Differences in Glucose Levels Between Left and Right Arm

Liu, Xiaohan; Shah, Sachin A; Eid, Tony; Shek, Allen

doi:10.1177/1932296819851123

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…First, since the Dexcom G6 reference device was on the participants' left arm, only data from the left arm was used in this analysis (data from both arms were used in the two previous studies). Since studies have shown that BG levels in the arms can be quite different [21], we restricted our study to only the arm corresponding to the reference device. Another difference is the way the data is split into test and training datasets.…”

Section: Comparison To Previous Know Labs Resultsmentioning

confidence: 99%

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹

Klyve

Currie

Ward

et al. 2023

Preprint

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Diabetes Mellitus (DM) is a highly prevalent and increasingly common disorder that can have dire health consequences if not properly managed. Management of DM involves monitoring of blood glucose levels which can be both cumbersome and invasive, limiting adherence. We present a validation for a novel sensor designed to measure blood glucose (BG) non-invasively using Radio Frequency (RF) waves. In this n=5 study, we trained a Light Gradient-Boosting Machine (lightGBM) model to predict BG values using 1,555 observations from over 130 hours of data collection from 5 participants. An observation is defined as data collected from 13 Bio-RFID sensor sweeps paired with a single Dexcom G6 value. Using this model, we were able to predict BG in the test set with a Mean Absolute Relative Difference (MARD) of 12.7% in the normoglycemic range and 14.0% in the hyperglycemic range. Overall, 70.7% of the estimates fell within 15% of the reference value, and 79.1% fell within 20% of the reference value. While this is a relatively small participant sample, these strong initial results indicate the efficacy of this technique, and that with further refinement and more data, there is promise to achieve a clinically relevant level of accuracy.

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Section: Comparison To Previous Know Labs Resultsmentioning

confidence: 99%

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹

Klyve

Currie

Ward

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Continuous glucose monitors on the market are approved for use on either arm, but a 2019 study by Liu et al reported significant differences between CGMs placed on the left and right arms of their participants. 8,37 Moving beyond real-time accuracy, our previous work examined CGM sensor agreement between different recommended locations in feedforward (FF) NN glycemic forecasting. In 10 of 13 participants, we observed significant differences in NN prediction performance from NNs trained on the right arm versus the left arm.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

Tucker

Erdman

Schreiner

et al. 2022

J Diabetes Sci Technol

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Background: Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN. Methods: In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location. Results: We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro—Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes ( P < .05). Conclusion: We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

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“…In particular, one recent study by Liu, et al demonstrates significant differences between left-arm and right-arm placed CGMs in their participants. 33 While previous studies have only implemented multiple sensor locations as redundancy, 34 we examine CGM sensor agreement between 2 different locations recommended by the manufacturer. Since an accurate, precise prediction is an important aspect of therapy, understanding the relevance of CGM placement is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Tucker

Erdman

Schreiner

et al. 2021

J Diabetes Sci Technol

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Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

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Differences in Glucose Levels Between Left and Right Arm

Cited by 6 publications

References 5 publications

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

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Differences in Glucose Levels Between Left and Right Arm

Cited by 6 publications

References 5 publications

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology1

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology1

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Contact Info

Product

Resources

About

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹

Algorithm Refinement in the Non-Invasive Detection of Blood Glucose via Bio-RFID™ Technology¹