Research master thesis | Psychology (research) (MSc)
open access
Evidence-based mental health programs have long conceptualized mental disorders as interactions between thoughts, feelings, behaviours and external factors. Idiographic network models are a...Show moreEvidence-based mental health programs have long conceptualized mental disorders as interactions between thoughts, feelings, behaviours and external factors. Idiographic network models are a relatively novel way of estimating such intra-individual psychological processes. These methods are not without limitations, and concerns have been raised about the stability and accuracy of estimated networks. The extend to which idiographic networks are stable, or vary over time, is unknown. We explored temporal network stability from three angles, exploring variation within people, across different stability metrics, and across people. We reanalysed daily symptom records of people with personality disorders. We fit graphical Vector Autoregressive models separately for the first and second 50 days of consecutive measurements. Contemporaneous but not temporal idiographic networks appeared to be relatively stable within people. The assessment of stability varied considerably across metrics applied. There was large variation in network stability of contemporaneous structures across people, which could not be explained by subject-specific variables. We illustrate the temporal changes in contemporaneous network structures of two participants with high and low network stability and discuss the most pressing questions to be considered by future research.Show less
Research master thesis | Psychology (research) (MSc)
open access
Eating disorders (EDs) are characterized by extreme symptom heterogeneity within diagnostic categories, which complicates treatment and inherently causes high relapse rates. The ability to predict...Show moreEating disorders (EDs) are characterized by extreme symptom heterogeneity within diagnostic categories, which complicates treatment and inherently causes high relapse rates. The ability to predict ED course in individuals would support clinicians in identifying early warning signals of relapse and to intervene accordingly. Traditional approaches have considered EDs as the shared origin of all symptoms which are reflective of a disorder, hindering prediction as it does not allow to unravel mechanisms of symptom progression. Network analysis provides new insights on EDs as it allows to model symptoms as networks of mutually causal relationships. However, most network analysis studies are limited as they only allow for conclusions on group-level at one single time point. By using time series data and intraindividual networks we can incorporate both individual and temporal information yielding insight in within-person variations over time. In this proof-of-concept study, we predicted ED severity using time series and intra-individual network features derived from ecological momentary assessment data in a transdiagnostic ED sample (n = 63). We explored whether time series and network features added to model performance on top of demographic and clinical features using machine learning and what features were most predictive of ED severity. Our findings show no convincing evidence that time series and network features improve predictive accuracy. Nonetheless, some time series and network features were identified as important, highlighting their potential clinical value. We consider our proposed combination of intra-individual networks and machine learning as a starting point towards personalized prediction of psychological outcomes.Show less
Research master thesis | Psychology (research) (MSc)
open access
Death by suicide a global health problem, often preceded with the experience of suicidal ideation. Both depression and anxiety increase the risk of experiencing suicidal ideation. However, the...Show moreDeath by suicide a global health problem, often preceded with the experience of suicidal ideation. Both depression and anxiety increase the risk of experiencing suicidal ideation. However, the specific relations between symptoms of depression and anxiety on the one hand, and suicidal ideation on the other, remain unexplored. Therefore, we investigated these relations both at the cross-sectional (N = 2981) and the temporal level (N = 2596), with a follow-up time of 2 years. We included data from the NESDA study and controlled for the covariates age and gender. To do so, we used unregularized network models, each consisting of 21 nodes. In each network, 10 nodes represented depression items, 10 nodes represented anxiety items, and one node represented suicidal ideation. Results showed that the relation between suicidal ideation and depression was stronger than the relation between suicidal ideation and anxiety. This held true at the cross-sectional and temporal level. Overall, depression and anxiety symptoms at baseline explained about 15% of suicidal ideation at the cross-sectional level, and up to 13% at the temporal level. However, these percentages are not directly comparable, because only for the temporal analyses did we control for previous suicidal ideation. Results should be replicated and further investigated in order to be able to draw firm conclusions.Show less
