cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20032663.v1
title: Perception of barriers to physical exercise in women population over 60

description: Abstract Aims This study evaluated the possible barriers to the permanence of physical exercise (PE) of old women. Methods The study population comprised 61 old women participants for at least one year of a supervised PE program, who underwent anamnesis, and applied the Barriers Questionnaire to Physical Activity Practice in the Elderly (QBPAFI). Exploratory factorial analysis was used to evaluate QBPAFI data. The analysis of principal component was applied to the 22 questions through orthogonal rotation to analyze the correlation between the questions. The Kaiser-Meyer-Olkin test was applied to evaluate the suitability of the sample size, and the Bartlett's test to assess whether the original matrix correlation is an identity matrix. Eigenvalues greater than 1 were considered for analysis. Results The motivational factor was the major determinant of perceived barriers (43.3%), followed by psychosocial (12.29%), facilities and appearance (8.75%), and exercise conditions (8.10%) factors. Conclusion Knowing the benefits of physical activity, and the main barriers that prevent the permanence of active old people to physical exercise programs, new strategies must be taken to increase the rate of adherence of this group.
DOI: 10.6084/m9.figshare.20032665
title: Quality of life of Brazilian wheelchair tennis athletes across competitive and elite levels

description: Abstract Aims This study aimed to identify Brazilian wheelchair tennis athletes' quality of life and to compare the perception scores between competitive and elite athletes. Methods Participants were wheelchair tennis athletes (N = 31) from Brazil (males = 25, females = 6) divided into two groups: competitive and elite. Data was collected using the Brazilian version of the WHOQOL-Bref. The WHOQOL-Bref asks two general questions, and is thereafter divided into four main domains: 1) physical, psychological, social, and environment, which give us an understanding of their overall quality of life perception. Results The results showed that overall, 29 out of the 31 athletes perceived their quality of life positively, regardless of the competitive level. However, statistically higher perceptions were found in the physical domain and the total score of quality of life of the elite group. Conclusion The findings of this study suggest that even though participation in high-performance adapted sports may offer a stressful and exhausting environment, elite wheelchair tennis athletes from Brazil perceive themselves as having a better QoL than competitive athletes.
DOI: 10.6084/m9.figshare.20032665.v1
title: Quality of life of Brazilian wheelchair tennis athletes across competitive and elite levels

description: Abstract Aims This study aimed to identify Brazilian wheelchair tennis athletes' quality of life and to compare the perception scores between competitive and elite athletes. Methods Participants were wheelchair tennis athletes (N = 31) from Brazil (males = 25, females = 6) divided into two groups: competitive and elite. Data was collected using the Brazilian version of the WHOQOL-Bref. The WHOQOL-Bref asks two general questions, and is thereafter divided into four main domains: 1) physical, psychological, social, and environment, which give us an understanding of their overall quality of life perception. Results The results showed that overall, 29 out of the 31 athletes perceived their quality of life positively, regardless of the competitive level. However, statistically higher perceptions were found in the physical domain and the total score of quality of life of the elite group. Conclusion The findings of this study suggest that even though participation in high-performance adapted sports may offer a stressful and exhausting environment, elite wheelchair tennis athletes from Brazil perceive themselves as having a better QoL than competitive athletes.
DOI: 10.6084/m9.figshare.20032668
title: Association between osteoporosis, health-related productivity loss and use of hospital services in outpatients of the Brazilian National Health System

