Estimated Cardiorespiratory Fitness Is Associated With Reported Depression in College Students

Depression is a serious but treatable health issue that affects college students at an alarming rate. Improved cardiorespiratory fitness (CRF) decreases depression risk and severity but this relationship has not been fully evaluated in the college student population. Non-exercise estimated CRF (eCRF) could be used to identify students at risk for or suffering from depression. This study investigated the associations of depression and eCRF in college students. Participants (N = 437) completed a survey which included demographic and student-status questions, eCRF variables, and a validated depression instrument. Descriptive, chi-square, t-test, regression, and odds ratio analyses were employed. Depression was associated with low-fitness (X2 = 4.660, P = 0.031) and eCRF below age-predicted CRF (t = 3.28, P... (see full abstract in article)

A New Powerful Nonparametric Rank Test for Ordered Alternative Problem

We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain

Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

Correlation Coefficients for a Study with Repeated Measures

Repeated measures are increasingly collected in a study to investigate the trajectory of measures over time. One of the first research questions is to determine the correlation between two measures. The following five methods for correlation calculation are compared: (1) Pearson correlation; (2) correlation of subject means; (3) partial correlation for subject effect; (4) partial correlation for visit effect; and (5) a mixed model approach. Pearson correlation coefficient is traditionally used in a cross-sectional study. Pearson correlation is close to the correlations computed from mixed-effects models that consider the correlation structure, but Pearson correlation may not be theoretically appropriate in a repeated-measure study as it ignores the correlation of the outcomes from multiple visits within the same subject. We compare these methods with regard to the average of correlation and the mean squared error. In general, correlation under the mixed-effects model with the compound symmetric structure is recommended as its correlation is close to the nominal level with small mean square error

Accurate Confidence Intervals for Risk Difference in Meta-Analysis With Rare Events

Background: Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. When the outcome is binary and it is rare (e.g., safety data in clinical trials), the traditionally used methods may have unsatisfactory performance. Methods: We propose using importance sampling to compute confidence intervals for risk difference in meta-analysis with rare events. The proposed intervals are not exact, but they often have the coverage probabilities close to the nominal level. We compare the proposed accurate intervals with the existing intervals from the fixed- or random-effects models and the interval by Tian et al. (2009). Results: We conduct extensive simulation studies to compare them with regards to coverage probability and average length, when data are simulated under the homogeneity or heterogeneity assumption of study effects. Conclusions: The proposed accurate interval based on the random-effects model for sample space ordering generally has satisfactory performance under the heterogeneity assumption, while the traditionally used interval based on the fixed-effects model works well when the studies are homogeneous

Sex Moderates the Relationship That Number of Professional Fights Has With Cognition and Brain Volumes.

Objective: Incidence of concussions and report of symptoms are greater among women across sports. While structural brain changes and cognitive declines are associated with repetitive head impact (RHI), the role of sex is not well-understood. This study aimed to determine if there is a moderating effect of sex on the relationship the number of professional fights has with cognitive functioning and regional brain volumes in a cohort of boxers, mixed martial artists, and martial artists. Methods: A total of 55 women were matched with 55 men based on age, years of education, ethnicity, and fighting style. Cognition was assessed via the CNS Vital Signs computerized cognitive battery and supplemental measures. Structural brain scans, demographic data, and number of professional fights (NoPF) were also considered. The matched pairs were compared via analysis of covariance, accounting for total brain volume. Within-subject moderation models were utilized to assess the moderating effect of sex on the relationship between NoPF and brain volumes and cognitive performance. Results: Men were observed to have poorer performance on measures of psychomotor speed when compared to women. On a series of analyses assessing the role of sex as a moderator of the relationship between NoPF and regional brain volumes/cognitive performance, a significant moderation effect was observed across multiple measures of cognitive functioning, such that men had poorer performance. Differences in numerous regional brain volumes were also observed, such that the relationship between NoPF and brain volumes was steeper among men. Conclusion: Sex was observed to be an important moderator in the relationship between NoPF, aspects of cognitive functioning, and volumes of numerous brain regions, suggesting that sex differences in neuroanatomic and cognitive response to RHI deserve further attention

Sex Differences in Cognitive Changes in De Novo Parkinson’s Disease

Objective: To evaluate the sex differences in cognitive course over 4 years in Parkinson’s disease (PD) patients with and without mild cognitive impairment (MCI) compared to controls. Methods: Four-year longitudinal cognitive scores of 257 cognitively intact PD, 167 PD-MCI, and 140 controls from the Parkinson’s Progression Markers Initiative were included. Longitudinal scores of men and women, and PD with and without MCI were compared. Results: Women had better verbal memory, men had better visuospatial function. There was no interaction between sex, diagnostic group, and/or time (4-year follow-up period). Conclusions: Sex differences in cognitive course in de novo PD are similar to healthy aging. Cognitive decline rates in PD with and without MCI are similar for the first 4 years of PD