Experts Explore the Potential of Wearable Tech in T1D Management

17th April, 2022.      //   Health, Technology  // 

Experts Explore the Potential of Wearable Tech in T1D Management

Wearable sensor technology has potential to aid in type 1 diabetes (T1D) management through noninvasive, seamless, and continuous data provision, according to a new study published in a Medical Internet Research.

Maintaining healthy glucose levels with upper and lower limits can be challenging for persons with T1D, especially because daily management imposes not only a physical but a cognitive burden on both the patients and their families. Sensing, the study authors noted, is a crucial part of disease management for 2 principal reasons: estimating the correct insulin dose and awareness of possible long-term complications.

Their literature review explored the potential of wearable health-related technology to enhance management for T1D. Such technology has already shown great success in heart rate, respiratory rate, and oxygen saturation monitoring, but this progress has not been carried over or thoroughly explored in T1D management. Can some of the physiological parameters monitored via noninvasive and wearable sensors also be used to enhance T1D management?

Comparing outcomes between persons with T1D and healthy controls, the authors saw that wearable technology detected heart rate variations more often in patients with T1D. In particular, during exercise. In addition, “cardiac depolarization and repolarization time intervals were shown to increase in T1D demonstrated through modified [electrocardiogram] features,” the authors wrote.

Further, persons with T1D were shown to have lower peak oxygen uptake and respiratory exchange ratios and impaired sensitivity to hypoxia, with the latter being a sign of a cardiorespiratory control imbalance, as well as a lower sudomotor function, which is a measure of thermal homeostasis. With a lower sudomotor function, sweat profile, body temperature, and skin temperature during exercise can be adversely affected.

The authors also found that the articles in their analysis indicated that using a sensor/machine learning (ML) algorithm based on pre-exercise heart rate and basal glucose could, to 80% accuracy, indicate exercise-induced hypoglycemia and that a ML algorithm accounting for heart rate and electrochemical skin conductance improved glucose prediction.

When noting the importance of their findings, the authors noted that the physiologic parameters from the studies in their analysis can accomplish 2 goals: They can be used to differentiate between persons with and without T1D and they have relationships to both glycemic control and T1D complications. Also, all of the identified parameters were associated in some way with aspects of diabetic complications and macrovascular disease, “with capacity for early risk prediction.”

“Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges exist,” the authors concluded. “Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.”

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