The role of perceived negative partner behavior in daily snacking behavior: A dynamical systems approach.

Past work suggested that psychological stress, especially in the context of relationship stress, is associated with increased consumption of energy-dense food and when maintained for long periods of time, leads to adverse health consequences. Furthermore, this association is moderated by a variety of factors, including emotional over-eating style. That being said, few work utilized a dynamical system approach to understand the intraindividual and interindividual fluctuations within this process. The current study utilized a 14-day daily diary study, collected between January-March 2020, where participants reported their partner's negative relationship behavior and their own snacking behavior. A differential equation model was applied to the daily dairy data collected. Results showed that snacking behavior followed an undamped oscillator model while negative relationship behavior followed a damped coupled oscillator model. In other words, snacking behavior fluctuated around an equilibrium but was not coupled within dyadic partners. Negative relationship behavior fluctuated around an equilibrium and was amplified over time, coupled within dyadic partners. Furthermore, we found a two-fold association between negative relationship behavior and snacking: while the association between the displacement of negative relationship behavior and snacking was negative, change in negative relationship behavior and snacking were aligned. Thus, at any given time, one's snacking depends both on the amount of negative relationship behaviors one perceives and the dynamical state a dyad is engaging in (i.e., whether the negative relationship behavior is "exacerbating" or "resolving"). This former association was moderated by emotional over-eating style and the latter association was not. The current findings highlight the importance of examining dynamics within dyadic system and offers empirical and methodological insights for research in adult relationships.

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis.

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

Separating Long-Term Equilibrium Adaptation from Short-Term Self-Regulation Dynamics Using Latent Differential Equations.

Self-regulating systems change along different timescales. Within a given week, a depressed person's affect might oscillate around a low equilibrium point. However, when the timeframe is expanded to capture the year during which they onboarded antidepressant medication, their equilibrium and oscillatory patterns might reorganize around a higher affective point. To simultaneously account for the meaningful change processes that happen at different time scales in complex self-regulatory systems, we propose a single model that combines a second-order linear differential equation for short timescale regulation and a first-order linear differential equation for long timescale adaptation of equilibrium. This model allows for individual-level moderation of short-timescale model parameters. The model is tested in a simulation study which shows that, surprisingly, the short and long timescales can fully overlap and the model still converges to the reasonable estimates. Finally, an application of this model to self-regulation of emotional well-being in recent widows is presented and discussed.

Stability and spread: A novel method for quantifying transitions within multivariate binary time series data.

We present a novel method for quantifying transitions within multivariate binary time series data, using a sliding series of transition matrices, to derive metrics of stability and spread. We define stability as the trace of a transition matrix divided by the sum of all observed elements within that matrix. We define spread as the number of all non-zero cells in a transition matrix divided by the number of all possible cells in that matrix. We developed this method to allow investigation into high-dimensional, sparse data matrices for which existing binary time series methods are not designed. Results from 1728 simulations varying six parameters suggest that unique information is captured by both metrics, and that stability and spread values have a moderate inverse association. Further, simulations suggest that this method can be reliably applied to time series with as few as nine observations per person, where at least five consecutive observations construct each overlapping transition matrix, and at least four time series variables compose each transition matrix. A pre-registered application of this method using 4 weeks of ecological momentary assessment data (N = 110) showed that stability and spread in the use of 20 emotion regulation strategies predict next timepoint affect after accounting for affect and anxiety's auto-regressive and cross-lagged effects. Stability, but not spread, also predicted next timepoint anxiety. This method shows promise for meaningfully quantifying two unique aspects of switching behavior in multivariate binary time series data.

Nonverbal Synchrony: An Indicator of Clinical Communication Quality in Racially-Concordant and Racially-Discordant Oncology Interactions.

