Suicide and self-injury are difficult to predict because at-risk individuals are often unable or unwilling to report their intentions. Therefore, tools to reliably assess risk without reliance on self-report are critically needed. Prior research suggests that people who engage in suicidal and nonsuicidal self-injury often implicitly (i.e., outside conscious control) associate themselves with self-harm and death, indicating that self-harm-related implicit cognition may serve as a useful behavioral marker for suicide risk. However, earlier studies left several critical questions about the robustness, sensitivity, and specificity of self-harm-related implicit associations unaddressed. We recruited a large sample of participants (N=7,015) via a public web-based platform called Project Implicit Mental Health to test several hypotheses about self-harm-related implicit associations using the Implicit Association Test (IAT). Participants were randomly assigned to complete one of three self-harm IATs (Self + Cutting using picture stimuli, Self + Suicide using word stimuli, Self + Death using word stimuli). Results replicated prior studies demonstrating that self-harm-related implicit associations were stronger among individuals with (vs. without) a history of suicide attempt and nonsuicidal self-injury. Results also suggested that self-harm-related implicit associations are robust (based on internal replication), are sensitive to recency and severity of self-harm history (e.g., stronger associations for more recent and more lethal prior suicide attempts), and correlate with specific types of self-harm behaviors. These findings clarify the nature of self-harm-related implicit cognition and highlight the IAT's potential to track current risk for specific types of self-harm in ways that more fixed risk factors cannot.

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Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.

Background and objectives Implicit associations are relatively uncontrollable associations between concepts in memory. The current investigation focuses on implicit associations in four mental health domains (alcohol use, anxiety, depression, and eating disorders) and how these implicit associations: a) relate to explicit associations and b) self-reported clinical symptoms within the same domains, and c) vary based on demographic characteristics (age, gender, race, ethnicity, and education). Methods Participants (volunteers over age 18 to a research website) completed implicit association (Implicit Association Tests), explicit association (self+psychopathology or attitudes toward food, using semantic differential items), and symptom measures at the Project Implicit Mental Health website tied to: alcohol use (N=12,387), anxiety (N=21,304), depression (N=24,126), or eating disorders (N=10,115). Results Within each domain, implicit associations showed small to moderate associations with explicit associations and symptoms, and predicted self-reported symptoms beyond explicit associations. In general, implicit association strength varied little by race and ethnicity, but showed small ties to age, gender, and education. Limitations This research was conducted on a public research and education website, where participants could take more than one of the studies. Conclusions Among a large and diverse sample, implicit associations in the four domains are congruent with explicit associations and self-reported symptoms, and also add to our prediction of self-reported symptoms over and above explicit associations, pointing to the potential future clinical utility and validity of using implicit association measures with diverse populations.

Although social anxiety and depression are common, they are often underdiagnosed and undertreated, in part due to difficulties identifying and accessing individuals in need of services. Current assessments rely on client self-report and clinician judgment, which are vulnerable to social desirability and other subjective biases. Identifying objective, nonburdensome markers of these mental health problems, such as features of speech, could help advance assessment, prevention, and treatment approaches. Prior research examining speech detection methods has focused on fully supervised learning approaches employing strongly labeled data. However, strong labeling of individuals high in symptoms or state affect in speech audio data is impractical, in part because it is not possible to identify with high confidence which regions of a long speech indicate the person’s symptoms or affective state. We propose a weakly supervised learning framework for detecting social anxiety and depression from long audio clips. Specifically, we present a novel feature modeling technique named NN2Vec that identifies and exploits the inherent relationship between speakers’ vocal states and symptoms/affective states. Detecting speakers high in social anxiety or depression symptoms using NN2Vec features achieves F-1 scores 17% and 13% higher than those of the best available baselines. In addition, we present a new multiple instance learning adaptation of a BLSTM classifier, named BLSTM-MIL. Our novel framework of using NN2Vec features with the BLSTM-MIL classifier achieves F-1 scores of 90.1% and 85.44% in detecting speakers high in social anxiety and depression symptoms.