Micro-randomization trial design under operational constraints

Micro-randomization is a common method used to design and tailor AI-driven digital health interventions. However, applying it in real-world clinical settings can be challenging particularly when there are operational or resource constraints. We propose a novel design that integrates micro-randomization with treatment allocation policies to address such constraints, inspired by a pediatric type 1 diabetes (T1D) program.

We evaluated the design's properties through an extensive simulation study and developed a simulation-based power calculator, MRThreshold, to support such trial designs.

Operational constraints that led to imbalance in treatment assignment affected efficiency. However, increasing resources had less impact relative to increases in study length (i.e., opportunities for micro-randomization). We observed a > 50% increase in power when lengthening a 16-week study to a 40-week study. Using our power calculator, we demonstrated that a 40-week study with 100 patients provides 84.0% power to detect a 2% change in time spent in glucose control, providing design considerations for our study.

Careful consideration of study length, sample size, and operational capacity is essential for thoughtful design. Our novel design and tool balance micro-randomization and treatment allocation under operational constraints.