Beyond fixed thresholds: optimizing summaries of wearable device data

Music City Center 

Wearable devices, such as actigraphy monitors and continuous glucose monitors (CGMs), capture high-frequency data, typically summarized by the percentage of time spent within fixed thresholds. For example, CGM data are categorized into hypoglycemia, normoglycemia, and hyperglycemia based on a standard glucose range of 70–180 mg/dL. Although scientific guidelines inform the choice of thresholds, it remains unclear whether this choice is optimal and whether the same thresholds should be applied across different populations. In this work, we define threshold optimality with loss functions that quantify discrepancies between the empirical distributions of wearable device measurements and threshold-based summaries. Using the Wasserstein distance as the base measure, we reformulate the loss minimization as optimal piecewise linearization of quantile functions, solved via stepwise algorithms and differential evolution. We also formulate semi-supervised approaches that incorporate some predefined thresholds based on scientific rationale. Applications to CGM data reveal that data-driven thresholds differ by population and improve discriminative power over fixed thresholds.

Amalgamation

Continuous glucose monitoring (CGM)

Histogram

Time-in-Range (TIR)

Piecewise linearization

Wasserstein distance 

ENAR