  Biosensor data has the potential ability to improve disease control and
detection. However, the analysis of these data under free-living conditions is
not feasible with current statistical techniques. To address this challenge, we
introduce a new functional representation of biosensor data, termed the
glucodensity, together with a data analysis framework based on distances
between them. The new data analysis procedure is illustrated through an
application in diabetes with continuous-time glucose monitoring (CGM) data. In
this domain, we show marked improvement with respect to state of the art
analysis methods. In particular, our findings demonstrate that i) the
glucodensity possesses an extraordinary clinical sensitivity to capture the
typical biomarkers used in the standard clinical practice in diabetes, ii)
previous biomarkers cannot accurately predict glucodensity, so that the latter
is a richer source of information, and iii) the glucodensity is a natural
generalization of the time in range metric, this being the gold standard in the
handling of CGM data. Furthermore, the new method overcomes many of the
drawbacks of time in range metrics, and provides deeper insight into assessing
glucose metabolism.
