scholarly journals Flash Glucose Monitoring Technology Impact on Diabetes Self-Care Behavior

2019 ◽  
Vol 14 (2) ◽  
pp. 130-132 ◽  
Author(s):  
Nicole D. White ◽  
Emily Knezevich
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Self Care ◽  
Innovative Technology ◽  
Future Research ◽  
Self Monitoring ◽  
Flash Glucose Monitoring ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Care Behavior

Individuals with diabetes play a significant role in the control of their condition by participating in their own care. Self-monitoring of blood glucose is of particular importance in maintaining adequate glycemic control but when obtained using traditional fingerstick methods, is often limited with by cost, fear of needles or pain and inconvenience. Flash glucose monitoring is an innovative technology available to address these barriers and help people with diabetes better manage their blood glucose levels. Data demonstrating increased frequency in glucose monitoring, patient perspectives related to self-care behaviors, and implications for practice and future research are described.

scholarly journals Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3319 ◽  
Author(s):  
Tiago M. Fernández-Caramés ◽  
Iván Froiz-Míguez ◽  
Oscar Blanco-Novoa ◽  
Paula Fraga-Lamas
Keyword(s):  
Blood Glucose ◽  
Fog Computing ◽  
Storage System ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitor ◽  
Self Monitoring ◽  
Mobile Crowdsourcing ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Diabetes Patients

Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.

Independent Glucose Monitoring by Functionally Blind Diabetics

1988 ◽  
Vol 82 (2) ◽  
pp. 50-53 ◽  
Author(s):  
S.V. Ponchillia ◽  
S. LaGrow
Keyword(s):  
Blood Glucose ◽  
Vision Loss ◽  
Glucose Monitoring ◽  
Test Strip ◽  
Individualized Instruction ◽  
Self Monitoring ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Severe Vision Loss ◽  
Adequate Coverage

Self-monitoring of blood glucose levels is the preferred practice for diabetics who wish to control the disease through diet and medication. However, some persons are not able to perform this task independently because of severe vision loss. Glucometers (devices to measure blood glucose levels) with an auditory output may prove to be a viable alternative for these persons. The purpose of this study was to determine if functionally blind diabetics could indeed monitor their glucose levels independently using a talking glucometer. Although results indicate that this is possible, some individualized instruction is needed to ensure adequate coverage of the test strip and, therefore, accurate glucose readings.

scholarly journals Self-Care IoT Platform for Diabetic Mellitus

2021 ◽  
Vol 11 (5) ◽  
pp. 2006
Author(s):  
Jai-Chang Park ◽  
Seongbeom Kim ◽  
Je-Hoon Lee
Keyword(s):  
Blood Glucose ◽  
Self Care ◽  
Target Range ◽  
Process Data ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Iot Platform ◽  
Number Of Patients ◽  
Glucose Management ◽  
The Relationship

Diabetes mellitus is a severe chronic disease, and the number of patients has increased. To manage blood glucose levels, patients should frequently measure their blood glucose and analyze which lifestyle habits affect blood glucose levels. However, it is hard to record and analyze the relationship between their blood glucose levels and lifestyle. The internet of things (IoT) is useful to interconnect, monitor, obtain, and process data between various devices used in everyday life to fulfill a common objective. This paper proposes an intelligent self-care platform using IoT technology that helps patients with chronic diabetes manage their blood glucose levels in their target range. In particular, we developed various devices called the self-care IoT pack. It consists of five different types of devices to obtain blood glucose levels, physical activities, food intake, medication, sleeping, and so on. They can collect blood glucose levels with lifestyles that automatically impact the patient’s blood glucose level. We also devised a self-care application to display and analyze the data obtained from the IoT pack. Consequently, the proposed self-care IoT platform collects the blood glucose levels and the lifestyles without any burden of record. By reviewing the accumulated information, the patients can find bad habits in blood glucose management and improve their lifestyle.

