Novel Energy Drink Improves Mood and Raises Blood Pressure, but has No Effect on Cardiac QTc Interval or Rate-Pressure Product in Young Adult Gamers

Novel energy drink formulations have been introduced to the market that are purported to have nootropic effects – including improving mood. Despite their rapidly growing popularity, especially among video gamers, there is minimal evidence supporting their efficacy or establishing their cardiovascular safety profiles. PURPOSE: We conducted a randomized, double-blind, placebo-controlled, crossover trial to investigate the effects of acute consumption of a non-caloric, novel energy drink (NED) containing 200 mg caffeine, citicoline, tyrosine, B-vitamins, and carboxylic acids on mood and cardiovascular safety outcomes. We hypothesized that NED would improve mood without significant adverse cardiovascular effects when compared to placebo. METHODS: Forty-five healthy young adults who routinely play video games (37M, 8F; mean ± SD, age = 25 ± 6 y) each completed two experimental study visits in randomized order where they consumed either NED or a placebo matched for volume, calories, taste, appearance, and mouthfeel. Resting systolic and diastolic blood pressure (SBP and DBP) and an electrocardiogram (ECG) were obtained from each participant after a 10-min quiescent period prior to and 45 minutes after consumption of NED or placebo. Resting heart rate (RHR) and corrected QT interval length (QTc) were derived from the ECG. Rate-pressure product (RPP) was determined as the product of HR and SBP. Mood was assessed using the Profile of Mood States at post-consumption after BP and ECG assessments. Paired t-tests or signed ranked tests (for non- normally distributed data) were used to examine between-condition differences in mood states, whereas 2 (condition) × 2 (time) ANOVAs were used to examine SBP, DBP, QTc, and RPP. RESULTS: Change scores are presented as mean absolute change ± 95% confidence interval. Relative changes are provided for mood data. Acute NED consumption improved fatigue-inertia (-1.4 ± 1.0 a.u. [+36%]; p = 0.004), vigor-activity (+2.4 ± 1.2 a.u. [+33%]; p \u3c 0.001), and friendliness (+0.7 ± 0.7 a.u. [+7%]; p = 0.04), without adverse effects on tension-anxiety, confusion-bewilderment, or depression-dejection (all p ≥ 0.27). RHR decreased from pre- to post-beverage consumption, and this decrease was greater following NED than placebo consumption (-6.0 ± 2.8 vs. -2.6 ± 1.4 bpm, p = 0.017). SBP (+3.7 ± 2.0 vs. -0.4 ± 2.0 mmHg; p = 0.002) and DBP (+3.7 ± 1.7 vs. -0.04 ± 1.4 mmHg; p = 0.003) increased following NED vs. placebo; however, RPP decreased independent of condition (-386.0 ± 229.0; p = 0.03), and there was no effect of beverage consumption on QTc (p = 0.44). CONCLUSION: Acute NED consumption improved mood states related to vigor, fatigue, and friendliness without affecting tension-anxiety, depression, or confusion in young adult gamers. While NED consumption produced mild increases in SBP and DBP, there were no effects on either QTc or RPP. Thus, overall, NED consumption produces mood-enhancing effects without markedly influencing cardiovascular safety outcomes

Habitual wearers of colored lenses adapt more rapidly to the color changes the lenses produce

The visual system continuously adapts to the environment, allowing it to perform optimally in a changing visual world. One large change occurs every time one takes off or puts on a pair of spectacles. It would be advantageous for the visual system to learn to adapt particularly rapidly to such large, commonly occurring events, but whether it can do so remains unknown. Here, we tested whether people who routinely wear spectacles with colored lenses increase how rapidly they adapt to the color shifts their lenses produce. Adaptation to a global color shift causes the appearance of a test color to change. We measured changes in the color that appeared “unique yellow”, that is neither reddish nor greenish, as subjects donned and removed their spectacles. Nine habitual wearers and nine age-matched control subjects judged the color of a small monochromatic test light presented with a large, uniform, whitish surround every 5 s. Red lenses shifted unique yellow to more reddish colors (longer wavelengths), and greenish lenses shifted it to more greenish colors (shorter wavelengths), consistent with adaptation “normalizing” the appearance of the world. In controls, the time course of this adaptation contained a large, rapid component and a smaller gradual one, in agreement with prior results. Critically, in habitual wearers the rapid component was significantly larger, and the gradual component significantly smaller than in controls. The total amount of adaptation was also larger in habitual wearers than in controls. These data suggest strongly that the visual system adapts with increasing rapidity and strength as environments are encountered repeatedly over time. An additional unexpected finding was that baseline unique yellow shifted in a direction opposite to that produced by the habitually worn lenses. Overall, our results represent one of the first formal reports that adjusting to putting on or taking off spectacles becomes easier over time, and may have important implications for clinical management

Parallel factor analysis of gait waveform data: a multimode extension of principal component analysis

Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three- or higher-mode data. Using three-mode joint angle waveform data (subjects ?? time ?? joints), we demonstrate Parafac???s ability to (a) determine interpretable components revealing the primary interrelationships between lower-limb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects??? gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns)

