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. 2025 Mar 18;46(3):142–148. doi: 10.1027/0227-5910/a000999

Exploring Predictors of Passive Versus Active Suicidal Ideation

Idiographic Analysis of Real-Time Data

Lena Spangenberg 1,*, Heide Glaesmer 1, Nina Hallensleben 1, Dajana Schreiber 2, Thomas Forkmann 2, Aleksa Kaurin 3
PMCID: PMC12096957  PMID: 40099509

Abstract

Abstract: Background: Passive and active suicidal ideation (SI) have been shown to be co-occurring but are distinguishable constructs with presumably differential sets of predictors. Aims: The present analysis integrates nomothetic and idiographic analyses to unravel the relations between passive and active SI and momentary affective states in real-time data to tap several knowledge gaps. Methods: 54 psychiatric inpatients rated their current passive and active SI and positive as well as negative affect for six consecutive days (10 random prompts daily) using ecological momentary assessments on smartphones. Data were analyzed using group iterative multiple model estimation (GIMME). Results: On subgroup level, only significant contemporaneous paths emerged (with no direct paths from affect to active SI). In general, the personalized models revealed large heterogeneity. The number, direction, and strengths of individual paths differed enormously (with fewer direct paths from affect to active SI than to passive SI overall). Passive and active SI were interrelated in the majority of individual models. Limitations: Findings are limited by item wording, co-occurence of passive and active SI, and the short observation interval. Conclusion: The heterogeneous individual models potentially reflect structural and functional differences in the development and maintenance of SI.

Keywords: suicide ideation, ecological momentary assessment, idiographic analysis, predictors

Suicidal ideation (SI) has a lifetime prevalence ranging from 6% to 14% (Nock et al., 2008). While SI is prevalent and distressing (Jobes & Joiner, 2019), suicide research typically emphasizes behavioral outcomes like nonlethal or lethal suicide attempts, often neglecting ideation (Mandel et al., 2023). On the contrary, studies employing ecological momentary assessments (EMA) usually focus on SI (Kivelä et al., 2022).

The nomenclature of Silverman et al. (2007) differentiates between suicide-related ideations varying in stability and content (casual, transient, passive, active, persistent) and the presence of suicidal intent (Silverman et al., 2007). Conceptually, suicide theories typically distinguish between passive (i.e. desire for death) and active SI (i.e. thoughts to kill oneself; van Orden et al., 2010), and recent empirical work has supported passive and active SI as closely linked but separable constructs (Wastler et al., 2023).

Consistent with a linear understanding of suicide risk progression, the severity of SI has generally been regarded as an indication of escalating suicide risk (i.e. passive SI acting as a gateway to active SI; Klonsky et al., 2018). Notably, passive SI, often perceived as low risk, has received limited research attention (Liu et al., 2020), and it has not been rigorously examined if passive SI is a prerequisite for active SI. With regard to theoretical approaches describing the development of suicide risk via nonlinear pathways (e.g., the Dual System Model of Suicidality; Brüdern et al., 2022), it seems also likely that passive and active SI are affected by both static and dynamic risk factors in a complex and dynamic interplay.

Among potential short-term predictors of SI, negative affective states have so far received the most attention and have been found to co-occur and predict SI in studies applying EMA (Kleiman et al., 2023). Yet, the majority of empirical studies fail to distinguish between passive and active SI (Wastler et al., 2023), resulting in a limited understanding of how constructs theoretically linked to SI development might specifically influence each aspect.

Relevance of Idiographic Approaches

Recent studies additionally highlighted that the emergence of SI and coping with suicidal urges seem to follow individual functionalities (Kaurin et al., 2022; Kuehn, Foster, et al., 2022) pointing out that suicidal crises appear to be highly idiosyncratic processes. Nomothetic approaches, that are mostly used to validate suicide theories (Millner et al., 2020), often result in group estimates which are likely to mask substantial variability on a personal level and produce misleading conclusions for individuals (Hamaker, 2012; Molenaar, 2004).

