Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data

BACKGROUND AND AIMS

Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets.

DESIGN, SETTING AND PARTICIPANTS

Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part.

MEASUREMENTS

Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks.

FINDINGS

Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09).

CONCLUSIONS

Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

A network analysis of catatonia symptoms across diagnoses

BACKGROUND

Catatonia, as a transdiagnostic construct, manifests across various psychiatric and non-psychiatric conditions. Understanding how symptom variations impact the catatonia construct and differ across primary diagnoses (schizophrenia, bipolar disorder, unipolar depression, and neurological/metabolic/immunological condition) is essential to refine diagnostic and therapeutic approaches. This study aims to compare the symptom networks and centrality measures of these diagnoses.

METHODS

We conducted a network analysis using Bush-Francis Catatonia Rating Scale (BFCRS) data from 118 patients, examining centrality measures and network comparisons across the four primary diagnostic groups.

RESULTS

In the general catatonia network, the three most central symptoms identified were Excitement (1.462), Perseveration (1.456), and Impulsivity (1.332). While the overall structure of the catatonia networks did not show significant differences between diagnoses in terms of symptom connections and centrality, variations in centrality measures were observed among the different networks.

CONCLUSIONS

The study reinforces the notion of catatonia as an independent syndrome relatively to psychiatric or non-psychiatric diagnoses. However, the variation in centrality of symptoms across different primary diagnoses provides critical insights that could aid clinicians in tailoring diagnostic and therapeutic strategies. Future research should further explore these relationships and develop more refined approaches to managing catatonia.

Dynamic patterns of COVID stress syndrome among university students during an outbreak: a time-series network analysis

OBJECTIVE

Assessing the progression of COVID stress syndrome (CSS) and understanding how cognitive and emotional factors play a role in the dynamic system is critical for prevention and intervention during the COVID-19 pandemic. We investigated the dynamic relationship between self-reported COVID stress syndrome, cognitive factors, and emotional factors through time-series network analysis.

METHODS AND MEASURES

Participants were 188 university students involved in an experience sampling method study that lasted 14 days, three times a day, during the COVID-19 pandemic period following a shift in pandemic prevention and control policy.

RESULTS

CSS symptoms are usually present simultaneously, and xenophobia is the most central node of the network. There is a complex mutual predictive relationship between CSS symptoms, in which traumatic stress symptoms are crucial in developing and maintaining the CSS symptom network. Negative affect was associated with CSS symptoms at the same time, and subjective health cognition was a significant predictor of CSS symptoms the next time.

CONCLUSIONS

Traumatic stress symptoms are essential nodes in the CSS symptom network, and negative emotions and subjective health influence the occurrence and development of CSS symptoms.

Contemporaneous symptom networks analysis in lymphoma patients during chemotherapy: protocol for a single-centre prospective cross-sectional study

BACKGROUND

Symptom networks offer a theoretical basis for developing personalised and precise symptom management strategies. However, symptom networks in lymphoma patients during chemotherapy have been rarely reported. This study intends to establish contemporaneous symptom networks in lymphoma patients during chemotherapy and explore the centrality indices and density in these symptom networks.

METHODS AND ANALYSIS

This is a single-centre prospective cross-sectional study. A total of 315 lymphoma patients admitted to the Lymphoma Department of Shanxi Bethune Hospital since 1 June 2024 will be selected as the study subjects. The patient-reported outcome measures of General Data Questionnaire and Lymphoma Symptom Assessment Scale will be assessed. R package will be used to construct a contemporaneous symptom network, explore the relationship between core and analysed symptoms and analyse the predictive role of network density on patient prognosis.

ETHICS AND DISSEMINATION

This study adheres to the principles of the Declaration of Helsinki and relevant ethical guidelines. Ethical approval has been obtained from Shanxi Bethune Hospital Ethics Committee (approval number: YXLL-2023-186). The final outcomes will be published in a peer-reviewed journal and disseminated through a conference.

Unveiling temporal dynamics of PTSD and its functional impairments: A longitudinal study in UK healthcare workers

PTSD has been associated with negative long-term consequences, including social and occupational impairments. Yet, a nuanced understanding of the interplay between PTSD symptoms and distinct domains of impairments on a short-term basis (weeks/ months) at the within-person level remains underexplored. In a large sample (nwave 1 = 1096, nwave 7 = 304) of UK healthcare workers assessed across seven assessment waves during the COVID-19 pandemic (spaced 6 weeks apart), we employed exploratory graphical vector autoregression models (GVAR) models to discern within-person temporal (across time) and contemporaneous (within same time window) dynamics between PTSD symptoms and functional impairment domains. The contemporaneous network highlighted strong co-occurrences between different symptoms and impairments. The temporal network revealed a mutually reinforcing cycle between intrusion and avoidance symptoms. Intrusion symptoms showed the highest out-strength (i.e., most predictive symptom), predicting avoidance symptoms, elevated sense of current threat, and various functional impairments. Avoidance symptoms, elevated after increased levels of intrusions, predicted work impairments that in turn were associated with difficulties in fulfilling other obligations. Our findings underscore the dynamics between perceived threat and intrusions, and the role intrusions may play in predicting a cascade of adverse effects. Targeted interventions aimed at mitigating intrusions may disrupt this negative cycle.

