Physical Activity Measured by Physical Activity Monitoring System Correlates with Glucose Trends Reconstructed from Continuous Glucose Monitoring

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.

Routine daily physical activity and glucose variations are strongly coupled in adults with T1DM

“…() reported findings similar to ours; that physical activity measured by a physical activity monitoring system was correlated with glucose variations in a group of subjects with T1DM (Zecchin et al. ). Zecchin et al.…”

“…Such knowledge may be important not only to inform routine clinical management of T1DM, but also to facilitate the evolution of closed loop systems for insulin administration. Zecchin et al (2013) reported findings similar to ours; that physical activity measured by a physical activity monitoring system was correlated with glucose variations in a group of subjects with T1DM (Zecchin et al 2013). Zecchin et al utilized a laboratory setting, allowing for standardization of multiple factors potentially impacting the relationship between glucose variation and physical activity; such as insulin therapy, meals and intensity of Figure 1.…”

“…The effects of structured physical activity (exercise) on glucose control in T1DM have been investigated with varying results (Yardley et al 2014). Most studies of this interac-tion have been conducted under controlled laboratory settings, which can be much different than real-life (Zecchin et al 2013). There has been much less investigation of the relationship between routine daily physical activity (as opposed to structured exercise regimens) and real-time glucose variations in real-life settings.…”

MagTrack

“…Existing monitoring systems are often limited because they rely on a single smartwatch, which can only monitor one hand. For example, doctors have been using smartwatches to monitor eating activities for patients with diabetes and heart diseases in order to control food intake [40,77]. An existing system has demonstrated good recognition accuracy [57], but it is limited to monitoring eating with a single hand, and it cannot monitor eating activities that involve the other hand, such as eating with a fork and knife.…”

A Five-Step Strategy to Combine Data Sources from Multiple Wearable Sensors

“…The patient's daily physical activity values ranges from 6 to 22 Activity Units (AU)/min with a mean of 14 and standard deviation of 4 AU/min [8] [13]. The analog-to-digital converter for this sensor was set as follows; +1 was sent for >19 AU/min, −1 was sent <9…”