Estimation of Visceral Fat via Ultrasound Sonography

Although visceral fat (VF) can be quantified via computed tomography or dual-energy X-ray absorptiometry (DXA), their application for frequent VF assessment is limited because both methods are radiating in nature. Research suggests that ultrasound imaging can be used to predict VF safely without the risk of radiation exposure. However, the complexity and/or lack of replicability limits such application. PURPOSE: To develop an easy-to-replicate ultrasound protocol and a regression model that can accurately estimate VF area (VFA, cm2). METHODS: Thirty healthy adults (9 males and 21 females, age: 23.2 ± 7.4 yr, body mass index: 22.3 ± 3.2 kg/m2, body fat percentage: 22.3 ± 5.9 %) fasted for 8 hours or more before a DXA scan and ultrasound imaging were performed to estimate VFA. Ultrasound imaging (with a 3.5-MHz convex-array probe) was used to measure the thickness of 15 different sites within the abdominal cavity. Thickness was defined as the distance in cm from the internal abdominal wall to the anterior aortic wall. Stepwise linear regression was utilized to develop a regression model for VFA using the estimated VFA by DXA as a dependent variable, followed by a Bland-Altman plot and Pearson correlation to compare the technique reliability. RESULTS: The developed regression model (F(4, 25) = 46.869, p = 0.001) was (37.677 + (1.456*Age) - (26.963*Sex) - (11.336*VFT2) + (13.554*VFT4)), where age = years, sex: 1 = male or 2 = female, and VFT2/4 = ultrasound probe placement 2 cm to the left (VFT2) and right (VFT4) of the superior umbilical border, respectively. The regression model had high accuracy (adjusted R2 = 0.864) and test reliability (r = 0.927, p = 0.001) at estimating VFA (31.4 ± 21.4 cm2) when compared to the VFA (31.1 ± 21.1 cm2) estimated by DEXA. CONCLUSION: Visceral fat area can be accurately estimated using an easy-to-replicate ultrasound protocol and regression model that eliminates the exposure to radiation caused by other body scanning methods

An Evaluation Of Static and Dynamic Yoga Training Programs

While traditional yoga programs focus on static stretching and core stability, Essentrics yoga relies more heavily on full-body stretch and strengthening regiments coupled with dynamic movements such as ceiling reaches, side-to-side bends, lunges, and side leg lifts. Through the incorporation of more dynamic movements, Essentrics yoga is thought to elicit greater improvements in overall body composition, flexibility, and balance. PURPOSE: To examine the benefits of a 6-weeks long Essentrics (dynamic) program compared to standard (static) Yoga on body composition, flexibility, and balance. METHODS: Thirty-one participants (24 females and 7 males, age = 20.4 ± 0.2yrs, and BMI = 22.58 ± 0.55kg/m2) were assigned to two groups – a standard Yoga (YOG, n = 20) and an Essentrics (ESS, n = 11) group. For 6 weeks, both groups attended a 45–50-minute class, 3 times per week. Body composition (dual-energy x-ray absorptiometry), flexibility (sit-and-reach), balance (lower extremity Y-balance), as well anthropometric measurements were assessed at the beginning and end of the 6-week program. Measurements of the balance test included 3 reaches and their combined values [anterior (ANT), posteromedial (PM), posterolateral (PL), and composite reach distance (CRD)]. All reaches were averaged for the right and left sides and then normalized to leg length. Data were analyzed using an ANOVA with repeated measures (p \u3c 0.05), and a post-hoc test was performed if any significant main or interaction effects were found. RESULTS: Interestingly, the total body fat percentage was significantly reduced only in the YOG group (24.44 ± 6.73 to 23.51 ± 6.32%, p=.002). There were no significant group differences between YOG and ESS in balance and flexibility. However, balance was improved after the 6-week workout programs; PM (87.13 ± 11.64cm to 92.25 ± 9.91cm, p=.001), PL (82.88 ± 11.28 to 88.62 ± 9.62cm, p=.002), CRD (225.96 ± 27.17 to 238.26 ± 22.98cm, p=.001), normalized PM (98.31 ± 11.68 to 104.27 ± 11.14%, p=.001), normalized PL (93.60 ± 11.98 to 100.15 ± 10.70%, p=.001), and normalized CRD (255.12 ± 27.89 to 269.21 ± 25.07%, p=.001). Additionally, flexibility was improved from 51.42 ± 8.24 to 53.38 ± 7.04cm (p=.010) after the 6-week workout programs, while total body fat percentage was significantly reduced only in the YOG group (24.44 ± 6.73 to 23.51 ± 6.32%, p=.002). CONCLUSION: Whether an individual prefers a static or dynamic yoga program, both show improvements in flexibility and balance; however, neither program had a significant benefit over the other

The Usage of Skeletal Muscle Oxygenation and Heart Rate Variability as Predictors of Aerobic Fitness.

