Modelling suicide ideation from beep to beep: application of network analysis to ecological momentary assessment data
Open accessAbstract
•EMA data were analyzed using network analysis (e.g., temporal networks).•Suicidal ideation at t was predicted by itself and perceived burdensomeness at t - 1.•Suicidal ideation at t - 1 predicted perceived burdensomeness, depression etc. at t.•At the same beep, suicidal ideation was related to all variables in the network.•Patients with higher average suicidal ideation had higher average hopelessness.
AI snapshot
Written from the paper’s full text.
TL;DR
This study re-analyzed ecological momentary assessment data from 74 psychiatric inpatients with depressive disorder and current/lifetime suicidal ideation using multilevel vector autoregression to estimate temporal, contemporaneous, and between-subjects networks. Suicidal ideation was prospectively predicted mainly by itself and perceived burdensomeness, while contemporaneously it was most strongly associated with hopelessness and related to all other assessed variables.
Key findings
- In the temporal network, suicidal ideation at time t was predicted only by perceived burdensomeness (fixed effects coefficient 0.06, p < .05) and suicidal ideation at t − 1 (0.26, p < .001). Suicidal ideation at t − 1 predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001); no temporal relation was found with thwarted belongingness or anxiety.
- In the contemporaneous network, suicidal ideation was most strongly associated with hopelessness and was associated with all other variables; positive affect had a strong negative correlation with depression.
- In the between-subjects network, participants with higher average hopelessness also had higher average suicidal ideation; no other between-subjects relation between suicidal ideation and any other variable was found.
- The sample completed 89.7% of EMA assessments on average (min = 75%, max = 100%), yielding 4295 valid observations.
Methods
- Seventy-four psychiatric inpatients with a primary unipolar depressive disorder and current or lifetime suicidal ideation were assessed over 6 days with 10 signal-contingent EMA prompts per day (maximum 60 assessments), randomly between 8:00 am and 8:00 pm with at least 30 minutes between prompts. Measures included suicidal ideation, perceived burdensomeness, thwarted belongingness, depressiveness, hopelessness, positive affect, and anxiety (16 items used for network analysis).
- Multilevel vector autoregression (mlVAR) models in R were used to estimate temporal, contemporaneous, and between-subjects networks. Variables were standardized and scaled within persons; networks were graphed with qgraph using the Fruchterman-Reingold algorithm. Data were not detrended because Dickey-Fuller tests indicated all variables were stationary (all p < .01).
Limitations
- The paper states the sample consisted only of inpatients with Major Depression, so findings should be generalized cautiously to other diagnoses; the inpatient setting may slightly diminish ecological validity. It also states uncertainty about whether the EMA design (10 assessments/day, 30–120 min intervals, 8 am–8 pm) captured the natural course of suicidal ideation, potentially missing late evenings, early mornings, and nights.
- The paper states that important risk factors such as defeat and entrapment were not included, only self-reports were used, and the study was likely underpowered to detect more subtle dynamic relations. It also states that network connections should be interpreted as hypothesis-generating indications of potential causality, not true causal relationships.
- Evident from the text: missing data were handled via listwise deletion, which may affect the analyzed sample; the paper does not discuss this as a limitation.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 38 cited sources checked · 0 supported · 0 flagged
- Findingown data
Suicidal ideation at time t−1 predicted positive affect at time t (fixed effects coefficient −0.08, p < .001).
“Suicidal ideation at t − 1 predicted perceived burdensomeness, depression etc. at t.”
- Also stated in: § Temporal network · plain
“SI, however, predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001) over time.”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingown data
Suicidal ideation at time t was predicted by perceived burdensomeness at time t−1 (fixed effects coefficient 0.06, p < .05).
“Suicidal ideation at t was predicted by itself and perceived burdensomeness at t – 1.”
- Also stated in: § Temporal network · plain
“SI at time point t was solely predicted by perceived burdensomeness (fixed effects coefficient: 0.06, p < .05) and SI at t − 1 (fixed effects coefficient: 0.26, p < .001).”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingown data
Patients with higher average suicidal ideation had higher average hopelessness.
“Patients with higher average suicidal ideation had higher average hopelessness.”
Quote in the paperNot a substantial claim - Findingown data
Suicidal ideation at time t−1 predicted hopelessness at time t (fixed effects coefficient 0.11, p < .001).
“Suicidal ideation at t − 1 predicted perceived burdensomeness, depression etc. at t.”
- Also stated in: § Temporal network · plain
“SI, however, predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001) over time.”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingown data
Suicidal ideation at time t was predicted by suicidal ideation at time t−1 (fixed effects coefficient 0.26, p < .001).
“Suicidal ideation at t was predicted by itself and perceived burdensomeness at t – 1.”
- Also stated in: § Temporal network · plain
“SI at time point t was solely predicted by perceived burdensomeness (fixed effects coefficient: 0.06, p < .05) and SI at t − 1 (fixed effects coefficient: 0.26, p < .001).”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingown data
Suicidal ideation at time t−1 predicted depressiveness at time t (fixed effects coefficient 0.08, p = .001).
“Suicidal ideation at t − 1 predicted perceived burdensomeness, depression etc. at t.”
- Also stated in: § Temporal network · plain
“SI, however, predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001) over time.”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingnone given
At the same beep, suicidal ideation was related to all variables in the network.
“At the same beep, suicidal ideation was related to all variables in the network.”
Quote in the paperSubstantial claim - Findingown data
Suicidal ideation at time t−1 predicted perceived burdensomeness at time t (fixed effects coefficient 0.10, p < .001).
“Suicidal ideation at t − 1 predicted perceived burdensomeness, depression etc. at t.”
- Also stated in: § Temporal network · plain
“SI, however, predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001) over time.”
Quote in the paperSubstantial claim - Also stated in: § Temporal network · plain
- Findingown data
In the contemporaneous network, positive affect had a strong negative correlation with depression.
“As expected, positive affect had a strong negative correlation with depression”
Quote in the paperNot a substantial claim - Findingown data
No other variables in the network predicted suicidal ideation at time t besides perceived burdensomeness and suicidal ideation at t−1.
“SI at time point t was solely predicted by perceived burdensomeness (fixed effects coefficient: 0.06, p < .05) and SI at t − 1 (fixed effects coefficient: 0.26, p < .001).”
Quote in the paperSubstantial claim - Findingown data
In the contemporaneous network, suicidal ideation is associated with all other variables.
“Furthermore, SI is associated with all other variables.”
Quote in the paperNot a substantial claim - Findingown data
In the between-subjects network, participants who on average scored high on hopelessness also scored high on average on suicidal ideation.
“participants that on average scored high on hopelessness also scored high on average on SI.”
- Also stated in: § Discussion · hedged
“Additionally, patients with higher average SI scores also tended to have higher hopelessness scores.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · hedged
- Findingown data
In the between-subjects network, participants who scored high on depression did not score high on perceived burdensomeness.
“They also scored low on positive affect and high on thwarted belongingness, whereas they did not score high on perceived burdensomeness”
Quote in the paperNot a substantial claim - Findingown data
In the between-subjects network, participants who scored high on depression also scored high on anxiety.