Comparing positive reappraisal and mindfulness in relation to daily emotions during COVID-19: An experience sampling study

Research has suggested that daily cognitive reappraisal and mindfulness are differentially associated with emotional experience. Nevertheless, the different relationship between these two emotion regulation strategies and emotional experience remains unexplored amidst the COVID-19 pandemic, when people were facing unprecedented challenges and disruptions in their everyday lives. The current study aimed to examine the potential unidirectional or bidirectional relations between two strategies and daily emotional experience during the COVID-19 pandemic and whether the associations between the two strategies and emotional experience varied. A total of 184 college students participated in this study. Daily positive reappraisal, mindful attention and awareness (MAA), positive and negative affect, and COVID-19-related stress were assessed utilizing experience sampling method (three times a day for 14 consecutive days). Results suggested that the directionality of the link between the two strategies and daily emotional experience differed. The links between positive reappraisal and positive affect, negative affect, and COVID-19-related stress were transactional. However, a unidirectional relation was observed between positive affect and subsequent MAA. The study provided support for the contextual perspective of emotion regulation by demonstrating that the efficacy of regulation strategies is contingent upon the context. The identification of optimal conditions for effective strategies remains a crucial area for future research.

Network analyses of ecological momentary emotion and avoidance assessments before and after cognitive behavioral therapy for anxiety disorders

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

A systematic review of interpersonal processes and their measurement within experience sampling studies of self-injurious thoughts and behaviours

Self-injurious thoughts and behaviours (SITBs) are a leading cause of death, and interpersonal processes (IPs) appear to play a role in SITBs. This systematic review synthesises the literature on IPs and SITBs in daily life and addresses four critical questions: (1) Which IPs have been assessed and how, (2) How are differences in IPs between individuals associated with SITBs?, (3) How are differences in IPs within individuals associated with SITBs? and (4) Do IPs relate differently to self-injurious thoughts than behaviours? Our review followed PRISMA guidelines and eligible literature was screened until 25 April 2024. We identified 58 Experience Sampling studies (32.76% daily-diary studies) of which most focused on IPs from major SITBs theories (e.g., thwarted belongingness) but largely used inconsistent operationalizations. Results from 39 studies investigating within-person associations were mixed. Based on 26 studies, whether differences in IPs between individuals relate to SITBs remains unclear. Three studies have investigated whether IPs relate to the transition from thoughts to behaviours, but temporal models are needed to draw firm conclusions. Studies investigating IPs and SITBs in daily life are largely inconclusive. Psychometrically validated measures are warranted, and future daily-life studies would benefit from drawing on ideation-to-action frameworks.

Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections

The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

Exploring mental health dynamics during eating disorder treatment: A psychometric network study with panel data

To explore mental health associations during eating disorder (ED) treatment. Based on the dual-continua model of mental health, general and ED-specific psychopathology, as well as emotional, psychological, and social well-being were considered as mental health domains.

Network analyses with panel data were applied to explore within- (temporal and contemporaneous networks) and between-person effects in a sample of 1250 female ED patients during 12 months of outpatient treatment. The associations between the domains and their centrality were examined. Autoregressive and cross-lagged effects were also estimated.

ED psychopathology was the most central domain in the temporal network. ED psychopathology changes predicted further ED psychopathology changes and small changes in the other domains. Weak bi-directional associations were found between changes in the well-being domains and general psychopathology. In contrast to the temporal network, ED psychopathology was the least central and psychological well-being the most central domain in the contemporaneous and between-subjects networks. This suggests a central role of psychological well-being for experiencing mental health within time points.

ED psychopathology may change relatively independent from other mental health domains. Well-being domains may be considered as more stable aspects of mental health.

Introducing FRED: Software for Generating Feedback Reports for Ecological Momentary Assessment Data

Ecological Momentary Assessment (EMA) is a data collection approach utilizing smartphone applications or wearable devices to gather insights into daily life. EMA has advantages over traditional surveys, such as increasing ecological validity. However, especially prolonged data collection can burden participants by disrupting their everyday activities. Consequently, EMA studies can have comparably high rates of missing data and face problems of compliance. Giving participants access to their data via accessible feedback reports, as seen in citizen science initiatives, may increase participant motivation. Existing frameworks to generate such reports focus on single individuals in clinical settings and do not scale well to large datasets. Here, we introduce FRED (Feedback Reports on EMA Data) to tackle the challenge of providing personalized reports to many participants. FRED is an interactive online tool in which participants can explore their own personalized data reports. We showcase FRED using data from the WARN-D study, where 867 participants were queried for 85 consecutive days with four daily and one weekly survey, resulting in up to 352 observations per participant. FRED includes descriptive statistics, time-series visualizations, and network analyses on selected EMA variables. Participants can access the reports online as part of a Shiny app, developed via the R programming language. We make the code and infrastructure of FRED available in the hope that it will be useful for both research and clinical settings, given that it can be flexibly adapted to the needs of other projects with the goal of generating personalized data reports.

Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study

BACKGROUND

Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables.

OBJECTIVE

This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep.

METHODS

Data were extracted from a questionnaire provided by the Réseau Morphée health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined.

RESULTS

Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes.

CONCLUSIONS

The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health.

The future of psychological treatments: The Marburg Declaration

Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.

Peer functioning difficulties may exacerbate symptoms of attention-deficit/hyperactivity disorder and irritability over time: a temporal network analysis

BACKGROUND

Children with attention-deficit/hyperactivity disorder (ADHD) have been consistently found to experience impairments in peer functioning. Irritability is highly prevalent in children with ADHD and may worsen social impairments given the frequent temper outbursts and low frustration tolerance characterizing irritability. However, it is still unclear how ADHD and irritability symptoms interact with peer functioning difficulties over time. Assessing these temporal dynamics using a novel longitudinal approach (i.e., temporal network analysis) may reveal precise targets for intervention.

