Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

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.

100 days of Adolescence: Elucidating Externalizing Behaviors Through the Daily Assessment of Inhibitory Control

Inhibitory control is a transdiagnostic risk factor for externalizing behaviors, particularly during adolescence. Despite advances in understanding links between inhibitory control and externalizing behaviors across youth on average, significant questions remain about how these links play out in the day-to-day lives of individual adolescents. The goals of the current study were to: (1) validate a novel 100-occasion measure of inhibitory control; (2) assess links between day-to-day fluctuations in inhibitory control and individual differences in externalizing behaviors; and (3) illustrate the potential of intensive longitudinal studies for person-specific analyses of adolescent externalizing behaviors. Participants were 106 youth (57.5% female, Mage = 13.34 years; SDage = 1.92) who completed a virtual baseline session followed by 100 daily surveys, including an adapted Stroop Color Word task designed to assess inhibitory control. Results suggested that the novel task was generally reliable and valid, and that inhibitory control fluctuated across days in ways that were meaningfully associated with individual differences in baseline impulsive behaviors. Results of illustrative personalized analyses suggested that inhibitory control had more influence in the daily networks of adolescents who used substances during the 100 days than in a matched set of adolescents who did not. This work marks a path forward in intensive longitudinal research by validating a novel inhibitory control measure, revealing that daily fluctuations in inhibitory control may be a unique construct broadly relevant to adolescent externalizing problems, and at the same time, highlighting that links between daily inhibitory control and impulsive behaviors are adolescent-specific.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

Using GIMME to model the emotional context of suicidal ideation based on clinical data: From research to clinical practice

Research and clinical experience highlight the variability of suicidal ideation (SI) within and between individuals. Although the idiographic emotional contexts in which SI occurs may offer explanations for its dynamic nature, most statistical methods focus on nomothetic patterns, making it difficult to advance our understanding of SI. Furthermore, the gap between nomothetic methods and a need for idiographic understanding of SI poses challenges to translating empirical knowledge into individualized clinical treatment. Group iterative multiple model estimation (GIMME) is a method that may bridge the idiographic-nomothetic divide by analyzing temporal relationships among a network of variables at both group- and individual-levels. This study explored the feasibility and clinical utility of GIMME applied to examine the relationships between various emotions and SI among individuals with borderline personality disorder who underwent Dialectical Behavior Therapy. We present graphic outputs that emerged throughout treatment and discuss how they could aid clinical assessment and case formulation (ClinicalTrials.gov Identifier: NCT03123198.).

Promoting Empathy and Affiliation in Relationships (PEAR) study: protocol for a longitudinal study investigating the development of early childhood callous-unemotional traits

INTRODUCTION

Children with callous-unemotional (CU) traits are at high lifetime risk of antisocial behaviour. Low affiliation (ie, social bonding difficulties) and fearlessness (ie, low threat sensitivity) are proposed risk factors for CU traits. Parenting practices (eg, harshness and low warmth) also predict risk for CU traits. However, few studies in early childhood have identified attentional or physiological markers of low affiliation and fearlessness. Moreover, no studies have tested whether parenting practices are underpinned by low affiliation or fearlessness shared by parents, which could further shape parent-child interactions and exacerbate risk for CU traits. Addressing these questions will inform knowledge of how CU traits develop and isolate novel parent and child targets for future specialised treatments for CU traits.

METHODS AND ANALYSIS

The Promoting Empathy and Affiliation in Relationships (PEAR) study aims to establish risk factors for CU traits in children aged 3-6 years. The PEAR study will recruit 500 parent-child dyads from two metropolitan areas of the USA. Parents and children will complete questionnaires, computer tasks and observational assessments, alongside collection of eye-tracking and physiological data, when children are aged 3-4 (time 1) and 5-6 (time 2) years. The moderating roles of child sex, race and ethnicity, family and neighbourhood disadvantage, and parental psychopathology will also be assessed. Study aims will be addressed using structural equation modelling, which will allow for flexible characterisation of low affiliation, fearlessness and parenting practices as risk factors for CU traits across multiple domains.

ETHICS AND DISSEMINATION

Ethical approval was granted by Boston University (#6158E) and the University of Pennsylvania (#850638). Results will be disseminated through conferences and open-access publications. All study and task materials will be made freely available on lab websites and through the Open Science Framework (OSF).

Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Data

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at high frequency. However, EMA data is often multi-dimensional, correlated, and hierarchical. Mixed-effects models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The Recurrent Temporal Restricted Boltzmann Machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the Heterogeneous-Dynamics Restricted Boltzmann Machine (HDRBM), to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world EMA data sets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for EMA studies.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

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.

Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations

OBJECTIVE

Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents.

METHOD

Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences.

RESULTS

A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035.

CONCLUSIONS

Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Blind Subgrouping of Task-based fMRI

Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

Investigating Individual Variation Using Dynamic Structural Equation Modeling: A Tutorial with Tinnitus

A growing body of research suggests that standard group-based models might provide little insight regarding individuals. In the current study, we sought to compare group-based and individual predictors of bothersome tinnitus, illustrating how researchers can use dynamic structural equation modeling (DSEM) for intensive longitudinal data to examine whether findings from analyses of the group apply to individuals. A total of 43 subjects with bothersome tinnitus responded to up to 200 surveys each. In multi-level DSEM models, survey items loaded on three factors (tinnitus bother, cognitive symptoms, and anxiety) and results indicated a reciprocal relationship between tinnitus bother and anxiety. In fully idiographic models, the three-factor model fit poorly for two individuals, and the multilevel model did not generalize to most individuals, possibly due to limited power. Research examining heterogeneous conditions such as tinnitus bother may benefit from methods such as DSEM that allow researchers to model dynamic relationships.

A scoping review on innovative methods for personality observation

Background

Personality’s investigation has always been characterized as a central area of research for psychology, such that it was established in the 1920s as an autonomous scientific-disciplinary field. Identifying and observing the people’s typical ways of “being in the world” has made possible to define the predictability of a pattern of behavioral responses related both to the possession of distinct characteristics of the agent subject and to specific environmental situations. In the actual scientific landscape, there is a strand of research that makes a description of personality through methodologies and indicators not usually used by psychology, but scientifically validated through standardized procedures. Such studies seem to be significantly increasing and reflect the emerging need to have to consider the human being in his or her complexity, whose existential and personal dimensions can no longer be traced to classification systems that are divorced from the epochal reference.

Objective

In this review, attention is focused on highlighting publications in the literature that have included the use of unconventional methods in the study of nonpathological personality, based on the Big Five theoretical reference model. To better understand human nature, an alternative based on evolutionary and interpersonal theory is presented.

Design

Online databases were used to identify papers published 2011–2022, from which we selected 18 publications from different resources, selected according to criteria established in advance and described in the text. A flow chart and a summary table of the articles consulted have been created.

Results

The selected studies were grouped according to the particular method of investigation or description of personality used. Four broad thematic categories were identified: bodily and behavioral element; semantic analysis of the self-descriptions provided; integrated-type theoretical background; and use of machine learning methods. All articles refer to trait theory as the prevailing epistemological background.

Conclusion

This review is presented as an initial attempt to survey the production in the literature with respect to the topic and its main purpose was to highlight how the use of observational models based on aspects previously considered as scientifically uninformative (body, linguistic expression, environment) with respect to personality analysis proves to be a valuable resource for drawing up more complete personality profiles that are able to capture more of the complexity of the person. What has emerged is a rapidly expanding field of study.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Integrating a functional view on suicide risk into idiographic statistical models

Acute risk of death by suicide manifests in heightened suicidal ideation in certain contexts and time periods. These increases are thought to emerge from complex and mutually reinforcing relationships between dispositional vulnerability factors and individually suicidogenic short-term stressors. Together, these processes inform clinical safety planning and our therapeutic tools accommodate a reasonable degree of idiosyncrasy when we individualize interventions. Unraveling these multifaceted factors and processes on a quantitative level, however, requires estimation frameworks capable of representing idiosyncrasies relevant to intervention and psychotherapy. Using, data from a 21-day ambulatory assessment protocol that included six random prompts per day, we developed personalized (i.e., idiographic) models of interacting risk factors and suicidal ideation via Group Iterative Multiple Model Estimation (GIMME) in a sample of people diagnosed with borderline personality disorder (N = 95) stratified for a history of high lethality suicide attempts. Our models revealed high levels of heterogeneity in state risk factors related to suicidal ideation, with no features shared among the majority of participants or even among relatively homogenous clusters of participants (i.e., empirically derived subgroups). We discuss steps toward clinical implementation of personalized models, which can eventually capture suicidogenic changes in proximal risk factors and inform safety planning and interventions.

