Avoidance is a key symptom of anxiety disorders. Maladaptive avoidance impairs general functioning acutely and maintains chronic anxiety. A better understanding of the mechanisms that elicit and maintain excessive avoidance might provide opportunities to improve treatment. Here, we discuss pathways through which avoidance might get amplified in the context of anxiety disorders: 1) increased threat appraisal; 2) enhanced threat avoidance tendencies; 3) impaired regulation of avoidance; 4) habitual avoidance; and 5) attempts at increasing psychological distance. Novel strategies for reducing avoidance are considered. These include memory reconsolidation interference, retraining of avoidance tendencies, mindfulness training and habit disruption approaches. Throughout the paper, we highlight a number of suggestions for future research on avoidance and how to achieve lasting behavior change.

Although college campuses are diversifying rapidly, students of color remain an underserved and understudied group. Online screening and subsequent allocation to treatment represents a pathway to enhancing equity in college student mental health. The purpose of the current study was to evaluate racial/ethnic differences in mental health problems and treatment enrollment within the context of a largescale screening and treatment research initiative on a diverse college campus. The sample was comprised of n = 2090 college students who completed an online mental health screening survey and were offered either free online or face-to-face treatment based on symptom severity as a part of a research study. A series of ordinal, binomial and multinomial logistic regression models were specified to examine racial/ethnic differences in mental health problems, prior treatment receipt, and enrollment in online and face-to-face treatment through the campus-wide research initiative. Racial/ethnic differences in depression, anxiety and suicidality endorsed in the screening survey were identified. Students of color were less likely to have received prior mental health treatment compared to non-Hispanic white students, but were equally likely to enroll in and initiate online and face-to-face treatment offered through the current research initiative. Rates of enrollment in online therapy were comparable to prior studies. Online screening and treatment may be an effective avenue to reaching underserved students of color with mental health needs on college campuses. Digital mental health tools hold significant promise for bridging gaps in care, but efforts to improve uptake and engagement are needed.

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R² ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.