Carrie R. Houts

Stress, Inflammation, and Yoga Practice

Objective: To address the mechanisms underlying hatha yoga’s potential stress-reduction benefits, we compared inflammatory and endocrine responses of novice and expert yoga practitioners before, during, and after a restorative hatha yoga session, as well as in two control conditions. Stressors before each of the three conditions provided data on the extent to which yoga speeded an individual’s physiological recovery. Methods: A total of 50 healthy women (mean age, 41.32 years; range, 30–65 years), 25 novices and 25 experts, were exposed to each of the conditions (yoga, movement control, and passive-video control) during three separate visits. Results: The yoga session boosted participants’ positive affect compared with the control conditions, but no overall differences in inflammatory or endocrine responses were unique to the yoga session. Importantly, even though novices and experts did not differ on key dimensions, including age, abdominal adiposity, and cardiorespiratory fitness, novices’ serum interleukin (IL)-6 levels were 41% higher than those of experts across sessions, and the odds of a novice having detectable C-reactive protein (CRP) were 4.75 times as high as that of an expert. Differences in stress responses between experts and novices provided one plausible mechanism for their divergent serum IL-6 data; experts produced less lipopolysaccharide-stimulated IL-6 in response to the stressor than novices, and IL-6 promotes CRP production. Conclusion: The ability to minimize inflammatory responses to stressful encounters influences the burden that stressors place on an individual. If yoga dampens or limits stress-related changes, then regular practice could have substantial health benefits. CRC = Clinical Research Center; CRP = C-reactive protein; HR = heart rate; hsCRP = high-sensitivity C-reactive protein; IL = interleukin; LPS = lipopolysaccharide; MASQ = Mood and Anxiety Symptom Questionnaire; PANAS = Positive and Negative Affect Scale; PBLs = peripheral blood leukocytes; sIL-6r = soluble IL-6 receptor; TEWL = transepidermal water loss; TNF = tumor necrosis factor; &OV0312;o2 max = maximum oxygen consumption.

The structure of the Autism Diagnostic Interview‐Revised: diagnostic and phenotypic implications

BACKGROUND Multivariate statistics can assist in refining the nosology and diagnosis of pervasive developmental disorders (PDD) and also contribute important information for genetic studies. The Autism Diagnostic Interview-Revised (ADI-R) is one of the most widely used assessment instruments in the field of PDD. The current study investigated its factor structure and convergence with measures of adaptive, language, and intellectual functioning. METHODS Analyses were conducted on 1,861 individuals with PDD between the ages of 4 and 18 years (mean = 8.3, SD = 3.2). ADI-R scores were submitted to confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Analyses were conducted according to verbal status (n = 1,329 verbal, n = 532 nonverbal) and separately for algorithm items only and for all items. ADI-R scores were correlated with scores on measures of adaptive, language, and intellectual functioning. RESULTS Several factor solutions were examined and compared. CFAs suggested that two- and three-factor solutions were similar, and slightly superior to a one-factor solution. EFAs and measures of internal consistency provided some support for a two-factor solution consisting of social and communication behaviors and restricted and repetitive behaviors. Measures of functioning were not associated with ADI-R domain scores in nonverbal children, but negatively correlated in verbal children. CONCLUSIONS Overall, data suggested that autism symptomatology can be explained statistically with a two-domain model. It also pointed to different symptoms susceptible to be helpful in linkage analyses. Implications of a two-factor model are discussed.

