Stress and diurnal cortisol among Latino/a college students: A multi-risk model approach.

The transition to college is a time of increased opportunity and stress spanning multiple domains. Adolescents who encounter significant stress during this transition may be vulnerable to adverse outcomes due to a "wear and tear" of the hypothalamic pituitary adrenal (HPA) axis. Latino/a students may be particularly at-risk for heightened stress exposure due to experiences of both minority-specific and general life stress. Despite this, little is known regarding the cumulative impact of multiple stressors on Latino/a students' HPA axis functioning. The present study employed a "multi-risk model" approach to examine additive, common, and cumulative effects of multiple stress forms (general, academic, social, financial, bicultural, ethnic/racial discrimination) on diurnal cortisol in a sample of first-year Latino/a college students (N = 196; 64.4% female; Mage = 18.95). Results indicated that no stress forms were additively associated with the cortisol awakening response (CAR), but general stress was associated with a flatter diurnal cortisol slope (DCS) and bicultural stress was linked with a steeper DCS. A college stress latent factor was associated with a lower CAR, whereas a latent factor of discrimination was not associated with diurnal cortisol. Cumulative risk was linked with a lower CAR. Findings highlight the physiological correlates of various stressors experienced by Latino/a college students.

Developmental trajectories of executive functions from preschool to kindergarten.

Executive functions (EF) are key predictors of long-term success that develop rapidly in early childhood. However, EF's developmental trajectory from preschool to kindergarten is not fully understood due to conceptual ambiguity (e.g., whether it is a single construct or multiple related constructs) and methodological limitations (e.g., previous work has primarily examined linear growth). Whether and how this trajectory differs based on characteristics of children and their families also remains to be characterized. In a primarily low-income, racially and ethnically diverse, typically developing, urban sample, the present study employed confirmatory factor analyses to examine the construct of EF and latent growth curve modeling to examine nonlinear growth across five time points. Results indicated that the development of a single EF construct with partial measurement invariance across time points was best characterized as nonlinear, with disproportionately more growth during the preschool year. There was individual variability in EF trajectories, such that children with higher EF at preschool entry showed relatively steeper growth during preschool compared to low-EF peers. However, children with less EF growth in preschool had steeper growth in kindergarten, attenuating the gains of high-EF preschoolers and resulting in some convergence in EF by the end of kindergarten. Findings have implications for (1) examining EF development in early childhood with more specificity in future studies, (2) informing the timing of EF interventions in early childhood, and (3) identifying children for whom such interventions might be especially beneficial.

The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models.

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates-which are unique to BLCS models-were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Explorations of Individual Change Processes and Their Determinants: A Novel Approach and Remaining Challenges.

Over the past 40 years there have been great advances in the analysis of individual change and the analyses of between-person differences in change. While conditional growth models are the dominant approach, exploratory models, such as growth mixture models and structural equation modeling trees, allow for greater flexibility in the modeling of between-person differences in change. We continue to push for greater flexibility in the modeling of individual change and its determinants by combining growth mixture modeling with structural equation modeling trees to evaluate how measured covariates predict class membership using a recursive partitioning algorithm. This approach, referred to as growth mixture modeling with membership trees, is illustrated with longitudinal reading data from the Early Childhood Longitudinal Study with the MplusTrees package in R.

Infant and Toddler Responses to Bitter-Tasting Novel Vegetables: Findings from the Good Tastes Study.

