From observation to optimization: behavioral metrics that matter in KPI based home cage monitoring

Most <i>in vivo</i> scientists would agree that digital biomarkers collected via home-cage monitoring generate valuable data. However, few can tell precisely how valuable. The gap between enthusiasm and evidence has slowed the adoption of digital biomarkers in preclinical research. This framework paper addresses that gap by providing explicit key performance indicators (KPIs), organized into scientific, operational, welfare, and financial categories. We show how return-on-investment calculations differ across pharmaceutical companies, contract research organizations (CROs), and academic institutions. Furthermore, we demonstrate the approach through a worked example in an Amyotrophic Lateral Sclerosis (ALS) mouse model that reduces full-time equivalent (FTE) requirements by half. When successfully integrated, digital biomarkers can generate richer datasets, reduce the number of animals, improve welfare, and enhance translational value. However, successful implementation requires clear performance metrics to justify investment and measure success. We also discuss what these technologies cannot do, because understanding limitations matters as much as understanding benefits.