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

Abstract

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.