Using Adaptive Bandit Experiments to Increase and Investigate Engagement
  in Mental Health
Harsh Kumar Tong Li Jiakai Shi Ilya Musabirov Rachel Kornfield Jonah Meyerhoff Ananya Bhattacharjee Chris Karr Theresa Nguyen David Mohr Anna Rafferty Sofia Villar Nina Deliu Joseph Jay Williams
Digital mental health (DMH) interventions, such as text-message-based lessons
and activities, offer immense potential for accessible mental health support.
While these interventions can be effective, real-world experimental testing can
further enhance their design and impact. Adaptive experimentation, utilizing
algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB)
problems, can lead to continuous improvement and personalization. However, it
remains unclear when these algorithms can simultaneously increase user
experience rewards and facilitate appropriate data collection for
social-behavioral scientists to analyze with sufficient statistical confidence.
Although a growing body of research addresses the practical and statistical
aspects of MAB and other adaptive algorithms, further exploration is needed to
assess their impact across diverse real-world contexts. This paper presents a
software system developed over two years that allows text-messaging
intervention components to be adapted using bandit and other algorithms while
collecting data for side-by-side comparison with traditional uniform random
non-adaptive experiments. We evaluate the system by deploying a
text-message-based DMH intervention to 1100 users, recruited through a large
mental health non-profit organization, and share the path forward for deploying
this system at scale. This system not only enables applications in mental
health but could also serve as a model testbed for adaptive experimentation
algorithms in other domains.