A complex systems perspective on chronic aggression and self-injury: case study of a woman with mild intellectual disability and borderline personality disorder.

BACKGROUND: Challenging behaviors like aggression and self-injury are dangerous for clients and staff in residential care. These behaviors are not well understood and therefore often labeled as "complex". Yet it remains vague what this supposed complexity entails at the individual level. This case-study used a three-step mixed-methods analytical strategy, inspired by complex systems theory. First, we construed a holistic summary of relevant factors in her daily life. Second, we described her challenging behavioral trajectory by identifying stable phases. Third, instability and extraordinary events in her environment were evaluated as potential change-inducing mechanisms between different phases. CASE PRESENTATION: A woman, living at a residential facility, diagnosed with mild intellectual disability and borderline personality disorder, who shows a chronic pattern of aggressive and self-injurious incidents. She used ecological momentary assessments to self-rate challenging behaviors daily for 560 days. CONCLUSIONS: A qualitative summary of caretaker records revealed many internal and environmental factors relevant to her daily life. Her clinician narrowed these down to 11 staff hypothesized risk- and protective factors, such as reliving trauma, experiencing pain, receiving medical care or compliments. Coercive measures increased the chance of challenging behavior the day after and psychological therapy sessions decreased the chance of self-injury the day after. The majority of contemporaneous and lagged associations between these 11 factors and self-reported challenging behaviors were non-significant, indicating that challenging behaviors are not governed by mono-causal if-then relations, speaking to its complex nature. Despite this complexity there were patterns in the temporal ordering of incidents. Aggression and self-injury occurred on respectively 13% and 50% of the 560 days. On this timeline 11 distinct stable phases were identified that alternated between four unique states: high levels of aggression and self-injury, average aggression and self-injury, low aggression and self-injury, and low aggression with high self-injury. Eight out of ten transitions between phases were triggered by extraordinary events in her environment, or preceded by increased fluctuations in her self-ratings, or a combination of these two. Desirable patterns emerged more often and were less easily malleable, indicating that when she experiences bad times, keeping in mind that better times lie ahead is hopeful and realistic.

The best of both worlds? General principles of psychopathology in personalized assessment.

A complex systems approach to psychopathology proposes that general principles lie in the dynamic patterns of psychopathology, which are not restricted to specific psychological processes like symptoms or affect. Hence, it must be possible to find general change profiles in time series data of fully personalized questionnaires. In the current study, we examined general change profiles in personalized self-ratings and related these to four measures of treatment outcome (International Symptom Rating, 21-item Depression Anxiety and Stress Scale, daily symptom severity, and self-reflective capacity). We analyzed data of 404 patients with mood and/or anxiety disorders who completed daily self-ratings on personalized questionnaires during psychotherapy. For each patient, a principal component analysis was applied to the multivariate time series in order to retrieve an univariate person-specific time series. Then, using classification and regression methods, we examined these time series for the presence of general change profiles. The change profile classification yielded the following distribution of patients: no-shift (n = 55; 14%), gradual-change (n = 52; 13%), one-shift (n = 233; 58%), reversed-shift (n = 39; 10%) and multiple-shifts (n = 25; 6%). The multiple-shift group had better treatment outcome than the no-shift group on all outcome measures. The one-shift and gradual-change groups had better treatment outcome than the no-shift group on two and three outcome measures, respectively. Overall, this study illustrates that person-specific (idiographic) and general (nomothetic) aspects of psychopathology can be integrated in a complex systems approach to psychopathology, which may combine "the best of both worlds." (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Complexity theory of psychopathology.

There is a renewed interest for complex adaptive system approaches that can account for the inherently complex and dynamic nature of psychopathology. Yet a theory of psychopathology grounded in the principles of complex adaptive systems is lacking. Here, we present such a theory based on the notion of dynamic patterns: patterns that are formed over time. We propose that psychopathology can be understood as a dynamic pattern that emerges from self-organized interactions between interdependent biopsychosocial processes in a complex adaptive system comprising a person in their environment. Psychopathology is emergent in the sense that it refers to the person-environment system as a whole and cannot be reduced to specific system parts. Psychopathology as a dynamic pattern is also self-organized, meaning that it arises solely from the interdependencies in the system: the interactions between countless biopsychosocial variables. All possible manifestations of psychopathology will correspond to a wide variety of dynamic patterns. Yet we propose that the development of these patterns over time can be described by general principles of pattern formation in complex adaptive systems. A discussion of implications for classification, intervention, and public health concludes the article. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

From Metaphor to Computation: Constructing the Potential Landscape for Multivariate Psychological Formal Models.

