Lifestyle intervention using Internet of Things (IoT) for the elderly: A study protocol for a randomized control trial (the BEST-LIFE study) (original) (raw)
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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.
Nagoya journal of medical science, 2017
The Internet of Things (IoT) allows collecting vast amounts of health-relevant data such as daily activity, body weight (BW), and blood pressure (BP) automatically. The use of IoT devices to monitor diabetic patients has been studied, but could not evaluate IoT-dependent effects because health data were not measured in control groups. This multicenter, open-label, randomized, parallel group study will compare the impact of intensive health guidance using IoT and conventional medical guidance on glucose control. It will be conducted in outpatients with type 2 diabetes for a period of 6 months. IoT devices to measure amount of daily activity, BW, and BP will be provided to IoT group patients. Healthcare professionals (HCPs) will provide appropriate feedback according to the data. Non-IoT control, patients will be given measurement devices that do not have a feedback function. The primary outcome is glycated hemoglobin at 6 months. The study has already enrolled 101 patients, 50 in the...
Clinical Epidemiology and Global Health, 2021
Introduction: Diabetes Mellitus is a major chronic disease associated with many complications and high morbidity. The need for lifestyle modification and regular adherent treatment makes the management of the disease difficult. This study examines the scope of m-Health in the management of type 2 diabetes mellitus in terms of glycemic control. Methods: A randomized controlled study was performed among the patients attending the outpatient department of a tertiary care hospital in Mysuru city. A mobile application named DIAGURU, mainly focusing on lifestyle modification and medication management was used for a period of 6 months from April 2019 to September 2019 by 150 patients in the study group while another 150 participants served as controls. The change in glycosylated haemoglobin levels was assessed after six months. Results: The mean HbA1C levels at the starting of the study was found to be 7.36% ± 1.04 in the study group and 7.84% ± 1.33% in the control group. A repeat HbA1c test after six months showed a mean level of 7.10% ± 0.96% among the participants who received the intervention and 7.97% ± 1.37% among the control group. The values were showing a trend of reduction in the intervention group, with a median reduction HbA1c of − 0.2% (− 0.3% to − 0.2%). Among the participants who did not receive our intervention, a median increase of 0.1% (0%-0.2%) in HbA1c was noticed. Mann Whitney U test was performed, and it showed a statistically significant association with a p value less than 0.001. Conclusion: From this study, we conclude that an intervention with a mobile application aimed at lifestyle modification and medication management in addition to regular medical treatment for type 2 diabetes mellitus patients resulted in a better glycemic control compared to a control group who did not receive the adjuvant intervention.
Diabetes and Technology for Increased Activity (DaTA) Study
Medicine and Science in Sports and Exercise, 2010
This study tested the hypothesis that an 8-week exercise intervention supported by mobile health (mHealth) technology would improve metabolic syndrome (MetS) risk factors and heart rate variability (HRV) in a population with MetS risk factors. Participants (n = 12; three male; aged 56.9 ± 7.0 years) reported to the laboratory for assessment of MetS risk factors and fitness (VO 2max) at baseline (V 0) and after 8-weeks (V 2) of intervention. Participants received an individualized exercise prescription and a mHealth technology kit for remote monitoring of blood pressure (BP), blood glucose, physical activity, and body weight via smartphone. Participants underwent 24-h ambulatory monitoring of R-R intervals following V 0 and V 2. Low and high frequency powers of HRV were assessed from the recording and the ratio of low-to-high frequency powers and low and high frequency powers in normalized units were calculated. One-way repeated measures analysis of variance showed that waist circumference (V 0 : 113.1 ± 11.0 cm, V 2 : 108.1 ± 14.7 cm; p = 0.004) and diastolic BP (V 0 : 81 ± 6 mmHg, V 2 : 76 ± 11 mmHg; p = 0.04) were reduced and VO 2max increased (V 0 : 31.3 ml/kg/min, V 2 : 34.8 ml/kg/min; p = 0.02) with no changes in other MetS risk factors. Low and high frequency powers in normalized units were reduced (V 0 : 75.5 ± 12.0, V 2 : 72.0 ± 12.1; p = 0.03) and increased (V 0 : 24.5 ± 12.0, V 2 : 28.0 ± 12.1; p = 0.03), respectively, with no other changes in HRV. Over the intervention period, changes in systolic BP were correlated negatively with the changes in R-R interval (r = −0.600; p = 0.04) and positively with the changes in heart rate (r = 0.611; p = 0.03), with no other associations between MetS risk factors and HRV parameters. Thus, this 8-week mHealth supported exercise intervention improved MetS risk factors and HRV parameters, but only changes in systolic BP were associated with improved autonomic function.
