Factor Structure and Psychometric Properties of Cognitive-Behavioral Scales in Caregivers of Persons with Dementia

Background/Purpose Caregiving can be costly to dementia caregivers\u27 well-being. Assessing the factor structure and psychometric properties of Cognitive-Behavioral Scales in dementia caregivers is an essential step in addressing the gap in the current state of research. Specifically, it is essential to determine first whether the factorial structure of the three measures used in this study namely, the Positive Thinking Skills Scale, the Revised Memory and Behavior Problems Checklist, and the Zarit Burden Interview are good representation of the data by studying the good model fit. Next, evaluating the reliability of each factor of the three measures used are essential to learn about the precision of the factors. Lastly, it is vital to study the factor correlation and its relevance to the theory used to determine the validity of the factors. Methods A descriptive, correlational, cross-sectional design in a convenience sample of 100 caregivers. Results Results indicated that the factorial structure of the three scales is a good representation of the data; an acceptable reliability of each factor of the three measures; and the factors correlated as expected and showed their relevance to the underlying theory. Conclusions Future studies might consider studying the mediating/moderating effects of positive thinking on care-recipients challenging behavior problems. The findings can be used as a guide to provide a positive thinking training intervention among caregivers

Developing Student, Family, and School Constructs From NLTS2 Data

The purpose of this study was to use data from the National Longitudinal Transition Study–2 (NLTS2) to (a) conceptually identify and empirically establish student, family, and school constructs; (b) explore the degree to which the constructs can be measured equivalently across disability groups; and (c) examine latent differences (means, variances, and correlations) in the constructs across disability groups. Conceptual analysis of NLTS2 individual survey items yielded 21 student, family, and school constructs, and 16 were empirically supported. Partial strong metric invariance was established across disability groups, and in the latent space, a complex pattern of mean and variance differences across disability groups was found. Disability group moderated the correlational relationships between multiple predictor constructs, suggesting the key role of disability-related characteristics in understanding the experiences of youth with disabilities. Implications for future research and practice are discussed

The Positive Thinking Skills Scale: A Screening Measure for Early Identification of Depressive Thoughts

Background Depression is currently considered the second leading cause of disability worldwide. Positive thinking is a cognitive process that helps individuals to deal with problems more effectively, and has been suggested as a useful strategy for coping with adversity, including depression. The Positive Thinking Skills Scale (PTSS) is a reliable and valid measure that captures the frequency of use of positive thinking skills that can help in the early identification of the possibility of developing depressive thoughts. However, no meaningful cutoff score has been established for the PTSS. Aim To establish a cutoff score for the PTSS for early identification of risk for depression. Methods This study used a receiver operating characteristic (ROC) curve to establish a PTSS cutoff score for risk for depression, using the Center for Epidemiological Studies–Depression Scale (CES-D) as the gold standard measure. Results In a sample of 109 caregivers, the ROC showed that the cutoff score of PTSS that best classify the participants is 13.5. With this PTSS score, 77.8% of the subjects with low CES-D are classify correctly, and 69.6% of the subjects with high CES-D are classify correctly. Since the PTSS score should be integer numbers, functionally the cutoff would be 13. Conclusion The study showed that a cut off score of 13 is a point at which referral, intervention, or treatment would be recommended. Consequently, this can help in the early identification of depressive symptoms that might develop because of the stress of caregiving

Effects of Positive Thinking on Dementia Caregivers’ Burden and Care-Recipients’ Behavioral Problems

Most dementia care is provided at home by family members. This caregiving places an additional burden on the family members, which can negatively impact their physical and psychological well-being. The caregivers’ burden can also contribute to behavioral problems in the care-recipients. The purpose of this study was to examine the mediating/moderating effects of positive thinking (PT) on the relationship between caregivers’ burden (embarrassment/anger, patient’s dependency, and self-criticism) and their care-recipients’ behavioral problems (memory, depression, and disruption) in a sample of 100 dementia caregivers. Results indicated that caregivers’ embarrassment, self-criticism, and perception of patient dependency predicts depression in care-recipients, and these relationships are moderated by PT. Results also indicated that as PT increases, the relationship between embarrassment and disruption goes down as well as does the relationship between self-criticism and depression. The study provided direction for the development of a PT training intervention to help caregivers to combat their burden

TWO-METHOD PLANNED MISSING DESIGNS FOR LONGITUDINAL RESEARCH

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a "gold standard" that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based) measure that contains systematic measurement bias (e.g., response bias). Using simulated data on 4 measurement occasions, we compared the cost-efficiency and validity of longitudinal designs where the gold standard is measured at one or more measurement occasions. We manipulated the nature of the response bias over time (constant, increasing, fluctuating), the factorial structure of the response bias over time, and the constraints placed on the latent variable model. Our results showed that parameter bias is lowest when the gold standard is measured on at least two occasions. When a multifactorial structure was used to model response bias over time, estimation difficulties were common. Almost all parameters in all conditions displayed high relative efficiency, suggesting that the 2-method design is an effective way to reduce costs and improve power and accuracy in longitudinal research

