This paper aims to develop a data-driven model for glucose dynamics taking into account the effects of physical activity (PA) through a numerical study. It intends to investigate PA's immediate effect on insulin-independent glucose variation and PA's prolonged effect on insulin sensitivity. We proposed a nonlinear model with PA (NLPA), consisting of a linear regression of PA and a bilinear regression of insulin and PA. The model was identified and evaluated using data generated from a physiological PA-glucose model by Dalla Man et al. integrated with the uva/padova Simulator. Three metrics were computed to compare blood glucose (BG) predictions by NLPA, a linear model with PA (LPA), and a linear model with no PA (LOPA). For PA's immediate effect on glucose, NLPA and LPA showed 45–160% higher mean goodness of fit (FIT) than LOPA under 30 min-ahead glucose prediction (P < 0.05). For the prolonged PA effect on glucose, NLPA showed 87% higher FIT than LPA (P < 0.05) for simulations using no previous measurements. NLPA had 25–37% and 31–54% higher sensitivity in predicting postexercise hypoglycemia than LPA and LOPA, respectively. This study demonstrated the following qualitative trends: (1) for moderate-intensity exercise, accuracy of BG prediction was improved by explicitly accounting for PA's effect; and (2) accounting for PA's prolonged effect on insulin sensitivity can increase the chance of early prediction of postexercise hypoglycemia. Such observations will need to be further evaluated through human subjects in the future.

References

1.
Thabit
,
H.
,
Tauschmann
,
M.
,
Allen
,
J. M.
,
Leelarathna
,
L.
,
Hartnell
,
S.
,
Wilinska
,
M. E.
,
Acerini
,
C. L.
,
Dellweg
,
S.
,
Benesch
,
C.
,
Heinemann
,
L.
,
Mader
,
J. K.
,
Holzer
,
M.
,
Kojzar
,
H.
,
Exall
,
J.
,
Yong
,
J.
,
Pichierri
,
J.
,
Barnard
,
K. D.
,
Kollman
,
C.
,
Cheng
,
P.
,
Hindmarsh
,
P. C.
,
Campbell
,
F. M.
,
Arnolds
,
S.
,
Peiber
,
T. R.
,
Evans
,
M. L.
,
Dunger
,
D. B.
, and
Hovorka
,
R.
,
2015
, “
Home Use of an Artificial Beta Cell in Type 1 Diabetes
,”
N. Engl. J. Med.
,
373
(
22
), pp.
2129
2140
.
2.
Doyle
,
F. J.
,
Huyett
,
L. M.
,
Lee
,
J. B.
,
Zisser
,
H. C.
, and
Dassau
,
E.
,
2014
, “
Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms
,”
Diabetes Care
,
37
(
5
), pp.
1191
1197
.
3.
Kudva
,
Y. C.
,
Carter
,
R. E.
,
Cobelli
,
C.
,
Basu
,
R.
, and
Basu
,
A.
,
2014
, “
Closed-Loop Artificial Pancreas Systems: Physiological Input to Enhance Next-Generation Devices
,”
Diabetes Care
,
37
(
5
), pp.
1184
1190
.
4.
Wang
,
Q.
,
Xie
,
J.
,
Molenaar
,
P.
, and
Ulbrecht
,
J. S.
,
2015
, “
Model Predictive Control for Type 1 Diabetes Based on Personalized Linear Time-Varying Subject Model Consisting of Both Insulin and Meal Inputs: An In Silico Evaluation
,”
J. Diabetes Sci. Technol.
9
(
4
), pp.
941
942
.
5.
Mallad
,
A.
,
Hinshaw
,
L.
,
Schiavon
,
M.
,
Dalla Man
,
C.
,
Dadlani
,
V.
,
Basu
,
R.
,
Lingineni
,
R.
,
Cobelli
,
C.
,
Johnson
,
M. L.
,
Carter
,
R.
,
Kudva
,
Y. C.
, and
Basu
,
A.