Hindawi, 2022
Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. e lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. us, the Internet of ings (IoT) has taken an utmost interest in managing diabetes. is research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. e independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy
Patients with diabetes mellitus have an increased risk of developing various serious health problems that could be lifethreatening. These problems are associated with the difficulty of these patients in managing their lifestyle, which may even lead to the abandonment of treatment. The present study was designed to evaluate the feasibility of a multipurpose activity control solution for home activity (home activity control system), which will provide information on the activities of daily living carried out outside in real time, to improve adherence to each of the therapeutic objectives agreed on with the diabetic patient. Patients and Methods: A pilot randomised controlled feasibility study will be carried out to evaluate a home activity control system (Beprevent) in managing patients with type 2 diabetes mellitus. Twenty patients with type 2 diabetes mellitus will be included (10 in the intervention group and 10 in the control group). Data on satisfaction with the tool will be collected from professionals and patients, as well as other clinical/epidemiological data from their digital health records and several questionnaires, at baseline and six months. In addition, data will also be recorded regarding the degree of adherence to the behaviors agreed on with the patients before starting the study to assess changes throughout the study and their relationship with clinical results (glycosylated haemoglobin (HbA1c), cholesterol, etc), and to compare these outcomes between two study groups. Discussion: This project involves the incorporation of telemedicine in the management of patients with diabetes. Thus, according to the currently published bibliography, the use of smart devices in this population could help improve the quality of life of these people, reduce medical visits and improve adherence to home care patterns for diabetes mellitus. There are currently no published clinical trials or protocols that monitor activities of daily living in patients with diabetes individually using artificial intelligence (AI) devices.
BMC public health, 2018
Physical activity can decrease the risk of complications related to diabetes type 2. Feasible and scalable strategies to implement support for a healthy lifestyle for patients in primary care are needed. The aim of the DiaCert-study is to evaluate a digital healthcare platform and the effect of a 12-week long smartphone-app physical activity intervention aiming at increasing physical activity (primary outcome) and improve levels of HbA1c (glycated hemoglobin), blood lipids, blood pressure, body composition, as well as other lifestyle factors and overall health in patients with diabetes type 2. The DiaCert-study is a two-arm, randomized controlled trial that will include 250 patients with diabetes type 2. At baseline, participants are randomized 1:1 to intervention, i.e. use of the smartphone-app, during 12 weeks, or to a control group receiving only standard care. Physical activity and sedentary behavior, is objectively measured using the Actigraph GT3X. Biomarkers including HbA1c a...
Amerta Nutrition, 2021
ABSTRACTBackground: Diabetes mellitus is a chronic disease which if not done properly, can cause microvascular and macrovascular disorders. Indicators of the accuracy of diabetes management in this scientific article include education, self-management (improving diet, increasing physical activity, and self-efficacy), and monitoring of HbA1c levels. Mobile devices have the potential as a tool for diabetes mellitus management in the era of the industrial revolution 4.0.Purpose: to provide the latest information regarding the effectiveness of using mobile devices in controlling risk factors for diabetes mellitus.Method: This study is a literature review study. The electronic databases used are Google Scholar, Science Direct, and Directory of Access Journals (DOAJ). Inclusion criteria: original research, a journal of at least 80% indexed by Sinta (Indonesian journal) and indexed by Scopus (international journal), published year 2010-2020, intervention using a mobile device, has an outpu...