Bethany A. Teachman’s research while affiliated with University of Virginia and other places

The American Psychological Association Advisory Steering Committee for Development of Clinical Practice Guidelines created a working group to consider the feasibility of, and appropriate methods for, developing an APA clinical practice guideline for interventions that address a transdiagnostic change process, rather than a categorical disorder. Specifically, the working group was asked to determine whether development of a clinical practice guideline focused on emotion regulation was worth pursuing at this time, given the state of the scientific literature, feasibility, and considerations of clinical need and utility. The working group identified a set of goals for a clinical practice guideline based on the idea that if a guideline could mostly achieve these goals, it would be an advance for the field and worth the investment to try to develop a clinical practice guideline based on a transdiagnostic change process or principle. Goals: 1) Inform providers, patients and their families, payers and other stakeholders about what the empirical data indicate regarding the efficacy of treatments targeting emotion regulation 2) Enhance clinical utility of a clinical practice guideline so we learn more than typical guidelines tell us about: ● What works, for whom, and under what circumstances ● What is known about widely-used treatments for which there is not rigorous evidence concerning efficacy ● What is known about change processes or principles underlying the effects of efficacious treatments After careful consideration of the opportunities and challenges likely to arise in developing a clinical practice guideline on emotion regulation, the working group recommends developing and publishing three review protocols that would lead to a clinical practice guideline with three components: Review 1. Systematic review of the efficacy data leading to recommendations, following the current best practices to reduce bias (and acknowledgement of biases not addressed with this approach). This would include the Population, Intervention, Comparator, Outcomes, Timing, and Setting (PICOTS) questions to assess applicability based on current best practices, and would include the usual review of literature on harms, patient preferences, etc. Note: Only in unusual cases when there is a good reason a priori to think that a given subgroup will have a unique response to a treatment will subgroup analyses, cohort or other studies (that don’t meet best practices to reduce bias) be included. In these cases, careful attention to likely confounds and differences in the analytic approach will need to be considered if these studies are used to guide recommendations. Review 2. Summary of current knowledge (with appropriate caveats about the quality of evidence) for select treatments that were not included in Review 1, when the treatments are both widely used and studies meet stringent, pre-specified inclusion and exclusion criteria. Review 3. Summary of current knowledge (with appropriate caveats about the quality of evidence) for literature on change processes or principles based on systematic inclusion and exclusion criteria to determine (observational) studies that rigorously try to account for confounding. This report outlines the working group’s process, rationale for a guideline on emotion regulation, review of challenges likely to arise when developing a guideline of this nature and how recognition of these challenges guided the current proposal. Next the proposal and rationale for the three reviews are discussed, along with consideration of how to manage scope and why the working group believes developing this guideline would be very challenging, but ultimately feasible. We close with recommendations for future research in the hopes that the available literature will one day better match the urgent clinical needs.

Background Interpretation bias modification (IBM) and approach bias modification (ApBM) cognitive retraining interventions can be efficacious adjunctive treatments for improving social anxiety and alcohol use problems. However, previous trials have not examined the combination of these interventions in a young, comorbid sample. Objective This study aims to describe the feasibility, acceptability, and preliminary efficacy of a web-based IBM+ApBM program for young adults with social anxiety and hazardous alcohol use (“Re-Train Your Brain”) when delivered in conjunction with treatment as usual (TAU). Methods The study involved a 3-arm randomized controlled pilot trial in which treatment-seeking young adults (aged 18-30 y) with co-occurring social anxiety and hazardous alcohol use were randomized to receive (1) the “integrated” Re-Train Your Brain program, where each session included both IBM and ApBM (50:50 ratio), plus TAU (35/100, 35%); (2) the “alternating” Re-Train Your Brain program, where each session focused on IBM or ApBM in an alternating pattern, plus TAU (32/100, 32%); or (3) TAU only (33/100, 33%). Primary outcomes included feasibility and acceptability, and secondary efficacy outcomes included changes in cognitive biases, social anxiety symptoms, and alcohol use. Assessments were conducted at baseline, after the intervention period (6 weeks after baseline), and 12 weeks after baseline. Results Both Re-Train Your Brain program formats were feasible and acceptable for young adults. When coupled with TAU, both integrated and alternating programs resulted in greater self-reported improvements than TAU only in anxiety interpretation biases (at the 6-week follow-up; Cohen d=0.80 and Cohen d=0.89) and comorbid interpretation biases (at the 12-week follow-up; Cohen d=1.53 and Cohen d=1.67). In addition, the alternating group reported larger improvements over the control group in generalized social anxiety symptoms (at the 12-week follow-up; Cohen d=0.83) and alcohol cravings (at the 6-week follow-up; Cohen d=0.81). There were null effects on all other variables and no differences between the intervention groups in efficacy outcomes. Conclusions Should these findings be replicated in a larger randomized controlled trial, Re-Train Your Brain has the potential to be a scalable, low-cost, and non–labor-intensive adjunct intervention for targeting interpretation and comorbidity biases as well as generalized anxiety and alcohol-related outcomes in the real world. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12620001273976; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364131 International Registered Report Identifier (IRRID) RR2-10.2196/28667

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.