,
2015
, “
Exercise Effects on Postprandial Glucose Metabolism in Type 1 Diabetes: A Triple-Tracer Approach
,”
Am. J. Physiol. Endocrinol. Metab.
,
308
(
12
), pp.
E1106
E1115
.
6.
Breton
,
M. D.
,
2008
, “
Physical Activity the Major Unaccounted Impediment to Closed Loop Control
,”
J. Diabetes Sci. Technol.
,
2
(
1
), pp.
169
174
.
7.
Goodyear
,
L. J.
, and
Kahn
,
B. B.
,
1998
, “
Exercise, Glucose Transport, and Insulin Sensitivity
,”
Annu. Rev. Med.
,
49
(
1
), pp.
235
261
.
8.
Metcalf
,
K. M.
,
Singhvi
,
A.
,
Tsalikian
,
E.
,
Tansey
,
M. J.
,
Zimmerman
,
M. B.
,
Esliger
,
D. W.
, and Janz, K. F.,
2014
, “
Effects of Moderate-to-Vigorous Intensity Physical Activity on Overnight and Next-Day Hypoglycemia in Active Adolescents With Type 1 Diabetes
,”
Diabetes Care
,
37
(
5
), pp.
1272
1278
.
9.
Roberts
,
A. J.
, and
Taplin
,
C. E.
,
2015
, “
Exercise in Youth With Type 1 Diabetes
,”
Curr. Pediatr. Rev.
,
11
(
2
), pp.
120
125
.
10.
Garcia-Garcia
,
F.
,
Kumareswaran
,
K.
,
Hovorka
,
R.
, and
Hernando
,
M.
,
2015
, “
Quantifying the Acute Changes in Glucose With Exercise in Type 1 Diabetes: A Systematic Review and Meta-Analysis
,”
Sports Med.
,
45
(
4
), pp.
587
599
.
11.
Bergman
,
R. N.
,
Ider
,
Y. Z.
,
Bowden
,
C. R.
, and
Cobelli
,
C.
,
1979
, “
Quantitative Estimation of Insulin Sensitivity
,”
Am. J. Physiol.
,
236
(
6
), pp.
E667
E677
.https://www.physiology.org/doi/abs/10.1152/ajpendo.1979.236.6.E667
12.
Dalla Man
,
C.
,
Breton
,
M. D.
, and
Cobelli
,
C.
,
2009
, “
Physical Activity Into the Meal Glucose Insulin Model of Type 1 Diabetes: In Silico Studies
,”
J. Diabetes Sci. Technol.
,
3
(
1
), pp.
56
67
.
13.
Dalla Man
,
C.
,
Rizza
,
R. A.
, and
Cobelli
,
C.
,
2007
, “
Meal Simulation Model of the Glucose-Insulin System
,”
IEEE Trans. Biomed. Eng.
,
54
(
10
), pp.
1740
1749
.
14.
Roy
,
A.
, and
Parker
,
R. S.
,
2007
, “
Dynamic Modeling of Exercise Effects on Plasma Glucose and Insulin Levels
,”
J. Diabetes Sci. Technol.
,
1
(
3
), pp.
338
347
.
15.
Ewings
,
S. M.
,
Sahu
,
S. K.
,
Valletta
,
J. J.
,
Byrne
,
C. D.
, and
Chipperfield
,
A. J.
,
2015
, “
A Bayesian Network for Modelling Blood Glucose Concentration and Exercise in Type 1 Diabetes
,”
Stat. Methods Med. Res.
,
24
(
3
), pp.
342
72
.
16.
Eren-Oruklu
,
M.
,
Cinar
,
A.
,
Rollins
,
D. K.
, and
Quinn
,
L.
,
2012
, “
Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms
,”
Automatica
,
48
(
8
), pp.
1892
1897
.
17.
Turksoy
,
K.
,
Bayrak
,
E. S.
,
Quinn
,
L.
,
Littlejohn
,
E.
