Randomized controlled trial for assessment of Internet of Things system to guide intensive glucose control in diabetes outpatients: Nagoya Health Navigator Study protocol (original) (raw)
Related papers
Internal Medicine, 2020
Objective Exercise therapy is used for glycemic control in type 2 diabetes mellitus (T2DM). We evaluated the effects of intensive health guidance using the Internet of things (IoT) among Japanese company workers with early T2DM. Methods Fifty-three men (mean age: 54 years) with glycated hemoglobin (HbA1c) levels of >6.5% were enrolled in a 6-month exercise therapy program between August 2016 and January 2017. They used activity meters, scales, and sphygmomanometers connected to the Internet by Bluetooth. These devices automatically and continuously recorded daily information, and the participants simultaneously received health guidance from a public health nurse twice a month. Results The number of daily steps significantly increased, whereas the amount of physical activity increased but was not significant. The mean decrease (±SD) in HbA1c levels after 3 and 6 months was estimated to be-0.40% (±0.45, p<0.0001) and-0.19% (±0.55, p=0.033), respectively, by a linear mixed model that included baseline HbA1c levels and age as covariates. The program failed to improve the body mass index and blood pressure of the participants. The percentage of active stage (action and maintenance stage) in stage of health behavior significantly increased from 48% to 68% (p=0.011). Conclusion Intensive lifestyle intervention using a wearable monitoring system and remote health guidance improved diabetic control in middle-aged company workers.
IOT Based Non-Invasive Health Monitoring System for Diabetics
IOT Based Non-Invasive Health Monitoring System for Diabetics, 2023
In this paper, we introduce a novel approach to this problem by describing a non-invasive health monitoring method for diabetics that is based on the Internet of Things. Being healthy and happy (SDG 3) and manufacturing, creativity, and connectivity (SDG 9) are two of the most important targets of our system. It changes the game when it comes to personal health management. providing an independent medical kit that incorporates several non-invasive sensors within a single, convenient gadget. This all-inclusive medical kit features a glucometer for checking your glucose levels, a SpO2 sensor for tracking oxygen saturation levels, and a thermometer sensor for monitoring the core of the body, and a BPM reader for monitoring the pace of the heart. Because these sensors are built in, people can easily keep tabs on their health and take charge of their wellbeing to a whole new level. Importantly, our technology is complemented by the Med X kit mobile application. which works with Android as well as iOS phones and tablets. Users who use the Med X kit can take a more all-encompassing approach to their health by recording their vitals, organizing their medication, and keeping a health journal. Our preliminary testing with the non-invasive glucose test has shown around 95% accuracy for fasting measures in both insulin and non-diabetic subjects. Our system's potential to provide people more control over their health is further demonstrated by the fact that our design displays real-time values for SpO2, Business Process Management, and the internal temperature. Our IoT-based Non-Invasive Monitoring of Health Technology for Diabetics is a fresh take on boosting health and well-being at a time of rapidly evolving technology and altering health priorities.
IoT technologies combining glucose control with physiological signal: comparative study
2020
Patients with type 1 diabetes mellitus (T1DM) have varying sensitivities to insulin and also varying responses to meals and exercises, an Artificial pancreas (AP) which is a closed loop system are used to control blood glucose concentration. With advances in continuous glucose monitoring (CGM) technologies, intelligent control and communication systems, AP have improved better postprandial glucose. Despite these advances, many researchers have developed a system able to keep Blood glucose concentration (BGC) in the target range during all the different situations (stress, during and after exercise and overnight…etc). These different situations present a major challenge in the development of closed loop AP system, because of their effect on the BGC are not well understood. IoT emergent technologies allow to create new trend in the AP system introducing physiological signals to the closed loop system. This started with the few studies that found some correlation between physiological signals such as electrocardiography (ECG), electroencephalography (EEG) and changes in BGC during different situations. many researchers aim to develop an Intelligent control system that predict and avoid automatically hypoglycemia and hyperglycemia episodes using biometric variables extracted from the physiological signal instead of CGM. In this paper we will present an overview and a comparison study between the different studies that use these physiological signals in AP systems, concluding that the ECG signal are the most appropriate physiological signal that can be used in combination with glucose control strategy for better prediction and prevention of hypoglycemia and hyperglycemia episodes.
IoT for monitoring diabetic patients
Diabetes is the most common disease in the world which will lead to death when there is no proper health care. Failure to control the disease not only results in long-term complications but also affects the life of the patients. If the glucose level is monitored day-by-day, it will help the patient to manage their health properly. The feedback information regarding their sugar level is helpful for the patient to take care of the health daily. In this paper we discuss about the system that is designed to monitor the glucose level, blood pressure and temperature of the person. By making the data available in cloud, it can be used by the doctors to get the historical data. This will help better health care management of their patients. This system is implemented by using the Raspberry Pi for reading data from the user. The ADC is also used to convert the analog signal into the digital signals. The project implementation details are discussed in this paper.
