Daily life constantly requires the resolution of ambiguity. For example, not getting a job or a friend not returning a call can be interpreted in multiple ways. The way in which individuals automatically resolve the countless such ambiguous situations encountered each day has a large impact on how they feel and what they do. A tendency to jump to negative conclusions (interpretation bias) can lead to anxiety, depression, and maladaptive coping. Cognitive therapy has targeted interpretation bias for decades, but it is time-intensive, difficult, and requires a trained clinician.
Funded by NIMH, we developed a smartphone app called “HabitWorks” to deliver an interpretation bias intervention personalized to the individual and with engaging features (e.g., instructional videos, progressing through levels). We have conducted pilot testing in patients attending the Behavioral Health Partial Hospital Program at McLean Hospital, parents of anxious kids, and adults who identify as Black or Hispanic/ Latino/a/x.
Parent-Child Anxiety Transmission
Funded by the NIMH, this R01 is testing how parents pass on anxiety to their kids. We are testing a specific path: Does parent interpretation bias lead to more anxiety-related parenting behaviors, and does this ultimately impact children's cognitive bias and anxiety?
Unlike most prior studies, we will be examining parent-child transmission in fathers and mothers, and are recruiting a diverse, representative sample.
The long-term goal of this work is to inform mechanistic and accessible interventions for parents and kids with anxiety.
Digital CBT Clinic
Led by Dr. Courtney Beard, we are currently testing the real world effectiveness of digital CBT platforms. Funded by the Combined Jewish Philanthropies of Greater Boston,
this project aims to address the increased demand for mental health treatment during COVID by providing low-intensity forms of CBT to at least 300 people in the community. We will pilot procedures for a sustainable digital CBT program at McLean. Finally, we will examine predictors of outcome and drop-out, as well as patterns of engagement.