,
Rollins
,
D.
, and
Cinar
,
A.
,
2013
, “
Hypoglycemia Early Alarm Systems Based on Multivariable Models
,”
Ind. Eng. Chem. Res.
,
52
(
35
), pp.
12329
12336
.
18.
Turksoy
,
K.
,
Quinn
,
L. T.
,
Littlejohn
,
E.
, and
Cinar
,
A.
,
2014
, “
An Integrated Multivariable Artificial Pancreas Control System
,”
J. Diabetes Sci. Technol.
,
8
(
3
), pp.
498
507
.
19.
Turksoy
,
K.
,
Quinn
,
L. T.
,
Littlejohn
,
E.
, and
Cinar
,
A.
,
2014
, “
Multivariable Adaptive Identification and Control for Arti- Ficial Pancreas Systems
,”
IEEE Trans. Biomed. Eng.
,
61
(
3
), pp.
883
891
.
20.
Dasanayake
,
I. S.
,
Seborg
,
D. E.
,
Pinsker
,
J. E.
,
Doyle
,
F. J.
, and
Dassau
,
E.
,
2015
, “
Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity
,”
Conference on Decision and Control
(
CDC
), Osaka, Japan, Dec. 15–18, pp.
3834
3839
.
21.
Lee
,
H.
, and
Bequette
,
B. W.
,
2009
, “
A Closed-Loop Arti- Ficial Pancreas Based on Model Predictive Control: Human friendly Identification and Automatic Meal Disturbance Rejection
,”
Biomed. Signal Process. Control.
,
4
(
4
), pp.
347
354
.
22.
Percival
,
M. W.
,
Bevier
,
W. C.
,
Wang
,
Y.
,
Dassau
,
E.
,
Zisser
,
H. C.
,
Jovanovic
,
L.
, and
Doyle
,
F. J.
, III
,
2010
, “
Modeling the Effects of Subcutaneous Insulin Administration and Carbohydrate Consumption on Blood Glucose
,”
J. Diabetes Sci. Technol.
,
4
(
5
), pp.
1214
1228
.
23.
Finan
,
D. A.
,
Zisser
,
H.
,
Jovanovic
,
L.
,
Bevier
,
W. C.
, and
Seborg
,
D. E.
,
2007
, “
Practical Issues in the Identification of Empirical Models From Simulated Type 1 Diabetes Data
,”
Diabetes Technol. Ther.
,
9
(
5
), pp.
438
450
.
24.
Xie
,
J.
, and
Wang
,
Q.
,
2016
, “
A Nonlinear Data-Driven Model of Glucose Dynamics Accounting for Physical Activity for Type 1 Diabetes: An in Silico Study
,”
ASME
Paper No. DSCC2016-9742.
25.
Breton
,
M.
, and
Kovatchev
,
B.
,
2008
, “
Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors
,”
J. Diabetes Sci. Technol.
,
2
(
5
), pp.
853
862
.
26.
Wang
,
Q.
,
Molenaar
,
P.
,
Harsh
,
S.
,
Freeman
,
K.
,
Xie
,
J.
,
Gold
,
C.
,
Rovine
,
M.
, and
Ulbrecht
,
J.
,
2014
, “
Personalized State-Space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake an Extended Kalman Filter Approach
,”
J. Diabetes Sci. Technol.
,
8
(
2
), pp.
331
345
.
27.
Schiavon
,
M.
,
Hinshaw
,
L.
,
Mallad
,
A.
,
Dalla Man
,
C.
,
Sparacino
,
G.
,
Johnson
,
M.
, Carter, R., Basu, R., Kudva, Y., Cobelli, C., and Basu, A.,
2013
, “
Postprandial Glucose Fluxes and Insulin Sensitivity During Exercise: A Study in Healthy Individuals
,”
Am. J. Physiol. Endocrinol. Metab.
,
305
(
4
), pp.
E557
E566
.
28.