Regression with Ordered Predictors via Ordinal Smoothing Splines

Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables in regression models, which is theoretically and/or computationally undesirable. In this paper, we discuss the benefit of taking a smoothing spline approach to the modeling of ordinal predictors. The purpose of this paper is to provide theoretical insight into the ordinal smoothing spline, as well as examples revealing the potential of the ordinal smoothing spline for various types of applied research. Specifically, we (i) derive the analytical form of the ordinal smoothing spline reproducing kernel, (ii) propose an ordinal smoothing spline isotonic regression estimator, (iii) prove an asymptotic equivalence between the ordinal and linear smoothing spline reproducing kernel functions, (iv) develop large sample approximations for the ordinal smoothing spline, and (v) demonstrate the use of ordinal smoothing splines for isotonic regression and semiparametric regression with multiple predictors. Our results reveal that the ordinal smoothing spline offers a flexible approach for incorporating ordered predictors in regression models, and has the benefit of being invariant to any monotonic transformation of the predictor scores

Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis

The current parameterization and algorithm used to fit a smoothing spline analysis of variance (SSANOVA) model are computationally expensive, making a generalized additive model (GAM) the preferred method for multivariate smoothing. In this thesis, I propose various approximations and algorithms to stabilize and speed-up the fitting of two-way (or higher-way) SSANOVA models. In particular, I propose (a) an efficient reparameterization of the smoothing parameters in SSANOVA models, (b) using strategically-selected knot grids instead of randomly selected knots, (c) including rounding parameters in the model, and (d) scalable algorithms for multiple-smoothing parameter selection in SSANOVA models. To validate my approximations and algorithms, I conduct three simulation studies comparing my methods to current implementations of SSANOVAs and GAMs that are available in R. The simulation results demonstrate that my approximations and algorithms can perform as well as the typical SSANOVA approximation, and can do so in a fraction of the time; furthermore, the simulation results reveal that a strategic SSANOVA can perform as well as or better than a GAM, and (using my algorithm) the strategic SSANOVA can be fit in a similar amount of time as a GAM. Finally, I present how these new approximations and algorithms make it possible to holistically analyze electroencephalography data collected during event-related potential experiments

Adding bias to reduce variance in psychological results: A tutorial on penalized regression

Regression models are commonly used in psychological research. In most studies, regression coefficients are estimated via maximum likelihood (ML) estimation. It is well-known that ML estimates have desirable large sample properties, but are prone to overfitting in small to moderate sized samples. In this paper, we discuss the benefits of using penalized regression, which is a form of penalized likelihood (PL) estimation. Informally, PL estimation can be understood as introducing bias to estimators for the purpose of reducing their variance, with the ultimate goal of providing better solutions. We focus on the Gaussian regression model, where ML and PL estimation reduce to ordinary least squares (OLS) and penalized least squares (PLS) estimation, respectively. We cover classic OLS and stepwise regression, as well as three popular penalized regression approaches: ridge regression, the lasso, and the elastic net. We compare the different penalties (or biases) imposed by each method, and discuss the resulting features each penalty encourages in the solution. To demonstrate the methods, we use an example where the goal is to predict a student's math exam performance from 30 potential predictors. Using a step-by-step tutorial with R code, we demonstrate how to (i) load and prepare the data for analysis, (ii) fit the OLS, stepwise, ridge, lasso, and elastic net models, (iii) extract and compare the model fitting results, and (iv) evaluate the performance of each method. Our example reveals that penalized regression methods can produce more accurate and more interpretable results than the classic OLS and stepwise regression solutions

Robust Permutation Tests for Penalized Splines

Penalized splines are frequently used in applied research for understanding functional relationships between variables. In most applications, statistical inference for penalized splines is conducted using the random effects or Bayesian interpretation of a smoothing spline. These interpretations can be used to assess the uncertainty of the fitted values and the estimated component functions. However, statistical tests about the nature of the function are more difficult, because such tests often involve testing a null hypothesis that a variance component is equal to zero. Furthermore, valid statistical inference using the random effects or Bayesian interpretation depends on the validity of the utilized parametric assumptions. To overcome these limitations, I propose a flexible and robust permutation testing framework for inference with penalized splines. The proposed approach can be used to test omnibus hypotheses about functional relationships, as well as more flexible hypotheses about conditional relationships. I establish the conditions under which the methods will produce exact results, as well as the asymptotic behavior of the various permutation tests. Additionally, I present extensive simulation results to demonstrate the robustness and superiority of the proposed approach compared to commonly used methods

Fast and Stable Multiple Smoothing Parameter Selection in Smoothing Spline Analysis of Variance Models With Large Samples

<p>The current parameterization and algorithm used to fit a smoothing spline analysis of variance (SSANOVA) model are computationally expensive, making a generalized additive model (GAM) the preferred method for multivariate smoothing. In this article, we propose an efficient reparameterization of the smoothing parameters in SSANOVA models, and a scalable algorithm for estimating multiple smoothing parameters in SSANOVAs. To validate our approach, we present two simulation studies comparing our reparameterization and algorithm to implementations of SSANOVAs and GAMs that are currently available in R. Our simulation results demonstrate that (a) our scalable SSANOVA algorithm outperforms the currently used SSANOVA algorithm, and (b) SSANOVAs can be a fast and reliable alternative to GAMs. We also provide an example with oceanographic data that demonstrates the practical advantage of our SSANOVA framework. Supplementary materials that are available online can be used to replicate the analyses in this article.</p