Averting the Next Credibility Crisis in Psychological Science: Within-Person Methods for Personalized Diagnostics and Intervention

Personalizing assessments, predictions, and treatments of individuals is currently a defining trend in psychological research and applied fields, including personalized learning, personalized medicine, and personalized advertisement. For instance, the recent pandemic has reminded parents and educators of how challenging yet crucial it is to get the right learning task to the right student at the right time. Increasingly, psychologists and social scientists are realizing that the between-person methods that we have long relied upon to describe, predict, and treat individuals may fail to live up to these tasks (e.g., Molenaar, 2004). Consequently, there is a risk of a credibility loss, possibly similar to the one seen during the replicability crisis (Ioannides, 2005), because we have only started to understand how many of the conclusions that we tend to draw based on between-person methods are based on a misunderstanding of what these methods can tell us and what they cannot. An imminent methodological revolution will likely lead to a change of even well-established psychological theories (Barbot et al., 2020). Fortunately, methodological solutions for personalized descriptions and predictions, such as many within-person analyses, are available and undergo rapid development, although they are not yet embraced in all areas of psychology, and some come with their own limitations. This article first discusses the extent of the theory-method gap, consisting of theories about within-person patterns being studied with between-person methods in psychology, and the potential loss of trust that might follow from this theory-method gap. Second, this article addresses advantages and limitations of available within-person methods. Third, this article discusses how within-person methods may help improving the individual descriptions and predictions that are needed in many applied fields that aim for tailored individual solutions, including personalized learning and personalized medicine.

Detecting Idiographic Personality Change

Personality changes across the lifespan, but strong evidence regarding the mechanisms responsible for personality change remains elusive. Studies of personality change and life events, for example, suggest that personality is difficult to change. But there are two key issues with assessing personality change. First, most change models optimize population-level, not individual-level, effects, which ignores heterogeneity in patterns of change. Second, optimizing change as mean-levels of self-reports fails to incorporate methods for assessing personality dynamics, such as using changes in variances of and correlations in multivariate time series data that often proceed changes in mean-levels, making variance change detection a promising technique for the study of change. Using a sample of N = 388 participants (total N = 21,790) assessed weekly over 60 weeks, we test a permutation-based approach for detecting individual-level personality changes in multivariate time series and compare the results to event-based methods for assessing change. We find that a non-trivial number of participants show change over the course of the year but that there was little association between these change points and life events they experienced. We conclude by highlighting the importance in idiographic and dynamic investigations of change.

Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus

The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.

Integrating and distinguishing personality and psychopathology

OBJECTIVE

We asked authors of this Special Issue to answer the following four questions: (1) Is there evidence that personality and psychopathology can be integrated? (2) Is integration important? (3) Can they be distinguished? and (4) How can the difference be measured?

METHOD

We review each of the papers and place the special issue in a historical context.

RESULTS

Authors uniformly agreed that personality and psychopathology can be integrated within a common structure and that this is important. The third and fourth questions were more challenging. Though authors generally agreed that there is a distinction between the person and their mental health problems, articulations of that distinction were fuzzy and it is clear that current methods cannot adequately delineate these domains.

CONCLUSIONS

We summarize the issue by offering five directions for future research: (1) develop measurement tools that distinguish between the person, the context, and their transaction, (2) measure behavior and context at multiple timescales, (3) distinguish behavior and dysfunction in measurement, (4) use multimethod data to tap different levels of behavior, and (5) examine person-specific processes. Each of these directions comes with challenges, but the payoff of resolving them will be a more principled, evidence-based, and clinically useful model for the distinction between personality and psychopathology.

Specifying exogeneity and bilinear effects in data-driven model searches

Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone's emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME.

