Bridging maladaptive social self-beliefs and social anxiety: a network perspective

The Role of Anxiety and Depression in Shaping the Sleep-Pain Connection in Patients with Nonspecific Chronic Spinal Pain and Comorbid Insomnia: A Cross-Sectional Analysis

(1) Background: This exploratory study aims to explore the relationship between nonspecific chronic spinal pain (nCSP) and insomnia symptoms, by examining the interconnections, strengths, and directional dependence of the symptoms. In addition, we aim to identify the key symptoms of the nCSP-insomnia relationship and shed light on the bidirectional nature of this relationship. (2) Methods: This study is a secondary analysis of the baseline data (cross-sectional) from a randomized controlled trial, which examined the added value of Cognitive Behavioral Therapy for Insomnia (CBT-I) combined with cognition-targeted exercise therapy, conducted in collaboration with the Universiteit Gent and Vrije Universiteit Brussel (Belgium). One hundred and twenty-three nCSP patients with comorbid insomnia were recruited through the participating hospitals, advertisements, announcements in local newspapers, pharmacies, publications from support groups, and primary care. To explore the interconnections and directionality between symptoms and the strengths of the relationships, we estimated a regularized Gaussian graphical model and a directed acyclic graph. (3) Results: We found only one direct, but weak, link between sleep and pain, namely, between average pain and difficulties maintaining sleep. (4) Conclusions: Despite the lack of strong direct links between sleep and pain, pain and sleep seem to be indirectly linked via anxiety and depression symptoms, acting as presumable mediators in the network of nCSP and comorbid insomnia. Furthermore, feeling slowed down and fatigue emerged as terminal nodes, implying their role as consequences of the network.

Examining predictors of cocaine withdrawal syndrome at the end of detoxification treatment in women with cocaine use disorder

BACKGROUND

Detoxification is frequently recommended as a treatment for moderate to severe Cocaine Use Disorder (CUD). However, the response to detoxification varies among patients, and previous studies have focused mostly on patterns of drug use behavior to test associations with treatment outcomes, overlooking the potential impact of psychosocial factors, other clinical variables, and individual life experiences. In this study we comprehensively examined several variables aiming to find the most relevant predictors to classify patients with severe versus non-severe cocaine withdrawal symptoms at the end of detoxification.

METHODS

Data from 284 women with CUD who enrolled in a 3-week detoxification program was used in this longitudinal study. Psychosocial, clinical, and drug use behavior characteristics were evaluated, generating a dataset with 256 potential predictors. We tested six different machine learning classification algorithms.

RESULTS

The best classification algorithm achieved an average accuracy and ROC-AUC of approximately 70%. The 16 features selected as best predictors were the severity of psychiatric, family, and social problems and the level of exposure to childhood maltreatment. Features associated with drug-use behavior included days consuming drugs and having craving symptoms in the last month before treatment, number of previous drug/alcohol-related treatments, and a composite score of addiction severity. The level of cocaine withdrawal syndrome at the beginning of detoxification was also a key feature for classification. A network analysis revealed the pattern of association between predictors.

CONCLUSION

These variables can be assessed in real-world clinical settings, potentially helping clinicians to identify individuals with severe cocaine withdrawal that is likely to be sustained over the course of detoxification.

The role of personality traits in mediating the relation between fear of negative evaluation and social interaction anxiety

Introduction

Social interaction anxiety and fear of negative evaluation have many maladaptive outcomes and, in order to counteract their effects, it is essential to identify those psychological or social factors that make people vulnerable to them. One of these factors is the individual's personality structure: some personality traits increase the individuals' vulnerability to symptoms of social anxiety, while others protect them.

Methods

The aim of this paper is to analyse the role of HEXACO personality traits in mediating the relationship between fear of negative evaluation and social anxiety, in a sample of 352 cadets from the Land Forces Academy of Sibiu. The relationships between these concepts were analysed using structural equation modeling (SEM) in several hypothetical models, two of which were ultimately validated.

Results

In the first model, the fear of negative evaluation has an indirect effect on social interaction anxiety through the mediation of extraversion, conscientiousness, and altruism, separately. Furthermore, extraversion, conscientiousness, and altruism play a serial mediating role in the association between the fear of negative evaluation and social interaction anxiety. In the second model, the fear of negative evaluation has an indirect effect on social interaction anxiety through the mediation of social boldness, liveliness, and organization, separately, but not through altruism. Social boldness, liveliness, and organization played a serial mediating role in the relationship between the two constructs, while altruism moderated the relationship between liveliness, organization, and social interaction anxiety.