OBJECTIVES: The aim of this cross-sectional study was to apply a novel software to measure and compare levels of nonverbal synchrony, as a potential indicator of communication quality, in video recordings of racially-concordant and racially-discordant oncology interactions. Predictions include that the levels of nonverbal synchrony will be greater during racially-concordant interactions than racially-discordant interactions, and that levels of nonverbal synchrony will be associated with traditional measures of communication quality in both racially-concordant and racially-discordant interactions. DESIGN: This is a secondary observational analysis of video-recorded oncology treatment discussions collected from 2 previous studies. SETTING: Two National Cancer Institute-designated Comprehensive Cancer Centers and another large urban cancer center. PARTICIPANTS: Participants from Study 1 include 161 White patients with cancer and 11 White medical oncologists. Participants from Study 2 include 66 Black/African-American patients with cancer and 17 non-Black medical oncologists. In both studies inclusion criteria for patients was a recent cancer diagnosis; in Study 2 inclusion criteria was identifying as Black/African American. MAIN OUTCOME MEASURES: Nonverbal synchrony and communication quality. RESULTS: Greater levels of nonverbal synchrony were observed in racially-discordant interactions than in racially-concordant interactions. Levels of nonverbal synchrony were associated with indicators of communication quality, and these associations were more consistently found in racially-discordant interactions. CONCLUSION: This study advances clinical communication and disparities research by successfully applying a novel approach capturing the unconscious nature of communication, and revealing differences in communication in racially-discordant and racially-concordant oncology interactions. This study highlights the need for further exploration of nonverbal aspects relevant to patient-physician interactions.

How nonshared environmental factors come to correlate with heredity.

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

Low emotion differentiation: An affective correlate of binge eating?

BACKGROUND: Low emotion differentiation (the tendency to experience vague affective states rather than discrete emotions) is associated with psychopathology marked by emotion regulation deficits and impulsive/maladaptive behavior. However, research examining associations between emotion differentiation and dysregulated eating is nascent and has yet to incorporate measures of clinically significant binge eating. Different measures of emotion differentiation have also been used, impeding cross-study comparisons. We therefore examined associations between several emotion differentiation measures and binge eating-related phenotypes across a spectrum of severity. METHODS: Women (N = 482) from the Michigan State University Twin Registry completed the Positive and Negative Affect Schedule (PANAS) daily for 45 consecutive days. Three measures of negative/positive emotion differentiation (NED/PED) were created using the intraclass correlation coefficient (ICC), average interitem correlation, and average daily variance between negative/positive emotion ratings on the PANAS. Associations between NED/PED measures and emotional eating (EE) and a history of binge eating episodes (BEs) were then examined, controlling for affect intensity and BMI. RESULTS: Lower PED was associated with greater odds of BEs across the ICC and average interitem correlation measures, and more EE on the daily variance measure. Findings involving NED were less consistent; lower NED was associated with greater EE and greater odds of BEs using the daily variance measure only. CONCLUSION: Low PED is associated with clinically significant binge eating, and some aspects of NED may also be relevant for binge eating-related phenotypes. Further research examining the constructs captured by different emotion differentiation measures and their relevance to binge eating is needed.

Increased rates of eating disorders and their symptoms in women with major depressive disorder and anxiety disorders.

BACKGROUND: Individuals with eating disorders (EDs) have increased rates of major depressive disorder (MDD) and anxiety disorders. Yet, few studies have investigated rates of EDs and their symptoms in individuals presenting with MDD/anxiety disorders. Identifying potential disordered eating in people with MDD/anxiety disorders is important because even subclinical disordered eating is associated with reduced quality of life, and undiagnosed eating pathology may hinder treatment progress for both MDD/anxiety disorders and comorbid EDs. METHOD: We compared rates of EDs (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding and eating disorders) and their symptoms in 130 women with, and 405 women without, lifetime MDD or an anxiety disorder (generalized anxiety disorder, obsessive-compulsive disorder, social phobia, specific phobia, panic disorder, agoraphobia, or post-traumatic stress disorder) recruited from the population-based Michigan State University Twin Registry. Lifetime ED and MDD/anxiety diagnoses, and lifetime clinically significant disordered eating behaviors (e.g., binge eating, excessive exercise) were assessed using the Structured Clinical Interview for DSM-IV (SCID). RESULTS: Among participants with lifetime MDD or any anxiety disorder, 13% met criteria for a lifetime ED and 39% reported engaging in at least one lifetime clinically significant disordered eating behavior (e.g., binge eating) on the SCID. In contrast, only 3% of participants without a history of MDD/an anxiety disorder met criteria for a lifetime ED, and only 11% reported lifetime clinically significant disordered eating behavior. DISCUSSION: Our findings suggest that women with MDD and anxiety disorders have elevated rates of EDs, and it is therefore imperative to screen for disordered eating in these populations.

Associations between ovarian hormones and emotional eating across the menstrual cycle: Do ovulatory shifts in hormones matter?

OBJECTIVE: Elevated ovarian hormone levels are associated with increased risk for binge eating (BE) and emotional eating (EE) during the midluteal phase of the menstrual cycle. However, past studies have not examined whether pronounced hormonal changes that precede the midluteal phase (i.e., the dramatic decrease in estradiol and increase in progesterone during/after ovulation) also influence midluteal increases in binge-related symptoms. Past theories and studies of phenotypes strongly related to BE (e.g., depression) suggest that these pronounced hormonal changes may also contribute. This study examined this possibility in 375 female twins (aged 15-25 years) from the Michigan State University Twin Registry. METHODS: Daily ratings of EE (assessed with the Dutch Eating Behavior Questionnaire) and daily saliva samples of estradiol and progesterone were collected for 45 consecutive days. RESULTS: No significant associations were found between pronounced changes in estradiol or progesterone across ovulation and changes in EE scores in the midluteal phase. Results remained unchanged after controlling for body mass index and negative affect and examining participants with clinical BE episodes or more extreme hormonal fluctuations. DISCUSSION: In aggregate, the current findings and past data suggest that hormone levels are more significant predictors of EE than pronounced hormonal changes across the menstrual cycle.

A Conversation between Theory, Methods, and Data.

The 10 year anniversary of the COGITO Study provides an opportunity to revisit the ideas behind the Cattell data box. Three dimensions of the persons × variables × time data box are discussed in the context of three categories of researchers each wanting to answer their own categorically different question. The example of the well-known speed-accuracy tradeoff is used to illustrate why these are three different categories of statistical question. The 200 persons by 100 variables by 100 occasions of measurement COGITO data cube presents a challenge to integrate theories and methods across the dimensions of the data box. A conceptual model is presented for the speed-accuracy tradeoff example that could account for cross-sectional between persons effects, short-term dynamics, and long-term learning effects. Thus, two fundamental differences between the time axis and the other two axes of the data box include ordering and time scaling. In addition, nonstationarity in human systems is a pervasive problem along the time dimension of the data box. To illustrate, the difference in nonstationarity between dancing and conversation is discussed in the context of the interaction between theory, methods, and data. An information theoretic argument is presented that the theory-methods-data interaction is better understood when viewed as a conversation than as a dance. Entropy changes in the development of a theory-methods-data conversation provide one metric for evaluating scientific progress.

Within-person changes in salivary testosterone and physical characteristics of puberty predict boys' daily affect.

Recent investigations highlighted the role of within-person pubertal changes for adolescents' behavior. Yet, little is known about effects on adolescents' daily affect, particularly regarding the hormonal changes underlying physical changes during puberty. In a study with 148 boys aged 10 to 20years, we tested whether within-person physical and hormonal changes over eight months predicted everyday affect fluctuations, measured with experience sampling. As expected, greater within-person changes in testosterone (but not in dehydroepiandrosterone) were associated with higher affect fluctuations in daily life. Additionally, greater physical changes predicted higher affect fluctuations for individuals in the beginning of puberty. The findings demonstrate the relevance of physical and hormonal changes in boys' affective (in)stability.

Genetic and environmental associations between body dissatisfaction, weight preoccupation, and binge eating: Evidence for a common factor with differential loadings across symptom type.

OBJECTIVE: Prior twin studies provide support for a single "common factor" that contributes genetic and environmental risk to a range of disordered eating symptoms. However, the common factor may be indexed less well by binge eating (BE) than other symptoms of eating disorders [i.e., body dissatisfaction (BD) and weight preoccupation (WP)]. We sought to explore the presence of a common factor and test whether loadings differed across three key symptoms (i.e., BE, BD, WP). METHOD: Disordered eating was assessed via self-report in 631 female twin pairs from the Michigan State University Twin Registry. RESULTS: We detected a common disordered eating factor that was influenced primarily by additive genetic and nonshared environmental influences. However, we observed different loadings on this common factor by symptom type, as factor loadings for BD and WP were stronger than that for BE. Moreover, the residual environmental and/or genetic variances (i.e., those that are independent of the common factor) were larger in BE than those of BD or WP. DISCUSSION: Although all three symptoms share a common set of genetic and environmental influences, risk for BE may involve additional genetic, biological, and environmental factors that are not shared with other symptoms of eating pathology. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:157-161).

Examining associations between negative urgency and key components of objective binge episodes.