Longitudinal associations between borderline personality disorder and five-factor model traits over 24 years

Are five-factor traits and borderline personality symptoms the same features with different names? The existing literature offers reasons to think they are the same and reasons to think they are different. We examined longitudinal associations between these variables in a sample of patients assessed 12 times over 24 years using latent curve models with structured residuals. Mean trajectories for all variables were in the direction of symptom reduction/personality maturation and could be parsed into an initial, rapid improvement phase and a subsequent, gradual improvement phase. We found robust between-person associations among intercepts and long-term slopes of traits and symptoms. Specifically, higher levels of neuroticism as well as lower levels of extraversion, agreeableness, and conscientiousness were associated with higher levels of borderline personality symptoms, and changes in these traits were correlated with reduction in symptoms over time. Associations among time-structured residuals allowed for examinations of within-person deflections from these general trends at briefer (two year) intervals. All variables exhibited robust within-person carry-over effects. Other within-person effects were more specific to certain traits. These results suggest that, despite their distinct theoretical and methodological bases, normal trait and psychiatric diagnostic approaches largely converged on a similar conception of borderline personality.

Comparing impulsivity in borderline personality, schizophrenia and obsessional-compulsive disorders: Who is ahead?

OBJECTIVE

Impulsivity impacts life domains and in a psychiatric context is often associated with disorders severity and stigmatization. Borderline personality disorder's (BPD), Schizophrenic disorder's (SZD), and obsessional compulsive disorder's (OCD) impulsivity issues relate to worse prognosis. This study aims to compare these disorders assessing their proneness to impulsivity and urgency.

METHODS

We recruited 90 patients among them OCD (n = 25), SZD (n = 23), and BPD (n = 50), and 24 healthy control participants (HC). We assessed the diagnosis according and measured the impulsivity level.

RESULTS

Our results showed that BPD was significantly more impulsive than HC, SZD, and OCD. HC, SZD, and OCD being equivalent on their global Urgency-Premeditation-Perseverance-Sensation seeking scores. For urgency, BPD was also superior to others, OCD was superior to HC, but SZD and HC were equivalent. The urgency was correlated to SZD's scale for SZD, no link appeared between borderline personality questionnaire and Yale-Brown Obsessive-Compulsive Scale's score.

CONCLUSION

These results question the existent literature relating impulsivity and SZD.

Symptoms as rapidly fluctuating over time: Revealing the close psychological interconnections among borderline personality disorder symptoms via within-person structures

Despite the clinical emphasis on processes happening within individuals, investigations into the psychological, structural connections between mental health symptoms have almost exclusively analyzed differences between people. These investigations have revealed important findings; however, they do not reveal the close connections among symptoms in an individuals' psychology. This study thus examined the psychological connections between symptoms directly, using borderline personality disorder (BPD) symptoms as an example. Participants (252; 74 with BPD) reported their momentary BPD symptoms five times daily, and 165 did so again 18 months later. In support of personalized medicine (Wright & Woods, 2020), individuals' BPD symptom structures differed considerably from each other and from the between-person structure. A novel technique revealed that differences were greater than expected by chance. Within-person structures tended to exhibit more symptom granularity (more factors and lower variance explained) and differing symptom meanings (patterns of loadings). For example, some individuals exhibited close connections between relationship turmoil and identity uncertainty, whereas other individuals exhibited close connections between relationship turmoil and impulsivity. Thus, conceptions of any given person's psychopathological processes using between-person structural findings will most likely be inaccurate. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Gimme’s ability to recover group-level path coefficients and individual-level path coefficients

The growing availability of intensive longitudinal data has increased psychological researchers' interest in ideographic-statistical methods that, for example, reveal the contemporaneous or lagged associations between different variables for a specific individual. However, when researchers assess several individuals, the results of such models are difficult to generalize across individuals. Researchers recently suggested an algorithm called GIMME, which allows for the identification of coefficients that exist across all individuals (group-level coefficients) or are specific to one or a subgroup of individuals (individual-level coefficients). In three simulation studies we investigated GIMME's performance in recovering group-level and individual-level coefficients. For the former, we found that GIMME performed well when the magnitude of the parameters was moderate to high and when the number of measurements was sufficiently large. However, GIMME had problems detecting individual-level coefficients or coefficients that occurred for a subset of individuals from the whole sample.