METHODS

This study investigates the dynamic associations between ADHD symptoms, irritability, and peer functioning in a community sample of 739 children (ages 8-11 years, Mage = 10.06 [SD = 0.59], 47.77% females) assessed at three timepoints, 6 months apart, in a school-based study. Parents reported their child's ADHD symptoms using the Swanson, Nolan, and Pelham Rating Scale (SNAP-IV), and irritability symptoms using the Child Behavior Checklist (CBCL) irritability items. Children's peer functioning (i.e., peer acceptance, peer rejection, number of friendships, and victimization) was measured via peer nomination. To estimate the longitudinal associations between the variables, we built a graphical vector autoregression model for panel data.

RESULTS

The longitudinal network highlighted that poor peer functioning contributed to increases in symptoms over time. Specifically, (1) physical victimization predicted increases in inattention, hyperactivity, and irritability; (2) peer rejection predicted increases in inattention, which in turn predicted increases in irritability; (3) peer acceptance predicted decreases in inattention and irritability; and (4) higher numbers of mutual friendships increased inattention.

CONCLUSIONS

These results suggest that a negative social environment involving physical bullying and rejection may aggravate ADHD and irritability symptoms. Conversely, positive social interactions, such as being liked by peers, may improve inattention and irritability symptoms. Fostering social-emotional skills and positive social interactions and environments in children with ADHD and irritability may be a promising target for future interventions to reduce symptoms.

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

PURPOSE

To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients.

METHODS

We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks.

RESULTS

Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001).

CONCLUSIONS

Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients.

IMPLICATIONS FOR CANCER SURVIVORS

We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

The dynamic relationships between well-being, behavioral restrictions, and health behaviors during the COVID-19 pandemic: A large-scale intensive longitudinal network study

The behavioral restrictions disrupting daily life during the COVID-19 pandemic have profoundly impacted well-being, and health behaviors have been advocated to prevent decline. To understand how processes related to fluctuation in well-being unfold within individuals, analyses on the within-person level are required. In this preregistered intensive longitudinal study, 1,709 individuals from the Norwegian adult population provided data daily over 40 consecutive days during the pandemic. The responses were modeled in a multilevel vector autoregressive model to estimate within-person networks, across and within-day, and a between-person network. All three networks revealed productivity, relatedness, and optimism as positively associated. Social distancing was contemporaneously negatively associated with productivity and relatedness. Among behavioral factors, being physically active predicted lower relatedness across days but displayed positive associations with relatedness, productivity, and optimism contemporaneously. Alcohol consumption predicted lower productivity across and within-day, although revealing a positive association with optimism within-day. Being social online and feeling related to others displayed a temporal negative bidirectional relationship. In contrast, being social online was positively associated with optimism, productivity, and relatedness contemporaneously. Our study emphasizes the dynamic nature of well-being and its complex associations with behavioral factors during the pandemic. The study shed light on opposing associations of behavioral factors at the within- and between-person level.

Using cross-validation methods to select time series models: Promises and pitfalls

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross-validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10-fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person-mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice.

Staging models in eating disorders: A systematic scoping review of the literature

Eating Disorders (ED) are characterized by low remission rates, treatment drop-out, and residual symptoms. To improve assessment and treatment of ED, the staging approach has been proposed. This systematic scoping review is aimed at mapping the existing staging models that explicitly propose stages of the progression of ED. A systematic search of PubMed, PsycINFO, Scopus was conducted with the terms staging, anorexia nervosa, bulimia nervosa, binge-eating disorders, eating disorders. Eleven studies met inclusion criteria presenting nine ED staging models, mostly for anorexia nervosa. Three were empirically tested, one of which was through an objective measure specifically developed to differentiate between stages. Most staging models featured early stages in which the exacerbation of EDs unfolds and acute phases are followed by chronic stages. Intermediate stages were not limited to acute stages, but also residual phases, remission, relapse, and recovery. The criteria for stage differentiation encompassed behavioral, psychological, cognitive, and physical features including body mass index and illness duration. One study recommended stage-oriented interventions. The current review underscores the need to empirically test the available staging models and to develop and test new proposals of staging models for other ED populations. The inclusion of criteria based on medical features and biomarkers is recommended. Staging models can potentially guide assessment and interventions in daily clinical settings.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder

BACKGROUND

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

AIMS

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

METHOD

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

RESULTS

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

CONCLUSIONS

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Subgrouping with Chain Graphical VAR Models

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Interrelatedness of Family and Parenting Risk Factors for Juvenile Delinquency: A Network Study in U.S. and Dutch Juveniles

Family interventions that address a diversity of family and parenting factors are often used to prevent juvenile delinquency, but are effective to only a limited extent. This study applied a network approach to risk factors for juvenile delinquency and examined the interrelatedness of specifically family and parenting risk factors in a U.S. and separate Dutch sample of juveniles and their family members. Differences in interrelatedness between these samples were examined as well. Secondary analyses were conducted on data collected in the United States with the Washington State Juvenile Court Assessment (WSJCA) and on data collected in the Netherlands with a Dutch-adapted translation of the WSJCA. Network analyses were performed, separately for the U.S. (N = 13,613) and Dutch (N = 3,630) sample, on seven risk factors that were assessed with a three-point Likert scale ranging from each factor's protective side to a corresponding risk side. In the U.S. sample network, "inadequate parental punishment" and "lack of parental supervision" that both refer to an authoritarian parenting style were the most "central" factors and had the strongest associations with the other risk factors. In the Dutch sample network, "the family not providing opportunities" and "inadequate parental reward" were the most "central" factors, which refer to an authoritative parenting style. The family and parenting factors identified as most central in the networks may be promising to address in family interventions, as it can be expected that both the directly addressed problems and their correlated problems will improve. The current results may inform attempts to strengthen family interventions for juvenile delinquency in the United States and the Netherlands.

Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder

BACKGROUND

A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.

METHODS

A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.

RESULTS

In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.

CONCLUSIONS

This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

A socioemotional network perspective on momentary experiences of family conflict in young adults

Family conflict is an established predictor of psychopathology in youth. Traditional approaches focus on between-family differences in conflict. Daily fluctuations in conflict within families might also impact psychopathology, but more research is needed to understand how and why. Using 21 days of daily diary data and 6-times a day experience-sampling data (N = 77 participants; mean age = 21.18, SD = 1.75; 63 women, 14 men), we captured day-to-day and within-day fluctuations in family conflict, anger, anxiety, and sadness. Using multilevel models, we find that days of higher-than-usual anger are also days of higher-than-usual family conflict. Examining associations between family conflict and emotions within days, we find that moments of higher-than-usual anger predict higher-than-usual family conflict later in the day. We observe substantial between-family differences in these patterns with implications for psychopathology; youth showing the substantial interplay between family conflict and emotions across time had a more perseverative family conflict and greater trait anxiety. Overall, findings indicate the importance of increases in youth anger for experiences of family conflict during young adulthood and demonstrate how intensive repeated measures coupled with network analytic approaches can capture long-theorized notions of reciprocal processes in daily family life.

Dynamic symptom associations in posttraumatic stress disorder: a network approach

Background and objective: The current study aimed to investigate the within-day symptom dynamics in PTSD patients, specifically focusing on symptoms that most predict changes in other symptoms. The study included a baseline diagnostic assessment, followed by an assessment using the experience sampling method (ESM) via a smartphone.Method: Participants answered questions related to their PTSD symptoms four times per day for 15 consecutive days (compliance rate 75%). The clinical sample consisted of 48 treatment-seeking individuals: 44 with PTSD as a primary diagnosis, and four patients with subsyndromal PTSD, all of whom had not yet begun trauma-focused treatment. The ESM assessment included the 20 items from the PTSD Checklist for DSM-5, five items from the International Trauma Questionnaire (ITQ) assessing disturbances in relationships and functional impairment, and two items from the Clinician-Administered PTSD Scale for DSM-5 assessing symptoms of depersonalization and derealization.Results: Temporal networks (prospective associations between symptoms) showed that changes in hypervigilance predicted changes in the greatest number of symptoms at the next time point. Furthermore, hypervigilance showed temporal connections with at least one additional symptom from each of the DSM-5 PTSD symptom clusters.Conclusions: Results show that the contemporaneous network (representing the relationship between given symptoms within the same assessment occasion) and the temporal network (representing prospective associations between symptoms) differ and that it is important to estimate both. Some findings from earlier research are replicated, but heterogeneity across studies remains. Future studies should include potential moderators.

An Ecological Mobile Momentary Intervention to Support Dynamic Goal Pursuit: Feasibility and Acceptability Study

BACKGROUND

Individuals can experience difficulties pursuing their goals amid multiple competing priorities in their environment. Effective goal dynamics require flexible and generalizable pursuit skills. Supporting successful goal pursuit requires a perpetually adapting intervention responsive to internal states.

OBJECTIVE

The purpose of this study was to (1) develop a flexible intervention that can adapt to an individual's changing short to medium-term goals and be applied to their daily life and (2) examine the feasibility and acceptability of the just-in-time adaptive intervention for goal pursuit.

METHODS

This study involved 3 iterations to test and systematically enhance all aspects of the intervention. During the pilot phase, 73 participants engaged in an ecological momentary assessment (EMA) over 1 month. After week 1, they attended an intervention training session and received just-in-time intervention prompts during the following 3 weeks. The training employed the Capability, Opportunity, Motivation, and Behavior (COM-B) framework for goal setting, along with mental contrasting with implementation intentions (MCII). Subsequent prompts, triggered by variability in goal pursuit, guided the participants to engage in MCII in relation to their current goal. We evaluated feasibility and acceptability, efficacy, and individual change processes by combining intensive (single-case experimental design) and extensive methods.

RESULTS

The results suggest that the digital intervention was feasible and acceptable to participants. Compliance with the intervention was high (n=63, 86%). The participants endorsed high acceptability ratings relating to both the study procedures and the intervention. All participants (N=73, 100%) demonstrated significant improvements in goal pursuit with an average difference of 0.495 units in the outcome (P<.001). The results of the dynamic network modeling suggest that self-monitoring behavior (EMA) and implementing the MCII strategy may aid in goal reprioritization, where goal pursuit itself is a driver of further goal pursuit.

