Inflammatory markers provide invaluable tools for studying health and disease across the lifespan. Inflammation is central to the immune system’s response to infection and wounding; it also can increase in response to psychosocial stress. In addition, depression and physical symptoms such as pain and poor sleep can promote inflammation and, because these factors fuel each other, all contribute synergistically to rising inflammation. With increasing age, persistent exposure to pathogens and stress can induce a chronic proinflammatory state, a process known as inflamm-aging. Inflammation’s relevance spans the life course, from childhood to adulthood to death. Infection-related inflammation and stress in childhood, and even maternal stress during pregnancy, may presage heightened inflammation and poor health in adulthood. In turn, chronically heightened inflammation in adulthood can foreshadow frailty, functional decline, and the onset of inflammatory diseases in older age. The most commonly measured inflammatory markers include C-reactive protein (CRP) and proinflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). These biomarkers are typically measured in serum or plasma through blood draw, which capture current circulating levels of inflammation. Dried blood spots offer a newer, sometimes less expensive collection method but can capture only a limited subset of markers. Due to its notable confounds, salivary sampling cannot be recommended. Inflammatory markers can be added to a wide range of lifespan developmental designs. Incorporating even a single inflammatory assessment to an existing longitudinal study can allow researchers to examine how developmental profiles and inflammatory status are linked, but repeated assessments must be used to draw conclusions about the associations’ temporal order and developmental changes. Although the various inflammatory indices can fluctuate from day to day, ecological momentary assessment and longitudinal burst studies have not yet incorporated daily inflammation measurement; this represents a promising avenue for future research. In conclusion, mounting evidence suggests that inflammation affects health and disease across the lifespan and can help to capture how stress “gets under the skin.” Incorporating inflammatory biomarkers into developmental studies stands to enhance our understanding of both inflammation and lifespan development.
Stephanie J. Wilson, Alex Woody, and Janice K. Kiecolt-Glaser
Gawon Cho, Giancarlo Pasquini, and Stacey B. Scott
The study of human development across the lifespan is inherently about patterns across time. Although many developmental questions have been tested with cross-sectional comparisons of younger and older persons, understanding of development as it occurs requires a longitudinal design, repeatedly observing the same individual across time. Development, however, unfolds across multiple time scales (i.e., moments, days, years) and encompasses both enduring changes and transient fluctuations within an individual. Measurement burst designs can detect such variations across different timescales, and disentangle patterns of variations associated with distinct dimensions of time periods. Measurement burst designs are a special type of longitudinal design in which multiple “bursts” of intensive (e.g., hourly, daily) measurements are embedded in a larger longitudinal (e.g., monthly, yearly) study. The hybrid nature of these designs allow researchers to address questions not only of cross-sectional comparisons of individual differences (e.g., do older adults typically report lower levels of negative mood than younger adults?) and longitudinal examinations of intraindividual change (e.g., as individuals get older, do they report lower levels of negative mood?) but also of intraindividual variability (e.g., is negative mood worse on days when individuals have experienced an argument compared to days when an argument did not occur?). Researchers can leverage measurement burst designs to examine how patterns of intraindividual variability unfolding over short timescales may exhibit intraindividual change across long timescales in order to understand lifespan development. The use of measurement burst designs provides an opportunity to collect more valid and reliable measurements of development across multiple time scales throughout adulthood.
Ananiev’s approach shares the Activity Theory (AT) paradigm, dominant in Soviet psychology. Ananiev builds on the main fundamentals of the AT paradigm, considering psyche as a special procreation of the matter, engendered by the active interaction of the individual with the environment. The unique feature of his approach to AT is that he turned it “toward the inside,” focusing on the relation of the human individual to his own physicality, to his own bodily substrate. Ananiev sought by his intention to keep a holistic vision of a human being, considering the latter in the context of his real life, that is, the bodily substrate in its biological specificity in context of the concrete sociohistorical life course of the personality. Like no other psychologist, Ananiev did not limit his research to the sphere of narrowly defined mental phenomena. He conducted a special kind of research, labeled as “complex,” in the course of which characteristics of the same subjects: sociological, socio-psychological, mental, physiological, and psychophysiological indicators—life events of the subjects—were monitored for many years. He focused on ontogenetic development in adulthood, which he, ahead of his time, considered as a period of dynamic changes and differentiated development of functions. The focus of his attention was on individual differences in the ontogenetic development of mental and psycho-physiological functions, especially those deviations from general regularities that resulted from the impact of the life course of the individual. Individualization, the increase of individual singularity, is the main effect of human development and its measure for Ananiev. Ananiev developed a number of theoretical models and concepts. The best-known of Ananiev’s heritage is his theoretical model of human development, often named the “individuality concept.” According to this model, humans do not have any preassigned “structure of personality” or “initial harmony.” The starting point of human development is a combination of potentials—resources and reserves, biological and social. The human creates himself in the process of interaction with the world. Specialization, individually specific development of functions, appears here not as a distortion of the pre-set harmony of the whole but as the way of self-determining progressive human development. The most important practical task of psychology he viewed as psychological support and provision in the process of developing a harmonious individuality, based on the individual potentials.
Oscar Gonzalez and David P. MacKinnon
Lifespan developmental research studies how individuals change throughout their lifetime and how intraindividual or interindividual change leads to future outcomes. Lifespan researchers are interested in how developmental processes unfold and how specific developmental pathways lead to an outcome. Developmental processes have been previously studied using developmental cascade models, concepts of equifinality and multifinality, and developmental interventions. Statistical mediation analysis also provides a framework for studying developmental processes and developmental pathways by identifying intermediate variables, known as mediators, that transmit the effect between early exposures and future outcomes. The role of statistical mediation in lifespan developmental research is either to explain how the developmental process unfolds, or to identify mediators that researchers can target in interventions so that individuals change developmental pathways. The statistical mediation model is inherently causal, so the relations between the exposures, mediators, and outcomes have to be correctly specified, and ruling out alternative explanations for the relations is of upmost importance. The statistical mediation model can be extended to deal with longitudinal data. For example, the autoregressive mediation model can represent change through time by examining lagged relations in multiwave datasets. On the other hand, the multilevel mediation model can deal with the clustering of repeated measures within individuals to study intraindividual and interindividual change. Finally, the latent growth curve mediation model can represent the variability of linear and nonlinear trajectories for individuals in the variables in the mediation model through time. As a result, developmental researchers have access to a range of models that could describe the theory of change they want to study. Researchers are encouraged to consider mechanisms of change and to formulate mediation hypotheses about lifespan development.
Gizem Hülür and Elisa Weber
Lifespan development is embedded in multiple social systems and social relationships. Lifespan developmental and relationship researchers study individual codevelopment in various dyadic social relationships, such as dyads of parents and children or romantic partners. Dyadic data refers to types of data for which observations from both members of a dyad are available. The analysis of dyadic data requires the use of appropriate data-analytic methods that account for such interdependencies. The standard actor-partner interdependence model, the dyadic growth curve model, and the dyadic dual change score model can be used to analyze data from dyads. These models allow examination of questions related to dyadic associations such as whether individual differences in an outcome can be predicted by one’s own (actor effects) and the other dyad member’s (partner effects) level in another variable, correlated change between dyad members, and cross-lagged dyadic associations, that is, whether one dyad member’s change can be predicted by the previous levels of the other dyad member. The choice of a specific model should be guided by theoretical and conceptual considerations as well as by features of the data, such as the type of dyad, the number and spacing of observations, or distributional properties of variables.