Comparing Signal-Contingent and Event-Contingent Experience Sampling Ratings of Affect in a Sample of Psychotherapy Outpatients

Experience sampling methods are widely used in clinical psychology to study affective dynamics in psychopathology. The present study examined whether affect ratings (valence and arousal) differed as a function of assessment schedule (signal- versus event-contingent) in a clinical sample and considered various approaches to modeling these ratings. A total of 40 community mental health center outpatients completed ratings of their affective experiences over a 21-day period using both signal-contingent schedules (random prompts) and event-contingent schedules (ratings following social interactions). We tested whether assessment schedules impacted 1) the central tendency (mean) and variability (standard deviation) of valence or arousal considered individually, 2) the joint variability in valence and arousal via the entropy metric, and 3) the between-person differences in configuration of valence-arousal landscapes via the Earth Mover's Distance (EMD) metric. We found that event-contingent schedules, relative to signal-contingent schedules, captured higher average levels of pleasant valence and emotional arousal ratings. Moreover, signal-contingent schedules captured greater variability within and between individuals on arousal-valence landscapes compared to event-contingent schedules. Altogether, findings suggest that the two assessment schedules should not be treated interchangeably in the assessment of affect over time. Researchers must be cautious in generalizing results across studies utilizing different experience sampling assessment schedules.

The dynamics, processes, mechanisms, and functioning of personality: An overview of the field

Personality psychology has long focused on structural trait models, but it can also offer a rich understanding of the dynamics, processes, mechanisms, and functioning of individual differences or entire persons. The field of personality dynamics, which works towards such an understanding, has experienced a renaissance in the last two decades. This review article seeks to act as a primer of that field. It covers its historical roots, summarizes current research strands - along with their theoretical backbones and methodologies - in an accessible way, and sketches some considerations for the future. In doing so, we introduce relevant concepts, give an overview of different topics and phenomena subsumed under the broad umbrella term 'dynamics', and highlight the interdisciplinarity as well as applied relevance of the field. We hope this article can serve as a useful overview for scholars within and outside of personality psychology who are interested in the dynamic nature of human behaviour and experience.

A developmental perspective on personality and psychopathology across the life span

Taking a developmental perspective, guided by core principles of developmental science and developmental psychopathology, is necessary to move the fields of personality science and psychopathology forward. Personality and psychopathology can be delineated using hierarchical models of individual differences, as evidenced by decades of converging evidence across community and psychiatric samples, countries and cultures, and ages and developmental periods. A large body of empirical research likewise documents associations between personality and various forms of psychopathology. Cross-sectional investigations of personality-psychopathology links in samples of adults now yield diminishing returns. Prospective, longitudinal investigations that assess personality, psychopathology, and their co-development across the life span are needed to determine their temporal ordering, capture dynamic associations over time and development, and elucidate causal origins and underlying mechanisms. We lay out a developmental framework that integrates across the developmental, personality, and psychopathology literatures in order to further understanding and guide future investigations of the nature of personality-psychopathology links.

Temporal networks of tobacco withdrawal symptoms during smoking cessation treatment

A recently developed network perspective on tobacco withdrawal posits that withdrawal symptoms causally influence one another across time, rather than simply being indicators of a latent syndrome. Evidence supporting a network perspective would shift the focus of tobacco withdrawal research and intervention toward studying and treating individual withdrawal symptoms and intersymptom associations. Here we construct and examine temporal tobacco withdrawal networks that describe the interplay among withdrawal symptoms across time using experience-sampling data from 1,210 participants (58.35% female, 86.24% White) undergoing smoking cessation treatment. We also construct person-specific withdrawal networks and capture individual differences in the extent to which withdrawal symptom networks promote the spread of symptom activity through the network across time using impulse response analysis. Results indicate substantial moment-to-moment associations among withdrawal symptoms, substantial between-person differences in withdrawal network structure, and reductions in the interplay among withdrawal symptoms during combination smoking cessation treatment. Overall, findings suggest the utility of a network perspective and also highlight challenges associated with the network approach stemming from vast between-person differences in symptom networks. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample

BACKGROUND

An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning).

METHODS

Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress).

RESULTS

Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days.

CONCLUSIONS

Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.

How are you doing? The person-specificity of daily links between neuroticism and physical health

OBJECTIVE

The mind and body function in tandem across days and development, and in unique ways for individuals, but most work on the relation between personality and physical health is cross-sectional, assuming homogeneity across time and people. For instance, although neuroticism is associated with poor health, the direction of the relation and whether it characterizes all people all of the time is unclear. The goal of this study is to fill knowledge gaps concerning the person-specific, day-to-day neuroticism-health link.

METHODS

A 75-occassion intensive longitudinal study was conducted in which 119 adults reported daily on 12 indicators of neuroticism and 3 symptoms of physical health. Person-specific network analyses, conducted using the multiple solutions version of group iterative multiple model estimation (GIMME-MS), were used to determine the presence, valence, daily lag, and direction of relations among the daily variables. Network features were compared within and between individuals.

