Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring

Chiara Zecchin, Andrea Facchinetti, Giovanni Sparacino, Chiara Dalla Man, Chinmay Manohar, James A. Levine, Ananda Basu, Yogish C. Kudva, Claudio Cobelli

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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. Subjects and 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.

Original languageEnglish (US)
Pages (from-to)836-844
Number of pages9
JournalDiabetes Technology and Therapeutics
Volume15
Issue number10
DOIs
StatePublished - Oct 1 2013
Externally publishedYes

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Glucose
Type 1 Diabetes Mellitus
Artificial Pancreas
Insulin
Hypoglycemia
Hypoglycemic Agents

ASJC Scopus subject areas

  • Endocrinology
  • Endocrinology, Diabetes and Metabolism
  • Medical Laboratory Technology

Cite this

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. / Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Dalla Man, Chiara; Manohar, Chinmay; Levine, James A.; Basu, Ananda; Kudva, Yogish C.; Cobelli, Claudio.

In: Diabetes Technology and Therapeutics, Vol. 15, No. 10, 01.10.2013, p. 836-844.

Research output: Contribution to journalArticle

Zecchin, C, Facchinetti, A, Sparacino, G, Dalla Man, C, Manohar, C, Levine, JA, Basu, A, Kudva, YC & Cobelli, C 2013, 'Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring', Diabetes Technology and Therapeutics, vol. 15, no. 10, pp. 836-844. https://doi.org/10.1089/dia.2013.0105
Zecchin, Chiara ; Facchinetti, Andrea ; Sparacino, Giovanni ; Dalla Man, Chiara ; Manohar, Chinmay ; Levine, James A. ; Basu, Ananda ; Kudva, Yogish C. ; Cobelli, Claudio. / Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. In: Diabetes Technology and Therapeutics. 2013 ; Vol. 15, No. 10. pp. 836-844.
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abstract = "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. Subjects and 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.",
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AU - Facchinetti, Andrea

AU - Sparacino, Giovanni

AU - Dalla Man, Chiara

AU - Manohar, Chinmay

AU - Levine, James A.

AU - Basu, Ananda

AU - Kudva, Yogish C.

AU - Cobelli, Claudio

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N2 - 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. Subjects and 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.

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