Idiographic models of EMA data are thus considered a promising avenue for personalizing intervention and treatment (Beltz et al., 2017; Kaurin et al., 2022; Kleiman et al., 2023; Kuehn, Foster, et al., 2022; Wright & Zimmermann, 2019). Analytical approaches such as group iterative multiple model estimation (GIMME, Beltz & Gates, 2017) allow modelling individual associations between up to 15 variables in narrow time frames in personalized networks. In addition, it is possible to build models on group and subgroup level to illustrate shared paths between variables (see Lane & Gates, 2017). Recent studies have successfully applied GIMME on EMA data and demonstrated that its ability to unravel individual dynamic processes relevant to STBs alongside a group-level perspective (Coppersmith et al., 2024; Kaurin et al., 2022; Kuehn et al., 2024; Yin et al., 2023).

Aims

Passive and active SI often co-occur but are distinguishable constructs with potentially different predictors (Wastler et al., 2023). This analysis explores whether passive SI precedes active SI, or whether the two constructs largely coincide, and if both are independently influenced by proximal risk factors (e.g., affective states). Using GIMME, we investigated the relationships between passive and active SI in the context of momentary affective dynamics at both (sub)group and individual levels in real-time. To our knowledge, no prior study has applied GIMME to EMA data distinguishing between passive and active SI (Coppersmith et al., 2024; Kaurin et al., 2022; Kuehn et al., 2024; Yin et al., 2023). Our approach was exploratory, without predefined assumptions about effect strengths or directions.

Methods

Seventy-four psychiatric inpatients were initially recruited into the study (28.4% male, Mage 37.6 years). All were diagnosed with a unipolar affective disorder and reported current or lifetime SI assessed by self-report and clinical interview. During their inpatient stay, participants used a loaned smartphone to rate passive and active SI and various affective states over six days via movisensXS. EMA surveys delivered semirandomly 10 times daily between 8:00 a.m. and 7:50 p.m. (minimum 30 min apart), totaled up to 60 prompts per participant. Surveys could be postponed by 15 min but expired after 20 min, if not completed. During the EMA period, participants were informed that their responses were not monitored in real time and advised to contact clinical staff if experiencing increasing suicidal thoughts or plans. No adverse events, such as suicide attempts or suicides, occurred during the study.

Each construct was assessed with two items on a five-point Likert scale (“At the moment I feel …” with responses ranging from 0 = not at all to 4 = extremely). The factorial validity of the assessed constructs (PSI—passive SI [life not worth living for me; there are more reasons to die than to live for me], ASI—active SI [I want to die; I think about taking my life], ANX—anxiety [afraid; nervous], DEP—depressive affect [downhearted; sad], and HOP—hopelessness [my future seems dark to me; I might as well give up because there is nothing I can do about making things better for myself], PA—positive affect [cheerful; happy]) was supported by multilevel confirmatory factor analysis and the measures demonstrated good to excellent reliability on the person-level (vs > 0.80s) and satisfying reliability on the prompt-level (vs > 0.70s with the exception of anxiety with v = 0.58; see Forkmann et al., 2018).

Data Analytic Approach

Data were analyzed using GIMME, which estimates relationships as bidirectional connections among observed variables (i.e., directional regressions across all possible pathways, see Table E1 in Electronic Supplementary Material 1 [ESM 1] (414.1KB, pdf) ).

Personalized models of seven variables (PSI, ASI, HOP, PA, DEP, ANX, time) were generated using the gimmeSEM function built into to the R package gimme (version 0.7-4; Lane et al., 2020). The GIMME algorithm employs a data-driven approach to establish unified structural equation models (uSEMs) among repeatedly assessed observed variables within an individual. It incorporates paths common among individuals (e.g., default 75%) and re-estimates models until no more group-level paths emerge. Each uSEM is individually estimated, making group-level path strengths unique. GIMME produces personalized networks for each individual, revealing significant associations and shared paths within the groups. Autoregressive paths are default estimations (represented by rounded arrows in Figure 1 and Figure 2). The validity of uSEM and GIMME hinges on assumptions, including weak stationarity in time series. Given potential violations in empirical datasets, including our own, we used the gimme R package's exogenous variable feature to adjust for time effects (detrend) in path estimates (Lane et al., 2020). We extended GIMME to identify subgroups with shared patterns in individual networks using a default 50% cutoff for subgroup identification. To assess the robustness of our identified subgroups, we utilized the perturbR package in R (Gates et al., 2019; R Core Team, 2017). The final model identified three subgroups (modularity = 0.04446, indicating the optimal cut-point based on the degree to which similarity within a cluster is high compared to the degree of similarity between clusters; Gates et al., 2017).

