Conference Agenda

Suicidology has produced a rich array of conceptual frameworks, such as the Integrated Motivational–Volitional (IMV) model, that describe why and how suicidal thoughts and behaviors emerge. However, many of these frameworks remain primarily verbal, which limits their ability to generate concrete predictions or link to clinical practice. One promising way forward is to formalize existing theories by expressing them in mathematical terms. Formalization translates qualitative concepts into precise mathematical relationships, making it possible to simulate individualized trajectories of suicidal thinking and responses to potential interventions.

As part of an ongoing project, we are developing a formalized model based on the IMV theory to represent the dynamic processes underlying suicidal thoughts and behaviors. This within-person model is not intended to be a stand-alone, but rather will form the psychological component of a broader agent-based model, allowing the exploration of how individual risk processes interact within social contexts. Although still at an early stage, this work aims to illustrate how formal modeling can be used as a new tool to understand patient variability and tailor interventions.

Background: Suicidal ideation (SI) fluctuates over time, yet traditional static risk factors poorly align with its onset. Understanding dynamic symptom patterns may advance knowledge of the temporal interplay between SI and co-occurring symptoms in adults with depressive and anxiety disorders.

Methods: We analyzed six waves (at baseline, and after 2, 4, 6, 9, and 13 years of follow-up) of the Netherlands Study of Depression and Anxiety (NESDA; n = 305, mean age 40.8 years, 62% female) in participants with any SI fluctuation. Variables included depressive symptoms, anxiety, mastery, and worry. Dynamic Time Warping (DTW) quantified within-person temporal alignment between SI and other symptoms, and undirected networks visualized co-fluctuations. Analyses were stratified by age and sex.

Results: SI co-fluctuated most strongly with affective and anhedonic depressive symptoms, including sad mood, low capacity for pleasure, low general interest, pessimism, quality of mood, and decreased appetite. Select anxiety (terrified/afraid) and worry (overwhelming worries) items also aligned with SI, whereas mastery items did not. Patterns were broadly consistent across age and sex subgroups. Networks indicated that SI is embedded in depressive clusters but bridges to acute fear and persistent worry.

Conclusions: SI is a dynamic phenomenon closely linked to specific depressive, anxiety, and worry symptoms. Interventions targeting mood instability, anhedonia, and uncontrollable worry, combined with real-time monitoring, may improve personalized suicide prevention. DTW provides a framework to identify temporally proximal symptom patterns, supporting more precise monitoring

Ecological momentary assessment (EMA) has become a central methodological approach in suicide research, enabling the real-time monitoring of suicidal thoughts and behaviors (STBs). However, likely due to the low base rate of suicidal behavior, only a small number of EMA studies have included suicide attempts (SA) as a behavioral outcome. The present analysis compares SA event rates derived from retrospective clinical interviews with those captured via event- and signal-contingent EMA prompts.

A sample of N = 331 psychiatric inpatients admitted due to a SA or suicidal crises was monitored for up to six months following discharge. Participants completed a high-frequency three-week EMA followed by a low-frequency 26-week EMA, assessing affective states and suicide ideation. Additionally, participants received a weekly signal-contingent prompt asking about SA during the preceding week and could report a SA event-contingent (i.e. self-initiate a survey) throughout the study. After both EMA phases, SAs were also assessed via structured clinical interviews conducted by telephone. Final follow-ups will be finished in April 2026.

So far, n = 31 attempts were reported in the interviews (by n = 28 participants). In EMA, higher event-rates appeared. Signal-contingent EMAs revealed n = 55 attempts (by 32 participants) whereas the self-initiated event-contingent EMA yielded 34 attempts (by 27 participants). Descriptive analyses and graphical representations will illustrate the degree of convergence and discrepancy among the three assessment approaches. Potential factors influencing reporting patterns - including affective dysregulation, reduced compliance or dropout, recall biases, and varying interpretations of what constitutes a SA - will be presented.

This study highlights key methodological challenges related to assessing and analyzing suicide attempts in EMA research. Findings will be highly relevant for evaluating the validity and completeness of behavioral data in suicide research and for improving the design of future real-time monitoring studies.

Background: Suicidal ideation (SI) is a significant global mental health concern, yet the dynamic interplay between SI and other symptoms remains poorly understood. Clarifying which symptoms co-fluctuate with SI over time may help to identify warning signals and actionable intervention targets.

Methods: Longitudinal data from three Dutch psychiatric cohorts of individuals with lifetime internalizing disorders spanning 16 waves from April 2020 until February 2022 (COVID-19 pandemic) were analyzed, including only participants with any fluctuation in SI over time. Variables included depressive, happiness, anxiety, loneliness, worry symptoms, and COVID-19-specific items. Dynamic Time Warping (DTW) was applied to quantify within-person (dis)similarity between symptom trajectories and SI, and results were subsequently aggregated at the group level.

Results: The 307 participants were on average 44.8 years and 61.6% was female. Their average SI levels increased over the 16 waves (p<0.001). SI was significantly aligned with four depressive symptoms (i.e., sad mood, low self-esteem, low interest, and reduced happiness), two anxiety-related symptoms (i.e., fear of losing control, faintness), feeling abandoned, and overwhelming worrying. In a directed DTW analysis, sad mood and hypersomnia showed significant temporal precedence over SI (p < 0.01), suggesting that changes in mood and sleep may precede shifts in suicidal ideation.

Conclusion: SI co-fluctuated with emotional, anxiety, social and worry related symptoms, indicating that SI is not limited to depression, but embedded in broader symptom networks that may determine its onset and course. Directional analyses further point to mood and sleep disturbances as potential early indicators of SI. These findings highlight the value of time-sensitive monitoring approaches for uncovering the dynamic mechanisms through which suicidality emerges and intensifies.

Suicide is a complex phenomenon influenced by several interacting risk factors. The risk is especially high after a suicide attempt or a non-suicidal self-harm episode. The Integrated Motivational-Volitional model addresses the complexity of suicidal behavior. Network analysis can help study the complexity of suicidal behavior as it allows for the analysis of both direct and indirect relations between variables. To date, the IMV model has mainly been validated using undirected networks on cross-sectional data. Under strict assumptions, causal networks can also be estimated, resulting in direction insights. The aim of the study was to explore the complexity of suicidal behavior by utilizing both undirected and causal networks within 366 patients treated in a hospital after an episode of self-harm. The findings indicate that internal entrapment appears to have a causal effect on external entrapment, perceived burdensomeness, and defeat, while perceived burdensomeness seems to further cause thwarted belongingness and impulsivity within the network. Additionally, depression, impulsivity, and suicidal ideation emerged as the most predictive factors of repeat suicidal behavior.