“participants that scored high on depression also scored high on anxiety and hopelessness.”
Quote in the paperNot a substantial claim - Findingown data
Suicidal ideation at t resulted in less positive affect at t − 1.
“We found that SI at t resulted in less positive affect at t − 1”
Quote in the paperSubstantial claim - Findingown data
In the between-subjects network, participants who scored high on depression also scored high on hopelessness.
“participants that scored high on depression also scored high on anxiety and hopelessness.”
Quote in the paperNot a substantial claim - Findingown data
All variables predicted themselves in the consecutive measurement.
“While all variables predicted themselves in the consecutive measurement, SI predicted itself in the strongest manner, as depicted by the thickest directed connection and the largest coefficient in the temporal network”
Quote in the paperNot a substantial claim - Findingown data
In the contemporaneous network, suicidal ideation is most strongly associated with hopelessness.
“SI is most strongly associated with hopelessness.”
Quote in the paperSubstantial claim - Findingown data
In the between-subjects network, participants who scored high on depression scored high on thwarted belongingness.
“They also scored low on positive affect and high on thwarted belongingness, whereas they did not score high on perceived burdensomeness”
Quote in the paperNot a substantial claim - Findingown data
In the temporal network, suicidal ideation at t − 1 predicted almost all the other variables at t.
“SI at t − 1 predicted almost all the other variables in the temporal network at t.”
Quote in the paperSubstantial claim - Findingown data
In the between-subjects network, no other relation between suicidal ideation and any other variables was found.
“No other between-subjects relation between SI and any other variables was found.”
Quote in the paperNot a substantial claim - Findingown data
Anxiety was predicted only by itself at time t−1.
“Anxiety was only predicted by itself, but neither influenced nor was influenced by any other variables at t − 1.”
Quote in the paperNot a substantial claim - Findingown data
Suicidal ideation had the strongest autoregressive effect among all variables in the temporal network.
“While all variables predicted themselves in the consecutive measurement, SI predicted itself in the strongest manner, as depicted by the thickest directed connection and the largest coefficient in the temporal network”
Quote in the paperNot a substantial claim - Findingown data
In the between-subjects network, participants who scored high on depression scored low on positive affect.
“They also scored low on positive affect and high on thwarted belongingness, whereas they did not score high on perceived burdensomeness”
Quote in the paperNot a substantial claim - Findingnone given
Positive affect was negatively related to all other variables.
“As expected, positive affect was negatively related to all other variables.”
Quote in the paperNot a substantial claim - Findingown data
There was considerable variance within person over time (at level 1, e.g., 48% of variance for thwarted belongingness, 38% of variance for perceived burdensomeness).
“However, we found considerable variance within person over time (at level 1, e.g., 48% of variance for TB, 38% of variance for PB), contradicting this assumption.”
Quote in the paperSubstantial claim - Findingown data
Anxiety and thwarted belongingness were the only variables in the temporal network that had no relation to suicidal ideation, at least in the study's timeframe of 30 to 120 min.
“Anxiety and thwarted belongingness were the only variables in the temporal network that had no relation to SI, at least in our study's timeframe of 30 to 120 min.”
Quote in the paperSubstantial claim - Findingown data
Positive affect was not confirmed to be related to all other variables over time in the network.
“For example, one would expect positive affect to be related to all other variables over time, but this was not confirmed in our network.”
Quote in the paperNot a substantial claim - Findingown data
We observed intra-individual differences in networks revealing different associations and risk factors per person.
“Interestingly, we observed intra-individual differences in networks revealing different associations and risk factors per person.”
Quote in the paperSubstantial claim - Findingown data
There was no support for a negative feedback loop, as less positive affect did not influence suicidal ideation over time.
“However, there was no support for a negative feedback loop, as we did not find that less positive affect in return also influenced SI over time.”
Quote in the paperSubstantial claim - Premisecitation
In a previous multilevel analysis, besides suicidal ideation and perceived burdensomeness, hopelessness at t − 1 was also predictive of suicidal ideation at t.
“where, besides SI and perceived burdensomeness, hopelessness at t − 1 was also predictive of SI at t (Hallensleben et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Hallensleben et al., 2019 source
Not checked in the free run.
- Premisecitation
Perceived burdensomeness, thwarted belongingness, depressive feelings, and hopelessness co-occurred with suicidal ideation within the same assessment, even when controlling for suicidal ideation at t−1.
“First, perceived burdensomeness, thwarted belongingness, depressive feelings, and hopelessness co-occurred with SI within the same assessment, even when controlling for SI at t − 1.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hallensleben et al., 2019 source
Not checked in the free run.
- Study design and conductown data
All variables were stationary over time according to the Dickey-Fuller test (all p < .01).
“We further did not detrend the data because the Dickey-Fuller test indicated all variables to be stationary over time (all p < .01).”
Quote in the paperSubstantial claim - Premisecitation
Traditional epidemiological studies neglected the dynamic nature of suicidal ideation and its psychological risk factors.
“but neglected the dynamic nature of suicidal ideation and its psychological risk factors.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Many more variables are directly related to suicidal ideation than current theories propose, even when controlling for all other symptoms in the network.
“Results showed that many more variables are directly related to suicidal ideation than current theories propose, even when controlling for all other symptoms in the network.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites De Beurs et al., 2019 source
Not checked in the free run.
- Interpretationown data
Our findings and previous findings by Kleiman and Nock (2018) and Kleiman et al. (2017) are not in line with the assumptions of the IPTS that perceived burdensomeness and thwarted belongingness are the main predictors of suicidal ideation over time.
“Our as well as previous findings by Kleiman and Nock (2018) and Kleiman et al. (2017) are not in line with the assumptions of the IPTS, stating that perceived burdensomeness and thwarted belongingness are the main predictors of SI over time (Joiner, 2007; Van Orden et al., 2010).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Joiner, 2007
Not checked in the free run.
- cites Van Orden et al., 2010 source
Not checked in the free run.
- Premisecitation
Traditional epidemiological studies gave insight into the role of more static risk factors of suicidal behavior, such as sex and a history of depression.
“Analysis from traditional epidemiological studies gave insight into the role of more static risk factors of suicidal behavior, such as sex and a history of depression (Brown et al., 2000; Wang et al., 2015)”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
A considerable amount of variance of suicidal ideation is caused by within-person processes over time.
“These results show that a considerable amount of variance of SI is caused by within-person processes over time.”
Quote in the paperSubstantial claim - Interpretationown data
Within a much shorter time frame than 30 to 120 min (lag-1), hopelessness is strongly associated with suicidal ideation.
“Within a much shorter time frame than 30 to 120 min (lag-1), hopelessness is strongly associated with SI.”
Quote in the paperSubstantial claim - Interpretationcitation
The findings are in line with an EMA study reporting that suicidal ideation mainly predicts itself, while other variables are less useful to predict suicidal ideation over time.
“The findings are in line with an EMA study by Kleiman et al. (2017), reporting that SI mainly predicts itself, while other variables are less useful in order to predict SI over time.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kleiman et al. (2017) source
Not checked in the free run.
- Interpretationown data
These results indicate that the temporal relation between psychological risk factors and suicidal ideation is more complex than theorized.