description: Abstract Aims To analyze factors associated with osteoporosis among outpatients of the Brazilian National Health System and to identify their association with hospital and labor economic outcomes. Methods Cross-sectional study carried out in the city of Presidente Prudente / SP. The sample consisted of 542 adults of both sexes and aged ≥ 50 years old. The occurrence of osteoporosis, health-related productivity loss, use of hospital services and level of physical activity were assessed using questionnaires. Statistical analysis was composed of chi-square test, binary logistic regression and Mann-Whitney test. The significance level adopted was p-value &lt;0.05. ResultsThe prevalence of osteoporosis was 14.4% (95% CI: 11.4% - 17.3%) and it was associated with female sex (p = 0.001), lower economic status (p-value = 0.036) and obesity (p-value = 0.003). Participants with osteoporosis showed a higher incidence of surgery in the last 12 months (OR = 2.13 [1.04 to 4.35]), productivity loss (OR = 1.91 [1.13 to 3.42]) and disability retirement (OR = 2.03 [1.20 to 3.43]). Over the past 12 months, the sum of direct and indirect economic loss was R$ 1,382,630.00. Conclusion The female sex, lower economic status and obesity were associated with a higher occurrence of osteoporosis, and consequent higher use of hospital services and significant economic losses.
DOI: 10.6084/m9.figshare.20032668.v1
title: Association between osteoporosis, health-related productivity loss and use of hospital services in outpatients of the Brazilian National Health System

description: Abstract Aims To analyze factors associated with osteoporosis among outpatients of the Brazilian National Health System and to identify their association with hospital and labor economic outcomes. Methods Cross-sectional study carried out in the city of Presidente Prudente / SP. The sample consisted of 542 adults of both sexes and aged ≥ 50 years old. The occurrence of osteoporosis, health-related productivity loss, use of hospital services and level of physical activity were assessed using questionnaires. Statistical analysis was composed of chi-square test, binary logistic regression and Mann-Whitney test. The significance level adopted was p-value &lt;0.05. ResultsThe prevalence of osteoporosis was 14.4% (95% CI: 11.4% - 17.3%) and it was associated with female sex (p = 0.001), lower economic status (p-value = 0.036) and obesity (p-value = 0.003). Participants with osteoporosis showed a higher incidence of surgery in the last 12 months (OR = 2.13 [1.04 to 4.35]), productivity loss (OR = 1.91 [1.13 to 3.42]) and disability retirement (OR = 2.03 [1.20 to 3.43]). Over the past 12 months, the sum of direct and indirect economic loss was R$ 1,382,630.00. Conclusion The female sex, lower economic status and obesity were associated with a higher occurrence of osteoporosis, and consequent higher use of hospital services and significant economic losses.
DOI: 10.6084/m9.figshare.20032637
title: Analyzed_dataset_road_orientation.csv

description: Analyzed dataset for: Mulero-Pázmány, M., Rollán, L., D’Amico, M., & González-Suárez, M. (2022) Road orientation affects the impact of roads on wildlife. <em>Wildlife Research</em>.
DOI: 10.6084/m9.figshare.20032637.v1
title: Analyzed_dataset_road_orientation.csv

description: Analyzed dataset for: Mulero-Pázmány, M., Rollán, L., D’Amico, M., & González-Suárez, M. (2022) Road orientation affects the impact of roads on wildlife. <em>Wildlife Research</em>.
DOI: 10.6084/m9.figshare.20032669
title: Differences in game statistics between winning and losing for male wheelchair tennis players in Paralympics Games

description: ABSTRACT Aims The aim of the present study was to analyze the differences in wheelchair tennis game statistics between winning and losing sets. Methods Data were collected from the 139 sets of 64 matches played by 64 males' players in the 2012 Paralympics Games. The variables studied were grouped in four groups: variables related to serve, to return, to winners and errors, and to net point. Data was collected from official website of the Paralympics Games. A univariate (Wilcoxon test) and multivariate (discriminant) analysis of data was done to the study the four groups of variable in relation to the result of the set (win or lose). Results The result showed that winning players commit fewer errors and achieved more winning shots than losing players. Conclusion The values presented could be used as a reference for practice and competition in wheelchair tennis players.
DOI: 10.6084/m9.figshare.20032669.v1
title: Differences in game statistics between winning and losing for male wheelchair tennis players in Paralympics Games

