An Exploratory Study of COVID-19’s Impact on Psychological Distress and Antisocial Behavior Among Justice-Involved Youth

Little is known about how justice-involved youth are coping with stress related to COVID-19. This study examined changes in psychological distress and antisocial behavior indicators among 557 youths on probation who completed two assessments during pre-COVID-19 conditions and two assessments during post-COVID-19 conditions. Drawing from Agnew’s General Strain Theory, the study used multivariate latent growth models to examine: (a) changes in psychological distress and antisocial behavior over time, (b) the associations of the changes, and (c) differences across sex, race, and ethnicity regarding changes in psychological distress. In support of General Strain Theory, results highlight that indicators of psychological distress and antisocial behavior increased during post-COVID-19 conditions when compared to pre-COVID-19 conditions for the full sample and for subsamples of youth categorized by sex, race, and ethnicity.

Exploring the Bidirectional Associations Between Short or Long Sleep Duration and Lower Cognitive Function: A 7-Year Cohort Study in China

Background: Sleep duration is linked to cognitive function, but whether short or prolonged sleep duration results from impaired cognition or vice versa has been controversial in previous studies. We aimed to investigate the bidirectional association between sleep duration and cognitive function in older Chinese participants.Methods: Data were obtained from a nationally representative study conducted in China. A total of 7984 participants aged 45 years or older were assessed at baseline between June 2011 and March 2012 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4). Nocturnal sleep duration was evaluated using interviews. Cognitive function was examined via assessments of global cognition, including episodic memory, visuospatial construction, calculation, orientation and attention capacity. Latent growth models and cross-lagged models were used to assess the bidirectional association between sleep duration and cognitive function.Results: Among the 7,984 participants who were followed in the four waves of the study, the baseline mean (SD) age was 64.7 (8.4) years, 3862 (48.4%) were male, and 6453 (80.7%) lived in rural areas. Latent growth models showed that both sleep duration and global cognition worsened over time. Cross-lagged models indicated that short or long sleep duration in the previous wave was associated with lower global cognition in the subsequent wave (standardized β = −0.066; 95% CI: −0.073, −0.059; P < 0.001; Wave 1 to 2) and that lower global cognition in the previous wave was associated with short or long sleep duration in the subsequent wave (standardized β = −0.106; 95% CI: −0.116, −0.096; P < 0.001; Wave 1 to 2).Conclusion: There was a bidirectional association between sleep duration and cognitive function, with lower cognitive function having a stronger association with long or short sleep duration than the reverse relationship. Global cognition was likely the major driver in these reciprocal associations.

Vertex-wise characterization of Non-Human Primate cortical development with prenatal insights

Characterization of the interwoven complexities of early cortical thickness development has been an ongoing undertaking in neuroscience research. Longitudinal studies of Non-Human Primates (NHP) offer unique advantages to categorizing the diverse patterns of cortical growth trajectories. Here, we used latent growth models to characterize the trajectories of typical cortical thickness development in Japanese macaques at each cortical surface vertex (i.e. grayordinate). Cortical thickness from 4 to 36 months showed regional specific linear and non-linear trajectories and distinct maturation timing across the cortex. Intriguingly, we revealed a “accumulation/ablation phenomenon” of cortical maturation where the most profound development changes in cortical thickness occur in the accumulation or ablation zones surrounding the focal points (i.e., a center of a delineated regions where cortical thickness is thickest or thinnest) throughout the brain. We further examined maternal diet and inflammation in the context of these typical brain trajectories and known network architecture. A well-controlled NHP model of a maternal “Western-style” diet was used alongside measures of inflammatory cytokine interleukin-6 (IL-6) in the mothers during gestation. We observed that these accumulation and ablation zones of variable change might be most susceptible to environmental effects. The maternal factors, diet and inflammation during pregnancy were distinctively associated with different aspects of offspring cortical development reflected in regions related to distinctive functional networks. Our findings characterize the versatile intricacies of typical cortical thickness development and highlight how the maternal environment plays a role in offspring cortical development.

