A recent review published in Frontiers in Behavioral Neuroscience proposes a practical framework for evaluating the impact of home-cage monitoring (HCM) and digital biomarkers in preclinical research. While the scientific benefits of continuous behavioral monitoring are increasingly recognized, the adoption of these technologies often requires clear evidence of their value to researchers, animal facilities and decision-makers.
The authors introduce a structured set of key performance indicators (KPIs) covering scientific, operational, welfare and financial outcomes. The framework provides measurable metrics for assessing improvements in data quality, reproducibility, animal welfare, labor efficiency and return on investment, helping organizations make informed decisions about implementing digital monitoring technologies.
The paper highlights how home-cage monitoring systems can generate continuous, longitudinal behavioral data while reducing animal handling and experimental variability. It also emphasizes the growing role of artificial intelligence and machine learning in extracting meaningful digital biomarkers from large-scale behavioral datasets.
To demonstrate the practical application of this approach, the authors present an ALS mouse model case study in which home-cage monitoring reduced workload requirements by approximately 50%, resulting in significant savings in labor time and operational costs while maintaining scientific value.
Overall, the study supports the transition toward a more data-driven approach to preclinical research, where technologies such as Tecniplast’s DVC® system can contribute not only to richer behavioral phenotyping and improved welfare, but also to measurable scientific and operational outcomes.
Find the study here (Frontiers in Behavioral Neuroscience, 2026)