description: ABSTRACT Aims The aim of the present study was to analyze the differences in wheelchair tennis game statistics between winning and losing sets. Methods Data were collected from the 139 sets of 64 matches played by 64 males' players in the 2012 Paralympics Games. The variables studied were grouped in four groups: variables related to serve, to return, to winners and errors, and to net point. Data was collected from official website of the Paralympics Games. A univariate (Wilcoxon test) and multivariate (discriminant) analysis of data was done to the study the four groups of variable in relation to the result of the set (win or lose). Results The result showed that winning players commit fewer errors and achieved more winning shots than losing players. Conclusion The values presented could be used as a reference for practice and competition in wheelchair tennis players.
DOI: 10.6084/m9.figshare.12851319
title: A Bottom-Up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, With Error Rate Control

description: Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that both helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given <i>p</i>-values from association tests for each individual OTU or ASV, we would like to know if we can declare a certain species, genus, or higher taxonomic group to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family, etc.) is required. We develop such a bottom-up testing algorithm that controls a novel error rate that we call the false selection rate. By simulation, we also show that our approach is better at finding <i>driver taxa</i>, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851319.v2
title: A Bottom-Up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, With Error Rate Control

description: Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that both helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given <i>p</i>-values from association tests for each individual OTU or ASV, we would like to know if we can declare a certain species, genus, or higher taxonomic group to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family, etc.) is required. We develop such a bottom-up testing algorithm that controls a novel error rate that we call the false selection rate. By simulation, we also show that our approach is better at finding <i>driver taxa</i>, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.15105114.v3
title: A Cross-Validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies

description: Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work, we address this problem by proposing a cross-validated ensemble of kernels (CVEK) that learns the space of appropriate functions for the main effects using a cross-validated ensemble approach. With a carefully chosen library of base kernels, CVEK flexibly estimates the form of the main-effect functions from the data, and encourages test power by guarding against over-fitting under the alternative. The method is motivated by a study on the interaction between metal exposures <i>in utero</i> and maternal nutrition on children’s neurodevelopment in rural Bangladesh. The proposed tests identified evidence of an interaction between minerals and vitamins intake and arsenic and manganese exposures. Results suggest that the detrimental effects of these metals are most pronounced at low intake levels of the nutrients, suggesting nutritional interventions in pregnant women could mitigate the adverse impacts of <i>in utero</i> metal exposures on the children’s neurodevelopment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.15105114
title: A Cross-Validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies

description: Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work, we address this problem by proposing a cross-validated ensemble of kernels (CVEK) that learns the space of appropriate functions for the main effects using a cross-validated ensemble approach. With a carefully chosen library of base kernels, CVEK flexibly estimates the form of the main-effect functions from the data, and encourages test power by guarding against over-fitting under the alternative. The method is motivated by a study on the interaction between metal exposures <i>in utero</i> and maternal nutrition on children’s neurodevelopment in rural Bangladesh. The proposed tests identified evidence of an interaction between minerals and vitamins intake and arsenic and manganese exposures. Results suggest that the detrimental effects of these metals are most pronounced at low intake levels of the nutrients, suggesting nutritional interventions in pregnant women could mitigate the adverse impacts of <i>in utero</i> metal exposures on the children’s neurodevelopment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.15105114.v2
title: A Cross-Validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies

description: Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work, we address this problem by proposing a cross-validated ensemble of kernels (CVEK) that learns the space of appropriate functions for the main effects using a cross-validated ensemble approach. With a carefully chosen library of base kernels, CVEK flexibly estimates the form of the main-effect functions from the data, and encourages test power by guarding against over-fitting under the alternative. The method is motivated by a study on the interaction between metal exposures <i>in utero</i> and maternal nutrition on children’s neurodevelopment in rural Bangladesh. The proposed tests identified evidence of an interaction between minerals and vitamins intake and arsenic and manganese exposures. Results suggest that the detrimental effects of these metals are most pronounced at low intake levels of the nutrients, suggesting nutritional interventions in pregnant women could mitigate the adverse impacts of <i>in utero</i> metal exposures on the children’s neurodevelopment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.12851385
title: The Hellinger Correlation