Discussion

Analysing the relationship between the individuals' personality traits, social anxiety, and fear of negative evaluation facilitated the identification of ways to cultivate desirable behaviours in social environments typified by compliance, discipline, uniformity, and rigor.

Neural evidence of face processing in social anxiety disorder: A systematic review with meta-analysis

Numerous previous studies have used event-related potentials (ERPs) to examine facial processing deficits in individuals with social anxiety disorder (SAD). However, researchers still need to determine whether the deficits are general or specific and what the dominant factors are behind different cognitive stages. Meta-analysis was performed to quantitatively identify face processing deficits in individuals with SAD. Ninety-seven results in 27 publications involving 1032 subjects were calculated using Hedges' g. The results suggest that the face itself elicits enlarged P1 amplitudes, threat-related facial expressions induce larger P2 amplitudes, and negative facial expressions lead to enhanced P3/LPP amplitudes in SAD individuals compared with controls. That is, there is face perception attentional bias in the early phase (P1), threat attentional bias in the mid-term phase (P2), and negative emotion attentional bias in the late phase (P3/LPP), which can be summarized into a three-phase SAD face processing deficit model. These findings provide an essential theoretical basis for cognitive behavioral therapy and have significant application value for the initial screening, intervention, and treatment of social anxiety.

Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic

BACKGROUND

This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions.

METHODS

A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented.

RESULTS

The connections between A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying", A6 "Becoming easily annoyed or irritable" and A7 "Feeling afraid as if something awful might happen", etc., were relatively strong; A2 "Not being able to stop or control worrying" and A3 "Worrying too much about different things" had the highest expected influence, and A2 "Not being able to stop or control worrying" had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 "Not being able to stop or control worrying" had the highest probabilistic priority; the lines from A2 "Not being able to stop or control worrying" to A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying" to A7 "Feeling afraid as if something awful might happen" represented the most important arrows.

CONCLUSION

There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 "Not being able to stop or control worrying" might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 "Not being able to stop or control worrying". GAD may have two "subsystems". The modes of interconnection among anxiety may be consistent between genders.

Affective and cognitive impact of social overinclusion: a meta-analytic review of cyberball studies

Belongingness is a central biopsychosocial system. Challenges to belongingness (i.e. exclusion/ostracism) engender robust negative effects on affect and cognitions. Whether overinclusion - getting more than one's fair share of social attention - favourably impacts affect and cognitions remains an open question. This pre-registered meta-analysis includes twenty-two studies (N = 2757) examining overinclusion in the context of the Cyberball task. We found that the estimated overall effect size of overinclusion on positive affect was small but robust, and the effect on fundamental needs cognitions (belongingness, self-esteem, meaningful existence and control) was moderate in size and positive in direction. Notably, the effect sizes of overinclusion were smaller than the corresponding effects of exclusion. Finally, the effects of overinclusion on positive affect were greater for high, as compared to low, socially anxious individuals. Exploring the sequelae of the full range of inclusion experiences - from exclusion to overinclusion - may enrich our understanding of the functioning of the belongingness system as well as its interaction with another central biosocial system - the social status system.

A network approach to climate change anxiety and its key related features

Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data-driven clues for the field's larger quest to establish the foundations of climate anxiety.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis

BACKGROUND

Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them.

AIMS

To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown.

METHODS

A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis.

RESULTS

After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms.

CONCLUSION

Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

The psychological impact of the COVID-19 pandemic in Portugal: The role of personality traits and emotion regulation strategies

Recent evidence suggests that both personality traits (PT) and emotion regulation (ER) strategies play an important role in the way people cope with the COVID-19 pandemic. The aim of this study was two folded. First, to longitudinally investigate the psychological distress (depression, anxiety, and stress levels) taking in consideration PT and ER strategies in 3 different moments: during the first lockdown period (April/20), at the first deconfinement (May/20) and 1-month after the first deconfinement (Jun/20)-Experiment I. Second, to cross-sectionally evaluate the impact of the pandemic in psychological distress and the correlates with PT and ER 6-months after the first deconfinement November/20 to February/21 -Experiment II. A total of 722 volunteers (Experiment I = 180; Experiment II = 542) aged 18 years or older participated in this online survey. The findings from Experiment I show that psychological distress decreased after the lockdown period, however, neuroticism traits predicted higher levels of depression, anxiety and stress symptoms, while difficulties in ER strategies were identified as a risk factor for depression and stress. For experiment II, neuroticism traits and being infected with COVID-19 were associated to higher levels of symptomatology, while unemployment and the use of emotional suppression strategies to cope with emotional situations were associated to depressive and anxiety symptoms. Although the psychological impact of the COVID-19 outbreak decreased over time in our sample, the current findings suggest that difficulties in emotional regulation and high levels of neuroticism traits might be potential risk factors for psychiatric symptomatology during the COVID-19 pandemic. Thus, people with difficulties in ER and neuroticism traits would benefit from psychological interventions that provide personality-appropriate support and promote emotion regulation skills during stressful events, such as the case of the global pandemic.