“If anything, these results indicate that the (temporal) relation between psychological risk factors and SI is more complex than theorized.”
Quote in the paperSubstantial claim - Study design and conductnone given
The sample only consisted of inpatients, which slightly diminishes ecological validity.
“Moreover, the sample only consists of inpatients also slightly diminishing ecological validity.”
Quote in the paperNot a substantial claim - Study design and conductnone given
Important risk factors for suicidal ideation, such as defeat and entrapment, were not included in the study.
“Another limitation is that important risk factors for SI, such as defeat and entrapment, were not included in the study.”
Quote in the paperNot a substantial claim - Premisecitation
Results of prospective studies on the effect of anxiety disorders on suicidal ideation are mixed, with some confirming and some falsifying the effect.
“The role of anxiety in the development of SI is less clear, since results of prospective studies are mixed – some confirmed and some falsified the effect of anxiety disorders on SI (Abreu et al., 2018; Bolton et al., 2010; Oquendo et al., 2004).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationown data
Taken together, the network analysis shows that the relation between known risk factors and suicidal ideation over time is more complex than theorized or presented in epidemiological studies.
“Taken together, the network analysis shows that the relation between known risk factors and SI over time is more complex than theorized or presented in epidemiological studies.”
Quote in the paperSubstantial claim - Premisecitation
Even when a node has no impact on other variables in the temporal network, it still can have an impact on subsequent variables by being central in the contemporaneous network.
“Moreover, even when a node has no impact on other variables in the temporal network, it still can have an impact on subsequent variables by being central in the contemporaneous network (Epskamp et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Epskamp et al., 2018 source
Not checked in the free run.
- Study design and conductnone given
The study only used self-reports.
“we only used self-reports.”
Quote in the paperNot a substantial claim - Study design and conductnone given
The study was likely underpowered to detect more subtle dynamic relations.
“It is also likely that we were underpowered to detect more subtle dynamic relations.”
Quote in the paperNot a substantial claim - Study design and conductnone given
The gap between the last assessment of one day and the first assessment of the consecutive day was taken into account statistically by not correlating them.
“Statistically, this gap has been taken into account by not correlating the last assessment of one day with the first assessment of the consecutive day.”
Quote in the paperNot a substantial claim - Interpretationown data
With regard to the IPTS, the networks show that the relationship between perceived burdensomeness and thwarted belongingness is not as straightforward as assumed in the theory.
“With regard to the IPTS, the networks show that the relationship between PB and TB is not as straight forward as assumed in the theory.”
Quote in the paperSubstantial claim - Interpretationcitation
Our findings regarding the assumptions of the IPTS are in line with findings reported by Kleiman et al. (2017), who examined inpatients admitted for suicide risk.
“On the other hand, our findings regarding the assumptions of the IPTS are in line with findings reported by other authors, for instance Kleiman et al. (2017), who examined inpatients, admitted for suicide risk.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kleiman et al. (2017) source
Not checked in the free run.
- May combine two claims. 18
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 20
May combine two claims. The quote only partly states this claim.
Findingown dataIn the temporal network, suicidal ideation at t was solely predicted by itself and the level of perceived burdensomeness at t − 1; only perceived burdensomeness had a direct temporal impact on suicidal ideation.
“SI at t was solely predicted by itself and the level of perceived burdensomeness at t − 1”
- Also stated in: § Discussion · plain
“Regarding the IPTS variables, only perceived burdensomeness had a direct temporal impact on SI.”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
May combine two claims.
Findingown dataAnxiety did not predict or was not predicted by any other variables at time t−1.
“Anxiety was only predicted by itself, but neither influenced nor was influenced by any other variables at t − 1.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe contemporaneous network revealed a direct relation between suicidal ideation, perceived burdensomeness, and thwarted belongingness; both thwarted belongingness and perceived burdensomeness are related to suicidal ideation at the same measurement.
“The contemporaneous network also revealed a direct relation between SI, perceived burdensomeness, and thwarted belongingness.”
- Also stated in: § Discussion · plain
“According to the results from the contemporaneous network, both thwarted belongingness and perceived burdensomeness are related to SI at the same measurement”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
Classified as probably not a substantial claim.
PremisecitationSuicidal behavior may emerge as the result of an interaction between many different psychological, social, and biological variables.
“Suicidal behavior may emerge as the result of an interaction between many different psychological, social, and biological variables (Hawton and Van Heeringen, 2000; O'Connor and Nock, 2014).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Hawton and Van Heeringen, 2000
Not checked in the free run.
- cites O'Connor and Nock, 2014
Not checked in the free run.
Classified as probably not a substantial claim.
PremisereasoningTemporal networks can be indicative of potential causality given that one variable preceded the other in time.
“These networks are called temporal networks, as they can be indicative of potential causality given that one variable preceded the other in time.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenNetwork analysis extends previously used multilevel analysis by controlling for autoregressive effects of all variables included in the network (not only autoregressive effects of SI), by revealing temporal and contemporaneous associations as well as between-subjects differences, and by offering a visual representation of the relations between all assessed variables.
“In sum, network analysis extends previously used multilevel analysis by controlling for autoregressive effects of all variables included in the network (not only autoregressive effects of SI), by revealing temporal and contemporaneous associations as well as between-subjects differences, and by offering a visual representation of the relations between all assessed variables.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationThe well-known psychological risk factors hopelessness, perceived burdensomeness, and thwarted belongingness co-occurred with suicidal ideation, but did not predict suicidal ideation at short-term follow up.
“Importantly, the well-known psychological risk factors hopelessness, perceived burdensomeness, and thwarted belongingness co-occurred with SI, but did not predict SI at short-term follow up.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kleiman et al., 2017 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationreasoningThe relation over time could also be explained by variables that had not been measured; therefore, temporal networks are mainly useful for hypothesis generating.
“The relation over time could also be explained by variables that had not been measured. Therefore, temporal networks are mainly useful for hypothesis generating.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationAccording to the network perspective in psychopathology, mental health problems (in this case suicidal ideation) develop as a result of interacting symptoms or risk factors.
“According to the network perspective in psychopathology, mental health problems (in our case SI) develop as a result of interacting symptoms or risk factors (Borsboom, 2017).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Borsboom, 2017 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationKleiman et al. (2017) reported similar results for psychological risk factors associated with suicidal ideation, such as hopelessness, perceived burdensomeness, and thwarted belongingness.
“Kleiman et al. (2017) reported similar results not only for SI, but also for psychological risk factors associated with SI, such as hopelessness, perceived burdensomeness, and thwarted belongingness.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kleiman et al., 2017 source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationOne proposed hypothesis from the network perspective on suicidal behavior is that a suicide attempt is the result of the interaction between different risk factors, such as hopelessness, depression, impulsivity, and suicidal ideation.
“One proposed hypothesis from the network perspective on suicidal behavior is that a suicide attempt is the result of the interaction between different risk factors, such as hopelessness, depression, impulsivity, and suicidal ideation (De Beurs, 2017).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites De Beurs, 2017
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationDifferences between cross-sectional and temporal analyses have been reported, leading Kleiman and Nock to distinguish between correlates, predictors, and predictors of change for suicidal ideation.