Attachment avoidance predicts inflammatory responses to marital conflict

Marital stress has been associated with immune dysregulation, including increased production of interleukin-6 (IL-6). Attachment style, ones expectations about the availability and responsiveness of others in intimate relationships, appears to influence physiological stress reactivity and thus could influence inflammatory responses to marital conflict. Thirty-five couples were invited for two 24-h admissions to a hospital research unit. The first visit included a structured social support interaction, while the second visit comprised the discussion of a marital disagreement. A mixed effect within-subject repeated measure model indicated that attachment avoidance significantly influenced IL-6 production during the conflict visit but not during the social support visit. Individuals with higher attachment avoidance had on average an 11% increase in total IL-6 production during the conflict visit as compared to the social support visit, while individuals with lower attachment avoidance had, on average, a 6% decrease in IL-6 production during the conflict visit as compared to the social support visit. Furthermore, greater attachment avoidance was associated with a higher frequency of negative behaviors and a lower frequency of positive behaviors during the marital interaction, providing a mechanism by which attachment avoidance may influence inflammatory responses to marital conflict. In sum, these results suggest that attachment avoidance modulates marital behavior and stress-induced immune dysregulation.

Deconstructing the PDD clinical phenotype: internal validity of the DSM‐IV

BACKGROUND Empirical studies of the structure of autism symptoms have challenged the three-domain model of impairment currently characterizing pervasive developmental disorders (PDD). The objective of this study was to assess the internal validity of the DSM as a conceptual model for describing PDD, while paying particular attention to certain subject characteristics. METHODS Parents and teachers completed a DSM-IV-referenced rating scale for 3- to 12-year-old clinic referrals with a PDD (n = 730). Ratings were submitted to confirmatory factor analysis and different models were assessed for fit. RESULTS Measures of fit indicated that the three-factor solution based on the DSM was superior to other models. Most indices of fit were acceptable, but showed room for improvement. Fit indices varied according to the rater (parent or teacher), childs age (preschool versus school aged), PDD subtype (autism, Aspergers, pervasive developmental disorder not otherwise specified (PDDNOS)), and IQ. CONCLUSIONS More research needs to be done before discarding current classification systems. Subject characteristics, modality of assessment, and procedural variations in statistical analyses impact conclusions about the structure of PDD symptoms.

Validity of DSM-IV syndromes in preschoolers with autism spectrum disorders

Behavior and emotional problems are often present in very young children with autism spectrum disorders (ASDs) but their nosology has been the object of scant empirical attention. The objective of this study was to assess the construct validity of select Diagnostic and Statistical Manual of Mental Disorders (DSM)—defined syndromes (ADHD, ODD, Mood disorder) in preschoolers with ASD (N = 229). Parents and teachers completed the Early Childhood Inventory—4, a behavior rating scale based on the DSM-IV, and ratings were submitted to confirmatory factor analysis. Results generally supported the DSM nosology in this population. There was some evidence that parent ratings were associated with better fit indices (e.g. RSMEA = .062) than teachers (e.g. RMSEA = .083). For both raters, fit indices appeared to improve when the ADHD factor was broken into its constituent parts. However, hyperactivity symptoms accounted for little unique additional variance in the model. Findings lend support to the DSM as a conceptual model for behavioral syndromes in preschoolers with ASDs and also reinforce the importance of source-specificity when considering psychiatric disorders in children with ASDs.

The Performance of Local Dependence Indices with Psychological Data

The violation of the assumption of local independence when applying item response theory (IRT) models has been shown to have a negative impact on all estimates obtained from the given model. Numerous indices and statistics have been proposed to aid analysts in the detection of local dependence (LD). A Monte Carlo study was conducted to evaluate the relative performance of selected LD measures in conditions considered typical of studies collecting psychological assessment data. Both the Jackknife Slope Index and likelihood ratio statistic G2 are available across the two IRT models used and displayed adequate to good performance in most simulation conditions. The use of these indices together is the final recommendation for applied analysts. Future research areas are discussed.

Development of the Contact Lens User Experience: CLUE Scales.

