A review of explicit and implicit assumptions when providing personalized feedback based on self-report EMA data

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure

Mental disorders as networks of problems:A review of recent insights

Purpose: The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years. Methods: This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention. Results: Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality—a metric that measures how connected and clinically relevant a symptom is in a network—is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies. Conclusions: We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients

Exploring the interconnectedness of fatigue, depression, anxiety and potential risk and protective factors in cancer patients:a network approach

Researchers have extensively studied fatigue, depression and anxiety in cancer patients. Several risk and protective factors have been identified for these symptoms. As most studies address these constructs, independently from other symptoms and potential risk and protective factors, more insight into the complex relationships among these constructs is needed. This study used the multivariate network approach to gain a better understanding of how patients’ symptoms and risk and protective factors (i.e. physical symptoms, social withdrawal, illness cognitions, goal adjustment and partner support) are interconnected. We used cross-sectional data from a sample of cancer patients seeking psychological care (n = 342). Using network modelling, the relationships among symptoms of fatigue, depression and anxiety, and potential risk and protective factors were explored. Additionally, centrality (i.e. the number and strength of connections of a construct) and stability of the network were explored. Among risk factors, the relationship of helplessness and physical symptoms with fatigue stood out as they were stronger than most other connections in the network. Among protective factors, illness acceptance was most centrally embedded within the network, indicating it had more and stronger connections than most other variables in the network. The network identified key connections with risk factors (helplessness, physical symptoms) and a key protective factor (acceptance) at the group level. Longitudinal studies should explore these risk and protective factors in individual dynamic networks to further investigate their causal role and the extent to which such networks can inform us on what treatment would be most suitable for the individual cancer patient

Critical slowing down as early warning for the onset and termination of depression

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression

A review of explicit and implicit assumptions when providing personalized feedback based on self-report EMA data

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure

Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't Like Parties if You Don't Like People

In one currently dominant view on personality, personality dimensions (e.g. extraversion) are causes of human behaviour, and personality inventory items (e.g. I like to go to parties and I like people) are measurements of these dimensions. In this view, responses to extraversion items correlate because they measure the same latent dimension. In this paper, we challenge this way of thinking and offer an alternative perspective on personality as a system of connected affective, cognitive and behavioural components. We hypothesize that these components do not hang together because they measure the same underlying dimension; they do so because they depend on one another directly for causal, homeostatic or logical reasons (e.g. if one does not like people and it is harder to enjoy parties). From this network perspective, personality dimensions emerge out of the connectivity structure that exists between the various components of personality. After outlining the network theory, we illustrate how it applies to personality research in four domains: (i) the overall organization of personality components; (ii) the distinction between state and trait; (iii) the genetic architecture of personality; and (iv) the relation between personality and psychopathology

Where are the breaks in translation from theory to clinical practice (and back) in addressing depression? An empirical graph-theoretic approach

BackgroundResearch in depression has progressed rapidly over the past four decades. Yet depression rates are not subsiding and treatment success is not improving. We examine the extent to which the gap between science and practice is associated with the level of integration in how depression is considered in research and stakeholder-relevant documents.MethodsWe used a network-science perspective to analyze similar uses of depression relevant terms in the Google News corpus (approximately 1 billion words) and the Web of Science database (120 000 documents).ResultsThese analyses yielded consistent pictures of insular modules associated with: (1) patient/providers, (2) academics, and (3) industry. Within academia insular modules associated with psychology, general medical, and psychiatry/neuroscience/biology were also detected.ConclusionsThese analyses suggest that the domain of depression is fragmented, and that advancements of relevance to one stakeholder group (academics, industry, or patients) may not translate to the others. We consider potential causes and associated responses to this fragmentation that could help to unify and advance translation from research on depression to the clinic, largely involving harmonizing employed language, bridging conceptual domains, and increasing communication across stakeholder groups.Merit, Expertise and Measuremen