BACKGROUND: Infants are born with the biological predisposition to reject bitterness. Dark green vegetables contain essential nutrients but also bitter compounds, making them more difficult to like. OBJECTIVE: The Good Tastes Study was designed to determine whether reducing bitterness by adding small amounts of sugar or salt would alter infant acceptance of kale purées. METHODS: Caregivers (n = 106, 94% mothers, 82% Non-Hispanic White) and children (53% male, aged 6-24 mo) participated in a videorecorded laboratory visit during which infants were offered 4 versions of puréed kale: plain, 1.2% or 1.8% added sugar, or 0.2% added salt. Caregivers rated their children's liking for each kale version. Videos were coded for the number of tastes accepted and for children's behaviors and acceptance of each kale version. A multilevel ordered logistic model was fit for the number of accepted tastes and caregiver ratings of child liking of kale versions with age, breastfeeding history, order effects, and kale version as predictors. RESULTS: Infants 6 to <12 mo accepted more tastes (b = 2.911, P < 0.001) and were rated by caregivers as liking the kale more than older toddlers (≥18 mo; b = 1.874, P = 0.014). The plain kale was more likely to be accepted (P < 0.001); also, the first version offered was more likely to be rejected (b = -0.586, P < 0.007). Older infants (≥18 mo) exhibited more avoidant behaviors (b = 1.279, P < 0.001), more playing (b = 2.918, P < 0.001), and more self-feeding (b = 1.786, P = 0.005) than younger infants (6 to <12 mo). Children who were reported to have been breastfed more in the last 7 d were more likely to self-feed (b = 0.246, P < 0.001) and play with food (b = 0.207, P < 0.005). CONCLUSIONS: Our findings support that there may be a sensitive period, during the early phase of complementary feeding, to improve success of introducing a novel, bitter, more difficult-to-like food. When low levels of sugar or salt were added, no advantage of bitterness reduction was observed. This study has been registered with ClinicalTrials.gov as NCT04549233.

Effects of the Family Check-Up 4 Health on Parenting and Child Behavioral Health: A Randomized Clinical Trial in Primary Care.

The Family Check-Up 4 Health (FCU4Health) is an adaptation of the Family Check-Up (FCU) for delivery in primary care settings. While maintaining the original FCU's focus on parenting and child behavioral health, we added content targeting health behaviors. This study evaluated whether the adapted FCU maintained positive effects on parenting (positive behavior support, limit setting, parental warmth) and child behavioral health (self-regulation, conduct problems, emotional problems). Pediatric (6-12 years) primary care patients with a BMI ≥ 85th%ile (n = 240) were recruited from primary care clinics in Phoenix. Children were 75% Latino, 49% female, and 73% Medicaid recipients. This type 2 effectiveness-implementation hybrid trial compared families randomized to FCU4Health (n = 141) or usual care (n = 99). FCU4Health was delivered over a period of 6 months. This study focuses on a priori secondary outcomes included parenting and child behavioral health targets of the original FCU, assessed at baseline and 3, 6, and 12 months. Significant improvements were found for the FCU4Health condition, compared to usual care, in parenting from baseline to the 3-month assessment [ß = .17 (.01; .32)]. Parenting predicted improvements in child self-regulation at 6-months [ß = .17 (.03; .30)], which in turn predicted reductions in conduct problems [ß = - .38 (- .51; - .23)] and emotional problems [ß = - .24 (- .38; - .09)] at 12 months. Ethnicity and language of delivery (English or Spanish) did not moderate these effects. The FCU4Health can improve parenting and child behavioral health outcomes when delivered in primary care.Trial Registration Trial registration number: NCT03013309 ClinicalTrials.gov.

Reliable Trees: Reliability Informed Recursive Partitioning for Psychological Data.

Recursive partitioning, also known as decision trees and classification and regression trees (CART), is a machine learning procedure that has gained traction in the behavioral sciences because of its ability to search for nonlinear and interactive effects, and produce interpretable predictive models. The recursive partitioning algorithm is greedy-searching for the variable and the splitting value that maximizes outcome homogeneity. Thus, the algorithm can be overly sensitive to chance associations in the data, particularly in small samples. In an effort to limit chance associations, we propose and evaluate a reliability-based cost function for recursive partitioning. The reliability-based cost function increases the likelihood of selecting variables that are more reliable, which should have more consistent associations with the outcome of interest. Two reliability-based cost functions are proposed, evaluated through simulation, and compared to the CART algorithm. Results indicate that reliability-based cost functions can be beneficial, particularly with smaller samples and when more reliable variables are important to the prediction, but can overlook important associations between the outcome and lower reliability predictors. The use of these cost functions was illustrated using data on depression and suicidal ideation from the National Longitudinal Survey of Youth.

Examination of Nonlinear and Functional Mixed-Effects Models with Nonparametrically Generated Data.

Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering individual trajectories when data were generated following a parametric structure. We extend this previous work and compare nonlinear mixed-effects (NMEM) and functional mixed-effects models' (FMEM) ability to recover underlying trajectories when generated from an inherently nonparametric process. Nonlinear trajectories were generated using B-splines, NMEMs and FMEMs were estimated, and the accuracy of the estimated curves was examined. Sample size, number of time points per curve, and measurement design were varied across simulation conditions. Results showed the FMEMs recovered the underlying mean curve more accurately than the NMEMs, and that, the FMEMs tended to recover the underlying individual curves more accurately than the NMEMs. Progesterone cycle data were then analyzed to demonstrate the utility of both approaches, and models performed similarly when analyzing these data.

School Readiness and Achievement in Early Elementary School: Moderation by Students' Temperament.

The goals of this study were to examine the longitudinal relations between school readiness and reading and math achievement and to test if these relations were moderated by temperament. The sample included socio-economically and ethnically diverse twins (N=551). Parents reported on school readiness when children were five years old. Teachers reported on temperament (effortful control, anger, and shyness) three years later. Standardized measures of reading and math were obtained when children were eight years old. Effortful control and shyness moderated the effect of school readiness on reading. Prediction of reading from school readiness was strongest when students were high in effortful control and low in shyness. Effortful control and shyness predicted math beyond school readiness. There were no relations involving anger. Findings demonstrate that temperament can potentiate the relations between school readiness and reading and highlight the importance of promoting school readiness and effortful control, while decreasing shyness.

Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches.

Developmental researchers often have research questions about cross-lag effects-the effect of one variable predicting a second variable at a subsequent time point. The cross-lag panel model (CLPM) is often fit to longitudinal panel data to examine cross-lag effects; however, its utility has recently been called into question because of its inability to distinguish between-person effects from within-person effects. This has led to alternative forms of the CLPM to be proposed to address these limitations, including the random-intercept CLPM and the latent curve model with structured residuals. We describe these models focusing on the interpretation of their model parameters, and apply them to examine cross-lag associations between reading and mathematics. The results from the various models suggest reading and mathematics are reciprocally related; however, the strength of these lagged associations was model dependent. We highlight the strengths and limitations of each approach and make recommendations regarding modeling choice.

Effortful Control Moderates the Relation Between Electronic-Media Use and Objective Sleep Indicators in Childhood.

Electronic-media use is associated with sleep disruptions in childhood and adolescence, although research relies primarily on subjective sleep. Effortful control, a dimension of self-regulation, may mitigate this association by helping children disengage from and regulate responses to media. We examined associations between media use and multiple actigraph-measured sleep parameters at mean and day levels and tested children's effortful control as a moderator of mean-level relations. We collected actigraph data and parents' diary reports of children's prebedtime television, video-game, laptop, desktop, cell-phone, and tablet use in 547 twin children (7-9 years old; 51.74% female). Mean-level media use was associated with bedtime and sleep duration. For the proportion of nights on which twins used media, but not the average number of media types, effortful control attenuated associations between media use and reduced sleep duration and efficiency. Day-level media use was related only to bedtime. Findings replicate and extend existing research and highlight self-regulation as a potential protective factor.

Reducing cultural mismatch: Latino students' neuroendocrine and affective stress responses following cultural diversity and inclusion reminder.

Cultural mismatch theory suggests that a poor fit between the cultural values endorsed by individuals and the institutions to which they belong results in emotional distress and activation of physiological stress processes, particularly for underrepresented groups. To test a novel paradigm for reducing perceptions of this cultural mismatch, the current experiment evaluated whether reminding first-year Latino university students (N = 84; Mage = 18.56; SD = 0.35; 63.1% female; 85.7% Mexican descent; 65.5% first-generation college students) about institutional support for cultural diversity and inclusion would reduce neuroendocrine and affective responses to psychosocial stress. Prior to completing a modified version of the Trier Social Stress Test, participants were randomly assigned to view either a video conveying university commitment to cultural diversity and inclusion (n = 45) or a control video (n = 39) depicting a campus tour. Five saliva samples assayed for cortisol and corresponding negative affect measures were collected to assess stress reactivity and recovery patterns (pre-task baseline, post-task +30 min, +45 min, +60 min, +75 min). Repeated measures data were analyzed using bilinear spline growth models. Viewing the culture video (compared to control) significantly reduced cortisol reactivity to the TSST and post-task negative affect levels, specifically for students endorsing higher Latino cultural values (e.g., familism, respect). Post-task cortisol levels were also reduced for students endorsing higher U.S. mainstream cultural values (e.g., self-reliance, competition). Results provide novel evidence for cultural diversity in stress responsivity and individual variation in approaches to reduce perceived cultural mismatch.

