  We evaluate the benefits of combining different offline and online data
assimilation methodologies to improve personalized blood glucose prediction
with type 2 diabetes self-monitoring data. We collect self-monitoring data
(nutritional reports and pre- and post-prandial glucose measurements) from 4
individuals with diabetes and 2 individuals without diabetes. We write online
to refer to methods that update state and parameters sequentially as nutrition
and glucose data are received, and offline to refer to methods that estimate
parameters over a fixed data set, distributed over a time window containing
multiple nutrition and glucose measurements.
  We fit a model of ultradian glucose dynamics to the first half of each data
set using offline (MCMC and nonlinear optimization) and online (unscented
Kalman filter and an unfiltered model---a dynamical model driven by nutrition
data that does not update states) data assimilation methods. Model parameters
estimated over the first half of the data are used within online forecasting
methods to issue forecasts over the second half of each data set.
  Offline data assimilation methods provided consistent advantages in
predictive performance and practical usability in 4 of 6 patient data sets
compared to online data assimilation methods alone; yet 2 of 6 patients were
best predicted with a strictly online approach. Interestingly, parameter
estimates generated offline led to worse predictions when fed to a stochastic
filter than when used in a simple, unfiltered model that incorporates new
nutritional information, but does not update model states based on glucose
measurements.
  The relative improvements seen from the unfiltered model, when carefully
trained offline, exposes challenges in model sensitivity and filtering
applications, but also opens possibilities for improved glucose forecasting and
relaxed patient self-monitoring requirements.
