Home cage monitoring (HCM) represents a paradigm shift in rodent behavioural research, replacing brief manual observations with continuous digital surveillance that captures animal behaviour in its natural context. This technological revolution generates vast amounts of data, offering both unprecedented opportunities and substantial analytical challenges. Effective HCM research depends on careful experimental design that often recognises the cage, rather than individual animals, as the fundamental experimental unit, with profound implications for statistical power. By adjusting cage density and employing randomised block designs, researchers can maximise statistical sensitivity while minimizing animal use. Translating continuous behavioural streams into meaningful biological insights requires sophisticated yet transparent preprocessing. Moving averages reveal patterns obscured by moment-to-moment variability, while normalisation techniques enable comparisons across experiments and facilities. Heatmaps elegantly capture weeks of circadian rhythms and behavioural changes in single, interpretable displays. The statistical framework emphasises linear mixed-effects models that account for correlated repeated measurements, alongside rhythmometric techniques extracting circadian parameters. A major strength of HCM is its capacity for continuous welfare assessment through composite indices that combine multiple physiological and behavioural streams, enabling early interventions and advancing refinement goals. The chapter concludes by exploring how emerging machine learning approaches can enhance pattern detection while maintaining the statistical rigor and biological interpretability essential for translational research. Overall, HCM emerges not merely as a data collection technology but as a comprehensive framework for ethical, statistically robust behavioural phenotyping that advances all three Rs (Replacement, Reduction, and Refinement) of animal research through continuous digital biomarkers, proactive welfare monitoring, and efficient experimental designs.