CONCLUSIONS

This pilot study demonstrated the feasibility and acceptability of a just-in-time adaptive intervention among a nonclinical adult sample. This intervention used self-monitoring of behavior, the COM-B framework, and MCII strategies to improve dynamic goal pursuit. It was delivered via an Ecological Momentary Intervention (EMI) procedure. Future research should consider the utility of this approach as an additional intervention element within psychological interventions to improve goal pursuit. Sustaining goal pursuit throughout interventions is central to their effectiveness and warrants further evaluation.

eHealth tools use and mental health: a cross-sectional network analysis in a representative sample

eHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18-87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.

Understanding the Risk of Sexual Reoffending in Adult Men: A Network-Based Model

The predominant approach to understand dynamic risk factors of sexual reoffending has been referred to as the Propensities Model (Thornton, 2016). According to this model, dynamic risk factors can be conceptualized as latent constructs whose change alters the risk of sexual reoffending. Despite its strengths and contributions to research, this model does not offer answers to the question of how dynamic risk factors contribute to the risk of sexual reoffending, or of how sustained change in risk might take place. In this paper we introduce the Network-Based Model of Risk of Sexual Reoffending (NBM-RSR), which addresses several limitations and constraints of the Propensities Model and offers empirically testable propositions regarding the nature and development of the risk of sexual reoffending. The NBM-RSR considers risk of sexual reoffending to involve a self-sustaining network of causally connected dynamic risk factors. Consistent with this, an increased risk of sexual reoffending is characterized through a network that contains more and stronger interconnected dynamic risk factors with a higher strength. Sustained change in risk of sexual reoffending occurs when activity in the network exceeds a critical point resulting in a new self-sustaining network. Propositions based on the NBM-RSR are introduced and translated into testable hypotheses. These propositions revolve around (a) risk of sexual reoffending resulting from the construction of a network of causally connected dynamic risk factors, (b) network stability, sudden changes, and critical transitions, and (c) dynamic risk factors' relative influence on risk of sexual reoffending.

Using experience sampling methodology (ESM) to improve our understanding of day-to-day intrusion frequency and related distress in survivors of trauma

BACKGROUND AND OBJECTIVES

Cognitive models of posttraumatic stress disorder (PTSD) suggest that appraisals of traumatic sequelae and subsequent distress drive the development and maintenance of PTSD. Posttraumatic research has relied heavily on macro-longitudinal designs, with weeks or months between assessments of trauma-related cognitions and symptoms. The present study uses experience sampling methodology (ESM) better understand the day-to-day experiences of trauma exposed individuals.

METHODS

One-hundred trauma exposed adults reported their posttraumatic symptoms, interpretations, and behaviours four times a day over a 10-day ESM period.

RESULTS

As anticipated, within-person fluctuations in negative appraisals of intrusions and maladaptive coping strategies (e.g., thought suppression) were significantly positively associated with intrusion frequency and related distress. In all cases, the associations for negative appraisals and maladaptive coping were stronger with intrusion related distress than intrusion frequency.

LIMITATIONS

The observed contemporaneous associations only demonstrate that variables reliably fluctuated together and cannot indicate causality.

CONCLUSIONS

The findings demonstrate that day-to-day fluctuations in trauma related perceptions and sequelae are significant and should be explored alongside broader individual differences to advance our understanding of the development, maintenance, and treatment of PTSD.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

The network dynamics of self-compassion components and psychological symptoms during an intervention

The change process of psychological interventions is complex and should be understood with a systems perspective. This study sought to examine the network dynamics of self-compassion components and psychological symptoms during an intervention. A total of 139 participants completed daily assessments during a 28-day intervention. Utilizing multilevel vector autoregressive (VAR) model, temporal and contemporaneous networks were generated and group differences in network dynamics were evaluated through descriptive assessment and permutation tests. The intervention group displayed a significant increase in self-compassion and decrease in psychological symptoms, with self-compassion mediating the intervention effects on symptoms. Network analysis revealed some network dynamics that might be relevant to desirable therapeutic changes in the intervention group. The intervention group demonstrated a significantly less connected contemporaneous depression network, indicating a decreased vulnerability to symptom activation. Additionally, the intervention group showed significantly more temporal connections from self-compassion to anxiety, indicating an increased influence of self-compassion on anxiety. These findings suggest that the intervention may have reshaped the interconnection pattern of symptoms and that between self-compassion components and symptoms.

A novel approach for constructing personalized networks from longitudinal perceived causal relations

Personalized networks of psychological symptoms aim to advance therapy by identifying treatment targets for specific patients. Statistical relations in such networks can be estimated from intensive longitudinal data, but their causal interpretation is limited by strong statistical assumptions. An alternative is to create networks from patient perceptions, which comes with other limitations such as retrospective bias. We introduce the Longitudinal Perceived Causal Problem Networks (L-PECAN) approach to address both these concerns. 20 participants screening positive for depression completed 4 weeks day of brief daily assessments of perceived symptom interactions. Quality criteria of this new method are introduced, answering questions such as "Which symptoms should be included in networks?", "How many datapoints need to be collected to achieve stable networks?", and "Does the network change over time?". Accordingly, about 40% of respondents achieved stable networks and only few respondents exhibited network structure that changed during the assessment period. The method was time-efficient (on average 7.4 min per day), and well received. Overall, L-PECAN addresses several of the prevailing issues found in statistical networks and therefore provides a clinically meaningful method for personalization.

