Supporting Electronic Mental Health with Artificial Intelligence

Supporting Electronic Mental Health with Artificial Intelligence: Thought Record Analysis and Guidance

Burger, Franziska (TU Delft Interactive Intelligence)

Neerincx, M.A. (promotor)
Brinkman, W.P. (promotor)

Delft University of Technology

2022-12-15

This thesis investigates how artificial intelligence can support e-mental health for depression, i.e. the delivery of treatment and prevention interventions for depression using technology. E-mental health for depression is a promising means for bridging the treatment gap since it addresses many of the barriers that prevent people in need of help from seeking or obtaining it. Additionally, many systems have been found to be effective in controlled trials. However, as human support for e-health interventions decreases so do their effectiveness and users’ adherence. While one possible explanation is that human support is a necessary ingredient of a successful intervention, another is that the technology is not satisfying the needs of users to the best of its abilities. This finding inspired us to take a closer look at the technological implementation of the functionality of these systems. To this end, we developed a set of scales that assess the technological sophistication of the functional components of systems, the e-mental health degree of technological sophistication (eHDTS) scales. In a systematic literature review of the field, we then divided all systems developed between 2000 and 2017 for the prevention or treatment of depression reported in the scientific literature into their functional components and rated those components with the eHDTS scales. We found that most systems that had been developed until 2017 were low-tech implementations, consisting mostly of psychoeducation and having a one-way information stream from system to user. This clearly contrasts with face-to-face therapy in which the therapist closely attends to the patient and provides his or her knowledge and insight strategically to signal understanding and empathy, foster self-reflection, teach, or obtain more information. Based on this consideration, we set out to develop a conversational agent capable of signaling to the user that it had processed the content of what it had been told when completing a thought record together with a user in dialog with the hypothesis that this would be able to motivate the user to complete more thought records and feel more engaged. Thought recording is a core technique of cognitive therapy in which patients are asked to systematically monitor their thinking in situations that caused a maladaptive response. Cognitive theory posits that the negative, cognitive appraisals that are responsible for the low mood experienced in patients with depression stem from maladaptive schemas, i.e., beliefs that we hold as truths about the world, ourselves, and the future. To get the conversational agent to “understand” the thoughts provided by the user from this cognitive theory perspective, we collected a corpus of thought records from Amazon Mechanical Turk workers, manually coded the thoughts with respect to the underlying schema, and trained various machine learning models to do the same labeling. A set of deep neural networks outperformed the other algorithms and was then deployed in the conversational agent. We used a between-subjects design to expose 308 participants recruited from Prolific to the conversational agent. The three conditions differed with respect to the feedback-giving capabilities of the conversational agent in response to a thought record: low feedback richness entailed an acknowledgment of the completion of the thought record (thanking the user), medium feedback richness entailed the acknowledgment plus feedback on the process (how many steps the user did in relation to his or her previous thought records), and rich feedback richness entailed medium feedback richness combined with feedback on the content (an interpretation of the thought record with respect to the underlying schema). While all users were able to complete the thought records with the conversational agent, we did not find supportive evidence that the agent’s feedback strategy could increase users’ motivation to complete more thought records or their self-reported engagement in self-reflection. Future research may investigate why we observed these null results by studying whether the feedback is processed correctly, whether a population with depression that is motivated by a wish to get healthy might behave or experience the system differently from our sample that was recruited online and did not meet diagnostic criteria for depression, or whether more advanced social and interaction capabilities need to accompany the complex feedback for it to be believable.

computerized therapy
conversational agents
natural language processing
cognitive therapy

https://doi.org/10.4233/uuid:6aed720a-b970-43c2-9c84-f028a8127230

978-94-6469-147-4

2022-11-24

Institutional Repository

doctoral thesis

© 2022 Franziska Burger