Heart rate variability (HRV) is used to assess the autonomic nervous system’s (ANS) activity on the heart, while skeletal muscle oxygenation (SmO2) measures how well muscles uptake oxygen from the blood. Both measurements have demonstrated strong associations with cardiorespiratory fitness and are altered with increased exercise workloads. Both have been used to assess athletic performance. While the gold standard for assessing cardiorespiratory fitness is VO2 max testing, several situations preclude the usage of a true VO2 max. Purpose: To determine if HRV and SmO2 possess predictive qualities to accurately assess cardiorespiratory fitness levels. Methods: Thirty-six healthy fit individuals (n = 22 men; n = 14 women; age 37.6 + 12.4 yr; BF% 19.2 + 7.1%; VO2max 41.8 + 7.4 ml/kg/min) completed a single VO2 max ramp protocol treadmill test while wearing an infrared oxyhemoglobin (MOXY) Sensor to assess SmO2 while HRV was assessed via Polar (Bluetooth monitor (Polar H7)) heart rate (HR) monitor. The MOXY Sensor was placed on the lateral-posterior belly of the gastrocnemius while the Polar HR monitor was placed on the distal third of the sternum using an elastic belt. The data was analyzed using a Pearson Correlation to compare SmO2, HRV indices, and VO2max associations. In addition, a multiple linear regression analysis was performed to examine the relationship between HRV indices and SmO2 to VO2 max. All analyses were performed using SPSS (v. 28.0.1.1). Results: There was a significant correlation between VO2 max, mean of RR intervals (mRR) (r = 0.440, p = 0.007), and THb Max (r = 0.509, p = 0.002). mRR and THb Max were able to significantly predictive (r2 = 0.365, p = 0.001) VO2 max outcomes. Conclusion: The combination of SmO2 measurements and HRV can assist in predicting VO2 max levels, but further research is needed to determine the accuracy at which it will predict. This can be a useful and simple method for predicting cardiorespiratory fitness when a VO2 max test is unavailable, or an individual is unfit to perform one. This can aid in better exercise prescription for chronic diseased individuals

Effects of Virtual Reality During Rowing Ergometry on Metabolic and Performance Parameters

Physical activity and moderate or intense exercise improve musculoskeletal and metabolic health; however, approximately 80% of Americans do not meet the minimum exercise recommendations from the American College of Sports Medicine (ACSM) or the Centers for Disease Control (CDC). Exercise intensity may be the most important factor in eliciting positive physical outcomes with exercise. PURPOSE: To assess the effectiveness of a proprietary virtual reality (VR) interface to increase metabolic and physical performance during rowing ergometry. METHODS: A novel VR software program for rowing ergometry was developed. Subsequently, sixteen apparently healthy, recreationally active individuals (12M, 4F; 35.5 ± 13.9 y; 174.5 ± 10.1 cm; 80.4 ± 12.8 kg; VO2max: 38.1 ± 5.6 mL/kg/min) were familiarized with the rowing ergometer and VR software, and then completed a VO2max test during two separate sessions. Finally, subjects performed four, 30-min rowing sessions in a randomized, counterbalanced order at maximal voluntary intensity in four different conditions: 1) no augmented visual or audio stimuli (CON), 2) no augmented visual stimuli with self-selected music (MUS), 3) screen-based environmental display (SB), and 4) a virtual reality environment (VR). Oxygen consumption, ventilation, heart rate, and the respiratory exchange ratio (RER) were measured continuously during the four experimental sessions; these data were then averaged over each 30-min testing period. Power output (W) and distance rowed (m) were measured and similarly reduced. Data (mean ± SD) were analyzed by repeated measures ANOVA and appropriate Tukey’s post hoc tests. Alpha was set at P \u3c 0.05. RESULTS: Oxygen consumption (CON: 2.23 ± 0.63 L/min; MUS: 2.30 ± 0.63 L/min; SB: 2.23 ± 0.71 L/min; VR: 2.19 ± 0.69 L/min), ventilation (CON: 74.2 ± 21.0 L/min; MUS: 77.5 ± 20.5 L/min; SB: 73.4 ± 23.9 L/min; VR: 71.7 ± 23.8 L/min), heart rate (CON: 154 ± 16 bpm; MUS: 156 ± 17 bpm; SB: 152 ± 23 bpm; VR: 154 ± 17 bpm), and RER (CON: 0.94 ± 0.04; MUS: 0.95 ± 0.04; SB: 0.94 ± 0.04; VR: 0.93 ± 0.05) were not different between conditions (all P \u3e 0.05). Performance outcomes also did not differ between conditions (CON: 126 ± 40 W, 6337 ± 763 m; MUS: 130 ± 42 W, 6486 ± 617 m; SB: 128 ± 46 W, 6358 ± 862 m; VR: 124 W ± 44 W, 6294 ± 849 m; all P \u3e 0.05). CONCLUSION: The pilot version of the VR software for rowing ergometry did not increase voluntary effort as determined by metabolic or physical performance outputs. Added features, such as greater immersion for reluctant exercisers, and competitive elements for highly motivated individuals, may elicit greater voluntary exertion with VR in rowing ergometry. Moreover, such applications may be more beneficial and improve exercise enjoyment in less experienced exercises who are not accustomed to high exercise intensities