Purpose The field of optometry has become increasingly interested in patient-reported outcomes, reflecting a common trend occurring across the spectrum of healthcare. This article reviews the development of the Contact Lens User Experience: CLUE system designed to assess patient evaluations of contact lenses. CLUE was built using modern psychometric methods such as factor analysis and item response theory. Methods The qualitative process through which relevant domains were identified is outlined as well as the process of creating initial item banks. Psychometric analyses were conducted on the initial item banks and refinements were made to the domains and items. Following this data-driven refinement phase, a second round of data was collected to further refine the items and obtain final item response theory item parameters estimates. Results Extensive qualitative work identified three key areas patients consider important when describing their experience with contact lenses. Based on item content and psychometric dimensionality assessments, the developing CLUE instruments were ultimately focused around four domains: comfort, vision, handling, and packaging. Item response theory parameters were estimated for the CLUE item banks (377 items), and the resulting scales were found to provide precise and reliable assignment of scores detailing users’ subjective experiences with contact lenses. Conclusions The CLUE family of instruments, as it currently exists, exhibits excellent psychometric properties.

A review of empirical research related to the use of small quantitative samples in clinical outcome scale development

IntroductionThere has been a notable increase in the advocacy of using small-sample designs as an initial quantitative assessment of item and scale performance during the scale development process. This is particularly true in the development of clinical outcome assessments (COAs), where Rasch analysis has been advanced as an appropriate statistical tool for evaluating the developing COAs using a small sample.MethodsWe review the benefits such methods are purported to offer from both a practical and statistical standpoint and detail several problematic areas, including both practical and statistical theory concerns, with respect to the use of quantitative methods, including Rasch-consistent methods, with small samples.ConclusionsThe feasibility of obtaining accurate information and the potential negative impacts of misusing large-sample statistical methods with small samples during COA development are discussed.

Measurement invariance, the lack thereof, and modeling change

PurposeMeasurement invariance issues should be considered during test construction. In this paper, we provide a conceptual overview of measurement invariance and describe how the concept is implemented in several different statistical approaches. Typical applications look for invariance over things such as mode of administration (paper and pencil vs. computer based), language/translation, age, time, and gender, to cite just a few examples. To the extent that the relationships between items and constructs are stable/invariant, we can be more confident in score interpretations.MethodsA series of simulated examples are reported which highlight different kinds of non-invariance, the impact it can have, and the effect of appropriately modeling a lack of invariance. One example focuses on the longitudinal context, where measurement invariance is critical to understanding trends over time. Software syntax is provided to help researchers apply these models with their own data.ResultsThe simulation studies demonstrate the negative impact an erroneous assumption of invariance may have on scores and substantive conclusions drawn from naively analyzing those scores.ConclusionsMeasurement invariance implies that the links between the items and the construct of interest are invariant over some domain, grouping, or classification. Examining a new or existing test for measurement invariance should be part of any test construction/implementation plan. In addition to reviewing implications of the simulation study results, we also provide a discussion of the limitations of current approaches and areas in need of additional research.

Scale development with small samples: a new application of longitudinal item response theory

PurposeMeasurement development in hard-to-reach populations can pose methodological challenges. Item response theory (IRT) is a useful statistical tool, but often requires large samples. We describe the use of longitudinal IRT models as a pragmatic approach to instrument development when large samples are not feasible.MethodsThe statistical foundations and practical benefits of longitudinal IRT models are briefly described. Results from a simulation study are reported to demonstrate the model’s ability to recover the generating measurement structure and parameters using a range of sample sizes, number of items, and number of time points. An example using early-phase clinical trial data in a rare condition demonstrates these methods in practice.ResultsSimulation study results demonstrate that the longitudinal IRT model’s ability to recover the generating parameters rests largely on the interaction between sample size and the number of time points. Overall, the model performs well even in small samples provided a sufficient number of time points are available. The clinical trial data example demonstrates that by using conditional, longitudinal IRT models researchers can obtain stable estimates of psychometric characteristics from samples typically considered too small for rigorous psychometric modeling.ConclusionCapitalizing on repeated measurements, it is possible to estimate psychometric characteristics for an assessment even when sample size is small. This allows researchers to optimize study designs and have increased confidence in subsequent comparisons using scores obtained from such models. While there are limitations and caveats to consider when using these models, longitudinal IRT modeling may be especially beneficial when developing measures for rare conditions and diseases in difficult-to-reach populations.