Jeremy William Eberle

Project Personality and the Action Brings Change Project are two web-based single-session interventions (SSIs) with theoretically distinct targets (growth mindset, behavioral activation, respectively) that have been shown to reduce adolescent depressive symptoms. However, the mechanisms through which they exert their effects remain unclear. In this preregistered exploratory analysis, we used completer data for each SSI (ns = 653 and 730) from a trial in depressed adolescents to examine the total effects of three theoretically derived candidate factors (pre-post changes in agency, pre-post changes in hopelessness, and postintervention acceptability) on 3-month changes in depressive symptoms. To further clarify potential mechanisms, we used commonality analysis to separate the explained variance unique to each predictor from that shared across multiple predictors. In both SSIs, changes in hopelessness and agency each demonstrated significant, small total effects on depressive symptom change (R² range = 2.89-4.92%). Both predictors contributed significant unique explanatory variance (1.01-1.80%) and shared explanatory variance with each other (1.23-1.99%). By contrast, acceptability demonstrated a significant, very small total effect (1.48%) in only one SSI (comprised of significant explanatory variance shared with both other predictors; 1.01%) and no significant unique effect in either SSI. Overall, findings suggest that changes in hopelessness and agency have partially distinct, yet overlapping associations with longer-term changes in depressive symptoms following completion of either SSI, despite the SSIs’ theoretically distinct targets. Future research should evaluate a broader set of candidate mechanisms using intensive longitudinal designs and methods that can further clarify the magnitude and nature of the mechanisms’ pathways.

Background. Co-occurrence of depressive and anxiety symptoms is common; however, the shortage of mental health professionals and high treatment costs means additions to in-person care are necessary to meet treatment needs. The present paper evaluates whether a web-based cognitive bias modification for interpretation (CBM-I) intervention intended for anxious adults—but which targets a shared cognitive mechanism of anxiety and depression—is superior to web-based psychoeducation at reducing co-occurring depressive symptoms. Methods. Latent growth curve modeling was used to assess for superiority of CBM-I over psychoeducation at reducing depressive symptoms among adults with elevated anxiety and depressive symptoms. Interventions were completed once weekly for five weeks with a 2-month follow-up assessment. Analyses were replicated in two separate datasets (Study 1: N = 1069; Study 2: N = 517) and employed multiple variations of CBM-I (Study 1: standard, added low-intensity coaching; Study 2: standard, shortened, added self-referential content, added psychoeducation). Results. Study 1 showed significant superiority of CBM-I over psychoeducation at post-intervention (between-group d = 0.31); this was not replicated in Study 2, but the pattern of effect sizes was similar (d = 0.21). Both interventions showed significant reductions in depressive symptoms at post-intervention (within-group ds from baseline = 0.59-0.92) that were maintained at 2-month follow-up (ds from baseline = 0.75-1.04). Conclusions. The current core CBM-I intervention, designed to target experiences of anxiety, appears preliminarily to be effective in reducing co-occurring depressive symptoms, though its effectiveness is not consistently superior to that of psychoeducation and needs replication in randomized trials.

Background: In recent years, there have been significant advancements in understanding the etiology, assessment, and treatment of borderline personality disorder [BPD]. However, the influence of culture has not been carefully considered. The present review is an attempt to identify cultural factors that may change the presentation, assessment, and response to treatment among adults and adolescents with BPD. Discussion: We discuss the relevance of examining the BPD diagnosis across cultures, define culture, and review studies on the prevalence of BPD across sociocultural groups. Conclusion: We provide a comprehensive list of assessments developed to capture BPD and the cultural adaptations and validations attempted thus far. We also summarize the evidence base for culturally sensitive treatments for BPD. Finally, we present suggestions for future research and clinical implications for our findings.

