UNASSIGNED: Longitudinal studies with annual follow-up including psychological and social variables in substance use disorder recovery are scarce. We investigated whether levels of substance use, satisfaction with life, and psychological distress fluctuate across five years in relation to having drug-free friends.
UNASSIGNED: A prospective naturalistic cohort study of change trajectories in a cohort of people diagnosed with substance use disorder and using multiple substances with quarterly and annual follow-up over five years. Two-hundred-and-eight patients were recruited from substance use disorder treatment in Rogaland, Norway. Out of these, 164 participants fulfilled the inclusion criteria. We used Bayesian two-level dynamic structural equation modelling. The variable \'drug-free friends\' was assessed by a self-reporting questionnaire, while psychological distress was assessed using the Symptoms Checklist 90 Revised. Satisfaction with life was assessed using the Satisfaction With Life Scale while drug use was assessed using the Drug Use Disorders Identification Test.
UNASSIGNED: The main findings are that higher-than-average psychological distress at a three-month lag credibly predicts higher-than-normal substance use at the concurrent time point t. Substance use and satisfaction with life seem to have synchronous trajectories over time, i.e. as the first decreases the latter increases and vice versa. During the five years after treatment, the participants mainly experienced a decrease in substance use and increase in satisfaction with life.
UNASSIGNED: Since the participants experienced positive and negative fluctuations for several years after treatment, it seems crucial to establish a dialogue with treatment professionals in order to create functional solutions for maintaining motivation and aiding recovery.

Although the Research Diagnostic Criteria (RDoC) framework proposes biological and environmental mechanisms intersect in the etiology of psychopathology, there is no guidance on how to define or measure experiences in the environment within the RDoC matrix. Interpersonal dynamics during caregiver-child interactions involve temporal coordination of interacting partners\' biobehavioral functioning; repeated experiences of signaling to caregivers and responding to caregivers\' signals shape children\'s subsequent socioemotional and brain development. We begin with a review of the extant literature on caregiver-child dynamics, which reveals that RDoC\'s units of analysis (brain circuits, physiology, behavior, and self-report) are inextricably linked with moment-to-moment changes in the caregiving environment. We then offer a proof-of-concept for integrating biobehavioral RDoC units and environmental components via caregiver-child dynamics. Our approach uses dynamic structural equation models to estimate within-dyad dynamics involving arousal, social, cognitive, and negative or positive affective processes based on second-by-second changes in parasympathetic activity (RSA) during a conflict discussion and a positive event-planning task. Our results illustrate variation in parent-child RSA synchrony, suggesting differences depending on the driver (i.e., child- or parent-led) and on the unique and intersecting domains involved (e.g., positive or negative affect valence systems). We conclude with recommendations for conducting robust, methodologically rigorous studies of interpersonal dynamics that advance the RDoC framework and provide a summary of the clinical implications of this research. Examining caregiver-child dynamics during and across multiple dyadic interaction paradigms that differentially elicit key domains of functioning can deepen understanding of how caregiver- and child-led interpersonal dynamics contribute to child psychopathology risk.

Given that noncompliance is the most common externalizing problem during middle childhood and reliably predicts significant conduct problems, innovations in elucidating its etiology are sorely needed. Evaluation of in-the-moment antecedents and consequences of child noncompliance improves traction on this goal, given that multiple theories contend that child noncompliance and parent behavior mutually influence each other through negative reciprocation as well as contingent praise processes. Among a sample of 140 families (child age: 6-10 years; 32.1% female), the present study capitalized on intensive repeated measures of observed child noncompliance and parent negative talk and praise objectively coded during three unique tasks. We employed dynamic structural equation modeling to evaluate within-dyad parent-child behavioral dynamics and between-dyad differences therein. Results provided mixed support for hypotheses and suggested that antecedents and consequences of child noncompliance differed according to task demands and child ADHD symptoms. Contrary to models of coercive cycles, during child-led play, parent negative talk was more likely following prior child noncompliance, but child noncompliance was less likely following prior parent negative talk. As expected, during parent-led play, parent praise was less likely following prior child noncompliance, which was also less likely following prior parent praise. Relative to youth with fewer symptoms, for children with elevated ADHD symptoms, during a challenging clean-up task, child noncompliance was less stable and less contingent on prior parent negative talk. Results are discussed in terms of their implications of real-time parent-child interactions for typical and atypical development of externalizing problems.

To design effective policies against COVID-19, there is a need for more evidence-based research. However, associations between actual policies and temporal behavior changes have remained underexplored. To fill this important research gap, a nationwide retrospective life-oriented panel survey on individuals\' behavior changes from April to September 2020 was implemented in Japan. Reliability of information sources, risk perceptions, and attitudes toward policymaking were also investigated. Valid data were collected from 2643 respondents residing in different parts of the country. Risks were reported about general infections and public transport use. Attitudes toward policymaking were mainly about policymaking capacity and PASS-LASTING based policy measures. A dynamic structural equation model (DSEM) was developed to quantify dynamic associations between individuals\' behavior changes over time and subjective assessments (i.e., attitudes) of policymaking. Survey results revealed that behavior changes are mostly characterized by avoidance behaviors. Modeling estimation results showed a statistically-significant sequential cause-effect relationship between accumulated behavior changes in the past, subjective factors, and the most recent behavior changes. The most recent behavior changes are mostly affected by accumulated behavior changes in the past. Effects of subjective assessments of policymaking on the most recent behavior changes are significant but moderate. Among attitudes toward policymaking, attitudes toward policymaking capacity are more influential than willingness to follow PASS-LASTING based policy measures. High risks of using public transport are found to significantly influence the most recent behavior changes, together with other risk perception factors. Insights into effective COVID-19 policymaking are summarized.

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.