scholarly journals Improved HbA1c and reduced glycaemic variability after 1-year intermittent use of flash glucose monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenhui Zhang ◽  
Yu Liu ◽  
Baosheng Sun ◽  
Yanjun Shen ◽  
Ming Li ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Final Visit ◽  
Glycaemic Variability ◽  
Self Monitoring ◽  
Flash Glucose Monitoring ◽  
Significant Difference ◽  
Mean Blood Glucose ◽  
Hba1c Measurement

AbstractFlash glucose monitoring (FGM) was introduced in China in 2016, and it might improve HbA1c measurements and reduce glycaemic variability during T1DM therapy. A total of 146 patients were recruited from October 2018 to September 2019 in Liaocheng. The patients were randomly divided into the FGM group or self-monitoring blood glucose (SMBG) group. Both groups wore the FGM device for multiple 2-week periods, beginning with the 1st, 24th, and 48th weeks for gathering data, while blood samples were also collected for HbA1c measurement. Dietary guidance and insulin dose adjustments were provided to the FGM group patients according to their Ambulatory Glucose Profile (AGP) and to the SMBG group patients according to their SMBG measurements taken 3–4 times daily. All of the participants underwent SMBG measurements on the days when not wearing the FGM device. At the final visit, HbA1c, time in range (TIR), duration of hypoglycaemia and the number of diabetic ketoacidosis (DKA) events were taken as the main endpoints. There were no significant difference in the baseline characteristics of the two groups. At 24 weeks, the HbA1c level of the FGM group was 8.16 ± 1.03%, which was much lower than that of the SMBG group (8.68 ± 1.01%) (p = 0.003). The interquartile range (IQR), mean blood glucose (MBG), and the duration of hypoglycaemia in the FGM group also showed significant declines, compared with the SMBG group (p < 0.05), while the TIR increased in the FGM group [(49.39 ± 17.54)% vs (42.44 ± 15.49)%] (p = 0.012). At 48 weeks, the differences were more pronounced (p < 0.01). There were no observed changes in the number of episodes of DKA by the end of the study [(0.25 ± 0.50) vs (0.28 ± 0.51), p = 0.75]. Intermittent use of FGM by T1DM patients can improve their HbA1c and glycaemic control without increasing the hypoglycaemic exposure in insulin-treated individuals with type 1 diabetes in an developing country.

scholarly journals A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6820
Author(s):  
Bushra Alsunaidi ◽  
Murad Althobaiti ◽  
Mahbubunnabi Tamal ◽  
Waleed Albaker ◽  
Ibraheem Al-Naib
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Daily Basis ◽  
Glucose Sensing ◽  
Blood Glucose Monitoring ◽  
Optical Systems ◽  
Glucose Levels ◽  
Non Invasive ◽  
Blood Glucose Levels ◽  
Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Modern technologies in diabetes management - continuous glucose monitoring and insulin pump therapy

2021 ◽  
Vol 87 (1) ◽  
pp. 50-55
Author(s):  
E.Yu. Pyankova ◽  
◽  
L.A. Anshakova ◽  
I.A. Pyankov ◽  
S.V. Yegorova ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Insulin Pump ◽  
Glucose Monitoring ◽  
Insulin Pump Therapy ◽  
Pump Therapy ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
The Individual ◽  
Glucose Lowering Therapy

The problems of complications of diabetes mellitus cannot be solved without constant monitoring of blood glucose levels. The evolution of additional technologies for the determination of glucose in the blood of the last decades makes it possible to more accurately predict the risks of complications, both in the individual and in the patient population as a whole. The article provides an overview of the methods used in modern diabetology, facilitating control over the variability of blood glucose levels and helping in a more accurate selection of glucose-lowering therapy. All presented methods are currently working in real clinical practice in the Khabarovsk Krai

Online Prediction of Blood Glucose Levels using Genetic Algorithm

2014 ◽  
pp. 299-310
Author(s):  
Khaled Eskaf ◽  
Tim Ritchings ◽  
Osama Bedawy
Keyword(s):  
Blood Glucose ◽  
Glucose Monitoring ◽  
Simulation Software ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Learning Techniques ◽  
Preliminary Study ◽  
Online Prediction