For psychological formal models, the stability of different phases is an important property for understanding individual differences and change processes. Many researchers use landscapes as a metaphor to illustrate the concept of stability, but so far there is no method to quantify the stability of a system's phases. We here propose a method to construct the potential landscape for multivariate psychological models. This method is based on the generalized potential function defined by Wang et al. (2008) and Monte Carlo simulation. Based on potential landscapes we define three different types of stability for psychological phases: absolute stability, relative stability, and geometric stability. The panic disorder model by Robinaugh et al. (2019) is used as an example, to demonstrate how the method can be used to quantify the stability of states and phases, illustrate the influence of model parameters, and guide model modifications. An R package, simlandr, was developed to provide an implementation of the method.

A Game-Based School Program for Mental Health Literacy and Stigma on Depression (Moving Stories): Cluster Randomized Controlled Trial.

BACKGROUND: Depressive symptoms are highly prevalent among adolescents in Western countries. However, although treatment for depressive symptoms is available, many adolescents do not seek help when they need it. Important barriers to help-seeking among adolescents include low mental health literacy and high stigma. Therefore, we have developed a game-based school program, Moving Stories, which combines mental health literacy training for depression with contact with someone with lived experience both in the digital and nondigital world. OBJECTIVE: The aim of this study is to conduct a first test of the effectiveness of the newly developed game-based program, Moving Stories, using a cluster randomized controlled trial. METHODS: A total of 185 adolescents participated, divided over 10 classes from 4 schools. Half of the classes were randomly selected to follow the Moving Stories program, whereas the other half were in the control group, where no intervention was provided. The adolescents filled out digital questionnaires at 4 time points, with questions on mental health literacy, stigma, depressive symptoms, and the program itself (before the program, after the program, 3-month follow-up, and 6-month follow-up). Using R (R Foundation for Statistical Computing), we ran linear mixed-effects models for all continuous outcome variables and generalized linear mixed-effects models for all binary outcome variables. RESULTS: Compared with the control group, participants in the Moving Stories group improved after the program in personal stigma (b=-0.53, 95% CI -1.02 to -0.03; t179.16=-2.08; P=.04). Effects on personal stigma lasted over time (3-month follow-up: b=-0.57, 95% CI -1.11 to -0.03; t174.39=-2.07; P=.04). Most adolescents in the Moving Stories group participated in the introduction (97/99, 98%) and contact session (93/99, 94%), played the game for 4 or 5 days (83/99, 83%), and indicated that they would recommend the game to their peers (90/98, 92%). CONCLUSIONS: The results of this study show the potential of Moving Stories as a stigma reduction program. With changes in the program to improve its effects on mental health literacy, Moving Stories could be implemented in schools to improve help-seeking in adolescents and reduce the negative consequences and burden of depressive symptoms. TRIAL REGISTRATION: Dutch Trial Register NTR7033; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7033. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11255.

Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.

Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.

Early warning signals and critical transitions in psychopathology: challenges and recommendations.

Empirical evidence is mounting that monitoring momentary experiences for the presence of early warning signals (EWS) may allow for personalized predictions of meaningful symptom shifts in psychopathology. Studies aiming to detect EWS require intensive longitudinal measurement designs that center on individuals undergoing change. We recommend that researchers (1) define criteria for relevant symptom shifts a priori to allow specific hypothesis testing, (2) balance the observation period length and high-frequency measurements with participant burden by testing ambitious designs with pilot studies, and (3) choose variables that are meaningful to their patient group and facilitate replication by others. Thoroughly considered designs are necessary to assess the promise of EWS as a clinical tool to detect, prevent, or encourage impending symptom changes in psychopathology.

