Dynamic Structural Equation Modeling Research Articles

Exploring daily mediating pathways of religious identity in the associations between maternal religious socialization and Muslim American adolescents' civic engagement.

Despite its implications for positive youth development, factors and processes that promote civic engagement are critically understudied, particularly among Muslim American adolescents for whom opportunities for civic engagement could be hindered by Islamophobia and hate crimes. Prior work has proposed that parents can strengthen adolescents' group belonging and motivate their civic engagement, but this mediating link has not been empirically tested. Moreover, parents' religious socialization and adolescents' religious identity remain understudied, especially with respect to possible daily fluctuations. We used experience sampling methods to: (a) explore momentary fluctuations and temporal relations between two dimensions of Muslim American adolescents' religious identity (i.e., private regard and centrality) over the course of 14 days (Phase 2), and (b) examine if Muslim American adolescents' momentary religious identity (Phase 2) mediated associations between their stable perceptions of maternal religious socialization (Phase 1) and subsequent civic engagement (Phase 3). Dynamic structural equation modeling revealed positive autoregressive and cross-lagged relations between religious private regard and centrality. Moreover, adolescents' momentary religious identities differentially mediated the associations between maternal religious socialization and civic attitudes and behaviors, suggesting specificity in these developmental processes. Our findings have important implications for programs and policies to support the self-empowerment and positive development of Muslim American adolescents. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Examining emotional pain among individuals with chronic physical pain: Nomothetic and idiographic approaches

ObjectiveEmotional pain (i.e., pain affect in response to psychological experiences such as rejection or loss) may be a component of chronic pain syndromes given high co-occurrence with depression and neurobiological overlaps in pain affect resulting from physical and emotional experiences. In the current set of studies, we examined the relationship between emotional and physical pain using both nomothetic (i.e., group-level) and idiographic (i.e., individual-level) approaches. MethodsIndividuals with chronic pain were recruited from the Washington University Pain Center. First, we assessed the relationship between emotional and physical pain at the group level. Then, three individuals from the group-level study completed ecological momentary assessment four times per day for at least four weeks. We assessed relationships between emotional and physical pain using correlations and dynamic structural equation modeling. ResultsEmotional pain severity was significantly positively correlated with physical pain at the group level. However, results from idiographic analyses suggested that one individual did not display a significant correlation between physical and emotional pain, and two individuals displayed correlations larger than expected based on the group-level data. Competing models suggested that emotional and physical pain represented distinct constructs for the individuals studied. ConclusionsThese results suggest that emotional pain may be an important component of chronic pain syndromes. However, idiographic models revealed heterogeneity that may have important implications for treatment. Further research is needed to understand whether idiographic relationships between emotional and physical pain can help identify effective treatment targets for individuals with co-occurring emotional and physical symptoms.

Relationship between decline in cognitive resources and physical activity.

This study aimed to test whether the level of cognitive resources explains engagement in physical activity across aging and whether the age-related decline of cognitive resources precede the decline in physical activity. Data from 105,206 adults aged 50 to 90 years from the Survey of Health, Ageing, and Retirement in Europe (SHARE) were used in adjusted linear mixed models to examine whether the engagement in moderate physical activity and its evolution across aging were dependent on cognitive resources. Cognitive resources and physical activity were measured 5 times over a 12-year period. Delayed recall, verbal fluency, and the level of education were used as indicators of cognitive resources. The frequency of engagement in moderate physical activity was self-reported. Dynamic structural equation models (SEM) were used to assess the temporal precedence of changes in cognitive resources and physical activity. Lower cognitive resources were associated with lower levels and steeper decreases in moderate physical activity across aging. Results further revealed a time-ordered effect with a stronger influence of cognitive resources (delayed recall and verbal fluency) on subsequent changes in moderate physical activity than the opposite. These findings suggest that, after age 50, the level of engagement in moderate physical activity and its trajectory depend on the availability of cognitive resources. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Quantitatively representing real-time emotion dynamics: Attachment-based differences in mothers' emotion experiences.

Given inconsistent findings emerging in the literature between motherhood and emotional well-being, it is important to employ cutting-edge methods to evaluate mothers' dynamic emotional experiences. As anticipated by theory, attachment anxiety and avoidance may uniquely predict fluctuations in mothers' positive emotion, which may be yoked in particular to 2 aspects of their experiences: their emotional closeness with their children and their perceptions of their children's positive emotion. In the current study, 144 mothers (41% Hispanic) of young children (mean [M] = 20.9 months) reported on their positive emotion, closeness/distance with their children, and perceptions of their children's positive emotion, up to 5 times per day for 10 days. We fit a dynamic structural equation model (DSEM) in order to evaluate attachment-based differences in mothers' emotional equilibrium (i.e., mean levels of positive emotion), intraindividual volatility in positive emotion, within-person emotional inertia, and cross-lagged emotion processes over time. Attachment anxiety was related to lower average maternal positive emotion ratings and to greater volatility in mothers' positive emotion and emotional closeness/distance. Attachment avoidance was related to higher average ratings of emotional distance, stronger inertia in mothers' positive emotion, and weaker inertia in mothers' emotional distance. Among mothers who were higher on attachment avoidance, emotional distance was related to greater subsequent feelings of positive emotion and perceived child positive emotion. The results are aligned with theory and have specific implications for attachment-informed parenting interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

735-P: Visible and Invisible Spousal Support in Daily Diabetes Self-Care

Aim: Prior research has yielded inconsistent findings on the effects of spousal support in type 2 diabetes (T2D) management, yet most studies were cross-sectional and relied exclusively on patient perceived support. This study aimed to examine the within-person effects of daily perceived and enacted spousal support in T2D self-care on glycemic control. Method: Adults with T2D (N = 63; age M = 61; 67% male; 70% Caucasian) and their spouses participated in a parent study. Patients wore continuous glucose monitors (CGM) for 7 days and concurrently, each evening, both partners responded to the following item: Did you/your partner say or do anything TODAY to try to influence your/spouse/partner’s behavior with regard to managing your/his/her diabetes? The patient item version represents perceived and spouse version enacted support, respectively. CGM data were used to calculate 4 indices of daily glycemic control: mean (M), standard deviation (SD), coefficient of variation (CV), and time in range (TR). Results: A multilevel dynamic structural equation model was estimated for each outcome. Within-person effects of perceived and enacted support were allowed to vary between persons and time was a covariate. Enacted support significantly predicted better glycemic control across all outcomes (βM = -.32, βSD = -.30, βCV = -.19, βTR = .42, ps < .05). Conversely, perceived support was unrelated to most outcomes (βM = .06, βSD = .17, βCV = .12, ps > .05), but significantly related to lower TR (βTR = -.40, p = .004). Conclusion: Results echo research on invisible (enacted, not perceived) versus visible (enacted, perceived) support: the former is consistently linked to positive outcomes, whereas visible support is often ineffective or detrimental. Indeed, perceiving support receipt may undermine patient self-efficacy for T2D management and in turn, reduce self-care adherence (e.g., medication, diet). Replication and more research are needed to determine the extent to which the support visibility model applies to the context of T2D. Disclosure E. Soriano: None. J. Laurenceau: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R21DK098679, F31DK120201)

The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?

Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data. We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points. The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations. We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.

Within- and Between-Persons Effects of Self-Esteem and Affective State as Antecedents and Consequences of Dysfunctional Behaviors in the Everyday Lives of Patients With Borderline Personality Disorder

Dysfunctional behaviors are conceptualized as maladaptive affective coping attempts in borderline personality disorder (BPD). The recent benefits-and-barriers model extended the affective function assumption by adding self-esteem as a barrier to engaging in dysfunctional behaviors. Patients with BPD ( N = 119) carried e-diaries to report their current self-esteem, emotional valence, tense arousal, and whether they engaged in dysfunctional behaviors 12 times a day for 4 days. Dynamic structural equation modeling revealed that on the within-person level, high momentary negative affect predicted dysfunctional behaviors, and on the between-person level, low trait self-esteem predicted dysfunctional behaviors. We also found an association between engaging in dysfunctional behaviors and momentary self-esteem and trait levels of valence and tense arousal. Moreover, our results indicate a deterioration of, rather than relief from, negative affective state after dysfunctional behaviors. These findings highlight the importance of emotion-regulation skills and reestablishing a positive self-view as important treatment targets to reduce dysfunctional behaviors in BPD.

Affective Dynamics Across Internalizing and Externalizing Dimensions of Psychopathology.

Little is known about pathogenic affective processes that cut across diverse mental disorders. The current study examines how dynamic features of positive and negative affect differ or converge across internalizing and externalizing disorders in a diagnostically diverse urban sample using bivariate dynamic structural equation modeling. One-hundred fifty-six young women completed semi-structured clinical interviews and a 21-day ecological momentary assessment protocol with seven assessments of affective states per day. Internalizing and externalizing dimensions of psychopathology were modeled using confirmatory factor analysis of mental disorders. After controlling for externalizing disorders, internalizing disorders were associated with higher negative affective mean intensity, higher negative affective variability (i.e., unique innovation variance), and lower positive affective variability. Conversely, externalizing disorders were associated with less persistent positive affect (i.e., lower inertia) and more variable positive emotionality. Results suggest internalizing and externalizing disorders have distinct affective dynamic signatures, which have implications for development of tailored interventions.

Dynamics of mother-adolescent and father-adolescent autonomy and control during a conflict discussion task.

Adolescence is characterized by increases in autonomy, yet we have limited knowledge about how parents and adolescents may navigate this transition in real time. We assessed dynamic bidirectional associations between parental behavior-specifically, autonomy support and control-and adolescent autonomy in both mother-adolescent and father-adolescent dyads during a 10-min conflict discussion task (N = 86, 32 girls). Observers rated parental autonomy support, parental control, and adolescent autonomy on 4-point scales in 30-s epochs. Residual dynamic structural equation (RDSEM) models revealed that increases in paternal autonomy support in a given 30-s epoch predicted increases in adolescent autonomy in the next epoch, after controlling for stability in father and adolescent behavior from one epoch to the next. Further, increases in adolescent autonomy in a given 30-s epoch predicted increases in maternal control in the next epoch. Findings highlight the importance of investigating bidirectional associations in parent-adolescent interactions as well as considering the divergent roles that mothers and fathers may play in the socialization of adolescent autonomy. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Fluctuations in Affective States and Self-Efficacy to Resist Non-Suicidal Self-Injury as Real-Time Predictors of Non-Suicidal Self-Injurious Thoughts and Behaviors.

IntroductionAlthough research over the past decade has resulted in significantly increased knowledge about distal risk factors for non-suicidal self-injury (NSSI), little is known about short-term (proximal) factors that predict NSSI thoughts and behaviors. Drawing on contemporaneous theories of NSSI, as well as the concept of ideation-to-action, the present study clarifies (a) real-time factors that predict NSSI thoughts and (b) the extent to which theoretically important momentary factors (i.e., negative affect, positive affect, and self-efficacy to resist NSSI) predict NSSI behavior in daily life, beyond NSSI thoughts.MethodsUsing experience sampling methodology, intensive longitudinal data was obtained from 30 young adults with frequent NSSI episodes in the last year. Participants completed assessments up to eight times per day for 12 consecutive days (signal-contingent sampling). This resulted in the collection of 2,222 assessments (median compliance = 79.2%) during which 591 NSSI thoughts and 270 NSSI behaviors were recorded. Using the dynamic structural equation modeling framework, multilevel vector autoregressive models were constructed.ResultsWithin the same assessment, negative affect was positively associated with NSSI thoughts, whereas positive affect and self-efficacy to resist NSSI were each negatively associated with NSSI thoughts. Across assessments, higher-than-usual negative affect and self-efficacy to resist NSSI were predictive of short-term change in NSSI thoughts. While fluctuations in both negative affect and positive affect prospectively predicted NSSI behavior, these factors became non-significant in models that controlled for the predictive effect of NSSI thoughts. In contrast, self-efficacy to resist NSSI incrementally predicted a lower probability of engaging in NSSI, above and beyond NSSI thoughts.DiscussionThis study provides preliminary evidence that affective fluctuations may uniquely predict NSSI thoughts but not NSSI behaviors, and point to the role of personal belief in the ability to resist NSSI in preventing NSSI behavior. These findings illustrate the need to differentiate between the development of NSSI thoughts and the progression from NSSI thoughts to behavior, as these are likely distinct processes, with different predictors.

A novel telehealth platform for alcohol use disorder treatment: preliminary evidence of reductions in drinking

ABSTRACT Background Alcohol use disorder (AUD) treatment remains greatly underutilized. Innovative strategies are needed to improve AUD treatment access and patient engagement. The Ria Treatment Platform (RTP) is a patient-centered telemedicine AUD treatment program accessed through a smartphone application (app) that includes a package of physician visits (with AUD prescriptions as appropriate), text- and phone-based support from a recovery coach, video monitoring of medication adherence, and Bluetooth-linked breathalyzer tracking of alcohol intake. Objectives The purpose of the current study is to examine changes in alcohol use among patients utilizing the RTP. Methods This study examines daily breathalyzer blood alcohol content (BAC) readings collected from 77 adult patients (50.7% male) over the first 90 days in treatment with the RTP. Data were analyzed using dynamic structural equation modeling. Results The treatment retention rate at 90 days was 55%. The best fit for the BAC data was given by a cubic curve, which showed that among patients who remained engaged for 90 days average BAC levels declined approximately 50% (from .091 to .045) from baseline to day 90. Conclusion This study provides preliminary evidence of substantial alcohol use reductions among patients utilizing the RTP, an innovative telemedicine program accessed via smartphone. Although other alcohol-reduction apps have shown promise from scientific evaluations, the RTP appears to be the only app that incorporates physician-prescribed medication and a recovery coach. Research incorporating random assignment and meaningful comparison groups is needed to further evaluate this promising strategy.

Investigating intraindividual pain variability: methods, applications, issues, and directions.

Pain is a dynamic experience subject to substantial individual differences. Intensive longitudinal designs best capture the dynamical ebb and flow of the pain experience across time and settings. Thanks to the development of innovative and efficient data collection technologies, conducting an intensive longitudinal pain study has become increasingly feasible. However, the majority of longitudinal studies have tended to examine average level of pain as a predictor or as an outcome, while conceptualizing intraindividual pain variation as noise, error, or a nuisance factor. Such an approach may miss the opportunity to understand how fluctuations in pain over time are associated with pain processing, coping, other indices of adjustment, and treatment response. The present review introduces the 4 most frequently used intraindividual variability indices: the intraindividual SD/variance, autocorrelation, the mean square of successive difference, and probability of acute change. In addition, we discuss recent development in dynamic structural equation modeling in a nontechnical manner. We also consider some notable methodological issues, present a real-world example of intraindividual variability analysis, and offer suggestions for future research. Finally, we provide statistical software syntax for calculating the aforementioned intraindividual pain variability indices so that researchers can easily apply them in their research. We believe that investigating intraindividual variability of pain will provide a new perspective for understanding the complex mechanisms underlying pain coping and adjustment, as well as for enhancing efforts in precision pain medicine. Audio accompanying this abstract is available online as supplemental digital content at http://links.lww.com/PAIN/A817.

Dynamic and bidirectional associations between maternal stress, anxiety, and social support: The critical role of partner and family support

BackgroundStress and anxiety in pregnancy and the postpartum period are associated with poor long-term maternal and child health outcomes. Social support has been shown to mitigate the effects of maternal stress and anxiety; however, the directionality and longitudinal associations among these variables are poorly understood. Using a novel multilevel modeling approach called dynamic structural equation modeling (DSEM), we examined within-person (state-level) autoregressive and cross-lagged associations among stress, anxiety, and social support in the perinatal period in order to elucidate directional associations over time. MethodsMothers from a longitudinal pregnancy cohort (N = 3,388) completed self-report measures of stress, anxiety, and social support across 4 time points from pregnancy to 12 months postpartum. ResultsHigher than average levels of stress and anxiety led to elevations in anxiety and stress and decreases in social support at subsequent time points. Importantly, earlier individual levels of partner and family support predicted subsequent decreases in stress and anxiety. LimitationsSupport was measured via maternal self-report thus extrapolations cannot be made to tangible or instrumental supports and lagged relationships represent average lags over time. ConclusionsUsing a novel statistical approach, these results suggest that increases in both partner and family support may be powerful protective factors for decreasing mental health difficulties in pregnancy and the postpartum, highlighting the importance of targeting and increasing this type of support from pregnancy to the postpartum period.

Intensive Longitudinal Data Analyses With Dynamic Structural Equation Modeling

Recent developments in theories and data collection methods have made intensive longitudinal data (ILD) increasingly relevant and available for organizational research. New methods for analyzing ILD have emerged under the multilevel modeling framework. In this article, we first delineate features of ILD (including autoregressive relationships, trends, cycles/seasons, and between-subject variability in temporal trends). We discuss the analytic challenges for handling ILD using traditional analytic tools familiar to organizational researchers (e.g., growth models, single-subject time series analyses). We then introduce a statistical approach for handling ILD from the multilevel modeling framework: dynamic structural equation modeling (DSEM). We provide three examples using simulated data sets to demonstrate how to apply DSEM to examine ILD with a software program familiar to organizational researchers (i.e., M plus). Finally, we discuss issues related to applying DSEM, including centering, missing data, and sample size.

A Nonlinear Dynamic Latent Class Structural Equation Model

In this article, we propose a nonlinear dynamic latent class structural equation modeling (NDLC-SEM). It can be used to examine intra-individual processes of observed or latent variables. These processes are decomposed into parts which include individual- and time-specific components. Unobserved heterogeneity of the intra-individual processes are modeled via a latent Markov process that can be predicted by individual- and time-specific variables as random effects. We discuss examples of sub-models which are special cases of the more general NDLC-SEM framework. Furthermore, we provide empirical examples and illustrate how to estimate this model in a Bayesian framework. Finally, we discuss essential properties of the proposed framework, give recommendations for applications, and highlight some general problems in the estimation of parameters in comprehensive frameworks for intensive longitudinal data.

Neuroticism and extraversion are differentially related to between- and within-person variation of daily negative emotion and physical symptoms

Negative emotion and physical symptoms reciprocally influence each other, and are associated with neuroticism and extraversion. It remains unclear, nevertheless, how they are associated with the daily dynamic links between negative emotion and physical symptoms at between- and with-person level. This study used a daily diary design of 14 consecutive days in a Chinese college sample (n = 139, 17–22 years old, 27% males) to investigate the associations of neuroticism and extraversion with the variation and cross-day links between negative emotion and physical symptoms with dynamic structural equation modeling. Results showed substantial between- and within-person variation in daily negative emotion and physical symptoms. Neuroticism was positively associated with daily average negative emotion and physical symptoms (between-person variation); extraversion attenuated the association of physical symptoms with the next day's negative emotion (within-person variation). The findings demonstrate the value of examining within-person variation of negative emotion and physical symptoms at the daily level, and that neuroticism and extraversion have differential associations with negative emotion and physical symptoms at different levels, which have implications for interventions that target these individual characteristics to promote physical and mental well-being among college students.

Dynamic Structural Equation Models for Directed Cyclic Graphs: the Structural Identifiability Problem.

Network systems are commonly encountered and investigated in various disciplines, and network dynamics that refer to collective node state changes over time are one area of particular interests of many researchers. Recently, dynamic structural equation model (DSEM) has been introduced into the field of network dynamics as a powerful statistical inference tool. In this study, in recognition that parameter identifiability is the prerequisite of reliable parameter inference, a general and efficient approach is proposed for the first time to address the structural parameter identifiability problem of linear DSEMs for cyclic networks. The key idea is to transform a DSEM to an equivalent frequency domain representation, then Masons gain is employed to deal with feedback loops in cyclic networks when generating identifiability equations. The identifiability result of every unknown parameter is obtained with the identifiability matrix method. The proposed approach is computationally efficient because no symbolic or expensive numerical computations are involved, and can be applicable to a broad range of linear DSEMs. Finally, selected benchmark examples of brain networks, social networks and molecular interaction networks are given to illustrate the potential application of the proposed method, and we compare the results from DSEMs, state-transition models and ordinary differential equation models.

Dynamic structural equation models for directed cyclic graphs: the structural identifiability problem

Dynamic structural equation models for directed cyclic graphs: the structural identifiability problem

A Quantitative Exploration of Two Teachers with Contrasting Emotions: Intra-Individual Process Analyses of Physiology and Interpersonal Behavior

Although the association between teacher-student relations, teacher emotions, and burnout has been proven on a general level, we do not know the exact processes underlying these associations. Recently there has been a call for intra-individual process measures that assess what happens from moment-to-moment in class in order to better understand inter-individual differences in emotions and burnout between teachers. This paper explored the use of process measures of teachers’ heart rate and their interpersonal behavior during teaching. Our aim was to illustrate different ways of analyzing and combining physiological and observational time-series data and to explore their potential for understanding between-teacher differences. In this illustration, we focused on two teachers who represented contrasting cases in terms of their self-reported teaching-related emotions (i.e., anxiety and relaxation) and burnout. We discuss both univariate process analyses (i.e., trend, autocorrelation, stability) as well as state-of-the-art multivariate process analyses (i.e., cross-correlations, dynamic structural equation modeling). Results illustrate how the two teachers differed in the nature of their physiological responses, their interpersonal behavior, and the association between these two process measures over time. Along implications and suggestions for further research, it is discussed how the process-based, dynamic assessment of physiology and interpersonal behavior may ultimately help to understand differences in more general teaching-related emotions and burnout.

Featured Article: Bidirectional Effects of Sleep and Sedentary Behavior Among Toddlers: A Dynamic Multilevel Modeling Approach.

To examine the bidirectional effects of objectively measured nighttime sleep and sedentary activity among toddlers. Actical accelerometer data were analyzed for 195 toddlers participating in an obesity prevention trial (mean age = 27 months). Toddlers wore the accelerometers for up to 7 consecutive days. Nighttime sleep was defined as the number of minutes asleep between the hours of 8 pm and 8 am the following morning. Sedentary behavior (in minutes) was defined using previously established Actical cut points for toddlers. Variables were lagged and parsed into latent within- and between-person components, using dynamic structural equation modeling (DSEM). Toddlers spent an average of 172 min (∼3 hr) in sedentary activity and slept an average of 460 min (∼8 hr) per night. An autoregressive cross-lagged multilevel model revealed significant autoregression for both sleep and sedentary activity. Cross-lagged values revealed that decreased sleep predicted increased next-day sedentary activity, and sedentary activity predicted that night's sleep. For 89% of the sample, the within-person standardized cross-lagged effects of sleep on sedentary were larger than the cross-lagged effects of sedentary on sleep. Results suggest that, on average, nighttime sleep is a stronger predictor of subsequent sedentary behavior (compared with the reverse), and this is the case for the majority of toddlers. Findings highlight the importance of interindividual associations between sleep and sedentary activity. The present study is an example of how DSEM methods can be used to ask questions about Granger-causal cross-lagged relations between variables, both within and between individuals.