Understanding the Information Needs and Practices of Human Supporters of an Online Mental Health Intervention to Inform Machine Learning Applications

TL;DR

This study explores how AI and machine learning can support human supporters in online mental health interventions by analyzing their work practices and information needs, aiming to enhance therapy outcomes.

Contribution

It provides a detailed understanding of supporter practices and identifies opportunities for ML to improve personalized support in digital mental health interventions.

Findings

Six themes summarizing supporter strategies and challenges

Concrete ML opportunities for each identified challenge

Reflections on social and emotional implications of ML integration

Abstract

In the context of digital therapy interventions, such as internet-delivered Cognitive Behavioral Therapy (iCBT) for the treatment of depression and anxiety, extensive research has shown how the involvement of a human supporter or coach, who assists the person undergoing treatment, improves user engagement in therapy and leads to more effective health outcomes than unsupported interventions. Seeking to maximize the effects and outcomes of this human support, the research investigates how new opportunities provided through recent advances in the field of AI and machine learning (ML) can contribute useful data insights to effectively support the work practices of iCBT supporters. This paper reports detailed findings of an interview study with 15 iCBT supporters that deepens understanding of their existing work practices and information needs with the aim to meaningfully inform the development of useful, implementable ML applications particularly in the context of iCBT treatment for depression and anxiety. The analysis contributes (1) a set of six themes that summarize the strategies and challenges that iCBT supporters encounter in providing effective, personalized feedback to their mental health clients; and in response to these learnings, (2) presents for each theme concrete opportunities for how methods of ML could help support and address identified challenges and information needs. It closes with reflections on potential social, emotional and pragmatic implications of introducing new machine-generated data insights within supporter-led client review practices.