Metacognition, cognition and social anxiety: A test of temporal and reciprocal relationships

Cognitive models of social anxiety give prominence to dysfunctional schemas about the social self as the key underlying factors in maladaptive self-processing strategies and social anxiety symptoms. In contrast, the metacognitive model argues that beliefs about cognition represent a central belief domain underlying psychopathology and cognitive schemas as products of a thinking style regulated by metacognition. The present study therefore evaluated the temporal and reciprocal relations between metacognitive beliefs, social self-beliefs, and social anxiety symptoms to shed light on possible causal relationships among them. Eight hundred and sixty-eight individuals gathered at convenience participated in a four-wave online survey with each measurement wave 6 weeks apart. Using autoregressive cross-lagged panel models, we found significant temporal and reciprocal relations between metacognition, social self-beliefs (schemas), and social anxiety. Whilst social self-beliefs prospectively predicted social anxiety this relationship was reciprocal. Metacognitive beliefs prospectively predicted both social interaction anxiety and social self-beliefs, but this was not reciprocal. The results are consistent with metacognitive beliefs causing social anxiety and social self-beliefs and imply that negative social self-beliefs might be a product of metacognition. The clinical implications are that metacognitive beliefs should be the central target in treatments of social anxiety.

What do measures of self-report interoception measure? Insights from a systematic review, latent factor analysis, and network approach

Recent conceptualizations of interoception suggest several facets to this construct, including "interoceptive sensibility" and "self-report interoceptive scales", both of which are assessed with questionnaires. Although these conceptual efforts have helped move the field forward, uncertainty remains regarding whether current measures converge on their measurement of a common construct. To address this question, we first identified -via a systematic review- the most cited questionnaires of interoceptive sensibility. Then, we examined their correlations, their overall factorial structure, and their network structure in a large community sample (n = 1003). The results indicate that these questionnaires tap onto distinct constructs, with low overall convergence and interrelationships between questionnaire items. This observation mitigates the interpretation and replicability of findings in self-report interoception research. We call for a better match between constructs and measures.

A network analysis of two conceptual approaches to the etiology of PTSD

Two prominent conceptual models of posttraumatic stress disorder (PTSD) are the cognitive model, associated with cognitive processing therapy (CPT; Resick & Schnicke, 1992), and the functional contextualist model, underlying acceptance and commitment therapy (ACT; Hayes et al., 1999). Network analysis was used to examine dynamic interactions among cognitive (relating to CPT) and functional contextualistic (relating to ACT) variables and PTSD symptoms in a sample of 722 trauma-exposed adults. Results from the cognitive networks highlighted the importance of maladaptive beliefs about threat in maintaining the co-occurrence of PTSD symptoms and cognitive variables. Additionally, PTSD symptoms were more likely to lead to cognitive variables, rather than the reverse direction. Results from the functional contextualist networks identified numerous associations amongst variables that contribute to the co-occurrence of PTSD symptoms and psychological inflexibility. Findings from this study may help generate causal hypotheses that can be tested further using a longitudinal study design.

Test anxiety, anxiety disorders, and school-related wellbeing: Manifestations of the same or different constructs?

Previous studies have shown that highly test anxious persons are more likely to meet criteria for an anxiety disorder and report more frequent symptoms of anxiety disorders than their low test anxious counterparts. However, it is unclear whether test anxiety should be treated as distinct to, or a manifestation of, anxiety disorders. Furthermore, the Dual Factor Model of Mental Health proposes that high subjective wellbeing cannot be solely inferred from the absence of psychopathology. To date, no studies have examined the Dual Factor Model in relation to test anxiety. In the present study, we examined how test anxiety, two common anxiety disorders (i.e., generalized anxiety disorder [GAD] and panic disorder [PD]), and subjective wellbeing in the school domain (i.e., school-related wellbeing) were related in a sample of 918 adolescents (M age = 15.77 years) using network analysis and latent profile analysis. Results from the network analysis indicated that test anxiety, GAD, PD, and school-related wellbeing were represented as distinct constructs. Bridge nodes were identified that linked test anxiety with GAD, PD, and school-related wellbeing. The latent profile analysis identified three of the four profiles predicted by the Dual Factor Model, including (a) troubled (i.e., low school-related wellbeing, high test anxiety, GAD, and PD), (b) complete mental health (i.e., high school-related wellbeing, low test anxiety, GAD, and PD), and (c) symptomatic but content (i.e., average school-related wellbeing, test anxiety, GAD, and PD). We concluded that test anxiety was distinct from, rather than a manifestation of, GAD and PD. We found support for the Dual Factor Model, albeit not unequivocal, using test anxiety as an additional indicator of psychopathology to that of GAD and PD.

