  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.