Dynamic Individual Risk Networks: Personalized Network Modelling Based on Experience Sampling Data

Following a network perspective, risk of sexual reoffending can be understood as a construct that emerges from the interactions between risk factors. If these interrelationships are validly mapped out, this leads to an increased understanding of the risk and thus may contribute to more effective and/or more efficient interventions. This paper reports on personalized network modeling mapping the interrelationships of dynamic risk factors for an individual convicted of sexual offenses, using experience sampling (ESM) based on Stable-2007 items. The longitudinal character of ESM enables both the assessment of interrelations between risk factors within a timeframe and the relationships between risk factors over time. Networks are calculated and compared to the clinical assessment of interrelationships between the risk factors.

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Dyslexia, the Amsterdam Way

The current aim is to illustrate our research on dyslexia conducted at the Developmental Psychology section of the Department of Psychology, University of Amsterdam, in collaboration with the nationwide IWAL institute for learning disabilities (now RID). The collaborative efforts are institutionalized in the Rudolf Berlin Center. The first series of studies aimed at furthering the understanding of dyslexia using a gamified tool based on an artificial script. Behavioral measures were augmented with diffusion modeling in one study, and indices derived from the electroencephalogram were used in others. Next, we illustrated a series of studies aiming to assess individuals who struggle with reading and spelling using similar research strategies. In one study, we used methodology derived from the machine learning literature. The third series of studies involved intervention targeting the phonics of language. These studies included a network analysis that is now rapidly gaining prominence in the psychopathology literature. Collectively, the studies demonstrate the importance of letter-speech sound mapping and word decoding in the acquisition of reading. It was demonstrated that focusing on these abilities may inform the prediction, classification, and intervention of reading difficulties and their neural underpinnings. A final section examined dyslexia, conceived as a neurobiological disorder. This analysis converged on the conclusion that recent developments in the psychopathology literature inspired by the focus on research domain criteria and network analysis might further the field by staying away from longstanding debates in the dyslexia literature (single vs. a multiple deficit, category vs. dimension, disorder vs. lack of skill).

A Network Analysis Study on the Structure and Gender Invariance of the Satisfaction with Life Scale among Spanish University Students

INTRODUCTION

The psychometric properties of the Satisfaction With Life Scale (SWLS) have been evaluated across numerous languages and population groups, primarily from a factor analysis perspective. In some studies, inconsistencies in structural invariance have been identified.

OBJECTIVE

This study aims to analyze the properties and gender invariance of the SWLS from a network analysis perspective.

METHOD

A total of 857 Spanish university students were obtained through a stratified random cluster sampling method in a cross-sectional survey design study. Descriptive analysis of the items, partial-correlation network, Bayesian network model estimation, and invariance analysis by gender were conducted.

RESULTS

The instrument did not exhibit any floor or ceiling effects. Each item can be considered univariately normally distributed, and all items clustered in a single and stable community. The partial-correlation network model and centrality measures were stable in the full sample and invariant across genders. Item 3 emerged as the most central node in the network with the highest predictability. The Bayesian network indicated that items 2 and 4 initiate the process, while item 5 acts as the sink, and items 1 and 3 act as mediators.

CONCLUSIONS

The SWLS can be used as a unidimensional measure, and the total score and relationships among items are stable and reliable. Any potential differences among genders cannot be associated with the functioning of the instrument. The predictability of every item was high, and the Bayesian network clearly identified different roles among the items.

COVID Protection Behaviors, Mental Health, Risk Perceptions, and Control Beliefs: A Dynamic Temporal Network Analysis of Daily Diary Data

BACKGROUND

To control infections, behavioral non-pharmaceutical interventions (NPIs) such as social distancing and hygiene measures (masking, hand hygiene) were implemented widely during the COVID-19 pandemic. At the same time, adherence to NPIs has also been implied in an increase in mental health problems. However, the designs of many existing studies are often poorly suited to disentangle complex relationships between NPI adherence, mental health symptoms, and health-related cognitions (risk perceptions, control beliefs).

PURPOSE

To separate between- and temporal within-person associations between mental health, health-related cognitions, and NPI adherence.

METHODS

Six-month ecological momentary assessment (EMA) study with six 4-day assessment bouts in 397 German adults. Daily measurement of adherence, mental health symptoms, and cognitions during bouts. We used dynamic temporal network analysis to estimate between-person, as well as contemporaneous and lagged within-person effects for distancing and hygiene NPIs.

RESULTS

Distinct network clusters of mental health, health cognitions, and adherence emerged. Participants with higher control beliefs and higher susceptibility were also more adherent (between-person perspective). Within-person, similar findings emerged, additionally, distancing and loneliness were associated. Lagged findings suggest that better adherence to NPIs was associated with better mental health on subsequent days, whereas higher loneliness was associated with better subsequent hygiene adherence.

CONCLUSIONS

Findings suggest no negative impact of NPI adherence on mental health or vice versa, but instead suggest that adherence might improve mental health symptoms. Control beliefs and risk perceptions are important covariates of adherence-both on between-person and within-person level.

