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Background: Bone density in the young-adult life is associated with risk for osteoporosis in later life. Next to genetic factors, lifestyle seems to play an important role for bone health. Aim of the study was to clarify the relationship between various lifestyle factors and bone parameters in university students., Methods: In 233 women and 52 men (age: 22.7±3.1 vs. 24.0±3.2 years; BMI: 22.2±2.9 vs. 24.2±3.1 kg∙m -2 ) the following parameters were assessed: calcaneal Stiffness Index ([SI]; quantitative ultrasound), body composition (bioelectrical impedance analysis), actual and past physical activity level ([PAL]; standardized questionnaire), and food/nutrient intake (3-day dietary record). Sex differences (Mann-Whitney-U-tests), relationships between SI and lifestyle factors (Spearman's rank correlation), and the influence of the factors on the variance of SI (multiple linear regression) were tested (α=0.05)., Results: 6.9% of the women and 5.8% of the men were classified as osteopenic (-2.5< T score <-1). Significant correlations between SI and BMI, absolute fat mass and fat-free mass, actual PAL, and physical activity during adolescence were found in women (r=0.18-0.24, all P<0.05), but not in men. Food/nutrient parameters did not significantly correlate with bone outcomes (P>0.05). In multiple regression analysis physical activity during adolescence, actual PAL, and BMI explained 8% of the variance of SI (P<0.0001): SI=32.2+3.5∙physical activity during adolescence [1: very low, 2: low, 3: medium, 4: high, 5: very high] +28.5∙PAL+0.8∙BMI [kg∙m -2 ]., Conclusions: Parameters of physical activity seem to be more important for bone health than nutrition factors. Therefore, high levels of physical activity during childhood, adolescence, and early-adult life are highly recommended to improve bone parameters.

Achieving a high bone mass during childhood and adolescence is important for the prevention of osteoporosis in later life. Herein, the purpose was to assess the relationship of various lifestyle factors and bone outcomes in school children. In 248 girls (13.4 ± 1.9 years, BMI: 20.2 ± 4.8 kg m -2 ) and 231 boys (13.6 ± 1.7 years, BMI: 19.3 ± 3.3 kg m -2 ), bone mass (stiffness index, SI; calcaneal quantitative ultrasonometry), body composition (bioelectrical impedance analysis), food intake (food frequency questionnaire), and physical activity level (PAL; standardized questionnaire) were assessed. The individual food intake of eight food groups was related to the German recommendations (Bone Healthy Eating Index, BoneHEI; 0-100 points). Relationships between SI and lifestyle factors (Spearman´s rank correlation) and the influence of the factors on the variance of SI (multiple linear regression) were tested (α = 0.05). SI correlated with age, BMI, absolute fat-free mass, relative fat mass, PAL, and puberty category score in both girls and boys ( r = 0.18-0.56, p < 0.01), but not with BoneHEI ( p > 0.05). Age, absolute fat-free mass, sex, and PAL explained 35% of the variance of SI ( p < 0.0001): SI = -0.60 + 2.97∙age (years) + 0.65∙fat-free mass (kg) + 6.21∙sex (0 = male, 1 = female) + 17.55∙PAL. Besides age and sex, PAL and fat-free mass are important factors relating to bone health. School children should perform regular physical activity to improve their bone status.

Background: Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group., Aim: of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice., Subjects and Methods: RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 +/- 5.7 y, BMI 26.4 +/- 3.7 kg/m(2)) and 107 male (age 66.9 +/- 5.1 y, BMI 26.3 +/- 3.1 kg/m(2)) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR., Results: In females there was no significant difference between our measured RMR (5504 +/- 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 +/- 440 kJ/d), whereas in males measured RMR (6831 +/- 779 kJ/d) was significantly higher than calculated RMR (6490 +/- 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 x body weight [kg] - 15.3 x age [y] + 746 x sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R(2)) of the variance in RMR and predicted RMR within +/- 486 kJ/d (SEE)., Conclusion: On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice.

Aim: A balanced diet is important for health and well-being. The aim of this study was to investigate dietary intake and physical activity in German university students. Subjects and methods: A cross-sectional nutrition study was conducted at the University of Education, Schwäbisch Gmünd (Germany). Food consumption (3-day estimated dietary record) and physical activity level (PAL, standardized questionnaire) were assessed in 329 female (f) (age 22.7±3.4 years) and 60 male (m) students (age 24.0±3.1 years) and compared with recommendations (one-sample t-test, α = 0.05). Results: On average, students consumed less vegetables (f: 177±119, m: 152±163 g/day), fruit (f: 169±117, m: 146±158 g/day), and potatoes/grain products (f: 303±126, m: 362±163 g/day) than recommended. Meat consumption was too high in men (1155±651 g/week), but not in women (490±455 g/week). The consumption of milk/dairy products was slightly too low in women (190±129 g/day), but in the recommended range for men (279±253 g/day). Nutrient recommendations were almost achieved (exceptions: fiber, vitamin D and folate [f, m], fat, iron and vitamin E [f], carbohydrate [m]). Leisure time physical activity was 259±222 min/week (f) and 359±236 min/week (m), and PAL was 1.60±0.11 (f) and 1.65±0.12 (m). Conclusions: Dietary intake and physical activity in university students is roughly equivalent to that in the average German population and needs to be improved. [ABSTRACT FROM AUTHOR]

Background: Reference standards for resting energy expenditure (REE) are widely used. Current standards are based on measurements made in the first part of the past century in various races and locations. Objective: The aim of the present study was to investigate the application of the World Health Organization (WHO) equations from 1985 in healthy subjects living in a modern, affluent society in Germany and to generate a new formula for predicting REE. Design: The study was a cross-sectional and retrospective analysis of data on REE and body composition obtained from 2528 subjects aged 5-91 y in 7 different centers between 1985 and 2002. Results: MeanREEvaried between 5.63 and 8.07 MJ/d in males and between 5.35 and 6.46 MJ/d in females. WHO prediction equations systematically overestimated REE at low REE values but underestimated REE at high REE values. There were significant and independent effects of sex, age, body mass or fat-free mass, and fat mass on REE. Multivariate regression analysis explained up to 75% of the variance in REE. Two prediction formulas including weight, sex, and age or fat-free mass, fat mass, sex, and age, respectively, were generated in a subpopulation and cross-validated in another subpopulation. Significant deviations were still observed for underweight and normal-weight subjects. REE prediction formulas for specific body mass index groups reduced the deviations. The normative data for REE from the Institute of Medicine underestimated our data by 0.3 MJ/d. Conclusions: REE prediction byWHOformulas systematically overand underestimates REE.REEprediction from a weight group-specific formula is recommended in underweight subjects. [ABSTRACT FROM AUTHOR]