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Chatbot-Delivered Cognitive Defusion versus Cognitive Restructuring for Negative Self-Referential Thoughts: A Pilot Study

APA Citation

Lavelle, J., Dunne, N., Mulcahy, H. E., & McHugh, L. (2021). Chatbot-delivered cognitive defusion versus cognitive restructuring for negative self-referential thoughts: A pilot study. Psychological Record, 72, 247–261.

Publication Topic
CBS: Empirical
Publication Type
Article
RCT
Language
English
Keyword(s)
cognitive defusion, cognitive restructuring, chatbot-delivered interventions, negative self-referential thoughts
Abstract

Conversational agents or chatbots are a novel, highly accessible, and low-resource method of psychological intervention delivery. The present research aims to compare two brief chatbot interventions that delivered cognitive restructuring and defusion interventions, respectively. It was hypothesized that a defusion chatbot would lead to reduced cognitive fusion and decreased thought believability relative to cognitive restructuring and a nonactive control. Participants (N = 223; M age of 28.01 [SD = 10.29]; 47 identified as male, 174 as female, and 2 as nonbinary) were randomized into one of three conditions (defusion, restructuring, control), engaged for 5 days completing thought and mood measures pre- and postintervention. Sixty-two participants (M age of 25.98; SD = 8.647 years) completed measures again at time 2 (49 identified as female, 12 as male, and 1 as nonbinary). No statistically significant differences were observed among groups on believability of thoughts (F[2, 25] = .79, p = .47, ηp2 = .06), negativity of thoughts (F[2,25] = 1.49, p = .25, η 2 = .11), discomfort associated with thoughts (F[2, 25] = .48, p = .62, ηp2 = .04), and willingness (F[2, 25] = 3.00, p = .07, ηp2 = .19) to have negative self-referential thoughts. Moreover, substantial attrition of 72% was observed. Acceptability and usability of the chatbots employed are discussed as contributing toward the limited effectiveness of interventions and elevated attrition. Various recommendations are presented to support researchers and clinicians in developing engaging and effective chatbots.