Complexity in psychological self-ratings: implications for research and practice.

BACKGROUND: Psychopathology research is changing focus from group-based "disease models" to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the "butterfly effect," the divergence of initially similar trajectories. METHODS: We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm. RESULTS: Self-ratings concerning psychological states (e.g., the item "I feel down") exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item "I am hungry") exhibited less complex dynamics and their behavior was more similar to random variables. CONCLUSIONS: Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are "moving targets" whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

Temporal dynamics of sitting behavior at work.

Sitting for prolonged periods of time impairs people's health. Prior research has mainly investigated sitting behavior on an aggregate level, for example, by analyzing total sitting time per day. By contrast, taking a dynamic approach, here we conceptualize sitting behavior as a continuous chain of sit-to-stand and stand-to-sit transitions. We use multilevel time-to-event analysis to analyze the timing of these transitions. We analyze ∼30,000 objectively measured posture transitions from 156 people during work time. Results indicate that the temporal dynamics of sit-to-stand transitions differ from stand-to-sit transitions, and that people are quicker to switch postures later in the workday, and quicker to stand up after having been more active in the recent hours. We found no evidence for associations with physical fitness. Altogether, these findings provide insights into the origins of people's stand-up and sit-down decisions, show that sitting behavior is fundamentally different from exercise behavior, and provide pointers for the development of interventions.

Sudden gains in day-to-day change: Revealing nonlinear patterns of individual improvement in depression.

OBJECTIVE: We examined individual overall trajectories of change and the occurrence of sudden gains in daily self-rated problem severity and the relation of these patterns to treatment response. METHOD: Mood disorder patients (N = 329, mean age = 44, 55% women) completed daily self-ratings about the severity of their complaints as a standard part of treatment, using the Therapy Process Questionnaire (TPQ). Per individual, the best-fitting defined (linear, log-linear, 1-step) trajectory was tested for significance: for change over time, and for specificity of the best-fitting trajectory. Two-hundred and three cases had ICD-10 Symptom Rating (ISR) depression scores posttreatment: a score ≤1 identified 114 treatment responders. Relation to response was examined for sudden gains and type of change trajectory. RESULTS: 138 cases (42%) had a significant decrease in problem severity, of which 54 cases (16%) had a defined trajectory: 50 cases with one-step improvement, and 4 with a linear improvement in daily problem severity. Sudden gains occurred in 28% of the total sample, and within 58% of improvement patterns. Specifically, sudden gains occurred in 68% of significant 1-step trajectories and 25% of the linear cases. Sudden gains and nonspecific change trajectories were significantly more frequent for treatment responders. CONCLUSIONS: At the day-level, patterns of improvement are nonlinear for most patients. Sudden gains occur within various forms of overall change and are associated with treatment response. Clinically relevant improvements in depression occur both gradually and abruptly, and this finding allows for the possibility that the remission process functions according to dynamical systems principles. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Destabilization in self-ratings of the psychotherapeutic process is associated with better treatment outcome in patients with mood disorders.

Objective: While destabilization periods characterized by high variability and turbulence in a patient's psychological state might seem obstructive for psychotherapy, a complex systems approach to psychopathology predicts that these periods are actually beneficial as they indicate possibilities for reorganization within the patient. The present study tested the hypothesis that destabilization is related to better treatment outcome.Method: 328 patients who received psychotherapy for mood disorders completed daily self-ratings about their psychotherapeutic process. A continuous measure of destabilization was defined as the relative strength of the highest peak in dynamic complexity, a measure for variability and turbulence, in the self-ratings of individual patients.Results: Destabilization was found to be related to better treatment outcome. When improvers and non-improvers were analyzed separately, destabilization was found to be related to better treatment outcome in improvers but not in non-improvers.Conclusions: Destabilization in daily self-ratings of the psychotherapeutic process is associated with better treatment outcome. The identification of destabilization periods in process-monitoring data is clinically relevant. During destabilization, patients are believed to be increasingly sensitive to the effects of therapy. Clinicians could tailor their interventions to these sensitive periods.