Effects of Virtual Reality During Rowing Ergometry on Presence, Perceived Exertion, and Exercise Enjoyment

Physical inactivity is associated with a host of negative health outcomes. Approximately 80% of Americans do not meet minimum levels of recommended physical activity. Virtual reality (VR) may improve exercise outcomes by enhancing presence, decreasing perceived exertion, and increasing exercise enjoyment. PURPOSE: To assess the effects of a proprietary VR interface on presence, perceived exertion, and exercise enjoyment during rowing ergometry. METHODS: First, we developed a novel VR software program for rowing ergometry. Subsequently, sixteen apparently healthy, recreationally active individuals (12M, 4F; 35.5 ± 13.9 y; 174.5 ± 10.1 cm; 80.4 ± 12.8 kg; VO2max: 38.1 ± 5.6 mL/kg/min) were familiarized with the rowing ergometer and VR software, and then completed a VO2max test during two separate sessions. Finally, subjects performed four, 30-min rowing sessions in a randomized, counterbalanced order at maximal voluntary intensity in four different conditions: 1) no augmented visual or audio stimuli (CON), 2) no augmented visual stimuli with self-selected music (MUS), 3) screen-based environmental display (SB), and 4) a virtual reality environment (VR). Presence (Spatial Presence Experience Scale), perceived exertion (Borg 6-20 scale), and enjoyment (Exercise-Induced Feelings Inventory) were assessed using questionnaires. Data (mean ± SD) were analyzed by repeated measures ANOVA and appropriate Tukey’s post hoc tests. Alpha was set at P \u3c 0.05. RESULTS: Eight of twenty spatial presence items indicated an enhanced experience in VR vs. SB (P \u3c 0.05). Perceived exertion (CON: 14.7 ± 2.1; MUS: 14.9 ± 2.0; SB: 15.2 ± 2.5; VR: 14.9 ± 1.7) and exercise-induced feelings were not different between conditions (P \u3e 0.05). CONCLUSION: The pilot version of the VR software for rowing ergometry did not reduce perceived exertion or increase exercise enjoyment in recreationally active individuals, although it did facilitate improved user presence compared to a screen-based enhanced environment. Added features, such as better coupling of rowing intensity to boat velocity in VR may further enhance presence and immersion, thereby decreasing perceived exertion and increasing exercise enjoyment

Comprehensive Analysis of Mental Toughness Predictors Using Machine Learning Techniques

Mental toughness (MT) is a critical determinant of success in various high-pressure environments. Traditional methods of assessing MT often rely on subjective metrics only (self-assessed questionnaire scores), which may lack the precision and objectivity necessary for a thorough understanding. PURPOSE: To develop an objective, quantitatively robust model to predict MT, by integrating physiological and psychological variables using machine learning (ML) techniques. METHODS: The study involved a sample of 50 participants, encompassing diverse demographic backgrounds and physiological (e.g., DEXA, complete metabolic and lipid panel, and complete blood count) and psychological (e.g., Mental Toughness and Self-compassion surveys) characteristics. The analysis began with descriptive statistics to understand the dataset’s structure, followed by handling missing values through imputation methods. Key variables identified included self-compassion (SC), white blood cell count (WBC), total protein, Android/Gynoid Ratio, and Trunk/Leg Fat Ratio. Principal Component Analysis (PCA) was employed for dimensionality reduction, ensuring the model’s efficiency, and addressing multicollinearity. A Random Forest Regression model was chosen for its ability to handle complex, non-linear relationships. The model underwent iterative tuning, adjusting parameters like the number of trees (300), tree depth (no limit), and minimum samples for node splitting (2) and leaf nodes (1). The process also included evaluating and comparing linear and non-linear approaches, cross-validation for robustness, and detailed performance metrics analysis. RESULTS: The final model (R2 = 0.74) indicates a high degree of variance explanation in MT scores. Key predictive factors included both physiological measures and psychological aspects, along with body fat distribution metrics. The Mean Squared Error was 0.29, reflecting the model’s accuracy and precision in prediction. CONCLUSION: This study illustrates the effective use of ML in integrating diverse physiological and psychological factors to predict MT with high accuracy. The findings provide a nuanced understanding of MT, suggesting that it is influenced by a complex interplay of mental and physiological health aspects. This model serves as a valuable tool for identifying key factors in MT, aiding in targeted interventions for performance enhancement and resilience training