Digital mental health interventions (DMHIs) have the potential to expand treatment access for anxiety but often have low user engagement. The present study analyzed differences in psychosocial outcomes for different behavioral engagement patterns in a free web-based cognitive bias modification for interpretation (CBM-I) program. CBM-I is designed to shift interpretation biases common in anxiety by providing practice thinking about emotionally ambiguous situations in less threatening ways. Using data from 697 anxious community adults undergoing five weekly sessions of CBM-I in a clinical trial, we extracted program use markers based on task completion rate and time spent on training and assessment tasks. After using an exploratory cluster analysis of these markers to create two engagement groups (whose patterns ended up reflecting generally more vs. less time spent across tasks), we used multilevel models to test for group differences in interpretation bias and anxiety outcomes. Unexpectedly, engagement group did not significantly predict differential change in positive interpretation bias or anxiety. Further, participants who generally spent less time on the program (including both training and assessment tasks) improved in negative interpretation bias (on one of two measures) significantly more during the training phase than those who spent more time (and post hoc tests found were significantly older and slightly less educated). However, participants who generally spent less time had a significant loss in training gains for negative bias (on both measures) by 2-month follow-up. Findings highlight the challenge of interpreting time spent as a marker of engagement and the need to consider cognitive and affective markers of engagement in addition to behavioral markers. Further understanding engagement patterns holds promise for improving DMHIs for anxiety.

Introduction: Anxiety is widespread but undertreated in Huntington’s disease (HD). Targeting negative interpretations of ambiguity may reduce anxiety in HD, but no valid measure of negative interpretation biases in HD exists. We developed the Movement Disorders Interpretation Bias Scale (MDIB) to measure negative interpretation biases in movement disorders (e.g., HD, Parkinson’s disease [PD]) and evaluated its validity in adults with HD (preregistration: https://osf.io/26a3h). Methods: We developed the MDIB based on qualitative interviews and expert review, then invited adults with HD (N = 65) to participate in an online survey study to evaluate its structural and external validity. Data were collected between June 2021 and January 2023. Results: Exploratory factor analyses support one factor representing external threats (i.e., social evaluation of movement disorder symptoms) and another factor representing internal threats (i.e., catastrophizing about such symptoms). Scores for both factors showed acceptable internal and test-retest reliabilities, and expected convergent and discriminant validity relations. The external bias score showed expected concurrent, incremental concurrent, and predictive validity relations, with mixed evidence for incremental predictive validity. Unexpectedly, the internal bias score’s concurrent and predictive validity relations were nonsignificant. Conclusions: Results support the MDIB as a measure of negative interpretation biases in HD, with some limitations. Further, associations between the MDIB external bias score and anxiety-related outcomes suggest that the MDIB can be used as a measure of target engagement in future trials evaluating cognitive behavioral treatments for anxiety. These results represent a critical step toward understanding and alleviating the significant burden of anxiety in HD.

Digital mental health interventions (DMHIs) have the potential to expand treatment access for anxiety but often have low user engagement. The present study analyzed differences in psychosocial outcomes for different behavioral engagement patterns in a free web-based cognitive bias modification for interpretation (CBM-I) program. CBM-I is designed to shift interpretation biases common in anxiety by providing practice thinking about emotionally ambiguous situations in less threatening ways. Using data from 697 anxious community adults undergoing five weekly sessions of CBM-I in a clinical trial, we extracted program use markers based on task completion rate and time spent on training and assessment tasks. After using an exploratory cluster analysis of these markers to create two engagement groups (whose patterns ended up reflecting generally more vs. less time spent across tasks), we used multilevel models to test for group differences in interpretation bias and anxiety outcomes. Unexpectedly, engagement group did not significantly predict differential change in positive interpretation bias or anxiety. Further, participants who generally spent less time on the program (including both training and assessment tasks) improved in negative interpretation bias (on one of two measures) significantly more during the training phase than those who spent more time (and post hoc tests found were significantly older and slightly less educated). However, participants who generally spent less time had a significant loss in training gains for negative bias (on both measures) by 2-month follow-up. Findings highlight the challenge of interpreting time spent as a marker of engagement and the need to consider cognitive and affective markers of engagement in addition to behavioral markers. Further understanding engagement patterns holds promise for improving DMHIs for anxiety.

Introduction. Some have suggested that labeling oneself as having clinical anxiety may promote avoidant coping, which is associated with poor clinical outcomes. However, the construct of anxiety identity centrality (seeing one’s anxiety as relatively important to one's self-concept) has rarely been assessed, and its relationship with avoidant coping has not been investigated. Methods. In a sample of 1,234 high-anxiety adults, we analyzed the relationship between anxiety identity centrality and situational and experiential avoidance (preregistration: https://osf.io/g8qh9/). A Bayesian ordered probit regression framework was used to model the relationships between ordinal variables. Results. As hypothesized, anxiety identity centrality showed small-to-medium positive correlations with situational (r = .42, p < .001) and experiential (r = .12, p < .001) avoidance. After controlling for anxiety symptom severity, anxiety identity centrality was still positively associated with situational (b = 0.34, 95% credible interval [0.22, 0.48]) and experiential (b = 0.13, 95% CI [0.03, 0.28]) avoidance. Discussion. Anxiety identity centrality is a correlate of avoidant coping that can be measured with a single-item ordinal measure. The present study is cross-sectional; we cannot infer causal or temporal relationships between anxiety identity centrality and avoidant coping. Future research should investigate their causal relationship.

