Studies of food habits and dietary intakes face a number of unique respondent and observer considerations at different stages from early childhood to late adolescence. Despite this, intakes have often been reported as if valid, and the interpretation of links between intake and health has been based, often erroneously, on the assumption of validity. However, validation studies of energy intake data have led to the widespread recognition that much of the dietary data on children and adolescents is prone to reporting error, mostly through under-reporting. Reporting error is influenced by body weight status and does not occur systematically across different age groups or different dietary survey techniques. It appears that the available methods for assessing the dietary intakes of children are, at best, able to provide unbiased estimates of energy intake only at the group level, while the food intake data of most adolescents are particularly prone to reporting error at both the group and the individual level. Moreover, evidence for the existence of subject-specific responding in dietary assessments challenges the assumption that repeated measurements of dietary intake will eventually obtain valid data. Only limited progress has been made in understanding the variables associated with misreporting in these age groups, the associated biases in estimating nutrient intakes and the most appropriate way to interpret unrepresentative dietary data. Until these issues are better understood, researchers should exercise considerable caution when evaluating all such data.

A variety of growth curves have been developed to model both unpredated, intraspecific population dynamics and more general biological growth. Most predictive models are shown to be based on variations of the classical Verhulst logistic growth equation. We review and compare several such models and analyse properties of interest for these. We also identify and detail several associated limitations and restrictions.A generalized form of the logistic growth curve is introduced which incorporates these models as special cases. Several properties of the generalized growth are also presented. We furthermore prove that the new growth form incorporates additional growth models which are markedly different from the logistic growth and its variants, at least in their mathematical representation. Finally, we give a brief outline of how the new curve could be used for curve-fitting.

This study demonstrated significant associations between low vitamin D status and markers of inflammation (including the ratio of IL-6 to IL-10) within elderly adults. These findings suggest that an adequate vitamin D status may be required for optimal immune function, particularly within the older adult population.

The accurate measurement of physical activity is fraught with problems in adults, but more especially in children because they have more complex and multi-dimensional activity patterns. In addition, the results of different studies are often difficult to interpret and compare, because of the diversity of methodological approaches, differences in data analysis and reporting, and the adoption of varying definitions of what constitutes an appropriate level of activity. Furthermore, inactivity is seldom quantified directly. Although there exists an extensive literature documenting the health benefits of regular physical activity in adults, activity-health relationships in children are not clear-cut. Current recommendations reinforce the concept of health-related activity, accumulating 30 min moderate-intensity exercise on at least 5 d/week (adults) and 1 h moderate-intensity exercise/d (children). Evidence suggests a high prevalence of inactivity in adults, but whether or not inactivity is increasing cannot be assessed currently. Similarly, no definite conclusions are justified about either the levels of physical activity of children, or whether these are sufficient to maintain and promote health. Data to support the belief that activity levels in childhood track into adulthood are weak. Inactivity is associated with an increased risk of weight gain and obesity, but causality remains to be established. In children there is strong evidence to demonstrate a dose-response relationship between the prevalence and incidence of obesity and time spent viewing television. Future research should focus on refining methodology for physical activity assessment to make it more sensitive to the different dimensions and contexts of activity in different age-groups.

Background The exploitation of synthetic data in health care is at an early stage. Synthetic data could unlock the potential within health care datasets that are too sensitive for release. Several synthetic data generators have been developed to date; however, studies evaluating their efficacy and generalizability are scarce. Objective This work sets out to understand the difference in performance of supervised machine learning models trained on synthetic data compared with those trained on real data. Methods A total of 19 open health datasets were selected for experimental work. Synthetic data were generated using three synthetic data generators that apply classification and regression trees, parametric, and Bayesian network approaches. Real and synthetic data were used (separately) to train five supervised machine learning models: stochastic gradient descent, decision tree, k-nearest neighbors, random forest, and support vector machine. Models were tested only on real data to determine whether a model developed by training on synthetic data can used to accurately classify new, real examples. The impact of statistical disclosure control on model performance was also assessed. Results A total of 92% of models trained on synthetic data have lower accuracy than those trained on real data. Tree-based models trained on synthetic data have deviations in accuracy from models trained on real data of 0.177 (18%) to 0.193 (19%), while other models have lower deviations of 0.058 (6%) to 0.072 (7%). The winning classifier when trained and tested on real data versus models trained on synthetic data and tested on real data is the same in 26% (5/19) of cases for classification and regression tree and parametric synthetic data and in 21% (4/19) of cases for Bayesian network-generated synthetic data. Tree-based models perform best with real data and are the winning classifier in 95% (18/19) of cases. This is not the case for models trained on synthetic data. When tree-based models are not considered, the winning classifier for real and synthetic data is matched in 74% (14/19), 53% (10/19), and 68% (13/19) of cases for classification and regression tree, parametric, and Bayesian network synthetic data, respectively. Statistical disclosure control methods did not have a notable impact on data utility. Conclusions The results of this study are promising with small decreases in accuracy observed in models trained with synthetic data compared with models trained with real data, where both are tested on real data. Such deviations are expected and manageable. Tree-based classifiers have some sensitivity to synthetic data, and the underlying cause requires further investigation. This study highlights the potential of synthetic data and the need for further evaluation of their robustness. Synthetic data must ensure individual privacy and data utility are preserved in order to instill confidence in health care departments when using such data to inform policy decision-making.