[Show abstract][Hide abstract] ABSTRACT: Novel sensor-based continuous biomedical monitoring technologies have a major role in chronic disease management for early detection and prevention of known adverse trends. In the future, a diversity of physiological, biochemical and mechanical sensing principles will be available through sensor device 'ecosystems'. In anticipation of these sensor-based ecosystems, we have developed Healthcare@Home (HH) - a research-phase generic intervention-outcome monitoring framework. HH incorporates a closed- loop intervention effect analysis engine to evaluate the relevance of measured (sensor) input variables to system-defined outcomes. HH offers real-world sensor type validation by evaluating the degree to which sensor-derived variables are relevant to the predicted outcome. This 'index of relevance' is essential where clinical decision support applications depend on sensor inputs. HH can help determine system-integrated cost-utility ratios of bespoke sensor families within defined applications - taking into account critical factors like device robustness / reliability / reproducibility, mobility / interoperability, authentication / security and scalability / usability. Through examples of hardware / software technologies incorporated in the HH end-to-end monitoring system, this paper discusses aspects of novel sensor technology integration for outcome-based risk analysis in diabetes.