Solutions for latent growth modeling following COVID-19-related discontinuities in change and disruptions in longitudinal data collection

Following the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, daily life significantly changed for the population. Accordingly, researchers interested in examining patterns of change over time may now face discontinuities around the pandemic. Researchers collecting in-person longitudinal data also had to cancel or delay data collection waves, further complicating analyses. Accordingly, the purpose of this article is to aid researchers aiming to examine latent growth models (LGM) in analyzing their data following COVID-19. An overview of basic LGM notions, LGMs with discontinuities, and solutions for studies that had to cancel or delay data collection waves are discussed and exemplified using simulated data. Syntax for R and Mplus is available to readers in online supplemental materials.

Study length, change process separability, parameter estimation, and model evaluation in hybrid autoregressive-latent growth structural equation models for longitudinal data

Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates to a degree. This study thus used Monte Carlo simulation methods to examine associations between change process separability, the number of time points in a model, and the consequences of misspecification, across three prominent hybrid autoregressive-latent growth models: the Latent Change Score model (LCS), the Autoregressive Latent Trajectory Model (ALT), and the Latent Growth Model with Structured Residuals (LGM-SR). Results showed that including more time points increased process separability and robustness to misspecification in the LCS and ALT, but typically not at a rate that would be practically feasible for most developmental researchers. Alternatively, regardless of how many time points were in the model process separability was high in the LGM-SR, as was robustness to misspecification. Overall, results suggest that the LGM-SR is the most effective of the three hybrid autoregressive-latent growth models considered here.

Evaluating Eight-Year Trajectories for Sense of Purpose in the Health and Retirement Study

Objectives: Though cross-sectional research has suggested that sense of purpose declines intoolder adulthood, it remains unclear whether inter-individual variability occurs in these trajectories, and what factors predict these trajectories. The current study provides one of the first longitudinal investigations into how individuals’ sense of purpose fluctuates in older adulthood. Method: Participants from the Health and Retirement Study (n = 4,234, mean age = 65 years), completed assessments of sense of purpose over three years, along with multiple potential predictors (health, personality, demographics) at the start.Results: Second-order latent growth models demonstrated both mean-level declines on purpose over time, as well as the capacity for inter-individual variability in change patterns for retired adults. Among this cohort, health status, educational attainment, and marital status were significant predictors of purpose trajectories over time, though broad personality trait dimensions failed to uniquely predict change in sense of purpose. However, measurement invariance tests suggest that the scale did not operate similarly across work status groups.Conclusion: Findings advance the previous literature by demonstrating inter-individual variability in sense of purpose for those participants who had retired. Future research should consider that purpose inventories may operate differently for those in the workplace versus retired adults.

Measurement Reactivity of Accelerometer-Based Sedentary Behavior and Physical Activity in 2 Assessment Periods

Background: The purposes of this study were to examine accelerometer measurement reactivity (AMR) in sedentary behavior (SB), physical activity (PA), and accelerometer wear time in 2 measurement periods and to quantify AMR as a human-related source of bias for the reproducibility of SB and PA estimates. Methods: In total, 136 participants (65% women, mean age = 54.6 y) received 7-day accelerometry at the baseline and after 12 months. Latent growth models were used to identify AMR. Intraclass correlations were calculated to examine the reproducibility using 2-level mixed-effects linear regression analyses. Results: Within each 7-day accelerometry assessment, the participants increased their time spent in SB (b = 2.4 min/d; b = 3.8 min/d) and reduced their time spent in light PA (b = −2.0 min/d; b = −3.2 min/d), but did not change moderate to vigorous PA. The participants reduced their wear time (b = −5.2 min/d) only at the baseline. The intraclass correlations ranged from .42 for accelerometer wear time to .74 for SB. The AMR was not identified as a source of bias in any regression model. Conclusions: AMR may influence SB and PA estimates differentially. Although 7-day accelerometry seems to be a reproducible measure, our findings highlight accelerometer wear time as a crucial confounder in analyzing SB and PA data.

Fitting latent growth models with small sample sizes and non-normal missing data

This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML estimators, “MLR” was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates and growth parameter coverage. However, the choice “MLMV” produced the most accurate p values for the χ2 test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (i.e., comparative fit index, root mean square error of approximation, and standardized root mean square residual) exhibited worse fit. When the sample size was very small (e.g., N < 60), the fit indices would imply that a proposed model fit poorly, when this might not be actually the case in the population.