“Differences between cross-sectional and temporal analyses have also been reported by Kleiman and Nock (2018) and Ben-Zeev et al. (2012), which led Kleiman and Nock to distinguish between correlates, predictors, and predictors of change for SI (Kleiman and Nock, 2018).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Kleiman and Nock, 2018
Not checked in the free run.
- cites Ben-Zeev et al., 2012 source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationHopelessness, perceived burdensomeness (but not thwarted belongingness), and suicidal ideation at t−1 prospectively predicted suicidal ideation at t.
“Second, hopelessness, perceived burdensomeness (but not thwarted belongingness), and SI at t − 1 prospectively predicted SI at t (Hallensleben et al., 2019).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hallensleben et al., 2019 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWhen the dynamical relations between risk factors are sufficiently strong, they can be self-sustained via a negative feedback loop.
“When the dynamical relations between risk factors are sufficiently strong, they can be self-sustained via a negative feedback loop (Borsboom, 2017).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Borsboom, 2017 source
Not checked in the free run.
May combine two claims.
Interpretationown dataAs the time between two assessments in this study was between 30 and 120 min, the temporal network offers insights into relations between variables within this short period of time.
“As in our study the time between two assessments was between 30 and 120 min, the temporal network offers insights into relations between variables within this short period of time.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataMomentary intrusive suicidal thoughts seem to make people feel hopeless and a burden for others 30 to 120 min afterwards, which in turn might make them feel isolated further 30 to 120 min later.
“At least with our design, momentary intrusive suicidal thoughts seem to make people feel hopeless and a burden for others 30 to 120 min afterwards, which in turn might make them feel isolated (in terms of higher thwarted belongingness) further 30 to 120 min later.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationFirst EMA studies demonstrated high fluctuations of suicidal ideation over time, even from hour to hour, and large intrapersonal differences in fluctuations.
“First EMA studies not only demonstrated high fluctuations of suicidal ideation (SI) over time, even from hour to hour (cf. preliminary data analysis by Hallensleben et al., 2017; Kleiman et al., 2017), but also large intrapersonal differences in fluctuations.”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationKleiman and Nock (2018) concluded that well-known risk factors, such as hopelessness and perceived burdensomeness, are most useful to predict suicidal ideation at the moment, but less useful to predict suicidal ideation over time.
“concluding that well-known risk factors, such as hopelessness and perceived burdensomeness, are most useful to predict SI at the moment, but less useful to predict SI over time.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kleiman and Nock (2018)
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationAccording to the Interpersonal Theory of Suicidal Behavior, high levels of perceived burdensomeness and thwarted belongingness can lead to suicidal ideation.
“According to the IPTS, high levels of perceived burdensomeness and thwarted belongingness can lead to SI.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Joiner, 2007
Not checked in the free run.
- cites Van Orden et al., 2010 source
Not checked in the free run.
May combine two claims.
PremisecitationAll risk factors within the network were connected with each other, none of them being isolated.
“Additionally, all risk factors within the network were connected with each other, none of them being isolated.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites De Beurs et al., 2019 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataParticipants completed 89.7% of the EMA assessments on average (min = 75%, max = 100%), resulting in 4295 valid observations.
“Participants completed 89.7% of the EMA assessments on average (min = 75%, max = 100%), resulting in 4295 valid observations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenTime order is a necessary (but not sufficient) condition for causality.
“This time order is a necessary (but not sufficient) condition for causality.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationnone givenNetwork analysis of EMA data extends previously used multilevel analysis by offering a visualization of the relation between all variables and an insight into both within and between-subjects effects.
“Network analysis of EMA data extends our previously used multilevel analysis by offering a visualization of the relation between all variables and an insight into both the within and between-subjects effects.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe connections within the networks should only be interpreted as indications of potential causal relationships and serve as hypothesis generating networks.
“Finally, the connections within the networks should only be interpreted as indications of potential causal relationships, and serve as hypothesis generating networks.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLiterature on thwarted belongingness suggests that it influences suicidal ideation via perceived burdensomeness.
“Literature on thwarted belongingness does indeed suggest that it influences SI via perceived burdensomeness (e.g., Rogers and Joiner, 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Rogers and Joiner, 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationRisk factors can be associated with, predictive of, or related to change in suicidal ideation.
“As stated by Kleiman and Nock (2018), risk factors can be associated with, predictive of, or related to change in SI.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kleiman and Nock (2018)
Not checked in the free run.
Classified as probably not a substantial claim.
Interpretationown dataBecause the sample only consisted of patients with Major Depression, conclusions should be cautious for patients with any other diagnosis and suicidal ideation.
“A limitation of this study is the sample only consisting of patients with a Major Depression. Hence, we should be cautious when concluding for patients with any other diagnosis and suicidal ideation.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationThe findings are in line with both EMA studies on suicidal ideation and more traditional epidemiological studies.
“This is in line with both EMA studies on SI (Hallensleben et al., 2019; Kleiman et al., 2017) and more traditional epidemiological studies (e.g. Franklin et al., 2017).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOther studies report perceived burdensomeness to be associated more directly with suicidal ideation compared to thwarted belongingness.
“This is in line with other studies reporting perceived burdensomeness to be associated more directly with SI compared to thwarted belongingness (Hames et al., 2015; Ma et al., 2016).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Hames et al., 2015 source
Not checked in the free run.
- cites Ma et al., 2016
Not checked in the free run.
The authors state this as an assumption.
Participants with high levels of perceived burdensomeness, thwarted belongingness, hopelessness, and depressiveness on average would also show high levels of suicidal ideation.
“Finally, we assumed that participants with high levels of perceived burdensomeness, thwarted belongingness, hopelessness, and depressiveness on average would also show high levels of SI.”
The paper relies on this without support.
This is the first study in suicide research applying network analysis on EMA data to better understand the relation between suicidal ideation and several psychological risk factors.
“To the best of our knowledge, this is the first study in suicide research applying network analysis on EMA data to better understand the relation between SI and several psychological risk factors.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most reports failed to establish independent temporal predictors of suicidal ideation severity: of twelve articles fitting temporal prediction models, four failed to establish significant predictors after accounting for ideation at the previous time point, and five did not control for prior ideation.
“Most reports failed to establish independent temporal predictors of suicidal ideation severity: of twelve articles fitting temporal prediction models (47, 51, 52, 55–57, 60, 73, 74, 76, 77), four failed to establish significant predictors after accounting for ideation at the previous time point (47, 52, 55), and five did not control for prior ideation (51, 60, 73, 74, 76).”
From Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research (2022) · cites this paper as [57]
The most consistent evidence for other predictors was found for momentary negative affect, hopelessness and burdensomeness, which predicted increased momentary suicidal ideation within-day.
“Regarding other predictors, the most consistent evidence was found for momentary negative affect, hopelessness and burdensomeness. These variables predicted increased momentary suicidal ideation within-day (47, 56, 57, 60).”
From Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research (2022) · cites this paper as [57]
Digital phenotyping of suicidal ideators based on EMA data has so far been implemented by few.
