Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.
BACKGROUND
In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored.


METHODS
In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA.


RESULTS
Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min.


CONCLUSIONS
Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.
1 Department of Information Engineering, University of Padova , Padova, Italy .
Chiara
C
Zecchin
Andrea
A
Facchinetti
Giovanni
G
Sparacino
Chiara
C
Dalla Man
Chinmay
C
Manohar
James A
JA
Levine
Ananda
A
Basu
Yogish C
YC
Kudva
Claudio
C
Cobelli
D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000453:epidemiology; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood* / Q000453:epidemiology; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D008297:Male; D008910:Minnesota / Q000453:epidemiology; D018670:Monitoring, Ambulatory; D008991:Monitoring, Physiologic; D009043:Motor Activity; D012307:Risk Factors
D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human