Changes in ventromedial prefrontal and insular cortex support the development of metamemory from childhood into adolescence.

Metamemory monitoring, or the ability to introspect on the accuracy of one's memories, improves considerably during childhood, but the underlying neural changes and implications for intellectual development are largely unknown. The present study examined whether cortical changes in key brain areas hypothesized to support metacognition contribute to the development of metamemory monitoring from late childhood into early adolescence. Metamemory monitoring was assessed among 7- to 12-y-old children (n = 145) and adults (n = 31). Children returned for up to two additional assessments at 8 to 14 y of age (n = 120) and at 9 to 15 y of age (n = 107) (n = 347 longitudinal scans). Results showed that metamemory monitoring continues to improve from childhood into adolescence. More pronounced cortical thinning in the anterior insula and a greater increase in the thickness of the ventromedial prefrontal cortex over the three assessment points predicted these improvements. Thus, performance benefits are linked to the unique patterns of regional cortical change during development. Metamemory monitoring at the first time point predicted intelligence at the third time point and vice versa, suggesting parallel development of these abilities and their reciprocal influence. Together, these results provide insights into the neuroanatomical correlates supporting the development of the capacity to self-reflect, and highlight the role of this capacity for general intellectual development.

Estimating New Quantities from Longitudinal Test Scores to Improve Forecasts of Future Performance.

Psychometric models for longitudinal test scores typically estimate quantities associated with single-administration tests, like ability at each time-point. However, models for longitudinal tests have not considered opportunities to estimate new quantities that are unavailable from single-administration tests. Specifically, we discuss dynamic measurement models - which combine aspects of longitudinal IRT, nonlinear growth models, and dynamic assessment - to directly estimate capacity, defined as the expected future score once the construct has fully developed. After discussing the history and connecting these areas into a single framework, we apply the model to verbal test scores from the Intergenerational Studies, which follow 494 people from 3 to 72 years old. The goal is to predict adult verbal scores (Age ≥ 34) from adolescent scores (Age ≤ 20). We held-out the adult data for prediction and compared predictions from traditional longitudinal IRT ability scores and proposed dynamic measurement capacity scores from models fit to the adolescent data. Results showed that the R2 from capacity scores were 2.5 times larger than the R2 from longitudinal IRT ability scores (43% vs. 16%), providing some evidence that exploring new quantities available from longitudinal testing could be worthwhile when an interest in testing is forecasting future performance.

An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data.

Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study - Kindergarten Class of 1998-1999.

Distinguishing between-individual from within-individual predictors of gun carrying among Black and White males across adolescence.

Longitudinal studies have found that male adolescents who deal drugs, associate with delinquent peers, and engage in aggressive behavior are at increased risk for carrying a gun (between-individual risks). However, it is unclear whether changes in these risk factors help to explain fluctuations in youth gun carrying across adolescence (within-individual risks). The current study examined this issue using a community sample of 970 adolescent males (58% Black, 42% White) assessed annually from ages 14 to 18. Multilevel models examined the extent to which between-individual differences and within-individual changes in drug dealing, peer delinquency, aggressive behavior, and neighborhood disadvantage were associated with gun carrying across adolescence. Each of these predictors, except for disadvantage, exerted a between-individual and within-individual influence for Black youth. For White youth, drug dealing was significant on both levels, peer delinquency was a significant between-individual predictor, and aggression was a significant within-individual predictor. Neighborhood disadvantage did not significantly predict gun carrying in the model, on either the between- or within-individual level, for Black or White youth. These results stress the importance of examining race-specific predictors of gun carrying among Black and White adolescents and point to drug dealing as a robust predictor of gun carrying, at both the between-individual and within-individual levels for youth of either race. Efforts to prevent drug market involvement and reduce aggressive behaviors in adolescence may in turn prove useful for preventing firearm violence. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Girls' pubertal development is associated with white matter microstructure in late adolescence.