Frohnauer
,
M. K.
,
Woodworth
,
J. R.
, and
Anderson
,
J. H.
, Jr
.,
2001
, “
Graphical Human Insulin Time-Activity Profiles Using Standardized Definitions
,”
Diabetes Technol. Ther.
,
3
(
3
), pp.
419
429
.
29.
Kovatchev
,
B. P.
,
Breton
,
M.
,
Dalla Man
,
C.
, and
Cobelli
,
C.
,
2009
, “
In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes
,”
J. Diabetes Sci. Technol.
,
3
(
1
), pp.
44
55
.
30.
Riddell
,
M. C.
,
Zaharieva
,
D. P.
,
Yavelberg
,
L.
,
Cinar
,
A.
, and
Jamnik
,
V. K.
,
2015
, “
Exercise and the Development of the Artificial Pancreas: One of the More Difficult Series of Hurdles
,”
J. Diabetes Sci. Technol.
,
9
(
6
), pp.
1217
26
.
31.
Tsalikian
,
E.
,
Kollman
,
C.
,
Tamborlane
,
W. B.
,
Beck
,
R. W.
,
Fiallo-Scharer
,
R.
, and
Diabetes Research in Children Network (DirecNet) Study Group,
2006
, “
Prevention of Hypoglycemia During Exercise in Children With Type 1 Diabetes by Suspending Basal Insulin
,”
Diabetes Care
,
29
(
10
), pp.
2200
2204
.
32.
Zakynthinaki
,
M. S.
,
2015
, “
Modelling Heart Rate Kinetics
,”
PLoS One
,
10
(
4
), p.
e0118263
.
33.
Cameron
,
F.
,
Bequette
,
B. W.
,
Wilson
,
D. M.
,
Buckingham
,
B. A.
,
Lee
,
H.
, and
Niemeyer
,
G.
,
2011
, “
A Closed-Loop Artificial Pancreas Based on Risk Management
,”
J. Diabetes Sci. Technol.
,
5
(
2
), pp.
368
379
.
34.
Magni
,
L.
,
Raimondo
,
D. M.
,
Bossi
,
L.
,
Dalla Man
,
C.
,
De Nicolao
,
G.
,
Kovatchev
,
B.
, and
Cobelli
,
C.
,
2007
, “
Model Predictive Control of Type 1 Diabetes: An in Silico Trial
,”
J. Diabetes Sci. Technol.
,
1
(
6
), pp.
804
812
.
35.
El-Khatib
,
F. H.
,
Russell
,
S. J.
,
Nathan
,
D. M.
,
Sutherlin
,
R. G.
, and
Damiano
,
E. R.
,
2010
, “
A Bihormonal Closed-loop Artificial Pancreas for Type 1 Diabetes
,”
Sci. Transl. Med.
,
2
(
27
), p.
27ra27
.
36.
Finan
,
D. A.
,
Palerm
,
C. C.
,
Doyle
,
F. J.
,
Seborg
,
D. E.
,
Zisser
,
H.
,
Bevier
,
W. C.
, and Jovanovic, L.,
2009
, “
Effect of Input Excitation on the Quality of Empirical Dynamic Models for Type 1 Diabetes
,”
AIChE J.
,
55
(
5
), pp.
1135
1146
.
37.
Clarke
,
W.
, and
Kovatchev
,
B.
,
2009
, “
Statistical Tools to Analyze Continuous Glucose Monitor Data
,”
Diabetes Technol. Ther.
,
11
(
Suppl. 1
), pp.
S-45
S-54
.
38.
Gore
,
C. J.
, and
Withers
,
R. T.
,
1990
, “
The Effect of Exercise Intensity and Duration on the Oxygen Deficit and Excess Post-Exercise Oxygen Consumption
,”
Eur. J. Appl. Physiol.
,
60
(
3
), pp.
169
174
.
39.
Saalasti
,
S.
,
2003
, “
Neural Networks for Heart Rate Time Series Analysis
,” Ph.D. dissertation, Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland.
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