RESULTS

Person-specific networks were heterogeneous. Participants were significantly more likely to have networks in which physical symptoms predicted indicators of neuroticism compared to the reverse; this was particularly true for next-day relations, and for women. Exploratory analyses suggested that participants with a disproportionate amount of these health-to-neuroticism relations scored high on conscientiousness.

CONCLUSIONS

Person-specific network mapping of ecologically-valid intensive longitudinal data revealed heterogeneity in day-to-day relations between indicators of neuroticism and physical health, with long-term implications for personalized healthcare. There was some consistency, however, in that "body" symptoms were more likely to predict "mind" features across people.

Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder

OBJECTIVE

Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual.

METHOD

We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24).

RESULTS

We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes.

CONCLUSIONS

The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Personalized Models of Psychopathology

The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially specific to each individual, and it seeks to develop formal idiographic statistical models to represent these individual processes. Although the personalized approach draws on long-standing influences in clinical psychology, there has been an explosion of research in recent years following the development of intensive longitudinal data capture and statistical techniques that facilitate modeling of the dynamic processes of each individual's pathology. Advances are also making idiographic analyses scalable and generalizable. We review emerging research using the personalized approach in descriptive psychopathology, precision assessment, and treatment selection and tailoring, and we identify future challenges and areas in need of additional research. The personalized approach to psychopathology holds promise to resolve thorny diagnostic issues, generate novel insights, and improve the timing and efficacy of interventions.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

Using ambulatory assessment to measure dynamic risk processes in affective disorders

BACKGROUND

Rapid advances in the capability and affordability of digital technology have begun to allow for the intensive monitoring of psychological and physiological processes associated with affective disorders in daily life. This technology may enable researchers to overcome some limitations of traditional methods for studying risk in affective disorders, which often (implicitly) assume that risk factors are distal and static - that they do not change over time. In contrast, ambulatory assessment (AA) is particularly suited to measure dynamic "real-world" processes and to detect fluctuations in proximal risk for outcomes of interest.

METHOD

We highlight key questions about proximal and distal risk for affective disorders that AA methods (with multilevel modeling, or fully-idiographic methods) allow researchers to evaluate.

RESULTS

Key questions include between-subject questions to understand who is at risk (e.g., are people with more affective instability at greater risk than others?) and within-subject questions to understand when risk is most acute among those who are at risk (e.g., does suicidal ideation increase when people show more sympathetic activation than usual?). We discuss practical study design and analytic strategy considerations for evaluating questions of risk in context, and the benefits and limitations of self-reported vs. passively-collected AA.

LIMITATIONS

Measurements may only be as accurate as the observation period is representative of individuals' usual life contexts. Active measurement techniques are limited by the ability and willingness to self-report.

CONCLUSIONS

We conclude by discussing how monitoring proximal risk with AA may be leveraged for translation into personalized, real-time interventions to reduce risk.

Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement

Ambulatory assessment (AA; also known as ecological momentary assessment) has enjoyed enthusiastic implementation in psychological research. The ability to assess thoughts, feelings, behavior, physiology, and context intensively and repeatedly in the moment in an individual's natural ecology affords access to data that can answer exciting questions about sequences of events and dynamic processes in daily life. AA also holds unique promise for developing personalized models of individuals (i.e., precision or person-specific assessment) that might be transformative for applied settings such as clinical practice. However, successfully translating AA from bench to bedside is challenging because of the inherent tension between idiographic and nomothetic principles of measurement. We argue that the value of applied AA will be most fully realized by balancing the ability to develop personalized models with ensuring comparability among individuals. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Real-Time Monitoring of Suicide Risk among Adolescents: Potential Barriers, Possible Solutions, and Future Directions

Recent advances in real-time monitoring technology make this an exciting time to study risk for suicidal thoughts and behaviors among youth. Although there is good reason to be excited about these methods, there is also reason for caution in adopting them without first understanding their limitations. In this article, we present several broad future directions for using real-time monitoring among youth at risk for suicide focused around three broad themes: novel research questions, novel analytic methods, and novel methodological approaches. We also highlight potential technical, logistical, and ethical challenges with these methodologies, as well as possible solutions to these challenges.

The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.