Figure 1. Paths on group and subgroup level. Group (black), subgroup (green), and individual (grey) path estimates in the three subgroups. Thickness of lines reflects the number of individuals sharing the paths (in relation to the group size). Solid lines represent contemporaneous paths, dashed lines lagged paths. Passive SI = pSI, active SI = aSI, positive affect = PA, anxiety = anx, hopelessness = hop, depression = dep. The color version of the figure is available in the online article.

Figure 1

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Figure 2. Selected individual network models. Three models per subgroup (62, 24, 41 from Subgroup 1; 37, 49, 71 from Subgroup 2; 5, 51, and 67 from Subgroup 3). Positive effects (blue) and negative effects (red) with thickness of lines reflecting the strength of the effect. Solid lines indicate contemporaneous effects; dashed lines indicate lagged effects. Passive SI = pSI, active SI = aSI, positive affect = PA, anxiety = anx, hopelessness = hop, depression = d. The color version of the figure is available in the online article.

Figure 2

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Data sets of 16 participants were excluded due to zero variability in active SI or positive affect. Four participants were initially excluded from subgroup analyses for interpretability, leaving data sets of 54 participants for the final GIMME analysis (see Table E2 in ESM 1 (414.1KB, pdf) for sample demographics). On average, participants completed 54 prompts (90.8% compliance). Passive SI only was endorsed in 37.8% (n = 841) of the observations, while active SI only was endorsed in 0.6% (n = 9) of the observations. Both passive and active SI (combined SI) were present in 42.8% (n = 1,386) of the observations across the EMA period.

Results

Paths on Group and Subgroup Level

At the group level, a significant contemporaneous path emerged between hopelessness and passive SI (see Figure 1). On the subgroup level, similar paths were observed in Subgroups 2 and 3, linking positive and depressive affect, depressive affect and hopelessness, and passive and active SI. Interestingly, no significant lagged relationships were found at either the group or subgroup level. In Subgroup 1, many individual lagged paths highlighted temporally interwoven constructs (see grey dashed lines in Figure 1), whereas Subgroups 2 and 3 primarily exhibited contemporaneous individual paths (see Table E1 in ESM 1 (414.1KB, pdf) for details). While most individuals (n = 47) displayed a direct link between passive and active SI (active SI regressed on passive SI), this connection was absent for others. A reverse pattern emerged for five individuals (see below and Table E1).

Subgroups 2 and 3 showed a typical mechanism in depressive disorders: low positive affect combined with high depressive and hopeless affect escalating to passive SI. However, Figure 1 highlights substantial individual variability and potential functional relationships in these pathways.

Predictors on Individual Level

Figure 2 illustrates a selection of the individual network models. These interpretations of functional relationships in different individuals highlight how different affective states might interact with passive and active SI over time (see Figure E1 in ESM 1 (414.1KB, pdf) ).

In terms of persistence, passive and active SI show a positive autoregressive effect in the majority of individual models (i.e., SI tends to persist in short time frames), although there is large variability across participants in how well current assessments of SI may be predicted by previous ones on the same schedule (e.g., ID 5 vs. ID 51). Interestingly, some participants have a negative autoregressive effect in active SI (ID 71, 37, and 24) or passive SI (ID 51) that might inform us about a tendency or capacity to downregulate SI after it is experienced. As exemplified for ID 5 or ID 51 in Figure E1 in ESM 1 (414.1KB, pdf) , however, negative autoregressive effects may also be indicative of very few endorsements of SI.

In the majority of models in Figure 2 (except in ID 62), passive and active SI co-occur (active SI regressed on passive SI which might indicate an escalation from death wishes to thoughts about killing oneself). Active SI is also directly influenced by other variables in the networks such as depressive affect (e.g., ID 5, 41 and 71) or hopelessness (e.g., ID 41). Additionally, active SI is regressed on anxious affect (e.g., ID 20 and 55, not shown in the figure but in the OSF project – blinded for review) which might indicate that also anxiety drives active SI independently of other negative affective states in some individuals. For some participants (e.g., IDs 71 and 51), anxious affect appears to be influenced by active SI, potentially indicating further escalation (e.g., agitation, arousal) following the experience of suicidal thoughts. While the directional regressions (e.g., the observed contemporaneous paths) serve as an indicator for the relationships between the variables under study, it has to be noted that only few lagged paths reached significance in the models. Consequently, the temporal relation between the variables remains unclear.