The Ability of Cardiac Autonomic Modulations Stress Index to Independently Predict VO2max in Cardiometabolically Healthy Individuals

Cardiac autonomic modulation (CAM) is crucial for heart health, mediated by the sympathetic and autonomic systems (SAS). The link between CAM and aerobic exercise underscores the importance of aerobic fitness assessments in optimizing training to enhance performance. Heart rate variability (HRV) assesses CAM in various healthy populations, with the Stress Index (SI) identified as key in determining the SAS regulation involvement in performance and recovery outcomes. The SI may provide a quick and non-invasive metric to assess aerobic performance. PURPOSE: To determine if the SI can accurately predict aerobic performance via VO2max in healthy individuals free of metabolic diseases. METHODS: fifty cardiometabolically healthy individuals (n = 30 males, n = 20 females; Age 37.8 + 12.7 years, %BF 24.9 + 4.0) completed a single maximal treadmill exercise protocol to determine VO2max. HRV was measured for 5 minutes in the supine position prior to performing the exercise protocol using an elastic belt and Bluetooth monitor (Polar H7). CardioMood software was used to process HRV indices; SI, high frequency (HF), low frequency (LF), and total power (TP) were assessed for the frequency domain, and standard deviation of all NN intervals (SDNN) and the square root of the mean of the squares of successive R-R interval differences (RMSSD) for the time domain. The data was analyzed using a multiple correlation and linear regression between HRV indices and VO2max to determine the relationship between the two. All analyses were performed using SAS (v. 28.0.1.1). RESULTS: HRV indices SI was not significantly correlated to VO2max (r = -0.118, p = 0.414). Additionally, SI and all other HRV indices were not able to independently or combined predict VO2max (R2 = 0.014, p = 0.414). CONCLUSION: The utilization of HRV to assess CAM has proven beneficial in multiple clinical and athletic settings. However, the utilization of the SI to predict aerobic performance via VO2max does not appear to be significant. Thus, there are potential limitations to HRV to non-invasively assess aerobic performance

Effects of Acute Bouts of Aerobic Exercise on Adipokines in Individuals with Mid-Spectrum Chronic Kidney Disease

Adipokines have been known to influence various health-related complications such as chronic kidney disease (CKD) and cardiovascular diseases. Fluctuations in adipokines are commonly seen from changes in body composition, however, some evidence shows acute changes may be seen from exercise. Individuals with CKD are commonly characterized by a decline in renal filtration and systemic inflammation. It may be possible that an acute bout of aerobic exercise may improve pro- and anti-inflammatory adipokine concentrations typically seen in individuals with moderate stages of CKD. PURPOSE: To determine the acute effects of aerobic exercise on adipokine concentrations in individuals with moderate stages of CKD. METHODS: Fourteen participants (8 females and 6 males, age = 58.7 ± 9.3 yrs., and %BF = 36.0 ± 9.6) were classified as having moderate stages of CKD (stages G3 and G4). Participants completed 30 min of steady-state moderate intensity exercise (SSE) at 65% VO2 reserve and high-intensity interval training (HIIE) at a 90% VO2 reserve separated by 2 min of slow walking (20% VO2 reserve) in a randomized, crossover design fashion. Venous blood samples were obtained at baseline, 1 h, and 24 h post-exercise. Data were analyzed using a repeated measures ANOVA (p \u3c 0.05) and a paired t-test. If any significant main or interaction effects were found, a post-hoc test was performed. RESULTS: There were no significant differences in adiponectin and leptin levels within treatments. However, significant differences were seen between baseline and 24 h omentin concentrations when performing HIIE (F(2,26) = 5.001, p = .015). Omentin rose significantly 24 h after an acute bout of HIIE (214.69 ± 83.28 to 252.04 ± 91.22, p = .034). A paired t-test showed no significant differences between SSE and HIIE for adiponectin and leptin. Although, there was a significant difference between 24 h omentin concentrations for SSE and HIIE (t = -2.327, p \u3c .037). Omentin concentrations were significantly higher when performing HIIE (252.04 ± 91.22) as opposed to SSE (218.70 ± 82.00, p \u3c .001). CONCLUSION: Omentin plays an anti-inflammatory role in chronic diseases. Thus, individuals experiencing systemic inflammation from moderate stages of CKD may see benefits after performing an acute bout of HIIE due to the up-regulated release of omentin 24 h post-exercise