“but so far implemented by few (50, 57, 70).”
From Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research (2022) · cites this paper as [57]
EMA data has revealed notable inter-individual differences in suicide symptom profiles.
“EMA data has revealed notable inter-individual differences in suicide symptom profiles (57)”
From Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research (2022) · cites this paper as [57]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Limitation says the sample consisted only of patients with major depression
MinorThe discussion characterizes the sample as consisting only of patients with major depression, but Table 1 lists five participants with dysthymia and 69 with major depression.
“A limitation of this study is the sample only consisting of patients with a Major Depression.”
“| Dysthymia | 5 | 6.8 | | 3 | 14.3 | | 2 | 3.8 | |”
Reported EMA completion rate does not match valid observations
MinorThe 4295 valid observations out of 4440 possible assessments represent 96.7%, not the reported average completion rate of 89.7%.
Reported 89.7 · recomputed 96.734234
“Participants completed 89.7% of the EMA assessments on average (min = 75%, max = 100%), resulting in 4295 valid observations.”
Discussion reverses the direction of the positive-affect finding
MinorThe discussion says SI at time t led to less positive affect at the preceding assessment. The results instead report that SI predicted positive affect at a subsequent assessment.
“We found that SI at t resulted in less positive affect at t − 1”
“SI, however, predicted perceived burdensomeness (0.10, p < .001), hopelessness (0.11, p < .001), depressiveness (0.08, p = .001), and positive affect (−0.08, p < .001) over time.”
Cites
The interpersonal theory of suicide.
Van Orden, K. A., Witte, T. K., Cukrowicz, K. C., Braithwaite, S. R., Selby, E. A., & Joiner, T. E. (2010) · Psychological Review · doi:10.1037/a0018697 · Cited by 5,292 · see citing works
Qgraph: Network visualizations of relationships in psychometric data
Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V., & Borsboom, D. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i04 · Cited by 4,844 · see citing works
Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017) · Psychological Bulletin · doi:10.1037/bul0000084 · Cited by 3,784 · see citing works
The Analysis of Time Series: An Introduction
Dickey, D. A., & Chatfield, C. (1991) · Technometrics · doi:10.2307/1268794 · Cited by 3,551 · see citing works
A network theory of mental disorders
Borsboom, D. (2017) · World Psychiatry · doi:10.1002/wps.20375 · Cited by 3,482 · see citing works
Joiner, T. E. (2007) · Harvard University Press eBooks · doi:10.2307/j.ctvjghv2f · Cited by 2,891 · see citing works
Osman, A., Bagge, C. L., Gutierrez, P. M., Konick, L. C., Kopper, B. A., & Barrios, F. X. (2001) · Assessment · doi:10.1177/107319110100800409 · Cited by 2,276 · see citing works
Handbook of research methods for studying daily life
Mehl, M. R., Conner, T. S., & Csíkszentmihályi, M. (2012) · Choice Reviews Online · doi:10.5860/choice.50-1159 · Cited by 2,121 · see citing works
The disaggregation of within-person and between-person effects in longitudinal models of change
Curran, P. J., & Bauer, D. J. (2011) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100356 · Cited by 2,056 · see citing works
Risk factors for suicide in psychiatric outpatients: A 20-year prospective study.
Brown, G. K., Beck, A. T., Steer, R. A., & Grisham, J. R. (2000) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.68.3.371 · Cited by 1,317 · see citing works
Chu, C., Buchman-Schmitt, J. M., Stanley, I. H., Hom, M. A., Tucker, R. P., Hagan, C. R., Rogers, M. L., Podlogar, M. C., Chiurliza, B., Ringer, F. B., et al. (2017) · Psychological Bulletin · doi:10.1037/bul0000123 · Cited by 1,231 · see citing works
The psychology of suicidal behaviour
O'Connor, R. C., & Nock, M. K. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70222-6 · Cited by 1,193 · see citing works
Association of Symptom Network Structure With the Course of Depression
van Borkulo, C. D., Boschloo, L., Borsboom, D., Penninx, B. W. J. H., Waldorp, L., & Schoevers, R. A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.2079 · Cited by 852 · see citing works
Kleiman, E. M., Turner, B. J., Fedor, S., Beale, E. E., Huffman, J. C., & Nock, M. K. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000273 · Cited by 831 · see citing works
The Analysis of Time Series: An Introduction
Chatfield, C. (1980) · Springer eBooks · doi:10.1007/978-1-4899-2923-5 · Cited by 796 · see citing works
A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data
Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F. P. M. L., Borsboom, D., & Tuerlinckx, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060188 · Cited by 679 · see citing works
Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis
Hoffman, L., & Stawski, R. S. (2009) · Research in Human Development · doi:10.1080/15427600902911189 · Cited by 664 · see citing works
Oquendo, M. A., Galfalvy, H., Russo, S., Ellis, S. P., Grunebaum, M. F., Burke, A., & Mann, J. J. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.8.1433 · Cited by 605 · see citing works
Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A.-M., Riese, H., & Cramer, A. O. J. (2018) · Clinical Psychological Science · doi:10.1177/2167702617744325 · Cited by 448 · see citing works
The international handbook of suicide and attempted suicide
Rosenman, S. J. (2001) · Australian & New Zealand Journal of Psychiatry · doi:10.1046/j.1440-1614.2001.0949k.x · Cited by 431 · see citing works
Exploring the idiographic dynamics of mood and anxiety via network analysis
Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000311 · Cited by 385 · see citing works
Ma, J., Batterham, P. J., Calear, A. L., & Han, J. (2016) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.04.008 · Cited by 384 · see citing works
Revealing the dynamic network structure of the Beck Depression Inventory-II
Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015) · Psychological Medicine · doi:10.1017/s0033291714001809 · Cited by 371 · see citing works
Improving the Short-Term Prediction of Suicidal Behavior
Glenn, C. R., & Nock, M. K. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.06.004 · Cited by 328 · see citing works
SKID. Strukturiertes Klinisches Interview für DSM-IV. Achse I und II. Handanweisung
Wïttchen, H., Zaudig, M., & Fydrich, T. (1997) · Max Planck Institute for Plasma Physics · Cited by 321 · see citing works
Don’t blame the model: Reconsidering the network approach to psychopathology.
Bringmann, L. F., & Eronen, M. I. (2018) · Psychological Review · doi:10.1037/rev0000108 · Cited by 300 · see citing works
Visualizing Psychological Networks: A Tutorial in R
Jones, P. J., Mair, P., & McNally, R. J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01742 · Cited by 271 · see citing works
Real-time assessment of suicidal thoughts and behaviors
Kleiman, E. M., & Nock, M. K. (2018) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2017.07.026 · Cited by 239 · see citing works
Time Series Analysis and its Applications
Lawton, R. (2001) · International Journal of Forecasting · doi:10.1016/s0169-2070(01)00083-8 · Cited by 217 · see citing works
Hallensleben (2019) · doi:10.1016/j.eurpsy.2018.11.003 · Cited by 210 · see citing works
Self-citationUse of the experience sampling method in the context of clinical trials
Verhagen, S. J. W., Hasmi, L., Drukker, M., van Os, J., & Delespaul, P. A. E. G. (2016) · Evidence Based Mental Health · doi:10.1136/ebmental-2016-102418 · Cited by 208 · see citing works
Wigman, J. T. W., van Os, J., Borsboom, D., Wardenaar, K. J., Epskamp, S., Klippel, A., MERGE, Viechtbauer, W., Myin‐Germeys, I., & Wichers, M. (2015) · Psychological Medicine · doi:10.1017/s0033291715000331 · Cited by 200 · see citing works
Changing dynamics: Time-varying autoregressive models using generalized additive modeling.