A network approach to the five-facet model of mindfulness

Despite the large-scale dissemination of mindfulness-based interventions, debates persist about the very nature of mindfulness. To date, one of the dominant views is the five-facet approach, which suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty remains regarding the potential interplay between these facets. In this study, we investigated the five-facet model via network analysis in an unselected sample (n = 1704). We used two distinct computational network approaches: a Gaussian graphical model (i.e., undirected) and a directed acyclic graph, with each model determining the relations between the facets and their relative importance in the network. Both computational approaches pointed to the facet denoting Acting with Awareness as playing an especially potent role in the network system. Altogether, our findings offer novel data-driven clues for the field's larger quest to ascertain the very foundations of mindfulness.

A network approach to parental burnout

BACKGROUND

The use of network analyses in psychology has increasingly gained traction in the last few years. A network perspective views psychological constructs as dynamic systems of interacting elements.

OBJECTIVE

We present the first study to apply network analyses to examine how the hallmark features of parental burnout - i.e., exhaustion related to the parental role, emotional distancing from children, and a sense of ineffectiveness in the parental role - interact with one another and with maladaptive behaviors related to the partner and the child(ren), when these variables are conceptualized as a network system.

PARTICIPANTS AND SETTING

In a preregistered fashion, we reanalyzed the data from a French-speaking sample (n = 1551; previously published in Mikolajczak, Brianda et al., 2018), focusing on seven specific variables: the three hallmark parental burnout features, partner conflict, partner estrangement, neglectful behavior toward children, and violent behavior toward children.

METHODS

We computed two types of network models, a graphical Gaussian model to examine network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the different variables.

RESULTS

Both network models pointed to emotional distance as an especially potent mechanism in activating all other nodes.

CONCLUSIONS

These results suggest emotional distance as critical to the maintenance of the parental burnout network and a prime candidate for future interventions, while affirming that network analysis can successfully expose the structure and relationship of variables related to parental burnout and its consequences related to the partner and the child(ren).

Why we should move from reductionism and embrace a network approach to parental burnout

Network science has allowed varied scientific fields to investigate and visualize complex relations between many variables, and psychology research has begun to adopt a network perspective. In this paper, we consider how leaving behind reductionist approaches and instead embracing a network perspective can advance the field of parental burnout. Although research into parental burnout is in its early stages, we argue that a network approach to parental burnout could set the scene for radically new vistas in parental burnout research. We claim that such an approach can allow simultaneous investigations (and clear visualizations) of many variables related to parental burnout and their interactions, integrates smoothly with prior family systems theories, and prioritizes dynamic research questions. We likewise discuss potential future clinical applications, such as interventions targeting central nodes and treatment personalized to a specific family's network system. We also review practical considerations, limitations, and future directions for researchers interested in applying a network approach to parental burnout research.

A Temporal Network Approach to Paranoia: A Pilot Study

Paranoid beliefs have been conceptualized as a central psychological process linked to schizophrenia and many mental disorders. Research on paranoia has indicated that it is pivotal to consider not only levels but also dynamic aspects of incriminated related mechanisms over time. In the present study, we conceptualized paranoia as a system of interacting elements. To do so, we used temporal network analysis to unfold the temporal dynamics between core psychological paranoia-related mechanisms, such as self-esteem, sadness, feeling close to others, and experiential avoidance. Time-series data of 23 participants with high scores in paranoia and/or interpersonal sensitivity were collected via experience sampling methodology (ESM). We applied a multilevel vector autoregressive (mlVAR) model approach and computed three distinct and complementary network models (i.e., contemporaneous, temporal, and between-subject) to disentangle associations between paranoia-related mechanisms in three different time frames. The contemporaneous model indicated that paranoia and sadness co-occurred within the same time frame, while sadness was associated with both low self-esteem and lack of closeness to others. The temporal model highlighted the importance of feeling close to others in predicting low paranoia levels in the next time frame. Finally, the between-subject model largely replicated an association found in both contemporaneous and temporal models. The current study reveals that the network approach offers a viable data-driven methodology for elucidating how paranoia-related mechanisms fluctuate over time and may determine its severity. Moreover, this novel perspective may open up new directions toward identifying potential targets for prevention and treatment of paranoia-related problems.