Lizbeth Benson and Nilam Ram
In ecological sciences, biodiversity is the dispersion of organisms across species and is used to describe the complexity of systems where species interact with each other and the environment. Some argue that biodiversity is important to cultivate and maintain because higher levels are indicative of health and resilience of the ecosystem. Because each species performs functional roles, more diverse ecosystems have greater capability to respond, maintain function, resist damage, and recover quickly from perturbations or disruptions. In the behavioral sciences, diversity-type constructs and metrics are being defined and operationalized across a variety of functional domains (socioemotional, self, cognitive, activities and environment, stress, and biological). Emodiversity, for instance, is the dispersion of an individual’s emotion experiences across emotion types (e.g., happy, anger, sad). Although not always explicitly labeled as such, many core propositions in lifespan developmental theory—such as differentiation, dedifferentiation, and integration—imply intraindividual change in diversity and/or interindividual differences in diversity. For example, socioemotional theories of aging suggest that as individuals get older, they increasingly self-select into more positive valence and low arousal emotion inducing experiences, which might suggest that diversity in positive and low arousal emotion experiences increases with age. When conceptualizing and studying diversity, important considerations include that diversity (a) provides a holistic representation of human systems, (b) differs in direction, interpretation, and linkages to other constructs such as health (c) exists at multiple scales, (d) is context-specific, and (e) is flexible to many study designs and data types. Additionally, there are also a variety of methodological considerations in study of diversity-type constructs including nuances pertaining theory-driven or data-driven approaches to choosing a metric. The relevance of diversity to a broad range of phenomena and the utility of biodiversity metrics for quantifying dispersion across categories in multivariate and/or repeated measures data suggests further use of biodiversity conceptualizations and methods in studies of lifespan development.
Thomas M. Hess, Erica L. O'Brien, and Claire M. Growney
Blood pressure is a frequently used measure in studies of adult development and aging, serving as a biomarker for health, physiological reactivity, and task engagement. Importantly, it has helped elucidate the influence of cardiovascular health on behavioral aspects of the aging process, with research demonstrating the negative effect of chronic high blood pressure on various aspects of cognitive functioning in later life. An important implication of such research is that much of what is considered part and parcel of getting older may actually be reflective of changes in health as opposed to normative aging processes. Research has also demonstrated that situational spikes in blood pressure to emotional stressors (i.e., reactivity) also have implications for health in later life. Although research is still somewhat limited, individual differences in personal traits and living circumstances have been found to moderate the strength of reactive responses, providing promise for the identification of factors that might ameliorate the effects of age-related changes in physiology that lead to normative increases in reactivity. Finally, blood pressure has also been successfully used to assess engagement levels. In this context, recent work on aging has focused on the utility of blood pressure as a reliable indicator of both (a) the costs associated with cognitive engagement and (b) the extent to which variation in these costs might predict both between-individual and age-related normative variation in participation in cognitively demanding—but potentially beneficial—activities. This chapter elaborates on these three approaches and summarizes major research findings along with methodological and interpretational issues.
Shevaun D. Neupert and Jennifer A. Bellingtier
Daily diary designs allow researchers to examine processes that change together on a daily basis, often in a naturalistic setting. By studying within-person covariation between daily processes, one can more precisely establish the short-term effects and temporal ordering of concrete daily experiences. Additionally, the daily diary design reduces retrospective recall bias because participants are asked to recall events that occurred over the previous 24-hour period as opposed to a week or even a year. Therefore, a more accurate picture of individuals’ daily lives can be captured with this design. When conclusions are drawn between people about the relationship between the predictors and outcomes, the covariation that occurs within people through time is lost. In a within-person design, conclusions can be made about the simultaneous effects of within-person covariation as well as between-person differences. This is especially important when many interindividual differences (e.g., traits) may exist in within-person relationships (e.g., states).
Daily diary research can take many forms. Diary research can be conducted with printed paper questionnaires, divided into daily booklets where participants mail back each daily booklet at the end of the day or entire study period. Previous studies have called participants on the telephone to respond to interview questions each day for a series of consecutive days, allowing for quantitative as well as qualitative data collection. Online surveys that can be completed on a computer or mobile device allow the researcher to know the specific day and time that the survey was completed while minimizing direct involvement with the collection of each daily survey. There are many opportunities for lifespan developmental researchers to adopt daily diary designs across a variety of implementation platforms to address questions of important daily processes. The benefits and drawbacks of each method along with suggestions for future work are discussed, noting issues of particular importance for aging and lifespan development.
Michaela Riediger and Antje Rauers
Experience-sampling methodology (ESM) captures everyday events and experiences during, or shortly after, their natural occurrence in people’s daily lives. It is typically implemented with mobile devices that participants carry with them as they pursue their everyday routines, and that signal participants multiple times a day throughout several days or weeks to report on their momentary experiences and situation. ESM provides insights into short-term within-person variations and daily-life contexts of experiences, which are essential aspects of human functioning and development. ESM also can ameliorate some of the challenges in lifespan-developmental methodology, in particular those imposed by age-comparative designs. Compared to retrospective or global self-reports, for example, ESM can reduce potential non-equivalence of measures caused by age differences in the susceptibility to retrospective memory biases. Furthermore, ESM maximizes ecological validity compared to studies conducted in artificial laboratory contexts, which is a key concern when different age groups may differentially respond to unfamiliar situations. Despite these strengths, ESM also bears significant challenges related to potential sample selectivity and selective sample attrition, participants’ compliance and diligence, measurement reactivity, and missing responses. In age-comparative research, these challenges may be aggravated if their prevalence varies depending on participants’ age. Applications of ESM in lifespan methodology therefore require carefully addressing each of these challenges when planning, conducting, and analyzing a study, and this article provides practical guidelines for doing so. When adequately applied, experience sampling is a powerful tool in lifespan-developmental methodology, particularly when implemented in long-term longitudinal and cross-sequential designs.
Stephanie J. Wilson, Alex Woody, and Janice K. Kiecolt-Glaser
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.
Joseph E. Gaugler, Colleen M. Peterson, Lauren L. Mitchell, Jessica Finlay, and Eric Jutkowitz
Mixed methods research consists of collecting and analyzing qualitative and quantitative data within a singular study. The “methods” of mixed methods research vary, but the ultimate goal is to provide greater understanding and explanation via the integration of qualitative and quantitative data. Mixed methods studies have the potential to advance our understanding of complex phenomena over time in adult development and aging (e.g., depression following the death of a spouse), but the utility of this approach depends on its application. The authors systematically searched the literature (CINHAL, Embase, Ovid/Medline, PubMed, PsychInfo, and ProQuest) to identify longitudinal mixed methods studies focused on aging. They identified 6,351 articles published between 1994 and 2017, of which 174 met the inclusion criteria. The majority of mixed methods studies reported on the evaluation of interventions or educational programs. Non-interventional studies tended to report on experiences related to the progression of various health conditions, the needs and experiences of caregivers, and the lived experiences of older adults. About half (n = 81) of the mixed methods studies followed a sequential explanatory design where a qualitative component followed quantitative evaluation, and most of these studies achieved “integration” by comparing qualitative and quantitative data in Results sections. There was considerable heterogeneity across studies in terms of overall design (randomized trials, program evaluations, cohort studies, and case studies). As a whole, the literature suffered from key limitations, including a lack of reporting on sample selection methodology and mixed methods design characteristics. To maximize the value of mixed methods in adult development in aging research, investigators should conform to recommended guidelines (e.g., depict participant study flow and use recommended notation) and consider more sophisticated mixed methods applications to advance the state of the art.
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.