Diurnal Cortisol in Left-Behind Adolescents: Relations to Negative Family Expressiveness and Internalizing Problems

Chronic exposure to stress hormones, whether it occurs during the prenatal period, infancy, childhood, adolescence, adulthood or aging, has an impact on brain structures involved in cognition and mental health. However, the specific effects on the brain, behaviour and cognition emerge as a function of the timing and the duration of the exposure, and some also depend on the interaction between gene effects and previous exposure to environmental adversity. Advances in animal and human studies have made it possible to synthesize these findings, and in this Review a model is developed to explain why different disorders emerge in individuals exposed to stress at different times in their lives.

<p>Here is a book that challenges the very basis of the way psychologists have studied child development. According to Urie Bronfenbrenner, one of the world’s foremost developmental psychologists, laboratory studies of the child’s behavior sacrifice too much in order to gain experimental control and analytic rigor. Laboratory observations, he argues, too often lead to “the science of the strange behavior of children in strange situations with strange adults for the briefest possible periods of time.” To understand the way children actually develop, Bronfenbrenner believes that it will be necessary to observe their behavior in natural settings, while they are interacting with familiar adults over prolonged periods of time.<br><br>This book offers an important blueprint for constructing such a new and ecologically valid psychology of development. The blueprint includes a complete conceptual framework for analysing the layers of the environment that have a formative influence on the child. This framework is applied to a variety of settings in which children commonly develop, ranging from the pediatric ward to daycare, school, and various family configurations. The result is a rich set of hypotheses about the developmental consequences of various types of environments. Where current research bears on these hypotheses, Bronfenbrenner marshals the data to show how an ecological theory can be tested. Where no relevant data exist, he suggests new and interesting ecological experiments that might be undertaken to resolve current unknowns.<br><br>Bronfenbrenner’s groundbreaking program for reform in developmental psychology is certain to be controversial. His argument flies in the face of standard psychological procedures and challenges psychology to become more relevant to the ways in which children actually develop. It is a challenge psychology can ill-afford to ignore.</p>

Study protocols in endocrinological research and the neurosciences often employ repeated measurements over time to record changes in physiological or endocrinological variables. While it is desirable to acquire repeated measurements for finding individual and group differences with regard to response time and duration, the amount of data gathered often represents a problem for the statistical analysis. When trying to detect possible associations between repeated measures and other variables, the area under the curve (AUC) is routinely used to incorporate multiple time points. However, formulas for computation of the AUC are not standardized across laboratories, and existing differences are usually not presented when discussing results, thus causing possible variability, or incompatibility of findings between research groups. In this paper, two formulas for calculation of the area under the curve are presented, which are derived from the trapezoid formula. These formulas are termed 'Area under the curve with respect to increase' (AUCI) and 'Area under the curve with respect to ground' (AUCG). The different information that can be derived from repeated measurements with these two formulas is exemplified using artificial and real data from recent studies of the authors. It is shown that depending on which formula is used, different associations with other variables may emerge. Consequently, it is recommended to employ both formulas when analyzing data sets with repeated measures.