Two-Method Planned Missing Designs for Longitudinal Research

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a “gold standard” that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based) measure that contains systematic measurement bias (e.g., response bias). Using simulated data on four measurement occasions, we compared the cost-efficiency and validity of longitudinal designs where the gold standard is measured at one or more measurement occasions. We manipulated the nature of the response bias over time (constant, increasing, fluctuating), the factorial structure of the response bias over time, and the constraints placed on the latent variable model. Our results showed that parameter bias is lowest when the gold standard is measured on at least two occasions. When a multifactorial structure was used to model response bias over time, it is necessary to have the “gold standard” measures included at every time point, in which case most of the parameters showed low bias. Almost all parameters in all conditions displayed high relative efficiency, suggesting that the 2-method design is an effective way to reduce costs and improve both power and accuracy in longitudinal research

Effectiveness of CenteringPregnancy on Breast-Feeding Initiation Among African Americans: A Systematic Review and Meta-analysis

While breastfeeding initiation rates for African American mothers are low, an innovative model of group prenatal care, CenteringPregnancy, holds promise to increase breastfeeding rates. The aim of this systematic review and meta-analysis was to examine the effects of CenteringPregnancy versus individual prenatal care on breastfeeding initiation among African American mothers. Using a systematic approach and PRISMA guidelines, 4 electronic databases were used to search the literature. English-language studies, comparing CenteringPregnancy and individual prenatal care, including African American participants, and specifying breastfeeding initiation as an outcome were screened for inclusion. Study strength and quality were assessed and 7 studies were systematically reviewed and meta-analyzed. Participation in CenteringPregnancy increased the probability of breastfeeding initiation by 53% (95% confidence interval = 29%-81%) (n = 8047). A subgroup analysis of breastfeeding initiationamong only African American participants was performed on 4 studies where data were available. Participation in CenteringPregnancy increased the probability of breastfeeding initiation by 71% (95% confidence interval = 27%-131%) (n = 1458) for African American participants. CenteringPregnancy is an effective intervention to increase breastfeeding initiation for participants, especially for African Americans. To close the racial gap in breastfeeding initiation, high-quality research providing specific outcomes for African American participants in CenteringPregnancy are needed

The impact of maternal BMI, gestational weight gain, and breastfeeding on early childhood weight: Analysis of a statewide WIC dataset

Early childhood obesity is a persistent health concern with more frequent and significant impact on low-income families. Maternal weight factors impact offspring weight status, but evidence on whether breastfeeding protects against this impact is mixed. This analysis examined a model to predict early childhood obesity risk, simultaneously accounting for maternal pre-pregnancy body mass index (BMI), gestational weight gain, and breastfeeding. The team analyzed 27,016 unique maternal-child dyadic records collected via the Supplemental Nutrition Program for Wisconsin Women, Infants, and Children (WIC) between 2009 and 2011. Generalized Linear Modeling, specifically logistic regression, was used to predict a child\u27s risk of obesity given the mother\u27s pre-pregnancy BMI, gestational weight gain, and duration of breastfeeding. For each 1 kg/m2 increase in pre-pregnancy BMI, there was a 4.5% increase in risk of obesity compared to children with mothers of normal BMI. Children whose mothers had excessive gestational weight gain were 50% more likely to have obesity compared to those whose mothers had ideal weight gain. For each week of additional breastfeeding, there was a 1.9% increased risk of obesity. The risk models did not differ by race. In this model, accounting for pre-pregnancy weight, gestational weight gain, and breastfeeding among a diverse, low-income sample, women with pre-pregnancy overweight and obesity or who had excessive gestational weight gain had the highest risk of early childhood obesity. While breastfeeding is healthy for many reasons, providers should focus on maternal weight-related behaviors when counseling mothers about how to avoid risk of early childhood obesity

Intra and Interindividual Variation Modeling: Bayesian Mixed-Effects Nonstationary Latent Differential Equation Model

Longitudinal analysis are powerful methods to estimate change over time. The combination of nomothetic and idiographic approaches within longitudinal analysis would allow to answer questions related to intra and interindividual variability in one integrated method. In order to have lag independent results, longitudinal analysis should be made with a continuous-time method. Continuous-time methods take into account that the phenomena of interest does not stop existing between measurement time points. Differential equations modeling is a method that studies intraindividual variability as a form of continuous-time modeling, which can be implemented as fixed-effects or mixed-effects. Mixed-effects models allows to integrate interindividual variability, and properly estimate non-dependent data. Latent Differential Equation (LDE) model is a method to estimate differential equation models from a familiar framework in psychology (Structural Equation Modeling). This dissertation tend to extend the LDE by adding the mixed-effects, estimating subject and sample parameters, including interindividual variability on parameters of interest. The analyses were implemented from the Bayesian framework, this framework provides several advantages, one of them being that allow us to make direct inference of the estimated parameters. The proposed model (Bayesian Mixed-Effects Nonstationary Latent Differential Equation Model, BMNLDE) was tested in a simulation, and exemplify with a real data describing the sedentary behavior in older adults. The simulation shows that the BMNLDE model estimate parameters with low bias, the 95\% Credible Interval coverage is unreliable when the model presents low frequency and high damping. The frequency of the oscillating processes was the main factor that affected bias and CI coverage. The BMNLDE model showed to be an appropriate method to include intra and interindividual variability. The simulation was capable to demonstrate the conditions in which the model performs as desire, and under which conditions the model does not perform as desire. The real data example shows an application of the BMNLDE model, were the BMNLDE model describes the oscillating behavior of the sedentary behavior. It also shows how it can used to compare parameters of interest between groups