Accumulated findings from studies in which implicit-bias measures correlate with discriminatory judgment and behavior have led many social scientists to conclude that implicit biases play a causal role in racial and other discrimination. In turn, that belief has promoted and sustained two lines of work to develop remedies: (a) individual treatment interventions expected to weaken or eradicate implicit biases and (b) group-administered training programs to overcome biases generally, including implicit biases. Our review of research on these two types of sought remedies finds that they lack established methods that durably diminish implicit biases and have not reproducibly reduced discriminatory consequences of implicit (or other) biases. That disappointing conclusion prompted our turn to strategies based on methods that have been successful in the domain of public health. Preventive measures are designed to disable the path from implicit biases to discriminatory outcomes. Disparity-finding methods aim to discover disparities that sometimes have obvious fixes, or that at least suggest where responsibility should reside for developing a fix. Disparity-finding methods have the advantage of being useful in remediation not only for implicit biases but also systemic biases. For both of these categories of bias, causes of discriminatory outcomes are understood as residing in large part outside the conscious awareness of individual actors. We conclude with recommendations to guide organizations that wish to deal with biases for which they have not yet found solutions.

Background Despite the growing literature about recovery from eating disorders (EDs), it is still unknown if women who report being recovered from EDs present with differing implicit attitudes about high-fat (vs. low-fat) food relative to women who report having a current ED and women who report never having had an ED. Methods Female volunteers (N = 2,785) to the Project Implicit Mental Health (PIMH) website (https://implicit.harvard.edu/) were divided into three ED groups: current ED (n = 335), prior ED (n = 393), and healthy controls (n = 1,843). Participants completed, between 2016 and 2017, a background questionnaire, together with the Implicit Association Test (IAT), measuring implicit associations between high-fat (vs. low-fat) food and shame (vs. acceptableness). Linear regression models were conducted to examine cross-sectional differences between groups. Results Women with prior EDs had stronger implicit associations relative to healthy controls (p = 0.041) and similar implicit associations relative to women with current EDs (p = 0.424). Discussion The implicit association between high-fat food and shame may not diminish over time among women with EDs. Future longitudinal studies are warranted to clarify whether an experience of EDs may leave a “scar,” manifested in specific implicit associations, that may potentially lead to recurrence after remission.

Cognitive Bias Modification to reduce threat interpretations (CBM-I) is a computer-based paradigm designed to train a less negative interpretation bias that has shown some success in the lab, but results for web-based CBM-I are often mixed. To test possible explanations for the poorer results online, participants high in social anxiety (N = 379) were recruited from Amazon’s Mechanical Turk to complete a single-session, proof-of-principle study to investigate: (1) whether web-based CBM-I can shift interpretations of social situations to be less negative and reduce anticipatory social anxiety, (2) whether a common “control” condition used in CBM-I studies is in fact inert by incorporating an alternate control condition, and (2) whether priming anxious imagery prior to training moderates CBM-I’s effects. Participants were randomly assigned to one of three training conditions: all positive, half positive/half negative, or neutral unemotional scenarios. Participants also received an anxious or neutral imagery prime before training. Although results were somewhat mixed across outcome measures, findings generally suggested that participants exhibited less negative interpretations of ambiguous social scenarios following positive training with an anxious imagery prime. There was also some evidence that the neutral training condition was associated with less negative interpretations, and evidence that the half positive/half negative training condition led to the least anticipatory anxiety, especially when paired with anxious imagery. Findings are discussed in light of different training effects for near- and far-transfer outcomes.

In prior work, we proposed that the related processes of status differentiation and stigmatization both diminish the status of negatively labeled groups, but that only stigma also engenders social rejection, in part via appraisals of abnormality (Lucas, & Phelan, 2012, Phelan, Lucas, Ridgeway, & Taylor, 2014). We found support for this model using behavioral measures of influence and social distance. In this article, we compare status and stigma processes using both explicit (self-reported, consciously controlled) and implicit (relatively less consciously controlled) associations in a large, online volunteer sample (N = 1,348) based on appraisals of “uneducated people” (low status) and “mentally ill people” (high stigma). As expected, we found that both uneducated and mentally ill people elicited negative explicit and implicit appraisals on the dimension of incompetence, and that mentally ill people elicited explicit and implicit appraisals of abnormality. Contrary to our prediction, uneducated people were also viewed, explicitly and implicitly, as abnormal, suggesting pervasive bias across two very disparate groups (uneducated and mentally ill), appraisal types (explicit and implicit), and appraisal dimensions (incompetent and abnormal). Notably, in follow-up manipulation checks to better understand the impact of the uneducated people category label, using independent samples, we showed that the term uneducated tends to elicit representation of individuals who did not complete high school, and we replicated the original findings that uneducated people (especially those with less than a high school education) are appraised as both incompetent and abnormal.

Socially anxious individuals exhibit cognitive performance impairments; it is unclear whether this is due to trait differences in abilities or effects of the experimental context. This study sought to determine how social context, individual differences in fear of negative evaluation (FNE), and task difficulty interact to influence working memory performance as indicated by effectiveness (accuracy) and efficiency (reaction times). Participants (N = 61) performed the n-back task at 2-back and 3-back difficulty levels under three conditions: alone (“Anonymous”), in presence of a non-evaluative experimenter (“Presence”), and under explicit performance evaluation by the experimenter (“Threat”). Overall, participants showed improved accuracy during Threat, but only on 2-back trials. FNE was positively associated with longer reaction times during Threat on 3-back trials. FNE did not relate to accuracy, suggesting that threat-related impairments tied to social anxiety may alter efficiency rather than effectiveness. Thus, social anxiety may elicit cognitive performance impairments even in minimally evaluative environments.