Detection of a Meal Using Continuous Glucose Monitoring

Dassau, Eyal; Bequette, B. Wayne; Buckingham, Bruce A.; Doyle, Francis J.

doi:10.2337/dc07-1293

Cited by 125 publications

(100 citation statements)

References 19 publications

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“…This study applies also the meal detection methods from Dassau et al (2008) and Lee and Bequette (2009). Similar to the GRID algorithm, these methods detect meals when CGM data and its first and second derivatives exceed defined thresholds.…”

Section: Meal Detection Methodsmentioning

confidence: 99%

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Impact of sensing and infusion site dependent dynamics on insulin bolus based meal compensation

2017

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Meals are most challenging in the regulation of blood glucose levels (BGL) in diabetes mellitus type 1, whether it is automated, semi-automated or manually controlled. The common subcutaneous (SC) route for glucose sensing and insulin administration suffers from large latencies. This paper investigates the impact of glucose sensing and insulin absorption dynamics on the achievable glucose regulation when insulin boluses are triggered by a meal detection system. In silico patients from the academic version of the UVa/Padova simulator are studied. The sub-models of glucose sensing and insulin absorption are adjusted to allow simulations with different time delays and time constants. Meals are detected with published methods based on threshold-checking of continuous glucose monitoring data. Slow glucose sensing dynamics delay the meal detection. Delayed meal detection can be compensated to some extent by exact knowledge about the insulin absorption. The combination of slow glucose sensing and slow insulin administration reduces the effect of insulin boluses on the postprandial BGL. The classical SC approach is, therefore, at high risk of large BGL excursions despite meal detection.

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Section: Meal Detection Methodsmentioning

confidence: 99%

“…Suggested methods for meal detection based on threshold checking use the raw CGM data either directly or revised by removing measurement noise using a linear noise model in a Kalman filter (KF) (Dassau et al, 2008;Lee and Bequette, 2009;Harvey et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Impact of sensing and infusion site dependent dynamics on insulin bolus based meal compensation

2017

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“…In the estimation section, the ROC of glucose is calculated using the first derivative of the 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as follows: 18,22 …”

Section: G Kmentioning

confidence: 99%

“…[18][19][20][21] In those cases, 1 minute sampling was used, which may increase the speed of detection and allow for increased accuracy. At this time, however, most CGMs provide data at a 5-minute sampling time.…”

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confidence: 99%

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Design of the Glucose Rate Increase Detector

Harvey

Dassau

Zisser

et al. 2014

J Diabetes Sci Technol

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The primary goal of the artificial pancreas (AP) is to eliminate the occurrence of severe hypoglycemia and reduce the time spent in hyperglycemia (>180 mg/dL) in an effort to improve quality of life and reduce long-term complications. 1 Safe and effective control of type 1 diabetes mellitus (T1DM) using an AP has been researched widely for several decades, with many advances, but several challenges remain. These challenges include overcoming large meal disturbances, the effects of exercise, and the delays associated with subcutaneous glucose sensing and insulin delivery. 2 One of the most difficult aspects of the diabetes therapy routine is dealing with meals, and it has been shown that inaccurate estimation of meal sizes occurs frequently, resulting in additional glucose fluctuations. 3 Recent behavioral studies have also shown that people with T1DM are interested in an automated system but are concerned with relinquishing full control. 4,5 Therefore, an automatic AP that is safe and robust to daily living conditions and is trusted by the users is critical.The AP is a multilayered device that will contain several features, including a core glucose controller, devices for monitoring of glucose and possibly other biologically relevant compounds or signals, software to interface with the user, safety systems to monitor the status of the system, and telemedicine to convey information about the system to the user and family and/or medical personnel. The core of the AP is the controller, the design of which has been explored by several research teams, with promising results. [6][7][8][9][10][11] Continuous glucose monitoring (CGM) devices and insulin pumps are continually being improved, and are at a performance level that enables automatic control. 12,13 Currently, longer clinical trials with several meals and exercise are being performed with good results. 6,14 Generally, the trials with meals larger than 50 g of carbohydrate (CHO) use a feed-forward approach, announcing meals and giving a full AbstractThe Glucose Rate Increase Detector (GRID), a module of the Health Monitoring System (HMS), has been designed to operate in parallel to the glucose controller to detect meal events and safely trigger a meal bolus. The GRID algorithm was tuned on clinical data with 40-70 g CHO meals and tested on simulation data with 50-100 g CHO meals. Active closed-and open-loop protocols were executed in silico with various treatments, including automatic boluses based on a 75 g CHO meal and boluses based on simulated user input of meal size. An optional function was used to reduce the recommended bolus using recent insulin and glucose history. For closed-loop control of a 3-meal scenario (50, 75, and 100 g CHO), the GRID improved median time in the 80-180 mg/dL range by 17% and in the >180 range by 14% over unannounced meals, using an automatic bolus for a 75 g CHO meal at detection. Under open-loop control of a 75 g CHO meal, the GRID shifted the median glucose peak down by 73 mg/dL and earlier by 120 min and reduced the time >...

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Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge‐spectrum eating disorders: A proof‐of‐concept study

Presseller,

Parker,

Zhang

et al. 2024

Euro Eating Disorders Rev

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ObjectiveGoing extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge‐spectrum eating disorders (B‐EDs). However, existing treatments for B‐EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just‐in‐time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning.MethodAdults with B‐EDs (N = 22) wore CGMs and reported eating episodes on self‐monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non‐eating episodes.ResultsThe optimal model distinguished eating and non‐eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94).ConclusionsThese findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B‐EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

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Detection of a Meal Using Continuous Glucose Monitoring

Cited by 125 publications

References 19 publications

Impact of sensing and infusion site dependent dynamics on insulin bolus based meal compensation

Impact of sensing and infusion site dependent dynamics on insulin bolus based meal compensation

Design of the Glucose Rate Increase Detector

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge‐spectrum eating disorders: A proof‐of‐concept study

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