The Acute Effects of Aerobic and Resistance Exercise on Cardiovascular Function and Arterial Stiffness

The cardiovascular system changes acutely to the stresses of exercise to support the increased metabolic demand of the working tissues. This is accomplished through the augmentation of several parameters including heart rate, blood pressure, and vascular tone such as arterial stiffness. Exercise training has been shown to elicit changes in arterial stiffness but the acute effects of exercise on arterial stiffness have not been thoroughly studied. The current study examined the acute effects of no (control), aerobic (30 minutes of cycling at ~70% maximum heart rate), and resistance exercise (30 minutes, 3 sets of 10 repetitions for 6 exercises) on arterial stiffness in healthy males (n=11) utilizing measures of carotid-femoral pulse wave velocity and pulse wave analysis at rest and during recovery for 60 minutes. The exercise sessions utilized were consistent with American College of Sports Medicine guidelines for exercise in healthy individuals. Carotid-femoral pulse wave velocity demonstrated no significant change from resting values throughout recovery for any of the activities (~9 m·s-1). Systemic arterial stiffness values (corrected to a heart rate of 75 bpm) were significantly higher post-resistance exercise than the control and aerobic exercise activities initially (34.2 ± 10.3% vs. 14.2 ± 10.9% and 3.2 ± 12.7%, p\u3c0.05) and remained statistically higher throughout recovery. These results indicate that resistance exercise alone resulted in an increase in systemic arterial stiffness that lasted for at least 60 minutes. In contrast, neither aerobic or resistance activity elicited a change in regional arterial stiffness. Further studies may clarify the time course and mechanisms for changes in arterial stiffness following acute and chronic exercise of various modalities and intensities

Relationship Between Food Craving And Food Selection

Objective: The primary aim of this study was to determine the influence of craving on food selection. Research Methods and Procedures: A total of 95 viable participants completed the food craving inventory (FCI), a restaurant meal selection questionnaire, and various demographic questions. Linear regression modeling was used to analyze the relationship between FCI craving score and various forms of caloric intake. Logistic regression models were utilized to analyze the relationship between “high-craving” status and food selection. Results: No significant findings resulted from modelling the relationship between craving category FCI scores and craved caloric intake using Pearson’s coefficient. Likewise, no significant relationships were observed between craving category FCI scores and total caloric intake. Various significant relationships resulted from modelling the relationship between “high-craver” status and food selection. “High-craver” status for CARB and SWEET were significant predictors of choosing a high-fat meal. “High-craver” status for FFF was found to be a significant predictor of choosing a high-FFF meal. “High-craver” status for FFF and SWEET were significant predictors for choosing a high-carb meal. Discussion: While no significant associations were observed using linear regression to model the relationship between FCI score and caloric intake, these insignificant relationships may not hold true when more robust dietary measures for food selection are utilized and a larger sample size is polled. A number of significant relationships were elucidated using logistic regression to assess the relationship between “high-craver” status and food selection. Some of these relationships were positive and others inverse; however, important ideas concerning craving and food choice can be garnered from each of these. Conclusion: There are a number of limitations associated with this study; however, despite these limitations, this study provides an important base for the relationship between the magnitude of an individual’s craving score and food selection. With more robust studies centering around the same topic matter, it is possible that more concrete relationships between craving and food selection can be illuminated

Characteristics of High-Risk Groups: Analysis of Norwalk Student Body Mass Index (BMI) Data

1 in 3 children in the United States is currently classified as overweight or obese, and this prevalence increases as age rises. Obesity varies by racial, environmental, ethnic and socioeconomic factors as well as genetic factors. Childhood obesity is more prevalent among African Americans, American Indians, and Mexican-Americans than in whites, as well as in lower income families.1 2 3 Connecticut has one of the lowest childhood obesity rates in the nation (~11%), yet Norwalk’s childhood obesity rate is well above this mark at 22%4. This project reflects a partnership between the Norwalk Health Department and Norwalk Public Schools to create a report on childhood obesity in Norwalk stratified by age, gender, race, and free and reduced-price lunch eligibility. The objectives of this project were to 1) Analyze BMI data to determine if disparities in obesity prevalence in Norwalk Public Schools exist by demographic characteristics and understand how trends in obesity prevalence have changed over time. 2) Conduct qualitative analyses to identify areas for improvement by both the Norwalk Health Department and the Norwalk Public School System.https://elischolar.library.yale.edu/ysph_pbchrr/1027/thumbnail.jp