Background: Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification programs that target interpretation biases (cognitive bias modification for interpretation [CBM-I]) can provide practice thinking about ambiguous situations in less threatening ways on the web without requiring a therapist. However, digital mental health interventions, including CBM-I, are often plagued with lack of sustained engagement and high attrition rates. New attrition detection and mitigation strategies are needed to improve these interventions. Objective: This paper aims to identify participants at a high risk of dropout during the early stages of 3 web-based trials of multisession CBM-I and to investigate which self-reported and passively detected feature sets computed from the participants interacting with the intervention and assessments were most informative in making this prediction. Methods: The participants analyzed in this paper were community adults with traits such as anxiety or negative thinking about the future (Study 1: n=252, Study 2: n=326, Study 3: n=699) who had been assigned to CBM-I conditions in 3 efficacy-effectiveness trials on our team’s public research website. To identify participants at a high risk of dropout, we created 4 unique feature sets: self-reported baseline user characteristics (eg, demographics), self-reported user context and reactions to the program (eg, state affect), self-reported user clinical functioning (eg, mental health symptoms), and passively detected user behavior on the website (eg, time spent on a web page of CBM-I training exercises, time of day during which the exercises were completed, latency of completing the assessments, and type of device used). Then, we investigated the feature sets as potential predictors of which participants were at high risk of not starting the second training session of a given program using well-known machine learning algorithms. Results: The extreme gradient boosting algorithm performed the best and identified participants at high risk with macro–F1-scores of .832 (Study 1 with 146 features), .770 (Study 2 with 87 features), and .917 (Study 3 with 127 features). Features involving passive detection of user behavior contributed the most to the prediction relative to other features. The mean Gini importance scores for the passive features were as follows: .033 (95% CI .019-.047) in Study 1; .029 (95% CI .023-.035) in Study 2; and .045 (95% CI .039-.051) in Study 3. However, using all features extracted from a given study led to the best predictive performance. Conclusions: These results suggest that using passive indicators of user behavior, alongside self-reported measures, can improve the accuracy of prediction of participants at a high risk of dropout early during multisession CBM-I programs. Furthermore, our analyses highlight the challenge of generalizability in digital health intervention studies and the need for more personalized attrition prevention strategies.

Web-based cognitive bias modification for interpretation (CBM-I) can improve interpretation biases and anxiety symptoms but faces high rates of dropout. This study tested the effectiveness of web-based CBM-I relative to an active psychoeducation condition and the addition of low-intensity telecoaching for a subset of CBM-I participants. 1,234 anxious community adults (Mage = 35.09 years, 81.2% female, 72.1% white, 82.6% not Hispanic) were randomly assigned at Stage 1 of a sequential, multiple-assignment randomized trial to complete five weekly sessions of CBM-I or psychoeducation on our team's public research website. After the first session, for Stage 2, an algorithm attempted to classify CBM-I participants as higher (vs. lower) risk for dropping out; those classified as higher risk were then randomly assigned to complete four brief weekly telecoaching check-ins (vs. no coaching). As hypothesized (https://doi.org/j2xr; Daniel, Eberle, & Teachman, 2020), CBM-I significantly outperformed psychoeducation at improving positive and negative interpretation biases (Recognition Ratings, Brief Body Sensations Interpretation Questionnaire) and anxiety symptoms (Overall Anxiety Severity and Impairment Scale, Anxiety Scale from Depression Anxiety Stress Scales-Short Form), with smaller treatment gains remaining significant at 2-month follow-up. Unexpectedly, CBM-I had significantly worse treatment dropout outcomes than psychoeducation, and adding coaching (vs. no coaching) did not significantly improve efficacy or dropout outcomes (notably, many participants chose not to interact with their coach). Web-based CBM-I appears effective, but supplemental coaching may not mitigate the challenge of dropout. (PsycInfo Database Record (c) 2024 APA, all rights reserved).