Bringmann, L. F., Hamaker, E. L., Vigo, D. E., AUBERT, A. E., Borsboom, D., & Tuerlinckx, F. (2016) · Psychological Methods · doi:10.1037/met0000085 · Cited by 190 · see citing works
To center or not to center? Investigating inertia with a multilevel autoregressive model
Hamaker, E. L., & Grasman, R. P. P. P. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01492 · Cited by 185 · see citing works
Bolton, J. M., Pagura, J., Enns, M. W., Grant, B., & Sareen, J. (2010) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2010.01.003 · Cited by 183 · see citing works
Bos, F. M., Snippe, E., de Vos, S., Hartmann, J., Simons, C. J. P., van der Krieke, L., de Jonge, P., & Wichers, M. (2017) · Psychotherapy and Psychosomatics · doi:10.1159/000453583 · Cited by 176 · see citing works
Exploring the psychology of suicidal ideation: a theory driven network analysis.
De Beurs, D., Fried, E. I., Wetherall, K., Cleare, S., O’ Connor, D. B., Ferguson, E., O'Carroll, R. E., & O’ Connor, R. C. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103419 · Cited by 171 · see citing works
Forkmann, T., Spangenberg, L., Rath, D., Hallensleben, N., Hegerl, U., Kersting, A., & Glaesmer, H. (2018) · Journal of Abnormal Psychology · doi:10.1037/abn0000381 · Cited by 165 · see citing works
Self-citationWang, Y.-Y., Jiang, N.-Z., Cheung, E. F. C., Sun, H.-W., & Chan, R. C. K. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.05.001 · Cited by 138 · see citing works
Hallensleben, N., Spangenberg, L., Forkmann, T., Rath, D., Hegerl, U., Kersting, A., Kallert, T. W., & Glaesmer, H. (2018) · Crisis · doi:10.1027/0227-5910/a000464 · Cited by 125 · see citing works
Self-citationReal-time predictors of suicidal ideation: mobile assessment of hospitalized depressed patients
Ben-Zeev, D., Young, M. A., & Depp, C. A. (2012) · Psychiatry Research · doi:10.1016/j.psychres.2011.11.025 · Cited by 122 · see citing works
Network Analysis: A Novel Approach to Understand Suicidal Behaviour
Beurs, D. P. D. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14030219 · Cited by 117 · see citing works
Personalized feedback on symptom dynamics of psychopathology: A proof-of-principle study
Kroeze, R., van der Veen, D. C., Servaas, M. N., Bastiaansen, J. A., Voshaar, R. C. O. V., Borsboom, D., Ruhe, H. G., Schoevers, R. A., & Riese, H. (2017) · Journal for Person-Oriented Research · doi:10.17505/jpor.2017.01 · Cited by 93 · see citing works
Snippe, E., Viechtbauer, W., Geschwind, N., Klippel, A., de Jonge, P., & Wichers, M. (2017) · Scientific Reports · doi:10.1038/srep46523 · Cited by 89 · see citing works
Kleiman, E. M., Coppersmith, D. D. L., Millner, A. J., Franz, P. J., Fox, K. R., & Nock, M. K. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.02.033 · Cited by 88 · see citing works
Rogers, M. L., & Joiner, T. E. (2018) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000373 · Cited by 71 · see citing works
Hallensleben, N., Spangenberg, L., Kapusta, N. D., Forkmann, T., & Glaesmer, H. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.01.045 · Cited by 64 · see citing works
Self-citationValidation of the Rasch-based Depression Screening in a large scale German general population sample
Forkmann, T., Boecker, M., Wirtz, M. A., Glaesmer, H., Brähler, E., Norra, C., & Gauggel, S. (2010) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-8-105 · Cited by 60 · see citing works
Self-citationNuij, C., van Ballegooijen, W., Ruwaard, J., Beurs, D. P. D., Mokkenstorm, J. K., van Duijn, E., de Winter, R. F. P., O’Connor, R. C., Smit, J. H., Riper, H. M., et al. (2018) · Internet Interventions · doi:10.1016/j.invent.2018.04.005 · Cited by 58 · see citing works
Beurs, D. P. D., van Borkulo, C. D., & O’Connor, R. C. (2017) · BJPsych Open · doi:10.1192/bjpo.bp.116.004275 · Cited by 43 · see citing works
Abreu, L. N., Oquendo, M. A., Galfavy, H., Burke, A., Grunebaum, M. F., Sher, L., Sullivan, G. M., Sublette, M. E., Mann, J., & Lafer, B. (2018) · European Psychiatry · doi:10.1016/j.eurpsy.2017.09.005 · Cited by 34 · see citing works
Beurs, D. P. D., Vancayseele, N., van Borkulo, C. D., Portzky, G., & van Heeringen, K. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.07.047 · Cited by 27 · see citing works
Time Series Regression and ARIMA Models
Shumway, R. H., & Stoffer, D. S. (2000) · Springer texts in statistics · doi:10.1007/978-1-4757-3261-0_2 · Cited by 22 · see citing works
Hames, J. L., Chiurliza, B., Podlogar, M. C., Smith, A. R., Selby, E. A., Anestis, M. D., & Joiner, T. E. (2015) · Journal of Clinical Psychology · doi:10.1002/jclp.22158 · Cited by 22 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research
Kivelä, L., van der Does, W. A. J., Riese, H., & Antypa, N. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.876595 · Cited by 163 · see citing works
Network structure of depression and anxiety symptoms in Chinese female nursing students
Ren, L., Wang, Y., Wu, L., Wei, Z., Cui, L., Wei, X., Hu, X., Peng, J., Jin, Y., Li, F., et al. (2021) · BMC Psychiatry · doi:10.1186/s12888-021-03276-1 · Cited by 166 · see citing works
Use of Ecological Momentary Assessment to Study Suicidal Thoughts and Behavior: a Systematic Review
Sedano-Capdevila, A., Porras-Segovia, A., Bello, H. J., Baca-García, E., & Barrigon, M. L. (2021) · Current Psychiatry Reports · doi:10.1007/s11920-021-01255-7 · Cited by 148 · see citing works
A network perspective on suicidal behavior: understanding suicidality as a complex system
de Beurs, D., Bockting, C., Kerkhof, A., Scheepers, F., O’Connor, R., Penninx, B., & van de Leemput, I. (2021) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12676 · Cited by 119 · see citing works
Suicide Risk and Protective Factors: A Network Approach
Holman, M., & Williams, M. N. (2020) · Archives of Suicide Research · doi:10.1080/13811118.2020.1774454 · Cited by 99 · see citing works
Kuehn, K. S., Dora, J., Harned, M. S., Foster, K. T., Song, F., Smith, M. R., & King, K. M. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01340-8 · Cited by 90 · see citing works
Using intensive time sampling methods to capture daily suicidal ideation: A systematic review
Ammerman, B. A., & Law, K. C. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.10.121 · Cited by 74 · see citing works
New methods for assessing rapid changes in suicide risk
Ballard, E. D., Gilbert, J. R., Wusinich, C., & Zarate, C. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.598434 · Cited by 63 · see citing works
Blanchard, M. A., Contreras, A., Kalkan, R. B., & Heeren, A. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01839-y · Cited by 59 · see citing works
The use of advanced technology and statistical methods to predict and prevent suicide
Kleiman, E. M., Glenn, C. R., & Liu, R. T. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00175-y · Cited by 63 · see citing works
Integrating a functional view on suicide risk into idiographic statistical models
