Readiness for treatment predicts depression outcomes in a partial hospital program.

Evidence-based interventions vary in effectiveness for individuals with depression, which has a large public health burden. Readiness for change or treatment can be an important individual difference predictor of depression outcomes. To inform public service initiatives targeting readiness for treatment, characterizing readiness across settings and levels of care is key. However, limited data exist on the role of readiness for treatment in acute psychiatric settings and in particular, partial hospital programs which are key points in the continuity of inpatient and outpatient care. The present study assessed readiness for treatment in terms of importance, confidence, and motivation to engage in a partial hospital program and tested whether higher levels of readiness were associated with better treatment outcomes among clients with depression. Participants (N = 192) with major depressive disorder rated their readiness for treatment (Readiness Rulers), depression (Patient Health Questionnaire-9), and global improvement (Clinical Global Impression Scale-Improvement Self-Report) while enrolled in a partial hospital program. Generalized linear regression models assessed the effect of baseline readiness on outcomes at discharge, adjusted for baseline level of the outcome, age, sex, race, and ethnicity. Greater baseline readiness predicted reduced depression and better global improvement at discharge. Higher confidence and motivation to engage in treatment, but not importance, were associated with better depression outcomes. Identifying and addressing readiness for treatment by leveraging public health systems and services (e.g., help lines, family interventions) prior to or upon starting a partial hospital program may be useful to maximize gains in treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Empathic accuracy of romantic partner negative affect is influenced by borderline personality symptoms.

The present study sought to examine the relation between borderline personality disorder (BPD) symptoms and empathic accuracy while improving on prior methodologies by using daily affect assessment in romantic partners. BPD symptoms were assessed in both members of 81 community couples who also reported on their own and their partner's negative and positive affect daily for 3 weeks. Data were analyzed using the Truth and Bias Model of Judgment, which allows the source of empathic accuracy to be parsed into partner affect (truth) and own affect (bias). Results provided evidence that individuals with higher BPD symptoms exhibited increased empathic accuracy for a partner's negative affect, particularly when partners also had higher BPD symptoms. The source of this accuracy stemmed more from bias forces than truth forces, indicating that participants' own affective states lead to more accurate judgments of partner affective state. The results suggest that this bias reduced the general tendency among participants to underestimate partner negative affect, thus leading to higher empathic accuracy. Overall, our results extend and provide support for previous research indicating that BPD symptoms are associated with heightened, not diminished, empathic accuracy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Operationalizing Engagement With an Interpretation Bias Smartphone App Intervention: Case Series.

BACKGROUND: Engagement with mental health smartphone apps is an understudied but critical construct to understand in the pursuit of improved efficacy. OBJECTIVE: This study aimed to examine engagement as a multidimensional construct for a novel app called HabitWorks. HabitWorks delivers a personalized interpretation bias intervention and includes various strategies to enhance engagement such as human support, personalization, and self-monitoring. METHODS: We examined app use in a pilot study (n=31) and identified 5 patterns of behavioral engagement: consistently low, drop-off, adherent, high diary, and superuser. RESULTS: We present a series of cases (5/31, 16%) from this trial to illustrate the patterns of behavioral engagement and cognitive and affective engagement for each case. With rich participant-level data, we emphasize the diverse engagement patterns and the necessity of studying engagement as a heterogeneous and multifaceted construct. CONCLUSIONS: Our thorough idiographic exploration of engagement with HabitWorks provides an example of how to operationalize engagement for other mental health apps.

Development of a cognitive bias modification intervention for anxiety disorders in primary care.

OBJECTIVES: There is a great need for low-intensity, scalable treatments in primary care, where most anxious patients first present for treatment. We describe Stage IA treatment development and a Stage IB feasibility trial of cognitive bias modification (CBM) for transdiagnostic anxiety in primary care. METHODS: The online intervention, Mental Habits, comprised eight sessions of a personalized CBM targeting attention and interpretation biases. Coaches assisted patients in using the website, monitored progress via a dashboard, and shared information with primary care providers. We evaluated Mental Habits in an open trial (N = 14) and a randomized controlled trial (RCT) (N = 40) in primary care patients with anxiety disorders. RESULTS: We compared results to a priori benchmarks of clinically meaningful outcomes. In the open trial, Mental Habits met feasibility, acceptability, and efficacy benchmarks. In the pilot RCT, there was greater dropout at one study site which ultimately closed. In the intent-to-treat analyses, Mental Habits met the benchmark for self-report, but not the interview measure of anxiety. Symptom Tracking did not meet the benchmark for self-report or interview measures of anxiety. In per-protocol analyses, Mental Habits exceeded the benchmark for both self-report and interview measures, whereas Symptom Tracking met the benchmark for self-report. Interpretation bias improved in the Mental Habits group, but not in Symptom Tracking. No effects were observed for attention bias. CONCLUSION: The online CBM intervention demonstrated good acceptability and, when delivered at a stable primary care clinic, preliminary effectiveness in primary care. A larger RCT is warranted to test effectiveness. PRACTITIONER POINTS: A personalized, transdiagnostic Cognitive Bias Modification (CBM) intervention for anxiety in primary care is acceptable to primary care patients with social anxiety disorder, generalized anxiety disorder, and/or panic disorder /agoraphobia. With training and supervision from licensed mental health clinicians, bachelor's-level coaches can assist primary care patients to self-administer CBM. Offering a low-intensity, self-directed anxiety intervention in primary care can greatly expand the reach of anxiety treatment, with minimal need for additional resources. Interpretation bias may be an important clinical target for primary care patients with anxiety.

HabitWorks: Development of a CBM-I Smartphone App to Augment and Extend Acute Treatment.

The month following discharge from acute psychiatric care is associated with increased risk of relapse, rehospitalization, and suicide. Effective and accessible interventions tailored to this critical transition are urgently needed. Cognitive bias modification for interpretation (CBM-I) is a low-intensity intervention that targets interpretation bias, a transdiagnostic process implicated in the development and maintenance of emotional disorders. We describe the development of a CBM-I smartphone app called HabitWorks as an augmentation to acute care that extends through the high-risk month postdischarge. We first obtained input from various stakeholders, including adults who had completed partial hospital treatment (patient advisory board), providers, CBM experts, and clinic program directors. We then iteratively tested versions of the app, incorporating feedback over three waves of users. Participants were recruited from a partial hospital program and completed CBM-I sessions via the HabitWorks app while attending the hospital program and during the month postdischarge. In this Stage 1A treatment development work, we obtained preliminary data regarding feasibility and acceptability, adherence during acute care, and target engagement. Pilot data met our a priori benchmarks. While adherence during acute treatment was good, it decreased during the postacute period. Qualitative feedback was generally positive and revealed themes of usability and helpfulness of app features. Participants varied in their perception of skill generalization to real-life situations. The feasibility and acceptability data suggest that a controlled trial of HabitWorks is warranted.