For some participants, the positive affect is decreased when passive SI (e.g. ID 41, 49 and 71) or active SI (e.g., ID 5) is reported (contemporaneous paths). With regard to increases or decrease in the various negative affective states, some participants experience increased depressive affect (e.g., ID 5), anxious (e.g. ID 51 and 71), or hopeless affect (e.g., ID 5 and 71) when active SI is prevalent.

For those participants, active SI seems to be not relieving but rather intensifying their negative affect in the same moment. Very few individual models contain an affect-regulatory path of SI. ID 41 experiences less depressive affect in the following prompt when active SI is reported or ID 71 experiences more positive affect in the following prompt when active SI is present.

In total, the models differ in density speaking to very different functional relationships between the examined variables. While some models appear rather sparse (e.g., ID 51, 37 and 67), others offer much more interdependencies and dynamics (e.g., ID 5, 71 or 41).

An illustration how these models may be used in a clinically intuitive manner by interpreting two models in more detail in ESM 1 (414.1KB, pdf) (M1).

Discussion

Main Findings

The analysis highlights two key findings: (1) the importance of distinguishing between passive and active SI, advocating for their inclusion in SI evaluations and short-term predictors, and (2) the value of idiographic analyses of EMA data in uncovering individual dynamics of suicidal processes, meriting further exploration.

Our models revealed significant heterogeneity in the within-person dynamics of suicide risk, with no consistent pathways for active SI emerging across individuals or subgroups with similar patterns. Among the affective states studied, hopelessness was most strongly associated with passive SI, likely due to their shared focus on negative appraisals of life and the future. Passive SI reflects existential despair, emphasizing life's perceived lack of worth and the balance of reasons for living versus dying, while hopelessness highlights a lack of agency and a pessimistic outlook. Both involve cognitive despair and a loss of meaning, but passive SI is present-focused, whereas hopelessness is future-oriented (Beck et al., 1974). This overlap suggests that hopelessness may amplify passive SI by reinforcing beliefs that life is unchangeable or not worth living.

Other affective states showed no consistent links to passive or active SI at the group or subgroup level. Instead, predictors of both passive and active SI varied widely in presence and strength across individual models.

We explored potential links between affective states and SI in selected individual models. While speculative and selective, we consider them helpful to generate hypotheses that can be either tested empirically or discussed with patients in person to gain detailed insight in the ebb and flow of symptoms (see M1 in ESM 1 (414.1KB, pdf) ). Our findings indicate that, for some participants, both passive and active SI correlate with a deterioration in symptoms, such as decreased positive affect and increased negative affect. While positive affect appears to offer a protective effect for a minority of individuals, our results align with existing inconclusive evidence regarding the affect-regulating function of SI. This underscores the importance of individual pathways in the development and maintenance of SI (Coppersmith et al., 2023; Kuehn, Dora, et al., 2022; Kuehn et al., 2024).

The major role of hopelessness for all participants warrants detailed comment. In line with previous findings, hopelessness is associated with increases in passive SI (Franklin et al., 2017; O’Connor & Nock, 2014). Yet, similarities in wording and operationalization of both constructs may also be accountable for this finding in our sample. The items assessing passive SI do reflect reasons to die/death wishes (Wastler et al., 2023) and might thus be more closely related to hopelessness in meaning because of the overlap of both constructs. Passive SI and hopelessness are primarily cognitive constructs involving thought patterns and beliefs, while also incorporating emotional elements like sadness or despair. On the other hand, being in a state of hopelessness might initiate or increase wishes to end the current life, even if these thoughts do not exactly meet the definition of passive SI recently suggested by Mandel et al. (2023).

