Work

Micro-randomization trial design under operational constraints

Year: 2026

Type: article

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

Source: Contemporary Clinical Trials

DOI: 10.1016/j.cct.2026.108351