Patterns of pubertal maturation have been linked to vulnerability for emotion dysregulation disorders in girls, as well as white matter (WM) development, suggestive of a potential mechanism between pubertal maturation and emotional health. Because pubertal processes begin at varying ages (i.e., status, timing) and proceed at varying rates (i.e., tempo), identifying individual differences in the pubertal course associated with subsequent WM microstructure development may reveal clues about neurobiological mechanisms of girls' emotional well-being. In a prospective cohort study of 107 girls, we examined associations between pubertal status at age 9, pubertal timing and tempo from ages 9-15, and WM microstructure at age 19. Tract-based spatial statistics revealed that girls with more advanced pubertal status at age 9, specific to gonadal-related physical changes, had higher fractional anisotropy, and lower mean diffusivity (MD) and radial diffusivity in tracts relevant to cognitive control and emotion regulation (e.g., the superior longitudinal fasciculus, external capsule, and uncinate fasciculus). Additionally, girls with earlier pubertal timing showed lower MD in the left anterior cingulum bundle. Tempo was unrelated to WM measures. These findings implicate specific aspects of pubertal maturation in subsequent neural signatures, suggesting possible neuroendocrine mechanisms relevant to emotional development. Future work incorporating longitudinal neuroimaging in parallel with pubertal measures may contribute to the understanding of individual variation in pubertal course and WM development.

Latent Growth and Dynamic Structural Equation Models.

Latent growth models make up a class of methods to study within-person change-how it progresses, how it differs across individuals, what are its determinants, and what are its consequences. Latent growth methods have been applied in many domains to examine average and differential responses to interventions and treatments. In this review, we introduce the growth modeling approach to studying change by presenting different models of change and interpretations of their model parameters. We then apply these methods to examining sex differences in the development of binge drinking behavior through adolescence and into adulthood. Advances in growth modeling methods are then discussed and include inherently nonlinear growth models, derivative specification of growth models, and latent change score models to study stochastic change processes. We conclude with relevant design issues of longitudinal studies and considerations for the analysis of longitudinal data.

Trajectories of the Expression of Negative Emotion from Kindergarten to First Grade: Associations with Academic Outcomes.

We examined individual trajectories, across four time points, of children's (N = 301) expression of negative emotion in classroom settings and whether these trajectories predicted their observed school engagement, teacher-reported academic skills, and passage comprehension assessed with a standardized measure in first grade. In latent growth curve analyses, negative expressivity declined from kindergarten to first grade with significant individual differences in trajectories. Negative expressivity in kindergarten inversely predicted first grade school engagement and teacher-reported academic skills, and the slope of negative expressivity from kindergarten to first grade inversely predicted school engagement (e.g., increasing negative expressivity was associated with lower school engagement). In addition, we examined if prior academic functioning in kindergarten moderated the association between negative expressivity (level in kindergarten and change over time) and academic functioning in first grade. The slope of negative expressivity was negatively associated with first grade school engagement and passage comprehension for children who had lower kindergarten school engagement and passage comprehension, respectively, but was unrelated for those with higher academic functioning in kindergarten. That is, for children who had lower kindergarten school engagement and passage comprehension, greater declines in negative expressivity were associated with higher first grade school engagement and passage comprehension, respectively. The findings suggest that negative emotional expressivity in school is associated with academic outcomes in first grade and, in some cases, this association is more pronounced for children who had lower kindergarten academic functioning.

Recursive Partitioning with Nonlinear Models of Change.

In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node. We give an overview of recursive partitioning, nonlinear mixed-effects models for longitudinal data, describe nLRP, and illustrate its use with empirical data from the Early Childhood Longitudinal Study-Kindergarten Cohort.