Within the ideation-to-action framework, passive SI is seen as a gateway to active SI, following a linear progression (Klonsky & May, 2014). However, our analysis shows this progression is not universal. While escalation from passive to active SI was frequent according to directional regressions (i.e., contemporaneous paths in 47 of 53 cases) and significant at the subgroup level, this link did not emerge for each individual and regression coefficients differed vastly in strength (range β = 0.12–0.86). Understanding the progression to suicide risk in these individuals is crucial. Recent theories suggest that suicidal thoughts and behaviors (STBs) may arise impulsively (Brüdern et al., 2022), challenging the assumption that SI always precedes suicidal actions. Our individualized models highlight diverse pathways, showing that in some participants, active SI is directly influenced by anxious, depressive, or hopeless affect, independent of passive SI.

Limitations

Several key limitations of our analysis should be noted. The wording of our SI items is in line with conceptualization of passive SI as death wishes and active SI as thoughts of killing oneself (van Orden et al., 2010; Wastler et al., 2023). More recent work by Mandel et al. (2023) conceptualized SI in a different way (cognitions that relate to killing oneself and imply a personal motivation). Before comparing and integrating findings on SI, it is thus advisable to examine the underlying conceptualization of SI. The strong connection between hopelessness and passive SI may suggest that these items are part of a nomological network of suicide-related cognitions commonly experienced by individuals with depressive disorders. Future work could examine how the exact wording of self-reported SI influences the results. Most importantly, the frequent co-occurrence of passive and active SI precludes direct testing of whether passive SI increases in severity before transitioning to active SI, although time-lagged analyses can generally indicate nuanced predictive relationships. Methodologically, the short observation period of six days during an inpatient stay limits the generalizability of results. The present study is a secondary analysis of data collected for another study. Thus, power was not analyzed a priori.

Conclusion

The heterogeneous individual models potentially reflect structural and functional differences in the development and maintenance of SI. It seems advisable to assess both passive and active SI in future studies and to adopt idiographic approaches to get further insight in individual processes related to the development of STBs.

Electronic Supplementary Material

The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/0227-5910/a000999

Biographies

Author Biographies

Lena Spangenberg, PhD, is a senior researcher and head of the suicide research group (together with Heide Glaesmer) in the Department of Medical Psychology and Medical Sociology at the University of Leipzig, Germany. She has particular expertise in real-time data collection of suicide-related thoughts and behaviors.

Heide Glaesmer, PhD, is the deputy head and head of the suicide research group (together with Lena Spangenberg) at the Department of Medical Psychology and Medical Sociology at the University of Leipzig, Germany. Her research focuses on the drivers of suicidal ideation and behavior as well as on male suicidal ideation and behaviors.

Nina Hallensleben, PhD, is a postdoc at the Department of Medical Psychology and Medical Sociology at the University of Leipzig, Germany, and part of the suicide research group. She is particularly interested in the short-term prediction of suicide-related thoughts and behaviors and in male-specific aspects of suicidality.

Dajana Schreiber, PhD, is a postdoc at the Department of Clinical Psychology and Psychotherapy at the University of Duisburg-Essen, Germany. Her research addresses processes related to the development of suicide ideation and behavior and incorporates implicit associations, ecological momentary assessments and network analysis.

Thomas Forkmann, PhD, is professor of clinical psychology and psychotherapy at the University of Duisburg-Essen, Germany, and head of the university outpatient clinic. His lab conducts research into the understanding, prediction, and treatment of suicidal ideation and behavior.

Aleksa Kaurin, PhD, is Chair of Clinical Child and Adolescent Psychology and Psychotherapy at the University of Wuppertal, Germany. She studies trajectories of socio-affective dysregulation, their links to developmental psychopathology, as well as their manifestation in the daily lives of children and adolescents using real-time monitoring techniques.

Conflict of Interest: The authors have no conflicts of interest to disclose.

Publication Ethics: The study received ethical approval from the responsible ethical committee ethical committee in Leipzig/Germany (No. 388-13-16122013). All participating patients provided written informed consent.

Authorship: All authors contributed substantially to this work (LS: design and conceptualization, data analysis and interpretation, first drafting; HG and TF design and conceptualization, reviewing the draft; NH and DS data acquisition and reviewing the draft; AK data analysis and interpretation and reviewing the draft). The submitted manuscript is approved by all authors and all of them agree to be accountable for this work. All authors approved the final version of the manuscript.

Open Data: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Funding Statement

Funding: This research was supported by research grants no. SP 1556/1-1, GL 818/1-1 and FO 784/1-1 from the German Research Foundation. Open access publication was enabled by University of Leipzig, Germany.

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