IEEE Access, 2019
Over 425 million people suffer from diabetes worldwide and this number is expected to increase over the years. Rigorous and extensive research has led to the development of increasingly advanced technologies, such as continuous glucose monitoring and glucose flash monitoring. These new technologies are more promising and efficient with respect to calculating the glycemic index and are more easier to use than the glucometer technology already established in the market. However, market solutions are often highly restrictive due to their costs. In an effort to address this challenge, this article describes the Freestyle Free sensor and the associated advantages of an integrated and low-cost environment that it offers patients. The proposed environment allows continuously monitoring the blood glucose rate and provides doctors and caregivers information remotely. Additionally, the data generated will allow the application of data mining techniques in efforts aimed at understanding the disease better. The integration between the patient and the integrated environment occurs through the near-field communication sensor over an Internet of Things card, which sends the data collected for the LibreMonitor mobile application. To evaluate the integrated environment, we compared the glucose rates measured with an official Freestyle Libre software during the same period. Based on the positive results, we propose that the integrated environment is a low-cost alternative for continuous glucose monitoring of patients with diabetes.
Personal and Ubiquitous Computing, 2011
Diabetes therapy management in AAL environments, such as old people and diabetes patients homes, is a very difficult task since many factors affect a patient's blood sugar levels. Factors such as illness, treatments, physical and psychological stress, physical activity, drugs, intravenous fluids and change in the meal plan cause unpredictable and potentially dangerous fluctuations in blood sugar levels. Right now, operations related to dosage are based on insulin infusion protocol boards, which are provided by physicians to the patients. These boards are not considering very influential factors such as glycemic index from the diet, consequently patients need to estimate the dosage leading to dose error, which culminates in hyperglycemia and hypoglycemia episode. Therefore, right insulin infusion calculation needs to be supported by the next generation of personal-care devices. For this reason, a personal device has been developed to assist and consider more factors in the insulin therapy dosage calculation. The proposed solution is based on Internet of things in order to, on the one hand, support a patient's profile management architecture based on personal RFID cards and, on the other hand, provide global connectivity between the developed patient's personal device based on 6LoWPAN, nurses/physicians desktop application to manage personal health cards, glycemic index information system, and patient's web portal. This solution has been evaluated by a multidisciplinary group formed by patients, physicians, and nurses.
Nagoya journal of medical science, 2018
Modification of lifestyle habits, including diet and physical activity, is essential for the prevention and control of type 2 diabetes mellitus (T2DM) in elderly patients. However, individualized treatment is more critical for the elderly than for general patients. This study aimed to determine lifestyle interventions that resulted in lowering hemoglobin A (HbA) in Japanese pre- and early diabetic elderly subjects. The BEST-LIFE trial is an ongoing, open-label, 6-month, randomized (1:1) parallel group trial. Subjects with HbA of ≥5.6%-randomly assigned to the intervention or control group -use wearable monitoring devices loaded with Internet of things (IoT) systems that aids them with self-management and obtaining monthly remote health guidance from a public health nurse. The primary outcome is changes in HbA after a 6-month intervention relative to the baseline values. The secondary outcome is the change of behavior modification stages. The background, rationale, and study design o...
IRJET- Internet of Things (IOT) Based Personal Device for Diabetes Mellitus Treatment & Management
IRJET, 2021
The importance of diabetes treatment in various conditions majorly included elder individuals and home patients with diabetes having very difficult conditions and the same number of variables influence blood glucose levels in a patient. Symptoms like sickness, medications, physical and mental pressure, physical movement, drugs, intravenous liquids, and dinner plan change cause unusual and conceivably hazardous variances in glucose levels. At this moment, dose related tasks are centered around methodology steps for insulin imbuement, which is given to the patients by specialists. These steps don't consider extremely compelling variables, for example the eating regimen glycemic list and by this way patients need to gauge the measurement prompting portion, which finishes in a partly scene of hyperglycemia and hypoglycemia. Thus, the estimation of the correct insulin dosage must be helped by the upcoming age of individual consideration gadgets. The glucose sugar level inside a human could be estimated by inserting IR radiations. The level of glucose focus inside the blood relies upon the power upon the frequency explicit of the radiation. For this purpose, a personal device was created to assist in the determination of insulin therapy dose and consider further factors. The arrangement proposed relies on the Internet of Things from one viewpoint also to help the particular with the board arrangement of a patient dependent on close to home RFID cards and the doctor to give worldwide network between the individual gadget made for the patient dependent on 6LoWPAN, program for attendants/doctors to screen individual wellbeing cards, the glycemic file data framework and the patient web-based interface. Along these lines, the proposed framework won't just assistance a person to oversee diabetes yet in addition to screen every single needed parameter and support forestall the difficulties that may emerge from diabetes.
An IoT-Based Glucose Monitoring Algorithm to Prevent Diabetes Complications
Applied Sciences
Diabetes mellitus (DM) is a metabolic disorder characterized by blood glucose levels above normal limits. The impact of this disease on the population has increased in recent years. It is already a public health problem worldwide and one of the leading causes of death. Recently, several proposals have been developed for better and regular monitoring of glucose. However, theses proposals do not discard erroneous readings and they are not able to anticipate a critical condition. In this work, we propose an algorithm based on the double moving average supported by an IoT architecture to prevent possible complications in elderly patients. The algorithm uses historical readings to construct a series. Given a number of periods, it is possible to calculate averages of different subsets and trends for the next periods and, in this way, the prognosis is obtained. With the prognosis, it is possible to notify the doctor and relatives in advance about a possible critical condition in the patien...