Exploring the Role of Mental Toughness in Bone Mineral Content: A Preliminary Study

Bone mineral content (BMC), a measure of the mineral content within a person’s bones, is an important parameter in the assessment of bone health. Changes in BMC can be indicative of bone-related conditions. Dual-energy X-ray absorptiometry (DXA) is one of the most widely used and accurate methods for measuring BMC. Sex, age, race, and BMI are known to influence BMC. Physical activity is positively related to BMC levels. Mental toughness (MT) is conceptualized as a state-like psychological resource conducive to goal-oriented pursuits and is positively linked to physical activity outcomes. The relationship between MT and BMC has not been explored. PURPOSE: To investigate the isolated effect of MT on BMC after eliminating the confounding effects of sex, age, race, and BMI. METHODS: A total of 95 individuals participated in the study across two study sites. The sample (Mage = 34.57, SD = 15.87) was predominantly White (64%), normal weight/overweight (MBMI = 25.96, SD = 4.88) males (54%). DXA scans were performed on calibrated scanners using standard procedures. MT was assessed via the Mental Toughness Index (MTI). To reduce measurement error, the MTI was administered twice, separated by a two-week interval. A linear regression model was used to analyze the relationship between BMC and the average of the two MTI scores, while controlling for sex, age, race, and BMI in MATLAB (R2023a). A Cohen’s d for MT and BMC was additionally conducted. RESULTS: The linear regression model was BMC ~ 1 + Sex + Age + Race + BMI + MT. The overall regression was statistically significant (R2 = 0.183, F(94, 88) = 2.78, p = .012). MT was found to significantly predict BMC (β = 0.093, p = .008, d = 2.7). CONCLUSION: The findings underscore the statistical significance of MT as a predictor of BMC, even when accounting for the influence of sex, age, race, and BMI. The effect size points to the practical significance of this relationship, suggesting that individuals with higher MT levels may exhibit greater BMC. Future investigations should consider incorporating demographic covariates to gain deeper insights into these relationships and conduct interventional studies to identify potential underlying mechanisms (e.g., how trainable MT could be linked, to some degree, with an increase in BMC)

Vitamin D Association with Renal Health and Filtration in Healthy Individuals Free of Cardiometabolic Diseases: A Pilot Study

The effect of vitamin D (VITD) on bone, muscle, and over health is well know in renal failure and chronic kidney disease (CKD). However, the influence of VITD on renal health and filtration (RHF) in healthy individuals is unclear. Currently, only serum creatinine concentration (sCr) methods are used to assess renal status in health individuals. However, newer biomarkers like serum Cystatin C (CyC) and urine epidermal growth factor (uEGF) show promise in evaluating baseline RHF. The impact of Vitamin D on filtration in healthy individuals of various ages is still unknown. PURPOSE: To determine the impact of VITD on RHF in healthy individuals of middle-aged status. METHODS: Thirty-six participants (n = 22 men; n = 14 women; age 37.6 + 12.4 yr; BF% 19.2 + 7.1%) agreed to participate in the research study. Blood and urine samples were obtained under standardized conditions for all individuals. VITD, CyC, uEGF, urine creatinine (uCr), uCr/uEGF ratio, sCR, and multiple estimates of glomerular filtration rate (eGFR) - modification of diet in renal disease (MDRD), CKD-EPI, CyC equations (CyC only and CyC combined with sCr) were assessed as a whole cohort and grouped (young = 20-39 yrs. (n = 22), older = 40-60 yrs. (n = 14)). Analysis was done using a paired sample t-tests, Pearson Correlation to compare VITD concentrations and markers of RHF. Linear regression analyses was performed to examine the relationship between VITD ability to predict RHF. All analyses were performed using SPSS (v. 28.0.1.1). RESULTS: There was no significant correlations found between VITD and markers of RHF in the entire cohort. Therefore, no predictive model was performed. The younger group showed strong negative correlation between VITD and MDRD (r = -0.575, p = 0.008), and that VITD was able to predict MDRD (R2 = 0.331, p = 0.008). No significant correlation observed in older group. CONCLUSIONS: VITD was correlated and able to predict a marker of RHF in healthy younger individuals, but not in older individuals. Based on the sample size and overall outcomes, continued research is needed to more accurately determine VITD effects on RHF in healthy populations