Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? A randomized controlled trial.

We investigated if improving a patient's memory for the content of their treatment, via the Memory Support Intervention, improves illness course and functional outcomes. The platform for investigating this question was major depressive disorder (MDD) and cognitive therapy (CT). Adults diagnosed with MDD (N = 178) were randomly allocated to CT + Memory Support (n = 91) or CT-as-usual (n = 87). Both treatments were comprised of 20-26, 50-min sessions over 16 weeks. Blind assessments were conducted before and immediately following treatment (post-treatment) and 6 months later (6FU). Patient memory for treatment, assessed with a free recall task, was higher in CT + Memory Support for past session recall at post-treatment. Both treatment arms were associated with reductions in depressive symptoms and functional impairment except: CT + Memory Support exhibited lower depression severity at 6FU (b = -3.09, p = 0.050, d = -0.27), and greater reduction in unhealthy days from baseline to 6FU (b = -4.21, p = 0.010, d = -1.07), compared to CT-as-usual. While differences in illness course and functional outcomes between the two treatment arms were limited, it is possible that future analyses of the type of memory supports and longer follow-up may yield more encouraging outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT01790919. Registered October 6, 2016.

Not just "big" data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions.

There is strong interest in developing a more efficient mental health care system. Digital interventions and predictive models of treatment prognosis will likely play an important role in this endeavor. This article reviews the application of popular machine learning models to the prediction of treatment prognosis, with a particular focus on digital interventions. Assuming that the prediction of treatment prognosis will involve modeling a complex combination of interacting features with measurement error in both the predictors and outcomes, our simulations suggest that to optimize complex prediction models, sample sizes in the thousands will be required. Machine learning methods capable of discovering complex interactions and nonlinear effects (e.g., decision tree ensembles such as gradient boosted machines) perform particularly well in large samples when the predictors and outcomes have virtually no measurement error. However, in the presence of moderate measurement error, these methods provide little or no benefit over regularized linear regression, even with very large sample sizes (N = 100,000) and a non-linear ground truth. Given these sample size requirements, we argue that the scalability of digital interventions, especially when used in combination with optimal measurement practices, provides one of the most effective ways to study treatment prediction models. We conclude with suggestions about how to implement these algorithms into clinical practice.

Rising to the Occasion of This COVID-19-Impacted Nation: Development, Implementation, and Feasibility of the Brief Assessment-Informed Skills Intervention for COVID-19 (BASIC).

The COVID-19 pandemic has had a profound impact on the global economy, physical health, and mental health. This pandemic, like previous viral outbreaks, has resulted in spikes in anxiety, depression, and stress. Even though millions of individuals face the physical health consequences of infection by COVID-19, even more individuals are confronted with the mental health consequences of this pandemic. This significantly increased demand for mental health services cannot be easily met by existing mental health systems, which often rely on courses of therapy to be delivered over months. Single session interventions (SSIs) may be one important approach to meeting this increased demand, as they are treatments designed to be delivered over the course of a single meeting. SSIs have been found to be effective for a range of mental health challenges, with durable effects lasting months to years later. Here, we describe an SSI designed for the COVID-19 pandemic. This Brief Assessment-informed Skills Intervention for COVID-19 (BASIC) program draws upon therapeutic skills from existing empirically supported treatments to target common presenting complaints due to this pandemic. We discuss the process of developing and implementing this intervention, as well as explore feasibility and initial clinical insights. In short, BASIC is an easy-to-adopt intervention that is designed to be effective in a single session, making it well-suited for handling the increased demand for mental health services due to COVID-19.

Multifactorial prediction of depression diagnosis and symptom dimensions.

While depression is a leading cause of disability, prior investigations of depression have been limited by studying correlates in isolation. A data-driven approach was applied to identify out-of-sample predictors of current depression from adults (N = 217) sampled on a continuum of no depression to clinical levels. The current study used elastic net regularized regression and predictors from sociodemographic, self-report, polygenic scores, resting electroencephalography, pupillometry, actigraphy, and cognitive tasks to classify individuals into currently depressed (MDE), psychiatric control (PC), and no current psychopathology (NP) groups, as well as predicting symptom severity and lifetime MDE. Cross-validated models explained 20.6% of the out-of-fold deviance for the classification of MDEs versus PC, 33.2% of the deviance for MDE versus NP, but -0.6% of the deviance between PC and NP. Additionally, predictors accounted for 25.7% of the out-of-fold variance in anhedonia severity, 65.7% of the variance in depression severity, and 12.9% of the deviance in lifetime depression (yes/no). Self-referent processing, anhedonia, and psychosocial functioning emerged as important differentiators of MDE and PC groups. Findings highlight the advantages of using psychiatric control groups to isolate factors specific to depression.

Neurocognitive predictors of self-reported reward responsivity and approach motivation in depression: A data-driven approach.

BACKGROUND: Individual differences in reward-related processes, such as reward responsivity and approach motivation, appear to play a role in the nature and course of depression. Prior work suggests that cognitive biases for valenced information may contribute to these reward processes. Yet there is little work examining how biased attention, processing, and memory for positively and negatively valenced information may be associated with reward-related processes in samples with depression symptoms. METHODS: We used a data-driven, machine learning (elastic net) approach to identify the best predictors of self-reported reward-related processes using multiple tasks of attention, processing, and memory for valenced information measured across behavioral, eye tracking, psychophysiological, and computational modeling approaches (n = 202). Participants were adults (ages 18-35) who ranged in depression symptom severity from mild to severe. RESULTS: Models predicted between 5.0-12.2% and 9.7-28.0% of held-out test sample variance in approach motivation and reward responsivity, respectively. Low self-referential processing of positively valenced information was the most robust, albeit modest, predictor of low approach motivation and reward responsivity. CONCLUSIONS: Self-referential processing of positive information is the strongest predictor of reward responsivity and approach motivation in a sample ranging from mild to severe depression symptom severity. Experiments are now needed to clarify the causal relationship between self-referential processing of positively valenced information and reward processes in depression.

Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? Study protocol for a randomized controlled trial.

BACKGROUND: The Memory Support Intervention was developed in response to evidence showing that: (1) patient memory for treatment is poor, (2) poor memory for treatment is associated with poorer adherence and poorer outcome, (3) the impact of memory impairment can be minimized by the use of memory support strategies and (4) improved memory for treatment improves outcome. The aim of this study protocol is to conduct a confirmatory efficacy trial to test whether the Memory Support Intervention improves illness course and functional outcomes. As a "platform" for the next step in investigating this approach, we focus on major depressive disorder (MDD) and cognitive therapy (CT). METHOD/DESIGN: Adults with MDD (n = 178, including 20% for potential attrition) will be randomly allocated to CT + Memory Support or CT-as-usual and will be assessed at baseline, post treatment and at 6 and 12 months' follow-up (6FU and 12FU). We will compare the effects of CT + Memory Support vs. CT-as-usual to determine if the new intervention improves the course of illness and reduces functional impairment (aim 1). We will determine if patient memory for treatment mediates the relationship between treatment condition and outcome (aim 2). We will evaluate if previously reported poor treatment response subgroups moderate target engagement (aim 3). DISCUSSION: The Memory Support Intervention has been developed to be "transdiagnostic" (relevant to a broad range of mental disorders) and "pantreatment" (relevant to a broad range of types of treatment). This study protocol describes a "next step" in the treatment development process by testing the Memory Support Intervention for major depressive disorder (MDD) and cognitive therapy (CT). If the results are promising, future directions will test the applicability to other kinds of interventions and disorders and in other settings. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT01790919 . Registered on 6 October 2016.