Online cognitive bias modification for interpretation to reduce anxious thinking during the COVID-19 pandemic.

Anxiety disorders are highly prevalent, and rates increased during the COVID-19 pandemic. However, most individuals with elevated anxiety do not access treatment due to barriers such as stigma, cost, and availability. Digital mental health programs, such as cognitive bias modification for interpretation (CBM-I), hold promise in increasing access to care. Before widely disseminating CBM-I, we must rigorously test its effectiveness and determine whom it is best positioned to benefit. The present study (which is a substudy of a parent trial) compared CBM-I against psychoeducation offered through the public website MindTrails, and also tested whether baseline anxiety tied to COVID-19 influenced the rate of change in anxiety and interpretation bias during and after each intervention. Adults with moderate-to-severe anxiety symptoms were randomly assigned to complete five sessions of either CBM-I or psychoeducation as part of a larger trial, and 608 enrolled in this substudy after Session 1. As predicted (https://osf.io/2dyzr), CBM-I was superior to psychoeducation at reducing anxiety symptoms (on the OASIS but not the DASS-21-AS: d = -0.31), reducing negative interpretation bias (d range = -0.34 to -0.43), and increasing positive interpretation bias (d = 0.79) by the end of treatment. Results also indicated that individuals higher (vs. lower) in baseline COVID-19 anxiety had stronger decreases in anxiety symptoms while receiving CBM-I but weaker decreases in anxiety symptoms (on the DASS-21-AS) while receiving psychoeducation. These findings suggest that CBM-I may be a useful anxiety-reduction tool for individuals experiencing higher anxiety tied to uncertain events such as the COVID-19 pandemic.

Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline's F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.

State and Trait Emotion Regulation Diversity in Social Anxiety.

Emotion regulation (ER) diversity, defined as the variety, frequency, and evenness of ER strategies used, may predict social anxiety (SA) severity. In a sample of individuals with high (n=113) or low (n=42) SA severity, we tested whether four trait ER diversity metrics predicted group membership. We generalized existing trait ER diversity calculations to repeated-measures data to test if state-level metrics (using two weeks of EMA data) predicted SA severity within the higher severity group. As hypothesized (osf.io/xadyp), higher trait ER diversity within avoidance-oriented strategies predicted greater likelihood of belonging to the higher severity group. At the state-level, higher diversity across all ER strategies, and within and between avoidance- and approach-oriented strategies, predicted higher SA severity (but only after controlling for number of submitted EMAs). Only diversity within avoidance-oriented strategies was significantly correlated across trait and state levels. Findings suggest that high avoidance-oriented ER diversity may co-occur with higher SA severity.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Cross-Domain Assessment of Distress Intolerance: Associations With Borderline Personality Disorder Features.

Distress tolerance (DT) is central to major etiological theories of, and popular treatments for, borderline personality disorder (PD), but empirical evidence for the connection between DT and borderline PD is inconclusive. Such inconsistency is partly due to limited concordance across DT indices from different measurement domains (e.g., behavioral, physiological). In a student sample (N = 267), we assessed subjective perceptions of DT capabilities, task performance on a distressing laboratory challenge, and borderline pathology. Subjective and behavioral indices of DT were largely unrelated. Further, borderline PD features were moderately associated with self-perceived DT (r = -.53); in contrast, they were weakly related to performance on the DT task (r = -.09). We conclude that there is mixed evidence for an association between borderline pathology and DT. Further, we propose a systematic approach to examining the construct validity of DT in multimethod, multimeasure research that might resolve the equivocal results from prior work.