A 2-year prospective analysis of insomnia as a mediator of the relationship between androgen deprivation therapy and perceived cognitive function in men with prostate cancer.

BACKGROUND: Androgen deprivation therapy (ADT) may affect cognitive function in men with prostate cancer (PCa). This study examined whether insomnia symptoms mediate the relationship between ADT and perceived cognitive function and whether depressive symptoms, fatigue severity, and physical activity moderate the strength of this relationship. METHODS: This was a prospective study of ADT recipients (n = 83) who were matched with control patients with PCa who were not on ADT (n = 92) and with controls with no history of cancer (n = 112) over a 2-year follow-up period. Perceived cognitive function and satisfaction were assessed with the Everyday Cognition Scale. Insomnia was assessed with the Insomnia Severity Index. Multilevel mediation analyses were conducted to estimate the indirect effect of ADT on perceived cognitive function through insomnia symptoms. Exploratory moderated mediation analyses assessed whether the indirect effect of ADT on perceived cognitive function through insomnia symptoms was dependent on levels of fatigue, depression, or physical activity. RESULTS: Insomnia symptoms significantly mediated the relationship between receipt of ADT and perceived cognitive function (P < .001) and satisfaction with cognition (P < .001) after controlling for comorbidities. Men with greater fatigue had a more pronounced association of ADT with insomnia severity. Men with greater depressive symptoms had a stronger association between insomnia severity and worse perceived cognitive function. Physical activity was not a significant moderator of the relationship between ADT and perceived cognitive function. CONCLUSIONS: Insomnia influenced the relationship between ADT and perceived cognitive abilities. Interventions to address insomnia, fatigue, and depression may improve perceived cognitive function.

Etiological Distinction Across Dimensions of Math Anxiety.

Analyses have suggested math anxiety is a multidimensional construct. However, previous behavioral genetic work examining math anxiety was unidimensional. Thus, the purpose of the present study was to examine different approaches for specifying behavioral genetic models of math anxiety as a multidimensional construct. Three models were compared: a unidimensional model, a three dimension multidimensional model, and a bi-factor model, which partitioned variance into one common factor shared across three dimensions of math anxiety and examined residual variance in each dimension. The best fitting model was a bi-factor AE model, which suggested moderate heritability of general math anxiety and that each dimension of math anxiety had unique etiological influences not accounted for by shared variance with the general math anxiety factor. Thus, while there was evidence of shared etiology, there was also evidence of some etiological distinction across dimensions of math anxiety. The results demonstrate the importance of taking into account the dimensionality of the scale when interpreting similarity across twins.

Estimating Complex Measurement and Growth Models Using the R Package PLmixed.

Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package PLmixed, which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package lme4 and function optim. We demonstrate the use of PLmixed through two examples before concluding with a brief overview of other possible models.

Exploring Bidirectional Relationships: Child Sleep Duration, Child Behavior Problems, and Parenting Stress in Families of Children with Autism Spectrum Disorder.

Background: Youth with Autism Spectrum Disorder (ASD) are at-risk for sleep and behavior problems, and their parents are at-risk for high stress. Child sleep duration, behavior problems, and parenting stress are interrelated; however, directionality of these associations is unclear and research including youth with ASD is lacking. Using a day-to-day, within-person design, this study explores the directionality of these relationships in families of children with ASD. Method: Twenty-six children (ages 3-5, 73.1% male, 65.4% Hispanic/Latino) with ASD and their mothers participated in a 14-day study. Child sleep duration (parent-report and actigraphy), behavior problems, and parenting stress were measured daily. Constructs were decomposed into their within- and between-person components and analyzed with random intercept cross-lagged panel models. Results: While between-person relationships were directionally expected in that shorter sleep, more behavior problems, and greater parenting stress were associated, within-person relationships were complicated. Better-than-average child behavior was associated with less next-day parenting stress, yet more parenting stress than average was associated with better next-day child behavior. As expected, longer-than-average child sleep was associated with less next-day parenting stress, while greater child behavior problems were associated with less sleep that night. Conclusions: Understanding the directionality of associations between child and parent factors allows for the optimization of interventions to improve the quality of life for families of children with ASD. Interventions that target child behavior and/or help parents manage stress while maintaining effective parenting strategies for sleep and behavior may be useful.

Psychological Pathways to Paranoia and Psychotic-Like Experiences in Daily-Life: The Mediating Role of Distinct Affective Disturbances.