Relations among symptoms of depression over time in at-risk youth

Depression consists of symptoms that may relate to each other in ways that go beyond simple co-occurrence. For example, some symptoms may precede and possibly contribute to the emergence of others. The present study examined several potential relations among the symptoms of depression. The overarching goals were to better understand how depression may unfold and to identify potential targets for intervention. The sample included 120 offspring of depressed parents. Youths' symptoms of depression were rated across 89 weeks. First, we investigated which symptoms preceded and potentially contributed to other symptoms 1 week later. This model revealed that sleep disturbance predicted the occurrence of other symptoms (e.g., sad mood, fatigue), and the occurrence of sad mood was predicted by other symptoms (e.g., worthlessness/guilt, psychomotor symptoms, sleep disturbance). Second, we investigated the within-person question of which symptoms tended to co-occur at the same time point. This model identified sad mood, irritability, and anhedonia as symptoms that tended to co-occur with each other and with many other depressive symptoms. Third, we investigated the between-person question of which symptoms tended to co-occur when averaged across time. This model identified worthlessness/guilt, fatigue, and anhedonia as symptoms strongly associated with other depressive symptoms across people irrespective of timing. Results indicate that the relations among the symptoms of depression vary, such that some symptoms preceded others by 1 week, some symptoms occurred at the same time, and other symptoms co-occurred in individuals. This more detailed view of the connections among depressive symptoms informs our understanding of depression as a dynamic set of unique indicators. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Developmental Trajectories of Internalizing and Externalizing Symptoms in Youth and Associated Gender Differences: A Directed Network Perspective

Psychopathology in youth is highly prevalent and associated with psychopathology in adulthood. However, the developmental trajectories of psychopathology symptoms, including potential gender differences, are markedly underspecified. The present study employed a directed network approach to investigate longitudinal relationships and gender differences among eight transdiagnostic symptom domains across three years, in a homogenous age sample of youth participants (n = 6,414; mean baseline age = 10.0 years; 78.6% White; Adolescent Brain Cognitive Development study). Anxious/depressed problems and aggressive behaviors were central symptoms and most predictive of increases in other symptom clusters at later timepoints. Rule-breaking behaviors, aggressive behaviors, and withdrawn/depressed problems emerged as bridge symptoms between externalizing and internalizing problems. Results supported cascade models in which externalizing problems predicted future internalizing problems, but internalizing problems also significantly predicted future externalizing problems, which is contrary to cascade models. Network structure, symptom centrality, and patterns of bridge symptoms differed between female and male participants, suggesting gender differences in the developmental trajectories of youth psychopathology. Results provide new insights into symptom trajectories and associated gender differences that may provide promising pathways for understanding disorder (dis)continuity and co-occurrence. The central and bridge symptoms identified here may have important implications for screening and early intervention for youth psychopathology.

A cross-disasters comparison of psychological distress: Symptoms network analysis

Large-scale traumatic events have the potential to trigger psychological distress, particularly among those in the affected areas. However, the manifestation of psychological distress may vary across different types of disasters. This study thus aimed to compare the symptoms network structure of psychological distress as assessed by the Kessler Psychological Distress Scale across three types of disasters: Terror (n = 5842), COVID-19 (n = 2428), and a nature-related disaster (n = 1001). Across disasters, two communities representing depression and anxiety symptoms were revealed. However, while after a nature-related disaster and the COVID-19 pandemic depression and anxiety items were interconnected via hopelessness, a terror attack resulted in more separated manifestations of anxiety and depression. Examination of symptom centrality showed that while in the Terror and the COVID-19 networks "depressed/no cheering up" was most connected to other symptoms, for the nature-related disaster network, two items were most central: "depressed/no cheering up" and "restless or fidgety". The results may point to different mechanisms of psychological distress structures after different disasters. Depending on the type of disaster, trauma-focused interventions may require targeted support and treatment.

Dynamic structure of posttraumatic growth among victims of the 2021 Henan floods: A 6-month, three-wave longitudinal study

Posttraumatic growth (PTG) following traumatic events is a dynamic and transformational process. However, its dynamic structure is currently unknown. The study aimed to estimate the dynamic structure of PTG at the nuance level based on PTG measurement items using network analysis. A three-wave longitudinal study was conducted from July 20, 2021, to January 30, 2022, among the victims experiencing the 2021 Henan floods. The final sample (n = 297) completed reports of PTG after 0, 3, and 6 months of the disaster. We employed the graphical vector autoregressive model approach to estimate extended network models. Contemporaneous network results revealed strong positive associations between domains of PTG in the same measurement window, especially between new possibilities and personal strength. Moreover, temporal network results-the internal interplays among PTG items across measurement windows-revealed that the domain of relating to others plays a central role in the dynamics of PTG. Although other domains predicted an increase in relating to others, relating to others inhibited the development of other domains, especially new possibilities and personal strength. Our study identifies the culture-specific process of PTG and provides empirical evidence on the explanatory models of PTG and the Janus-Face model of PTG.

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

Combining experience sampling with temporal network analysis to understand inertia of negative emotion in dysphoria

BACKGROUND

Emotional inertia refers to the resistance to update or change an emotional state and is a hallmark of maladaptive emotional dynamics in psychopathology. Little is known, however, about the role of emotion regulation in negative emotional inertia in dysphoria. The current study aimed to explore the association between inertia of discrete negative emotions, and emotion-specific emotion regulation strategy selection use and efficacy in dysphoria.

METHODS

The Center for Epidemiologic Studies Depression Scale (CESD) was used to divide university students into a dysphoria (N = 65) and non-dysphoria control (N = 62) group. Using an experience sampling approach delivered via a smartphone app, participants were queried semi-randomly regarding negative emotions and emotion regulation strategies 10 times a day for 7 consecutive days. Temporal network analysis was employed to estimate autoregressive connections for each discrete negative emotion (inertia of negative emotion) and the bridge connections between negative emotion and emotion regulation clusters.