description: In this article, the defining properties of any valid measure of the dependence between two continuous random variables are revisited and complemented with two original ones, shown to imply other usual postulates. While other popular choices are proved to violate some of these requirements, a class of dependence measures satisfying all of them is identified. One particular measure, that we call the Hellinger correlation, appears as a natural choice within that class due to both its theoretical and intuitive appeal. A simple and efficient nonparametric estimator for that quantity is proposed, with its implementation publicly available in the R package HellCor. Synthetic and real-data examples illustrate the descriptive ability of the measure, which can also be used as test statistic for exact independence testing. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851385.v2
title: The Hellinger Correlation

description: In this article, the defining properties of any valid measure of the dependence between two continuous random variables are revisited and complemented with two original ones, shown to imply other usual postulates. While other popular choices are proved to violate some of these requirements, a class of dependence measures satisfying all of them is identified. One particular measure, that we call the Hellinger correlation, appears as a natural choice within that class due to both its theoretical and intuitive appeal. A simple and efficient nonparametric estimator for that quantity is proposed, with its implementation publicly available in the R package HellCor. Synthetic and real-data examples illustrate the descriptive ability of the measure, which can also be used as test statistic for exact independence testing. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851313
title: Estimation of Low Rank High-Dimensional Multivariate Linear Models for Multi-Response Data

description: In this article, we study low rank high-dimensional multivariate linear models (LRMLM) for high-dimensional multi-response data. We propose an intuitively appealing estimation approach and develop an algorithm for implementation purposes. Asymptotic properties are established to justify the estimation procedure theoretically. Intensive simulation studies are also conducted to demonstrate performance when the sample size is finite, and a comparison is made with some popular methods from the literature. The results show the proposed estimator outperforms all of the alternative methods under various circumstances. Finally, using our suggested estimation procedure we apply the LRMLM to analyze an environmental dataset and predict concentrations of PM2.5 at the locations concerned. The results illustrate how the proposed method provides more accurate predictions than the alternative approaches.
DOI: 10.6084/m9.figshare.12851313.v2
title: Estimation of Low Rank High-Dimensional Multivariate Linear Models for Multi-Response Data

description: In this article, we study low rank high-dimensional multivariate linear models (LRMLM) for high-dimensional multi-response data. We propose an intuitively appealing estimation approach and develop an algorithm for implementation purposes. Asymptotic properties are established to justify the estimation procedure theoretically. Intensive simulation studies are also conducted to demonstrate performance when the sample size is finite, and a comparison is made with some popular methods from the literature. The results show the proposed estimator outperforms all of the alternative methods under various circumstances. Finally, using our suggested estimation procedure we apply the LRMLM to analyze an environmental dataset and predict concentrations of PM2.5 at the locations concerned. The results illustrate how the proposed method provides more accurate predictions than the alternative approaches.
DOI: 10.6084/m9.figshare.12984285
title: Estimating Number of Factors by Adjusted Eigenvalues Thresholding

description: Determining the number of common factors is an important and practical topic in high-dimensional factor models. The existing literature is mainly based on the eigenvalues of the covariance matrix. Owing to the incomparability of the eigenvalues of the covariance matrix caused by the heterogeneous scales of the observed variables, it is not easy to find an accurate relationship between these eigenvalues and the number of common factors. To overcome this limitation, we appeal to the correlation matrix and demonstrate, surprisingly, that the number of eigenvalues greater than 1 of the population correlation matrix is the same as the number of common factors under certain mild conditions. To use such a relationship, we study random matrix theory based on the sample correlation matrix to correct biases in estimating the top eigenvalues and to take into account of estimation errors in eigenvalue estimation. Thus, we propose a tuning-free scale-invariant adjusted correlation thresholding (ACT) method for determining the number of common factors in high-dimensional factor models, taking into account the sampling variabilities and biases of top sample eigenvalues. We also establish the optimality of the proposed ACT method in terms of minimal signal strength and the optimal threshold. Simulation studies lend further support to our proposed method and show that our estimator outperforms competing methods in most test cases. Supplementary materials for this article are available online.