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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.

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.

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BACKGROUND Engagement with mental health smartphone apps is an understudied, yet critical, construct to understand in the pursuit of more efficacious mental health apps. OBJECTIVE In this manuscript we examine engagement as a multidimensional construct, as well as strategies to enhance engagement for a novel app HabitWorks. HabitWorks delivers a personalized cognitive bias modification for interpretation bias intervention and was originally tested in people traversing the challenging transition from acute psychiatric care to daily life. METHODS Using a case series we evaluate three domains of engagement- behavioral, cognitive, and affective- for three HabitWorks participants. RESULTS This manuscript highlights various strategies to enhance engagement such as human support, personalization, self-monitoring, and privacy and security measures. Our cases illustrate the heterogeneity of engagement patterns and clinical outcomes. CONCLUSIONS With rich participant-level data we emphasize the necessity of studying engagement as a multifaceted construct, and the complexity of the relationship between overall engagement and psychosocial outcomes. Our thorough idiographic exploration of engagement with HabitWorks provides an example of how to optimize and operationalize engagement for other mHealth apps.