RESULTS

Participants with dysphoria showed stronger inertia for anger and sadness in the context of the use of emotion-specific regulation strategies. Specifically, individuals with dysphoria displaying greater inertia of anger were more likely to ruminate about the past to cope with anger, and to ruminate on the past and future when experiencing sadness.

LIMITATIONS

Lack of a clinical depression patient group for comparison.

CONCLUSIONS

Our findings suggest an inflexibility to adaptively shift attention from discrete negative emotions in dysphoria and provide important insights for development of interventions to support wellbeing in this population.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Roles for Alexithymia, Emotion Dysregulation and Personality Features in Gambling Disorder: A Network Analysis

Although there is a growing interest in exploring the specific role of both emotional regulation processes and alexithymia in gambling disorder (GD), evidence remains scarce. In order to delve deeper into the complex interactions between these factors, the present study aimed at exploring a network of the core GD-related features, including GD symptomatology and severity, emotion dysregulation, alexithymia, and personality features. The sample included N = 739 treatment-seeking patients with GD (691 men and 48 women), aged 18-78 years (mean age = 39.2, SD = 13.2). The DSM-5 diagnostic criteria were assessed in, and the South Oaks Gambling Screen, Difficulties in Emotion Regulation Scale (DERS), and Temperament and Character Inventory-Revised were administered to, participants. A network analysis was conducted to reveal inter-relationships between these elements. Three nodes related to emotion dysregulation showed the most critical position in the whole network of the present study: "lack of emotional awareness", "non-acceptance of emotional responses", and "difficulties engaging in goal-directed behaviors". When analyzing emotional dysregulation using the different DERS subscales, two independent clusters were identified. One cluster encompassed alexithymia dimensions ("lack of awareness" and "lack of clarity"), while the other cluster included all other emotion-dysregulation dimensions. Identification of the emotion-dysregulation- and GD-related features with the highest centrality/linkage may be particularly useful for developing valid measurement tools and precise management plans for individuals with GD.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

A pilot randomized controlled trial of transdiagnostic network-informed personalized treatment for eating disorders versus enhanced cognitive behavioral therapy

OBJECTIVE

Eating disorders (EDs) are serious mental illnesses with high mortality and relapse rates and carry significant societal and personal costs. Nevertheless, there are few evidence-based treatments available. One aspect that makes treatment difficult is the high heterogeneity in symptom presentation. This heterogeneity makes it challenging for clinicians to identify pertinent treatment targets. Personalized treatment based on idiographic models may be well-suited to address this heterogeneity, and, in turn, presumably improve treatment outcomes.

METHODS

In the current randomized controlled trial, participants will be randomly assigned to either 20 sessions of enhanced cognitive behavioral therapy (CBT-E) or transdiagnostic network-informed personalized treatment for EDs (T-NIPT-ED). Assessment of ED symptoms, clinical impairment, and quality of life will occur at pre-, mid-, posttreatment, and 1-month follow-up.

RESULTS

We will examine the acceptability and feasibility of T-NIPT-ED compared to CBT-E. We also will test the initial clinical efficacy of T-NIPT-ED versus CBT-E on clinical outcomes (i.e., ED symptoms and quality of life). Finally, we will test if the network-identified precision targets are the mechanisms of change.

DISCUSSION

Ultimately, this research may inform the development and dissemination of evidence-based personalized treatments for EDs and serve as an exemplar for personalized treatment development across the broader field of psychiatry.

PUBLIC SIGNIFICANCE

Current evidence-based treatments for eating disorders result in low rates of recovery, especially for adults with AN. Our study aims to test the feasibility, acceptability, and clinical efficacy of a data-driven, individualized approach to ED treatment, network-informed personalized treatment, compared to the current evidence-based treatment for EDs, Enhanced CBT. Findings have the potential to improve treatment outcomes for EDs by identifying and targeting core symptoms maintaining EDs.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

The Temporal Relationship between Depressive Symptoms and Loneliness: The Moderating Role of Self-Compassion

Loneliness and depression are significant mental health challenges among college students; however, the intricate relationship between these phenomena remains unclear, particularly in the context of self-compassion. In this comprehensive study, we employ a cross-lagged panel network (CLPN) analysis to investigate the symptom-level association between depression and loneliness while exploring the potential moderating influence of self-compassion. Our sample consisted of 2785 college students, who were categorized into high- and low-self-compassion groups based on scores from the Self-Compassion Scale. Depressive symptoms were assessed using the Patient Health Questionnaire-9, while the UCLA Loneliness Scale-8 measured loneliness expressions. Our findings indicate that self-compassion plays a crucial role in the relationship between depression and loneliness. Specifically, we observed distinctive patterns within the high and low-self-compassion groups. In the low-self-compassion group, "energy" emerged as the most influential symptom, whereas "motor function" exhibited the highest influence in the high-self-compassion group. Furthermore, among individuals with high self-compassion, the pathway from depression to loneliness was characterized by "guilt-being alone when desired," while the reverse path from loneliness to depression encompassed "left out-feeling sad" and "left out-anhedonia." Conversely, in the low-self-compassion group, depression and loneliness demonstrated a more intricate mutual triggering relationship, suggesting that self-compassion effectively moderates the association between these variables. This study provides valuable insights into the underlying mechanisms driving the interplay between depression and loneliness, shedding light on the pivotal role of self-compassion in this intricate dynamic.