INVESTIGATING INDICATOR ASSOCIATIONS AND USABILITY CHARACTERISTICS FOR MEASURING CARDIOVASCULAR DISEASE RISK FACTORS IN CHILDREN

Purpose: This study examined the relationship between various indicators of CVD risk in children in order to determine the most efficient and useful set of measurements to characterize CVD risk. Method: A total of 354 children (193 girls and 161 boys; age: 9-12 years) participated in the (S)Partners for Health Project from 2010 to 2018 were included in this study. Blood pressure, lipids, anthropometric measurements, and cardiorespiratory fitness were obtained from the children. Usability characteristics were based on the time of obtaining data, time of training, price, and participant likeability for each measurement. Descriptive statistics, variable level correlations, and factor analysis were used to determine the most useful variables to characterize CVD risk. Results: Weight, mean arterial pressure, total cholesterol/high-density lipoprotein ratio, total cholesterol, and cardiorespiratory fitness were included as the CVD risk factors that contained the most useful information. The Measurement Usefulness Index revealed weight had a higher value, which indicates a measure that is easy to assess and significantly associated with CVD risk. Conclusion: The Measurement Usefulness Index is an important tool that future studies can use to design assessment protocols. The results allow researchers and clinicians to make more informed decisions about what indicators to include in the CVD risk assessment, based on both statistical and usability characteristics. Future studies on metabolic syndrome and cardiovascular risk should consider and examine the usability of the variables included

Determining independence and associations among various cardiovascular disease risk factors in 9-12 years old school-children: a cross sectional study

BACKGROUND: Cardiovascular disease (CVD) risk assessment of children typically includes evaluating multiple CVD risk factors some of which tend to correlate each other. However, in older children and young adolescents, there are little data on the level of independence of CVD risk factors. The purpose of this study was to examine the relationships among various CVD risk factors to determine the level of independence of each risk factor in a sample of 5-grade public school students. METHOD: A cross-sectional analysis of 1525 children (856 girls and 669 boys; age: 9-12 years) who participated in baseline CVD risk assessment for the (S)Partners for Heart Health program from 2010 - 2018. Thirteen CVD risk factor variables were used in the analysis and included blood lipids [low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), and triglycerides], resting systolic and diastolic blood pressure (BP); anthropometrics [height, weight, body mass index (BMI), % body fat, waist circumference (WC)]. Additionally, acanthosis nigricans (a marker insulin resistance and diabetes), and cardiorespiratory fitness (VO2 ml/kg) was estimated using the PACER. Descriptive statistics, bivariate Pearson correlations, and principal component analysis were used to determine the relationships among these variables and the independence. RESULTS: Parallel analysis indicated two components should be extracted. Among the two components extracted, WC, % body fat, and BMI loaded highest on component 1, which explained 34% of the total variance. Systolic BP and diastolic BP loaded predominantly on component 2 and accounted for 17% of the variance. Cardiorespiratory fitness, acanthosis nigricans, HDL, and triglycerides loaded highest on the first component (loadings between 0.42 and 0.57) but still suggest some non-shared variance with this component. Low-density lipoprotein had low loadings on each component. Factor loadings were stable across sex. CONCLUSION: Among the various CVD risk indicators, measures of adiposity loaded highest on the component that explained the largest proportion of variability in the data reinforcing the importance of assessing adiposity in CVD risk assessment. In addition, blood pressure loaded highest on the second component, suggesting their relative independence when assessing CVD risk. The data also provide support and rationale for determining what CVD risk factors to include- based on resource needs. For example, researchers or public health programs may choose to assess WC instead of lipid profile for cardiovascular related problems if ease of assessment and cost are considerations

Effect of Rehydration with Mineral Water on Cardiorespiratory Fitness Following Exercise-Induced Dehydration in Athletes

Background: The aim of the present study was to examine the effect of rehydration with mineral water on cardiorespiratory fitness in athletes. Methods: Twenty athletes (age 21.7 ± 3 years) underwent a random, crossover design experimental trial. Three visits were arranged, with the first for baseline measurement. The second visit included three phases (pre-dehydration, post-dehydration, and post-rehydration), with either Zamzam (mineral water) or bottled water (control water) used. The third visit was similar to the second visit, but with an exchange of the type of water used. Cardiorespiratory fitness and blood parameters were evaluated. Results were compared between Zamzam water and bottled water, and between the phases for each type of water. Results: No significant difference was found between Zamzam and bottled water for the cardiorespiratory fitness markers. However, Zamzam water maintained cardiorespiratory functions including VO2peak, VT1, VT2, and VEpeak, even with rehydration equivalent to 100% of the loss in body weight following exercise-induced dehydration (>2% loss in body weight). Rehydration with bottled water was associated with a significant reduction in both the VO2peak and VEpeak. Conclusions: Rehydration with mineral water such as Zamzam is unlikely to impair cardiorespiratory fitness, even with an intake equal to 100% of the loss in body weight

The Relationship between Accelerometry, Global Navigation Satellite System, and Known Distance: A Correlational Design Study

: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960-0.999, R = 0.922-0.999; RMSE = 0.047-0.242, \u3c 0.001), while GNSSD (small circle, R = 0.999, R = 0.997, RMSE = 0.047 \u3c 0.001; large circle, R = 0.999, R = 0.999, RMSE = 0.027, \u3c 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R = 0.983, RMSE = 0.112, \u3c 0.001; large circle, R = 0.997, R = 0.995, RMSE = 0.064, \u3c 0.001) performed best among all models. This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking