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BackgroundMethodResultsConclusionsAnxiety disorders are prevalent among youth and adults. Increasing studies examined the dynamic associations between momentary fluctuations of anxiety and well-being, primarily focusing on the severity of general anxiety. Scant research has explored the co-fluctuations between different anxiety symptoms and mental health outcomes.The current study evaluated the multi-level factor structure and assessed the subclinical symptoms of generalized anxiety disorder (GAD), social phobia (SP), and panic disorder (PD) in a sample of non-clinical young adults (N = 271, Mage = 18 years, 72% female, 68% non-White) who participated in a 30-day daily diary study.Between persons, GAD, SP, and PD were positively correlated with depressive symptoms, stress, as well as emotional and peer problems. Within persons, both SP and PD were positively associated with stress, peer and emotional problems on the same day. Across days, there was positive reciprocal relation between PD and stress, whereas negative reciprocal link was observed between SP and emotional problems.Current findings showed dynamic and distinct patterns in the associations between different anxiety symptoms and several mental health outcomes, which emphasizes the need to disentangle between- and within-person variation of anxiety symptoms with intensive longitudinal designs. [ABSTRACT FROM AUTHOR]

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM. [ABSTRACT FROM AUTHOR]

Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e., 1-1-1, 2-1-1, and 2-2-1) of intensive longitudinal mediation (ILM) analyses based on DSEM and RDSEM models. Specifically, each ILM model was demonstrated with a simulated example and illustrated with the corresponding annotated Mplus codes. We compared two types of detrending methods in mediation analyses and showed that RDSEM was superior to DSEM because the latter included the timetj variable as a Level 1 predictor. Lastly, we extended ILM analyses based on DSEM and RDSEM to multilevel autoregressive mediation models, cross-classified DSEM, and intensive longitudinal moderated mediation models. [ABSTRACT FROM AUTHOR]

Drawing on self-determination theory, this study focuses on the person- and occasion-specific components of the daily dynamics of employees' global psychological need satisfaction at work. Predictors (job demands related to information and communication technologies, segmentation norms, and workload) and outcomes (perceived productivity, psychological detachment, work-family conflict, job satisfaction, and personal satisfaction) were also examined across both levels to better grasp the mechanisms underlying these short-term dynamics. A total of 129 French employees filled out questionnaire surveys at the end of each workday for five days (521 observations). Results from Dynamic Structural Equation Modeling (DSEM) showed clear associations between need satisfaction, the predictors, and the outcomes at the person-specific level. However, and although need satisfaction levels were found to fluctuate on a daily basis, they seemed immune to the effects of daily fluctuations in predictor levels, and unlikely to generate matching fluctuations in outcome levels. These results suggest strong homoeostatic processes protecting employees' functioning against daily fluctuations, but that the accumulation of such fluctuations over the work week may jeopardize these processes. [ABSTRACT FROM AUTHOR]

Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person's level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects. [ABSTRACT FROM AUTHOR]

Dynamic Structural Equation Modeling (DSEM) was recently introduced as an advanced multilevel time series method to analyze Intensive Longitudinal Data (ILD). Although measurement error is likely to be embedded in ILD, many applications of intensive longitudinal studies often rely on composite scores without modeling measurement error. However, measurement error can be captured in the DSEM framework using a single indicator or multiple indicators. In this paper, we discussed and compared three multilevel DSEM models that analyze within-person dynamic process of a construct with different approaches to addressing measurement error: (1) single-indicator multilevel AR(1) model without measurement error; (2) single-indicator multilevel AR(1) model with measurement error; (3) multiple-indicator multilevel dynamic factor AR(1) model. The difference between (1) and (2) and the similarity between (2) and (3) were highlighted. An experience sampling data set was analyzed to show the difference and similarity of the numerical results from the three approaches. [ABSTRACT FROM AUTHOR]

Objectives: This study investigated how COVID-19-related media exposure during the COVID-19 crisis was related to same-day and next-day COVID-19-related worries. Design: A 21-day diary study was conducted between late March and late April 2020 in Germany. Main Outcome Measures: Hypotheses were tested in a sample of 561 participants (Mage = 42.79, SDage = 6.12). Every evening, participants indicated their exposure to COVID-19-related media (e.g., TV, print, online) and their COVID-19-related worries. Results: Same-day analyses showed that participants reported more COVID-19-related worries on days with higher exposure to COVID-19-related media. Dynamical structural equation models provided evidence for a reciprocal cycle across days: Higher media exposure at one day predicted higher worries the next day, and higher worries at one day also predicted higher media exposure the next day. Individuals with high trait anxiety reported an enhanced general level of media exposure during the 21 days of assessment, and individuals high in neuroticism and anxiety reported an enhanced level of worries. Conclusion: These findings suggest a self-reinforcing cycle whereby consuming crisis-related media and worrying reciprocally influence each other across days, possibly amplifying adverse effects of the COVID-19 crisis and other crises alike on mental and physical health. [ABSTRACT FROM AUTHOR]

The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, namely the mixed-effects TSO (ME-TSO). Hence, the ME-TSO model is a measurement model suitable to analyze intensive longitudinal data that allows studying the psychometric properties of the indicators per individual, the heterogeneity of psychological dynamics, and the person–situation interaction effects. We showcase how to use the model by analyzing the items of positive affect activation of the crowdsourcing study HowNutsAreTheDutch (HoeGekisNL). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]