Assessing the disproportionality of depressive reactions to life stress

Clinical Judgments of Response Profiles: Do They Tell Us What Matters for Whom?

DeRubeis and colleagues (2014a) proposed that psychotherapy research has been limited by underappreciated variability in how patients respond to psychotherapy. They proposed that the relationship between the quality of therapy and outcome varies according to patient response profiles. In a study of cognitive-behavioral therapy (CBT) for depression, we tested clinician ratings of this construct as a moderator of the relationship between therapist adherence to cognitive or behavioral methods in predicting symptom change. Patients (N = 125) participated in CBT for depression. Assessors rated response profiles following the intake and therapists rated them after the first session. We collected data on adherence at the first five sessions and symptoms at the first six sessions. Therapist ratings following the first session, but not assessor ratings at intake, moderated the relationship between each form of adherence and symptom change. Patients given lower ratings (identifying them as spontaneous remitting or easy patients) had a stronger relationship between adherence and greater symptom change, with this relationship reversed such that adherence was related to less robust symptom change for those with the highest ratings (intractable or challenging patients). Our findings suggest promise for clinical evaluation of response profiles. We encourage future research evaluating refinements to such measures.

Predicting anxiety and depression over 12 months of the COVID-19 pandemic: A machine learning study

OBJECTIVES

The coronavirus disease 2019 (COVID-19) pandemic was associated with substantial increases in anxiety and depressive symptoms. To understand individual risk, we examined a large set of potential risk factors for anxiety and depression in the pandemic context.

METHODS

Adults in the United States (N = 1200) completed eight online self-report assessments over 12 months of the COVID-19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic-related baseline variables.

RESULTS

Cumulative anxiety severity was most strongly explained by stress and depression-related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important.

CONCLUSIONS

By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions.

Adhering to COVID-19 health guidelines: Examining demographic and psychological predictors of adherence

The effort to limit the spread of the coronavirus (COVID-19) has relied heavily on the general public's compliance with health guidelines limiting social contact and mitigating risk when contact occurs. The aim of this study was to identify latent variables underlying adherence to COVID-19 guidelines and to examine demographic and psychological predictors of adherence. A sample of US adults (N = 1,200) were surveyed in late April to mid-May 2020. The factor structure of adherence was examined using exploratory factor analysis. Machine learning regression models using elastic net regularization were used to examine predictors of adherence. Two factors characterized adherence: avoidance and cleaning. Elastic net models identified differential demographic and psychological predictors of these two forms of adherence. Religious affiliation, denial coping, full-time employment, substance use coping, and being 60 or older predicted lower avoidance adherence. Behavioral and mindfulness emotion regulation skills, agreeableness, and Democrat political affiliation predicted greater avoidance adherence. For cleaning adherence, interpersonal and behavioral emotion regulation skills and conscientiousness emerged as strong predictors of greater cleaning. Efforts to promote compliance with COVID-19 health guidelines may benefit from distinguishing avoidance and cleaning adherence and considering predictors of each of these aspects of adherence.