Diabetes mellitus is one of the most common chronic diseases. The number of cases of diabetes in the world is likely to increase more than two fold in the next 30 years: from 115 million in 2000 to 284 million in 2030. This chapter is concerned with helping diabetic patients to manage themselves by developing a computer system that predicts their Blood Glucose Level (BGL) after 30 minutes on the basis of their current levels, so that they can administer insulin. This will enable the diabetic patient to continue living a normal daily life, as much as is possible. The prediction of BGLs based on the current levels BGLs become feasible through the advent of Continuous Glucose Monitoring (CGM) systems, which are able to sample patients' BGLs, typically 5 minutes, and computer systems that can process and analyse these samples. The approach taken in this chapter uses machine-learning techniques, specifically Genetic Algorithms (GA), to learn BGL patterns over an hour and the resulting value 30 minutes later, without questioning the patients about their food intake and activities. The GAs were invested using the raw BGLs as input and metadata derived from a Diabetic Dynamic Model of BGLs supplemented by the changes in patients' BGLs over the previous hour. The results obtained in a preliminary study including 4 virtual patients taken from the AIDA diabetes simulation software and 3 volunteers using the DexCom SEVEN system, show that the metadata approach gives more accurate predictions. Online learning, whereby new BGL patterns were incorporated into the prediction system as they were encountered, improved the results further.

scholarly journals Flash glucose monitoring helps achieve better glycemic control than conventional self-monitoring of blood glucose in non-insulin-treated type 2 diabetes: a randomized controlled trial

2020 ◽  
Vol 8 (1) ◽  
pp. e001115 ◽  
Author(s):  
Eri Wada ◽  
Takeshi Onoue ◽  
Tomoko Kobayashi ◽  
Tomoko Handa ◽  
Ayaka Hayase ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Analysis Of Covariance ◽  
Self Monitoring ◽  
Flash Glucose Monitoring ◽  
Baseline Values ◽  
The Mean

IntroductionThe present study aimed to evaluate the effects of flash glucose monitoring (FGM) and conventional self-monitoring of blood glucose (SMBG) on glycemic control in patients with non-insulin-treated type 2 diabetes.Research design and methodsIn this 24-week, multicenter, open-label, randomized (1:1), parallel-group study, patients with non-insulin-treated type 2 diabetes at five hospitals in Japan were randomly assigned to the FGM (n=49) or SMBG (n=51) groups and were provided each device for 12 weeks. The primary outcome was change in glycated hemoglobin (HbA1c) level, and was compared using analysis of covariance model that included baseline values and group as covariates.ResultsForty-eight participants in the FGM group and 45 in the SMBG group completed the study. The mean HbA1c levels were 7.83% (62.1 mmol/mol) in the FGM group and 7.84% (62.2 mmol/mol) in the SMBG group at baseline, and the values were reduced in both FGM (−0.43% (−4.7 mmol/mol), p<0.001) and SMBG groups (−0.30% (−3.3 mmol/mol), p=0.001) at 12 weeks. On the other hand, HbA1c was significantly decreased from baseline values in the FGM group, but not in the SMBG group at 24 weeks (FGM: −0.46% (−5.0 mmol/mol), p<0.001; SMBG: −0.17% (−1.8 mmol/mol), p=0.124); a significant between-group difference was also observed (difference −0.29% (−3.2 mmol/mol), p=0.022). Diabetes Treatment Satisfaction Questionnaire score was significantly improved, and the mean glucose levels, SD of glucose, mean amplitude of glycemic excursions and time in hyperglycemia were significantly decreased in the FGM group compared with the SMBG group.ConclusionsGlycemic control was better with FGM than with SMBG after cessation of glucose monitoring in patients with non-insulin-treated type 2 diabetes.Trial registration numberUMIN000026452, jRCTs041180082.

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