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Context: Previous work suggests that balance behavior is a sex-dependent, complex process that can be characterized by linear and nonlinear metrics. Although a certain degree of center of pressure variability may be expected based on sexual dimorphism, there is evidence to suggest that these effects are obscured by potential interactions between sex and anthropometric factors. To date, no study has accounted for such interactive effects using both linear and nonlinear measures. Objective: This investigation sought to analyze interactive models featuring sex, height, and weight as predictors of linear and nonlinear aspects of postural control. Design: Cross-sectional study. Setting: Controlled laboratory. Participants: A total of 26 males (23.80 [3.44] y, 177.87 [6.44] cm, 81.70 [10.80] kg) and 28 females (21.14 [2.03] y, 169.57 [8.80] cm, 64.48 [8.86] kg) were sampled from a healthy university population. MainOutcomeMeasures: Linear (range [RNG], velocity [VEL], and SD) and nonlinear (detrended fluctuation analysis scaling exponent, multivariate multiscale sample entropy [MMSECI]) summary metrics of center of pressure time series. Procedure: Participants stood on a force plate for 20 seconds in 3 conditions: double (D), single (S), and tandem (T) stance. Data for each stance condition were analyzed using regression models with interaction terms for sex × height and sex × weight. In D, weight had a positive, significant main effect on VELy, MMSECId, and MMSECIv. In men, height was observed to have a positive effect on SDy (S), RNGy (S), and RNGx (T) and a negative effect on MMSECIv (T). In women, weight was observed to have a positive effect on SDy and VELx (both T). Conclusions: Our findings suggest that men and women differ with respect to certain linear and nonlinear aspects of balance behavior, and that these differences may reflect sex-specific behavioral patterns in addition to effects related to sexual dimorphism. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine cardio hypothalamic-pituitary coupling and to better understand how the temporal relations between these systems are altered during rest and exercise conditions. An intensive within subjects study design was used. Seven adult males completed two visits, each consisting of either a 24 h period of complete rest or a 24 h period containing a high-intensity exercise bout. An intravenous catheter was used to collect serum samples every 10 min throughout the 24 h period (i.e., 145 samples/person/condition) to assess growth hormone (GH) dynamics throughout the 24 h period. Cardiac dynamics were also collected throughout the 24 h period and epoched into 3 min windows every 10 min, providing serial short-time measurements of heart rate variability (HRV) concurrent to the GH sampling. The standard deviation of the normal RR interval (SDNN), the root mean square of successive differences (rMSSD), and sample entropy (SampEn) was calculated for each epoch and used to create new profiles. The dynamics of these profiles were individually quantified using SampEn and recurrence quantification analysis (RQA). To address our central question, the coupling between these profiles with GH was assessed using cross-SampEn and cross-RQA (cRQA). A comparison between the epoched HRV profiles indicated a main effect between profiles for sample entropy (p < 0.001) and several measures from RQA. An interaction between profile and condition was observed for cross-SampEn (p = 0.04) and several measures from cRQA. These findings highlight the potential application of epoched HRV to assess changes in cardiac dynamics, with specific applications to assessing cardio hypothalamic-pituitary coupling. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic function that has been extensively studied in a variety of populations. However, HRV analyses require stationarity--thus, limiting the conditions in which these data can be analyzed in physiologic and health research (e.g. postexercise). To provide evidence and clarity on how non-stationarity affects popular indices of variability and complexity. Simulations within physiologic (restricted to values similar to exercise and recovery RR-intervals) and non-physiologic parameters, with homoscedastic and heteroscedastic variances, across four sample lengths (200, 400, 800, and 2000), and four trends (stationary, positivelinear, quadratic, and cubic) were detrended using 1-3 order polynomials and sequential differencing. Measures of variability [standard deviation of normal intervals (SDNN) and root mean square of successive differences (rMSSD)] as well as complexity [sample entropy (SampEn)] were calculated on each of the raw and detrended time-series. Differential effects of trend, length, and fit were observed between physiologic and non-physiologic parameters. rMSSD was robust against trends within physiologic parameters while both SDNN and SampEn were positively and negatively biased by trend, respectively. Within nonphysiologic parameters, the SDNN, rMSSD, and SampEn of the raw time-series were all biased, highlighting the effect of the scale between these two sets of parameters. However, indices of variability and complexity on the original (trended) times-series were furthest from those of the stationary time-series, with indices coming closer to the known values as fit become more optimal. Detrending with polynomial functions provide reliable and accurate methods of assessing the variability and complexity of non-stationary time-series--such as those immediately following exercise. [ABSTRACT FROM AUTHOR]

Previous work has shown that fractal patterns in gait can be altered by entraining to a fractal stimulus. However, little is understood about how long those patterns are retained or which factors may influence stronger entrainment or retention. In experiment one, participants walked on a treadmill for 45 continuous minutes, which was separated into three phases. The first 15 minutes (pre-synchronization phase) consisted of walking without a fractal stimulus, the second 15 minutes consisted of walking while entraining to a fractal visual stimulus (synchronization phase), and the last 15 minutes (post-synchronization phase) consisted of walking without the stimulus to determine if the patterns adopted from the stimulus were retained. Fractal gait patterns were strengthened during the synchronization phase and were retained in the post-synchronization phase. In experiment two, similar methods were used to compare a continuous fractal stimulus to a discrete fractal stimulus to determine which stimulus type led to more persistent fractal gait patterns in the synchronization and post-synchronization (i.e., retention) phases. Both stimulus types led to equally persistent patterns in the synchronization phase, but only the discrete fractal stimulus led to retention of the patterns. The results add to the growing body of literature showing that fractal gait patterns can be manipulated in a predictable manner. Further, our results add to the literature by showing that the newly adopted gait patterns are retained for up to 15 minutes after entrainment and showed that a discrete visual stimulus is a better method to influence retention. [ABSTRACT FROM AUTHOR]

Abstract: Variable practice may be beneficial for learning novel motor patterns. The purpose of this study was to determine the effect of a variable practice intervention during treadmill walking on the dynamic properties of gait. Using a counterbalanced design, 16 subjects with hemiparesis following chronic stroke performed 20min of treadmill walking in constant speed training (CST) and variable speed training (VST) conditions. The dynamic properties of the hip and knee sagittal plane angles for two minutes before (pre-data) and two minutes after (post-data) CST and VST were examined using detrended fluctuation analysis and sample entropy. A main effect for time was observed for sample entropy of the knee angle; no other differences were observed between the pre/post data for the CST or VST conditions. While variable practice conditions are intended to promote movement errors for improved learning, we were unable to detect immediate changes in movement variability as a function of practice condition following a single session of gait training for individuals post-stroke. [Copyright &y& Elsevier]