Kaurin, A., Dombrovski, A. Y., Hallquist, M. N., & Wright, A. G. C. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104012 · Cited by 53 · see citing works
Castro, D., Gysi, D. M., Ferreira, F., Ferreira‐Santos, F., & Ferreira, T. B. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297058 · Cited by 51 · see citing works
Schönfelder, A., Rath, D., Forkmann, T., Paashaus, L., Lucht, L., Teismann, T., Stengler, K., Juckel, G., & Glaesmer, H. (2021) · British Journal of Clinical Psychology · doi:10.1111/bjc.12293 · Cited by 39 · see citing works
Self-citationParrish, E. M., Chalker, S. A., Cano, M., Moore, R. C., Pinkham, A. E., Harvey, P. D., Joiner, T. E., Lieberman, A. J., Granholm, E. L., & Depp, C. A. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.06.022 · Cited by 40 · see citing works
The interplay between psychopathological symptoms: transdiagnostic cross-lagged panel network model
Chavez‐Baldini, U., Verweij, K. J. H., Beurs, D. P. D., Bockting, C., Lok, A., Sutterland, A. L., van Rooijen, G., van Wingen, G. A., Denys, D. A. J. P., Vulink, N. C. C., et al. (2022) · BJPsych Open · doi:10.1192/bjo.2022.516 · Cited by 39 · see citing works
Brüdern, J., Glaesmer, H., Berger, T., & Spangenberg, L. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.899500 · Cited by 37 · see citing works
Self-citationLi, Y., & Kwok, S. Y. C. L. (2022) · Journal of Youth and Adolescence · doi:10.1007/s10964-022-01698-y · Cited by 37 · see citing works
Heterogeneity in suicide risk: Evidence from personalized dynamic models
Coppersmith, D. D. L., Kleiman, E. M., Millner, A. J., Wang, S. B., Arizmendi, C., Bentley, K. H., DeMarco, D., Fortgang, R. G., Zuromski, K. L., Maimone, J. S., et al. (2024) · Behaviour Research and Therapy · doi:10.1016/j.brat.2024.104574 · Cited by 41 · see citing works
Pelton, M., Crawford, H., Bul, K., Robertson, A., Adams, J., Beurs, D. P. D., Rodgers, J., Baron‐Cohen, S., & Cassidy, S. (2023) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12954 · Cited by 32 · see citing works
Badal, V. D., Parrish, E. M., Holden, J. L., Depp, C. A., & Granholm, E. L. (2021) · Schizophrenia · doi:10.1038/s41537-021-00189-6 · Cited by 24 · see citing works
Al-Dajani, N., & Uliaszek, A. A. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.114027 · Cited by 22 · see citing works
Janssens, J. J., Kiekens, G., Jaeken, M., & Kirtley, O. J. (2024) · Clinical Psychology Review · doi:10.1016/j.cpr.2024.102467 · Cited by 26 · see citing works
van Ballegooijen, W., Littlewood, D. L., Nielsen, E., Kapur, N., & Gooding, P. A. (2022) · BJPsych Open · doi:10.1192/bjo.2022.68 · Cited by 23 · see citing works
Kyron, M. J., Hooke, G. R., Bryan, C. J., & Page, A. C. (2021) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12814 · Cited by 19 · see citing works
Hu, Z., Shan, N., & Jiao, R. (2023) · European Journal of Psychology of Education · doi:10.1007/s10212-023-00703-7 · Cited by 21 · see citing works
Preventing comorbidity between distress and suicidality: a network analysis
Junus, A., & YIP, P. S. F. (2023) · npj Mental Health Research · doi:10.1038/s44184-023-00022-1 · Cited by 17 · see citing works
Rath, D., Teismann, T., Höller, I., Glaesmer, H., Paashaus, L., Schönfelder, A., Juckel, G., & Forkmann, T. (2021) · Journal of Clinical Psychology · doi:10.1002/jclp.23159 · Cited by 17 · see citing works
Self-citationPsychosocial work environment as a dynamic network: a multi-wave cohort study
Elovainio, M. J., Hakulinen, C. A., Komulainen, K., Mika Kivimaki, B., Virtanen, M., Ervasti, J. M., & Oksanen, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-17283-z · Cited by 17 · see citing works
Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior
Carretero, P., Campaña-Montes, J. J., & Artés-Rodrı́guez, A. (2020) · Current topics in behavioral neurosciences · doi:10.1007/7854_2020_170 · Cited by 15 · see citing works
Kivelä, L. M. M., Fried, E. I., van der Does, W. A. J., & Antypa, N. (2024) · Psychological Medicine · doi:10.1017/s003329172400151x · Cited by 15 · see citing works
Smith, A. R., Forrest, L. N., Kinkel‐Ram, S. S., Grunewald, W., Tubman, S. D., Esche, A., & Levinson, C. A. (2024) · Psychological Medicine · doi:10.1017/s0033291724000734 · Cited by 12 · see citing works
Mandel, A. A., Revzina, O., Hunt, S., & Rogers, M. L. (2024) · Behaviour Research and Therapy · doi:10.1016/j.brat.2024.104602 · Cited by 11 · see citing works
Elovainio, M. J., Kuula, L., Halonen, R., & Pesonen, A. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.07.050 · Cited by 10 · see citing works
Choo, T.-H. J., Wall, M. M., Brodsky, B. S., Herzog, S., Mann, J. J., Stanley, B., & Galfalvy, H. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.05.093 · Cited by 10 · see citing works
Smith, A. R., Hunt, R. A., Grunewald, W., Jeon, M. E., Stanley, I. H., Levinson, C. A., & Joiner, T. E. (2021) · Archives of Suicide Research · doi:10.1080/13811118.2021.1993398 · Cited by 10 · see citing works
Risk factors of suicidal behaviors in a high‐risk longitudinal veteran sample: A network analysis
Saulnier, K. G., Volarov, M., Velimirović, M., Bauer, B. W., Kolnogorova, K., Ashrafioun, L., Stecker, T. A., & Allan, N. P. (2022) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.12918 · Cited by 9 · see citing works
The use of psychological network analysis in informal dementia care: an empirical illustration
Gérain, P., Wawrziczny, É., & Antoine, P. (2022) · Aging & Mental Health · doi:10.1080/13607863.2022.2134294 · Cited by 9 · see citing works
van den Brink, B., Jongkind, M., Delespaul, P., Braam, A. W., Schaap‐Jonker, H., & Giltay, E. J. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.05.139 · Cited by 8 · see citing works
Disorder agnostic network structure of psychopathology symptoms in youth
Wang, E., Reardon, B., Cherian, B., George, W. T., & Xavier, R. M. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.09.026 · Cited by 7 · see citing works
Nuij, C., Van Ballegooijen, W., Smit, A. C., De Beurs, D., De Winter, R. F. P., O’Connor, R. C., Kerkhof, A., Smit, J. H., & Riper, H. (2023) · Journal for Person-Oriented Research · doi:10.17505/jpor.2023.25265 · Cited by 6 · see citing works
Kraiss, J. T., Glaesmer, H., Forkmann, T., Spangenberg, L., Hallensleben, N., Schreiber, D., & Höller, I. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.07.050 · Cited by 6 · see citing works
Self-citationCoexisting service-related factors preceding suicide: a network analysis
Rex, M., Brezicka, T., Carlström, E., Wærn, M., & Ali, L. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-050953 · Cited by 6 · see citing works
Kyron, M. J., Hooke, G. R., Bryan, C. J., Kiekens, G., Chen, W., Hasking, P. A., & Page, A. C. (2023) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000812 · Cited by 6 · see citing works
Relations among symptoms of depression over time in at-risk youth.