OBJECTIVE: Negative urgency (NU; tendency to act impulsively when experiencing negative emotions) is a risk factor for binge eating, although few studies have examined interviewer-assessed objective binge episodes (OBEs). Moreover, research has not investigated how NU relates to the core components of OBEs: loss of control (LOC) eating and objective overeating (OO). Understanding the relationship between NU and these core components will enhance etiologic models of eating disorder development. Thus, the current study examined the associations between NU, OBEs, and the components of OBEs by comparing levels of NU in women with OBEs, LOC eating only, and OO only to women with no pathological eating. METHOD: Participants were 612 women who endorsed lifetime OBEs (5.4%), LOC eating outside of OBEs (5.7%), OO only (2.8%), or none of these eating episodes (85.9%). RESULTS: Women with OBEs, LOC only, and OO only had significantly higher levels of NU than women without these episodes, suggesting that NU is associated with both the LOC and OO components of OBEs. DISCUSSION: NU relates to the spectrum of pathology present in women with OBEs. Future research should examine the mechanisms underlying these associations, including impaired behavioral/psychological control and/or increased reward sensitivity in response to negative affect.

The effects of ovarian hormones and emotional eating on changes in weight preoccupation across the menstrual cycle.

OBJECTIVE: Previous research has shown that fluctuations in ovarian hormones (i.e., estradiol and progesterone) predict the changes in binge eating and emotional eating across the menstrual cycle. However, the extent to which other eating disorder symptoms fluctuate across the menstrual cycle and are influenced by ovarian hormones remains largely unknown. This study sought to examine whether the levels of weight preoccupation vary across the menstrual cycle and whether the changes in ovarian hormones and/or other factors (i.e., emotional eating and negative affect) account for menstrual cycle fluctuations in this eating disorder phenotype. METHOD: For 45 consecutive days, 352 women (age, 15-25 years) provided daily ratings of weight preoccupation, negative affect, and emotional eating. Saliva samples were also collected on a daily basis and assayed for levels of estradiol and progesterone using enzyme immunoassay techniques. RESULTS: Weight preoccupation varied significantly across the menstrual cycle, with the highest levels in the premenstrual and menstrual phases. However, ovarian hormones did not account for within-person changes in weight preoccupation across the menstrual cycle. Instead, the most significant predictor of menstrual cycle changes in weight preoccupation was the change in emotional eating. DISCUSSION: Fluctuations in weight preoccupation across the menstrual cycle appear to be influenced primarily by emotional eating rather than ovarian hormones. Future research should continue to examine the relationships among ovarian hormones, weight preoccupation, emotional eating, and other core eating disorder symptoms (e.g., body dissatisfaction, compensatory behaviors) in an effort to more fully understand the role of these biological and behavioral factors for the full spectrum of eating pathology.

Coregulation of respiratory sinus arrhythmia between parents and preschoolers: differences by children's externalizing problems.

The coordination of physiological processes between parents and infants is thought to support behaviors critical for infant adaptation, but we know little about parent-child physiological coregulation during the preschool years. The present study examined whether time-varying changes in parent and child respiratory sinus arrhythmia (RSA) exhibited coregulation (across-person dynamics) accounting for individual differences in parent and child RSA, and whether there were differences in these parasympathetic processes by children's externalizing problems. Mother-child dyads (N = 47; Child age M = 3½ years) engaged in three laboratory tasks (free play, clean up, puzzle task) for 18 min, during which RSA data were collected. Multilevel coupled autoregressive models revealed that mothers and preschoolers showed positive coregulation of RSA such that changes in mother RSA predicted changes in the same direction in child RSA and vice versa, controlling for the stability of within-person RSA over time and individual differences in overall mean RSA. However, when children's externalizing behaviors were higher, coregulation was negative such that changes in real-time mother and child RSA showed divergence rather than positive concordance. Results suggest that mothers and preschoolers do coregulate RSA during real-time interactions, but that children's higher externalizing behavior problems are related to disruptions in these processes.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE).

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant's personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual's data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.

Modular Open-Source Software for Item Factor Analysis.

This paper introduces an Item Factor Analysis (IFA) module for OpenMx, a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation and manipulation of models. Modular organization of the source code facilitates the easy addition of item models, item parameter estimation algorithms, optimizers, test scoring algorithms, and fit diagnostics all within an integrated framework. Three short example scripts are presented for fitting item parameters, latent distribution parameters, and a multiple group model. The availability of both IFA and structural equation modeling in the same software is a step toward the unification of these two methodologies.