BACKGROUND AND HYPOTHESIS: Influential models of psychosis indicate that the impact of putative causal factors on positive symptoms might be explained partly through affective disturbances. We aimed to investigate whether pathways from stress and self-esteem to positive symptoms, as well as reversal pathways from symptoms to stress and self-esteem, were mediated through specific affective disturbances across the extended psychosis phenotype. STUDY DESIGN: Using experience sampling methodology, 178 participants (65 high-schizotypy, 74 at-risk mental state, and 39 first-episode psychosis) were assessed on levels of momentary stress, self-esteem, anxiety, sadness, psychotic-like experiences (PLE), and paranoia. Multilevel mediation models were fit to examine indirect effects of each of these pathways. Considering evidence of mediation, each indirect pathway will be combined in a single model to explore their relative contributions. STUDY RESULTS: Anxiety, sadness, and self-esteem mediated the pathways from stress to PLE and paranoia in daily-life. In the pathway to paranoia, sadness, and self-esteem showed larger contributions than anxiety. Pathways from self-esteem to PLE and paranoia were mediated by anxiety and sadness, the later showing a larger contribution. Pathways from symptoms to stress, but not from symptoms to self-esteem, were differently explained by emotional states; sadness lost its mediating effect and anxiety was the most important mediator. Few differences across groups were found. CONCLUSIONS: This study lends support to psychological models of psychosis that highlight the relevance of affective disturbances in the risk and expression of psychosis. Furthermore, specific influences of different negative emotional states were identified, which could enhance psychological treatments.

Evaluating the Full Plate Living lifestyle intervention in low-income monolingual Latinas with and without food insecurity.

INTRODUCTION: Food insecurity has long been associated with poor physical and mental health, especially among women from underrepresented minorities. Despite efforts to reduce food insecurity, rates continue to rise and remain disproportionately high among Latinx living in the United States, a group reporting worse mental health symptoms than any other ethnic group during the COVID-19 pandemic. The need to reduce the health burden associated with food insecurity among Latinas is urgent and requires a more targeted and innovative approach. Interventions using a popular education approach have proven effective among underserved populations, especially when these are delivered by community health workers. However, food insecurity status of the participants is often unreported and it is not clear whether or not results vary between those with and without food insecurity. OBJECTIVES: The aim of this quasi-experimental study was to examine physical and mental health changes among Latinas with, and without, food insecurity following a multicomponent health intervention led by community health workers using a popular education approach. METHODS: Enrolled obese Latinas (N = 98) with and without food insecurity responded to demographic, health behaviors and mental health surveys and completed biometric measurements at baseline, immediately following the intervention and at 3 months. RESULTS: At baseline, participants with food insecurity reported more anxiety and depression than those without, but average body mass index was comparable. Depression, anxiety and body mass index were lower at 3 months post and no statistically significant differences were seen between the groups. Participants with food insecurity benefited as much from the intervention as those without.We found that, although community health workers are not licensed healthcare professionals, with proper training and support, they were able to successfully reduce the risk of chronic diseases and improve mental health symptoms among food-insecure Latinas. CONCLUSION: Given the promising results, similar interventions should be implemented on a larger scale in Latino communities among food insecure women. Long-term sustainability should also be explored.

Efficient Likelihood Estimation of Generalized Structural Equation Models with a Mix of Normal and Nonnormal Responses.

A maximum likelihood estimation routine is presented for a generalized structural equation model that permits a combination of response variables from various distributions (e.g., normal, Poisson, binomial, etc.). The likelihood function does not have a closed-form solution and so must be numerically approximated, which can be computationally demanding for models with several latent variables. However, the dimension of numerical integration can be reduced if one or more of the latent variables do not directly affect any nonnormal endogenous variables. The method is demonstrated using an empirical example, and the full estimation details, including first-order derivatives of the likelihood function, are provided.

Maximum Likelihood Estimation of Multilevel Structural Equation Models with Random Slopes for Latent Covariates.

A maximum likelihood estimation routine for two-level structural equation models with random slopes for latent covariates is presented. Because the likelihood function does not typically have a closed-form solution, numerical integration over the random effects is required. The routine relies upon a method proposed by du Toit and Cudeck (Psychometrika 74(1):65-82, 2009) for reformulating the likelihood function so that an often large subset of the random effects can be integrated analytically, reducing the computational burden of high-dimensional numerical integration. The method is demonstrated and assessed using a small-scale simulation study and an empirical example.

Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation.

There have been numerous treatments in the clinical research literature about various design, analysis, and interpretation considerations when testing hypotheses about mechanisms and contingencies of effects, popularly known as mediation and moderation analysis. In this paper we address the practice of mediation and moderation analysis using linear regression in the pages of Behaviour Research and Therapy and offer some observations and recommendations, debunk some popular myths, describe some new advances, and provide an example of mediation, moderation, and their integration as conditional process analysis using the PROCESS macro for SPSS and SAS. Our goal is to nudge clinical researchers away from historically significant but increasingly old school approaches toward modifications, revisions, and extensions that characterize more modern thinking about the analysis of the mechanisms and contingencies of effects.