Quinn, M. E., Liu, Q., Cole, D. A., McCauley, E., Diamond, G. S., & Garber, J. (2023) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0000847 · Cited by 5 · see citing works
Blacutt, M. E., Jacobucci, R., & Ammerman, B. A. (2025) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0001048 · Cited by 5 · see citing works
Katzenmajer-Pump, L., Komáromy, D., & Balázs, J. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.969164 · Cited by 5 · see citing works
Hendley, T., Gerner, J. L., Bolner, J., Moscardini, E. H., Tucker, R. P., & Hill, R. M. (2025) · Journal of Psychopathology and Behavioral Assessment · doi:10.1007/s10862-025-10214-y · Cited by 5 · see citing works
Network Analysis of Psychopathological Dimensions in Patients with and Without Suicidal Ideation
Cuadrado, E. H., Paniagua-Granados, T., Romero, M., Fernández‐Arias, I., & Larroy, C. (2025) · Behavioral Sciences · doi:10.3390/bs15070946 · Cited by 5 · see citing works
Weires, E., Edershile, E., Goel, D., Walsh, R. F. L., Burke, T. A., & Liu, R. T. (2025) · Psychological Medicine · doi:10.1017/s0033291725102547 · Cited by 2 · see citing works
MacNeil, S., Renaud, J., & Gouin, J. (2024) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.2024.43.5.492 · Cited by 3 · see citing works
Proximal Risk for Suicide: Protocol for an Ecological Momentary Assessment Study
Sharma, P., Peck, R., Sinicrope, A. R., Pavey, T., & Muehlenkamp, J. J. (2022) · JMIR Research Protocols · doi:10.2196/37583 · Cited by 3 · see citing works
Suicidal Behavior from a Complex System Perspective: Individual, Dynamical, and Contextual
Beurs, D. P. D., de Winter, R. F. P., Helbich, M., & Bockting, C. (2022) · Springer eBooks · doi:10.1007/978-3-030-42003-1_35 · Cited by 3 · see citing works
Zhou, P., Hu, Z., Chen, Y., Liu, K., & Wang, Y. (2023) · Health & Place · doi:10.1016/j.healthplace.2023.103170 · Cited by 4 · see citing works
Scott, L. N., & Brown, S. L. (2025) · Suicide and Life-Threatening Behavior · doi:10.1111/sltb.70027 · Cited by 3 · see citing works
Shou, Y., Gendi, M., Borschmann, R., Ma, J. S., Larsen, M. E., Han, J., Calear, A. L., Farrer, L. M., & Batterham, P. J. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.06.022 · Cited by 2 · see citing works
Validating the German version of the entrapment scale among suicidal psychiatric inpatients
Wichelhaus, E., Schreiber, D., Paashaus, L., Schönfelder, A., Juckel, G., Glaesmer, H., Teismann, T., & Forkmann, T. (2024) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2024.100862 · Cited by 2 · see citing works
Self-citationSuicidal Behavior in University Students in Spain: a Network Analysis
Soto‐Sanz, V., del Castillo‐López, Á. G., Pineda, D., Falcó, R., Rodríguez‐Jiménez, T., Marzo, J. C., & Piqueras, J. A. (2025) · Brain and Behavior · doi:10.1002/brb3.70457 · Cited by 3 · see citing works
Suicidal Behavior from a Complex System Perspective: Individual, Dynamical, and Contextual
Beurs, D. P. D., de Winter, R. F. P., Helbich, M., & Bockting, C. (2021) · Springer eBooks · doi:10.1007/978-3-030-41319-4_35-1 · Cited by 2 · see citing works
Núñez, R., Marcelo Arancibia, M., & Rees, R. (2021) · Revista chilena de neuro-psiquiatría · doi:10.4067/s0717-92272021000100002 · Cited by 1 · see citing works
Ren, L., Wang, Y., Wu, L., Wei, Z., Cui, L.-B., Wei, X., Hu, X., Peng, J., Jin, Y., Li, F., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14707870
Factors that impact risk for suicide in U.S. veterans: Exploring differences between men and women
Kintzle, S. M., Alday, E., Lee, J. J., Goncalves, V. T., Mamey, M. R., & Castro, C. A. (2026) · Military Psychology · doi:10.1080/08995605.2026.2648345
Harris, K., Haddock, G., Peters, S., Mulligan, L. D., & Gooding, P. A. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08092-z
Lin, T., Shariyf, L. R., Kautz, M. M., Baker, J. C., Khazem, L. R., Bryan, C. J., & Brown, L. A. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122133
Kim, H., Smith, J. L., McInnis, M. G., & Sperry, S. H. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122334
Pasquariello, K., Dalton, A. G., Barnhart, W. R., Engel, S. G., Wonderlich, S. A., Laam, L. A., Yee, S., Graver, H., Wang, S. B., Chenaux-Repond, A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10529603/v1
Knorr, A. C., Wang, L., Valentino, K., & Ammerman, B. A. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122522
- Authors
- Rath, D., de Beurs, D., Hallensleben, N., Spangenberg, L., Glaesmer, H., & Forkmann, T.
- Affiliations
- University of Duisburg-Essen (DE)
- Leipzig University (DE)
- Corresponding author
- Rath, Dajana
- Venue
- Internet Interventions
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 18
- Pages
- 100292
- ISSN
- 2214-7829 (ISSN-L)
- PMC
- PMC6889482
- OpenAlex
- W2991246777
- Cited by
- 66
- References
- 54
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 11 · 2023: 8 · 2022: 14 · 2021: 17 · 2020: 3
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Internet Interventions (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- German Research Foundation (SP 1556/1-1, GL 818/1-1, FO 784/1-1)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- suicidal ideation, perceived burdensomeness, hopelessness, depression, dynamic networks, ecological momentary assessment, network analysis, suicide risk prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
