the mean of the response variable. We propose a penalised spline (P-spline) estimation for generalised partially linear single-index models, which extend the generalised linear models to include nonlinear effect for some predictors. The proposed models can allow flexible dependence on some predictors while overcome the “curse of dimensionality”. We investigate the P-spline profile likelihood estimation using the readily available R package mgcv, leading to straightforward computation. Simulation studies are considered under various link functions. In addition, we examine different choices of smoothing parameters. Simulation results and real data applications show effectiveness of the proposed approach. Finally, some large sample properties are established.     

The expected bit complexity of the von Neumann rejection algorithm

In 1952, von Neumann introduced the rejection method for random variate generation. We revisit this algorithm when we have a source of perfect bits at our disposal. In this random bit model, there are universal lower bounds for generating a random variate with a given density to within an accuracy \(\epsilon \) derived by Knuth and Yao, and refined by the authors. In general, von Neumann’s method fails in this model. We propose a modification that insures proper behavior for all Riemann-integrable densities on compact sets, and show that the expected number of random bits needed behaves optimally with respect to universal lower bounds. In particular, we introduce the notion of an oracle that evaluates the supremum and infimum of a function on any rectangle of \({\mathbb {R}}^{d}\), and develop a quadtree-style extension of the classical rejection method.     

Prior specification of neighbourhood and interaction structure in binary Markov random fields

We formulate a prior distribution for the energy function of stationary binary Markov random fields (MRFs) defined on a rectangular lattice. In the prior we assign distributions to all parts of the energy function. In particular we define priors for the neighbourhood structure of the MRF, what interactions to include in the model, and for potential values. We define a reversible jump Markov chain Monte Carlo (RJMCMC) procedure to simulate from the corresponding posterior distribution when conditioned to an observed scene. Thereby we are able to learn both the neighbourhood structure and the parametric form of the MRF from the observed scene. We circumvent evaluations of the intractable normalising constant of the MRF when running the RJMCMC algorithm by adopting a previously defined approximate auxiliary variable algorithm. We demonstrate the usefulness of our prior in two simulation examples and one real data example.     

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its self-adjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.     

Is education a risk factor or social vaccine against HIV/AIDS in Sub-Saharan Africa? The effect of schooling across public health periods

Early in the 30-year HIV/AIDS pandemic in Sub-Saharan Africa, epidemiological studies identified formal education attainment as a risk factor: educated Sub-Saharan Africans had a higher risk of contracting HIV/AIDS than their less educated peers. Later demographic research reported that by the mid-1990s the education effect had reversed, and education began to function as a social vaccine. Recent counter-evidence finds a curvilinear pattern, with the association between educational attainment and HIV/AIDS infection changing from positive to negative across the education gradient. To reconcile these inconsistent conclusions, a hypothesis is developed and tested that education at early stages functioned as a risk factor and later functioned (and continues to function) as a social vaccine. We reason that this shift in the direction of the education effect was concurrent with changes in the public health environment in SSA that early on heightened material benefits from educational attainment but later heightened cognitive benefits from schooling. Using the 2003/2004 Demographic Health Surveys from four Sub-Saharan African countries (Cameroon, Ghana, Kenya and Tanzania), we tested this hypothesis (differential effects of schooling) using non-linear regression analysis (probit), identifying the different public health periods and controlling for confounding factors. The results support the hypothesis that the education effect shifted historically in the HIV/AIDS pandemic in SSA as we hypothesized.