The Predictive Role of Raw Bioelectrical Impedance Variables in Disordered Eating Attitudes in Female Ballet Dance Students.

The present study used receiver operating characteristic (ROC) curve analysis to investigate the accuracy of body composition and raw bioelectrical impedance analysis (BIA) in correctly classifying disordered eating attitudes (DEA) in dance students. Participants were 81 female dancers assigned in two groups: beginner training (BT; age (mean ± SD) = 10.09 ± 1.2 years, n = 32) and advanced training (AT; age = 15.37 ± 2.1 years, n = 49). Fat mass (FM) was estimated by Slaughter's equation and skeletal muscle with Poortman's equation. Impedance (Z), resistance (R), reactance (Xc) and phase angle (PhA) were obtained through multifrequency BIA at a frequency of 50 kHz. Fat-free mass (FFM) was assessed using Sun's equation. For evaluation of DEA, the Eating Attitudes Test-26 (EAT-26) questionnaire was performed. We defined an EAT-26 score ≥ 20 as positive for DEA. Comparisons between groups were performed by a one-way ANOVA test or Kruskall-Wallis test. Spearman's rank correlation coefficients were performed to assess associations between variables. ROC curve analysis was utilized to test the accuracy of body composition and BIA variables in predicting DEA. In the BT group, Xc and PhA demonstrated high accuracy in predicting DEA with an area under the curve (AUC) of 0.976 (95% confidence interval (CI): 0.85-1.00) and 0.957 (95% CI: 0.82-0.99), respectively, (both p < 0.0001). FFM Sun had an AUC of 0.836 (95% CI: 0.66-0.94) (p < 0.0001) in the BT group and FFM Slaughter was 0.797 (95% CI: 0.66-0.90) (p < 0.001) in the AT group. Reactance and Phase angle were excellent and useful predictors of DEA in the BT group.

Alcohol intake reduction for controlling hypertension.

BACKGROUND: High blood pressure constitutes one of the leading causes of mortality and morbidity all over the world. At the same time, heavy drinking increases the risk for developing cardiovascular diseases, including cardiomyopathy, hypertension, atrial arrhythmias, or stroke. Several studies have already assessed specifically the relationship between alcohol intake and hypertension. However, the potential effect on blood pressure of alcohol intake reduction interventions is largely unknown. OBJECTIVES: To assess the effect of any intervention to reduce alcohol intake in terms of blood pressure decrease in hypertensive people with alcohol consumption compared to a control intervention or no intervention at all. To determine additional effects related to mortality, major cardiovascular events, serious adverse events, or quality of life. SEARCH METHODS: The Cochrane Hypertension Information Specialist searched the following databases for randomised controlled trials up to June 2020: the Cochrane Hypertension Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (Issue 5, 2020), MEDLINE Ovid (from 1946), MEDLINE Ovid Epub Ahead of Print, and MEDLINE Ovid In-Process, Embase Ovid (from 1974), ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform. Trial authors were contacted when needed and no language restrictions were applied. SELECTION CRITERIA: We included randomised controlled trials with minimum 12 weeks duration and including 50 or more subjects per group with quantitative measurement of alcohol consumption and/or biological measurement of the outcomes of interest. Participants were adults (16 years of age or older) with systolic blood pressure (SBP) greater than 140 mmHg and diastolic blood pressure (DBP) greater than 90 mmHg, and SBP ≥ 130 or DBP ≥ 80 mmHg in participants with diabetes. We included any intervention implemented to reduce their alcohol intake. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed search results and extracted data using standard methodological procedures adopted by Cochrane. MAIN RESULTS: A total of 1210 studies were screened. We included one randomised controlled trial involving a total of 269 participants with a two-year follow-up. Individual patient data for all participants were provided and used in this review. No differences were found between the cognitive-behavioural intervention group and the control group for overall mortality (RR 0.72, 95% CI 0.16 to 3.17; low-certainty evidence), cardiovascular mortality (not estimable) and cardiovascular events (RR 0.80, 95% CI 0.36 to 1.79; very low-certainty evidence). There was no statistical difference in systolic blood pressure (SBP) reduction (Mean Difference (MD) -0.92 mmHg, 95% confidence interval (CI) -5.66 to 3.82 mmHg; very low-certainty evidence) or diastolic blood pressure (DBP) decrease (MD 0.98 mmHg, 95% CI -1.69 to 3.65 mmHg; low-certainty evidence) between the cognitive-behavioural intervention group and the control group. We also did not find any differences in the proportion of subjects with SBP < 140 mmHg and DBP < 90 mmHg (Risk Ratio (RR) 1.21, 95% CI 0.88 to 1.65; very low-certainty evidence). Concerning secondary outcomes, the alcohol intake was significantly reduced in the cognitive-behavioural intervention compared with the control group (MD 191.33 g, 95% CI 85.36 to 297.30 g). We found no differences between the active and control intervention in the proportion of subjects with lower-risk alcohol intake versus higher-risk and extreme drinkers at the end of the study (RR 1.04, 95% CI 0.68 to 1.60). There were no estimable results for the quality of life outcome. AUTHORS' CONCLUSIONS: An intervention for decreasing alcohol intake consumption did not result in differences in systolic and diastolic blood pressure when compared with a control intervention, although there was a reduction in alcohol intake favouring the active intervention. No differences were found either for overall mortality, cardiovascular mortality or cardiovascular events. No data on serious adverse events or quality of life were available to assess. Adequate randomised controlled trials are needed to provide additional evidence on this specific question.

Prediction of Performance in a Short Trail Running Race: The Role of Body Composition.

The aim of the present study was to examine the role of the classical physiological model of endurance running performance - maximal oxygen uptake (VO2max), %VO2max at ventilatory thresholds (VT), work economy, lactate levels, and body composition on the prediction of short trail running performance. Eleven male trail runners (age 36.1 ± 6.5 years, sport experience 6.6 ± 3.8 years, and mean ± standard deviation) were examined for fat mass and skeletal muscle mass, and performed a graded exercise test to measure VO2max, vVO2max, and VT. Also, they participated in a short 27 km trail run with a positive elevation of +1750 m. Age, years of training and skeletal muscle mass did not correlate with race time (P > 0.05), and fat mass and body mass index (BMI) showed significant correlations with race time (P < 0.05). Heart rate, velocity and VT1 and VT2 were not associated with race time (P > 0.05). Only vVO2max (P = 0.005) and VO2max (P = 0.007) is correlated to race time. Multiple regression models for VO2max accounted for 57% of the total variance. The vVO2max model variable accounted for 60% and the fat mass model for 59.5%. Finally, the combined VO2max and fat mass model explained 83.9% of the total variance (P < 0.05 in all models). The equation for this model is "race time (min) = 203.9956-1.9001 × VO2max + 10.2816 × Fat mass%" (R 2 = 0.839, SEE = 11.1 min, and P = 0.0007). The classical variable VO2max together with fat mass percent are two strong predictors for short trail running performance.