Do Patterns and Types of Negative Affect During Hospitalization Predict Short-Term Post-Discharge Suicidal Thoughts and Behaviors?

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

Person-specific dynamics between negative emotions and suicidal thoughts

INTRODUCTION

Recent technology has enabled researchers to collect ecological momentary assessments (EMA) to examine within-person correlates of suicidal thoughts. Prior studies examined generalized temporal dynamics of emotions and suicidal thinking over brief periods, but it is not yet known how variable these processes are across people.

METHOD

We use data EMA data delivered over two weeks with youth/young adults (N = 60) who reported past year self-injurious thoughts/behaviors. We used group iterative multiple model estimation (GIMME) to model group- and person-specific associations of negative emotions (i.e., fear, sadness, shame, guilt, and anger) and suicidal thoughts.

RESULTS

29 participants (48.33%) reported at least one instance of a suicidal thought and were included in GIMME models. In group level models, we consistently observed autoregressive effects for suicidal thoughts (e.g., earlier thoughts predicting later thoughts), although the magnitude and direction of this link varied from person-to-person. Among emotions, sadness was most frequently associated with contemporaneous suicidal thoughts, but this was evident for less than half of the sample, while other emotional correlates of suicidal thoughts broadly differed across people. No emotion variable was linked to future suicidal thoughts in >14% of the sample, CONCLUSIONS: Emotion-based correlates of suicidal thoughts are heterogeneous across people. Better understanding of the individual-level pathways maintaining suicidal thoughts/behaviors may lead to more effective, personalized interventions.

Daily life affective dynamics as transdiagnostic predictors of mental health symptoms: An ecological momentary assessment study

BACKGROUND

Affective dynamics have been identified as a correlate of a broad span of mental health issues, making them key candidate transdiagnostic factors. However, there remains a lack of knowledge about which aspects of affective dynamics - especially as they manifest in the course of daily life - relate to a general risk for mental health issues versus specific symptoms.

METHODS

We leverage an ecological momentary assessment (EMA) study design with four measures per day over a two-week period to explore how negative affect levels, inertia, lability, and reactivity to provocation and stress in the course of daily life relate to mental health symptoms in young adults (n = 256) in the domains of anxiety, depression, psychosis-like symptoms, behaviour problems, suicidality, and substance use.

RESULTS

Dynamic structural equation modelling (DSEM) suggested that negative affect levels in daily life were associated with depression, anxiety, indirect and proactive aggression, psychosis, anxiety, and self-injury; negative affective lability was associated with depression, physical aggression, reactive aggression, suicidal ideation, and ADHD symptoms; negative affective inertia was associated with depression, anxiety, physical aggression, and cannabis use; and emotional reactivity to provocation was related to physical aggression.

LIMITATIONS

The cross-sectional design, the limited span of mental health issues included, and the convenience nature and small size of the sample are limitations.

CONCLUSIONS

Findings suggest that a subset of mental health symptoms have shared negative affective dynamics patterns. Longitudinal research is needed to rigorously examine the directionality of the effects underlying the association between affective dynamics and mental health issues.

The use of advanced technology and statistical methods to predict and prevent suicide

In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.

Clinical Considerations for Digital Resources in Care for Patients With Suicidal Ideation

Smartphone apps offer accessible new tools that may help prevent suicide and that offer support for individuals with active suicidal ideation. Numerous smartphone apps for mental health conditions exist; however, their functionality is limited, and evidence is nascent. A new generation of apps using smartphone sensors and integrating real-time data on evolving risk offers the potential of more personalized support, but these apps present ethical risks and currently remain more in the research domain than in the clinical domain. Nevertheless, clinicians can use apps to benefit patients. This article outlines practical strategies to select safe and effective apps for the creation of a digital toolkit that can augment suicide prevention and safety plans. By creating a unique digital toolkit for each patient, clinicians can help ensure that the apps selected will be most relevant, engaging, and effective.

Real-time digital monitoring of a suicide attempt by a hospital patient

Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before, during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention efforts.

Understanding suicidal pathways through the lens of a Dual-System Model of Suicidality in real-time: The potential of ecological momentary assessments

Within the ideation-to-action framework, existing theories of suicidal thoughts and behaviors (STBs) primarily focus on the linear progression of suicide risk. This, however, neglects growing evidence that many suicidal individuals do not experience their suicide attempt as a planned action, and in some instances deny even having experienced any suicidal thoughts. Furthermore, recent research has found that risk factors differ substantially between persons and that this is reflected in the variety of suicidal pathways. Considering the strong variability of STBs, new innovative theoretical concepts and assessment methods are needed to advance our understanding of multiple suicidal pathways. In this review, we apply a dual-system framework to suicidality, the Dual-System Model of Suicidality (DSMS), which accounts for two different systems of information processing and behavior. The first of these described is the reflective system, whereby STBs are viewed from a self-regulation perspective and thusly considered as maladaptive coping behavior to perceived discrepancies regarding important goals. Applying a feedback-based view such as this to STBs provides a deeper understanding into underlying psychological processes involved in the development of STBs. The second system described by the DSMS is the impulsive system. Here, STBs are seen as a maladaptive self-organizing pattern that gets activated in high-risk situations of acute stress, negative affect, and when resources of the reflective system are depleted. In this context, the DSMS is informed by a strength model of self-regulation, which assumes that self-regulation resources are limited, an aspect with important theoretical and clinical implications for the development of STBs. In order to demonstrate the theoretical and practical utility of the DSMS, this review draws mainly on studies using ecological momentary assessment (EMA), a technology that allows to investigate moment-to-moment changes in STBs, and is therefore well suited for capturing the complex interplay of self-regulatory and impulsive processes proposed by the DSMS. The application of a dual-system framework to suicide research represents an innovative and integrative approach for expanding our knowledge about fundamental processes and how their dynamics lead to STBs. The usefulness of the DSMS, implications for future suicide research with EMA, and clinical implications are discussed.