(1) Background: Biomechanics during landing tasks, such as the kinematics and kinetics of the knee, are altered following anterior cruciate ligament (ACL) injury and reconstruction. These variables are recommended to assess prior to clearance for return to sport, but clinicians lack access to the current gold-standard laboratory-based assessment. Inertial sensors serve as a potential solution to provide a clinically feasible means to assess biomechanics and augment the return to sport testing. The purposes of this study were to (a) develop multi-sensor machine learning algorithms for predicting biomechanics and (b) quantify the accuracy of each algorithm. (2) Methods: 26 healthy young adults completed 8 trials of a double limb jump landing task. Peak vertical ground reaction force, peak knee flexion angle, peak knee extension moment, and peak sagittal knee power absorption were assessed using 3D motion capture and force plates. Shank- and thigh- mounted inertial sensors were used to collect data concurrently. Inertial data were submitted as inputs to single- and multiple- feature linear regressions to predict biomechanical variables in each limb. (3) Results: Multiple-feature models, particularly when an accelerometer and gyroscope were used together, were valid predictors of biomechanics (R² = 0.68–0.94, normalized root mean square error = 4.6–10.2%). Single-feature models had decreased performance (R² = 0.16–0.60, normalized root mean square error = 10.0–16.2%). (4) Conclusions: The combination of inertial sensors and machine learning provides a valid prediction of biomechanics during a double limb landing task. This is a feasible solution to assess biomechanics for both clinical and real-world settings outside the traditional biomechanics laboratory.

Introduction: Resting heart rate (HR_rest), heart rate variability (HRV), and HR recovery (HRR) from exercise provide valuable information about cardiac autonomic control. RR-intervals during acute recovery from exercise (RR_rec) are commonly excluded from HRV analyses due to issues of non-stationarity. However, the variability and complexity within these trends may provide valuable information about changes in HR dynamics.Purpose: Assess the complexity of RR_rec and determine what physiologic and demographic information are associated with differences in these indices in young adults.Methods: RR-intervals were collected throughout maximal treadmill exercise and recovery in young adults (n = 92). The first 5 min of RR_rec were (1) analyzed with previously reported methods that use 3-interval lengths for comparison and (2) detrended using both differencing_(diff) and polynomial regression_(res). The standard deviation of the normal interval (SDNN), root mean square of successive differences (rMSSD), root mean square (RMS) of the residual of regression, and sample entropy (SampEn) were calculated. Repeated measures analysis of covariance (ANCOVA) tested for differences in these indices for each of the methodological approaches, controlling for race, body fat, peak oxygen uptake (VO_2peak), and resting HR (HR_rest). Statistical significance was set at p < 0.05.Results: VO_2peak and HR_rest were significantly correlated with traditional measures of HRR and the variability surrounding RR_rec. SampEn_diff and SampEn_res were correlated with VO_2peak but not HR_rest or HRR. The residual-method provided a significantly (p = 0.04) lower mean standard error (MSE) (0.064 ± 0.042) compared to the differencing-method (0.100 ± 0.033).Conclusions: Complexity analysis of RR_rec provides unique information about cardiac autonomic regulation immediately following the cessation of exercise when compared to traditional measures of HRR and both HRrest and VO2peak influence these results.

Background: The influence of prolonged and repeated water immersions on heart rate variability (HRV) and complexity was examined in 10 U.S. Navy divers who completed six-hour resting dives on five consecutive days. Pre-dive and during-dive measures were recorded daily. Methods: Dependent variables of interest were average heart rate (HR), time-domain measures of HRV [root mean square of successive differences of the normal RR (NN) interval (RMSSD), standard deviation of the NN interval (SDNN)], frequency-domain measures of HRV [low-frequency power spectral density (psd) (LFpsd), low-frequency normalized (LFnu), high-frequency psd (HFpsd), high-frequency normalized (HFnu), low-frequency/high-frequency ratio (LF/HF)], and non-linear dynamics of HRV [approximate entropy (ApEn)]. A repeated-measures ANOVA was performed to examine pre-dive measure differences among baseline measures. Hierarchical linear modeling (HLM) was performed to test the effects of prolonged and repeated water immersion on the dependent variables. Results: Pre-dive HR (P=0.005) and RMSSD (P<0.001) varied significantly with dive day while changes in SDNN approached significance (P=0.055). HLM indicated that HR decreased during daily dives (P=0.001), but increased across dive days (P=0.011); RMSSD increased during daily dives (P=0.018) but decreased across dive days (P<0.001); SDNN increased during daily dives (P<0.001); LF measures increased across dive days (LFpsd P<0.001; LFnu P<0.001), while HF measures decreased across dive days (HFpsd P<0.001; HFnu P<0.001); LF/HF increased across dive days (P<0.001); ApEn decreased during daily dives (P<0.02) and across dive days (p<0.001). Conclusion: These data suggest that the cumulative effect of repeated dives across five days results in decreased vagal tone and a less responsive cardiovascular system.

The incidence of pre-diabetes (PD) and Type-2 Diabetes Mellitus (T2D) is a worldwide epidemic. African American (AA) individuals are disproportionately more likely to become diabetic than other ethnic groups. Over the long-term, metabolic complications related to diabetes result in significant alterations in growth hormone (GH) and insulin-like growth factor-1 (IGF-1). Considering the limited exercise-related studies in the area of gene expression changes with disease progression, the objective of this study was to examine differences in exercise-induced gene expression related to the GH and IGF-1 pathways in peripheral blood mononuclear cells (PBMCs) of healthy (CON) and PD AA individuals. Design: Ten subjects [5 PD (age = 35±9.3 yr, BMI = 32.1±4.0, FBG = 101.8±1.3 mg/dl) and 5 CON (age = 31±9.4 yr, BMI = 29.4±5.2, FBG = 82.8±9.7 mg/dl)] had blood drawn for RNA isolation prior to exercise (Pre), immediately following acute moderate intensity exercise on a treadmill (Post-1), 6-hours post (Post-6), and 24-hours post (Post-24). Isolation of mRNA from PBMCs was performed using ficoll separation, while the profiling of mRNA expression was performed using Illumina beadchip arrays with standard protocols. Scan results were statistically analyzed for a specific list of genes related to GH and IGF-1. GH and IGF-1 protein levels were also assessed in each sample. To address issues of normality, all GH and IGF-1 data were log-transformed prior to analysis. Statistical significance was set at p<0.05. Results: Group differences for GH2 variant 2 (p = 0.070) and GH2 variant 3 (p = 0.059) were coupled with significant alterations in IGF-1 mRNA over time (p = 0.024). A significant interaction between group and time was observed for GHRH mRNA (p = 0.008). No group differences were observed in GH AUC (p = 0.649), ΔGH (p = 0.331), GH_rec (p = 0.294), or IGF-1 AUC (p = 0.865), representing a similar exercise-induced GH and IGF-1 response for both groups. Conclusions: Analysis of GH and IGF-1 related-gene expression indicates that mild elevations in fasting blood glucose and exercise-induced alterations in gene expression are impacted by the prediabetic state.

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.