scholarly journals Hypoglycemic Detection by Human Breath: A Mobile Health App that Alerts Diabetics of Low Blood Glucose

2019 ◽  
Vol 6 (18) ◽  
pp. 162220
Author(s):  
A. Faiola ◽  
H. Vatani ◽  
M. Agarwal
Keyword(s):  
Blood Glucose ◽  
Mobile Health ◽  
Human Breath

scholarly journals Internet of Things Based on Electronic and Mobile Health Systems for Blood Glucose Continuous Monitoring and Management

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 175116-175125 ◽  
Author(s):  
Jose Jorge Rodrigues Barata ◽  
Roberto Munoz ◽  
Rafael D. De Carvalho Silva ◽  
Joel J. P. C. Rodrigues ◽  
Victor Hugo C. De Albuquerque
Keyword(s):  
Blood Glucose ◽  
Internet Of Things ◽  
Health Systems ◽  
Mobile Health ◽  
Continuous Monitoring ◽  
Mobile Health Systems

scholarly journals Effectivness of specific mobile health applications (mHealth-apps) in gestational diabtetes mellitus: a systematic review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Claudia Eberle ◽  
Maxine Loehnert ◽  
Stefanie Stichling
Keyword(s):  
Blood Glucose ◽  
Mobile Health ◽  
Postprandial Blood Glucose ◽  
Control Groups ◽  
Health Related ◽  
Mobile Health Applications ◽  
Short And Long Term ◽  
Mhealth Apps ◽  
Health Applications

Abstract Background Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), metabolic syndrome as well as cardiovascular diseases. Against this background, mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM. Therefore, we analyzed the clinical effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes in mother and child. Methods A systematic literature search in Medline (PubMed), Cochrane Library, Embase, CINAHL and Web of Science Core Collection databases as well as Google Scholar was performed. We selected studies published 2008 to 2020 analyzing women diagnosed with GDM using specific mHealth-Apps. Controlled clinical trials (CCT) and randomized controlled trials (RCT) were included. Study quality was assessed using the Effective Public Health Practice Project (EPHPP) tool. Results In total, n = 6 publications (n = 5 RCTs, n = 1 CCT; and n = 4 moderate, n = 2 weak quality), analyzing n = 408 GDM patients in the intervention and n = 405 in the control groups, were included. Compared to control groups, fasting blood glucose, 2-h postprandial blood glucose, off target blood glucose measurements, delivery mode (more vaginal deliveries and fewer (emergency) caesarean sections) and patient compliance showed improving trends. Conclusion mHealth-Apps might improve health-related outcomes, particularly glycemic control, in the management of GDM. Further studies need to be done in more detail.

scholarly journals A Novel Mobile Health App to Educate and Empower Young People With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Mixed Methods Pilot Study

JMIR Diabetes ◽  
10.2196/29739 ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. e29739
Author(s):  
Vinutha B Shetty ◽  
Wayne H K Soon ◽  
Alison G Roberts ◽  
Leanne Fried ◽  
Heather C Roby ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Young People ◽  
Mobile Health ◽  
Quality Score ◽  
Total Quality ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Perceived Impact

Background Empowering young people with type 1 diabetes (T1D) to manage their blood glucose levels during exercise is a complex challenge faced by health care professionals due to the unpredictable nature of exercise and its effect on blood glucose levels. Mobile health (mHealth) apps would be useful as a decision-support aid to effectively contextualize a blood glucose result and take appropriate action to optimize glucose levels during and after exercise. A novel mHealth app acT1ve was recently developed, based on expert consensus exercise guidelines, to provide real-time support for young people with T1D during exercise. Objective Our aim was to pilot acT1ve in a free-living setting to assess its acceptability and functionality, and gather feedback on the user experience before testing it in a larger clinical trial. Methods A prospective single-arm mixed method design was used. Ten participants with T1D (mean age 17.7 years, SD 4.2 years; mean HbA1c, 54 mmol/mol, SD 5.5 mmol/mol [7.1%, SD 0.5%]) had acT1ve installed on their phones, and were asked to use the app to guide their exercise management for 6 weeks. At the end of 6 weeks, participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS). All semistructured interviews were transcribed. Thematic analysis was conducted whereby interview transcripts were independently analyzed by 2 researchers to uncover important and relevant themes. The uMARS was scored for 4 quality subscales (engagement, functionality, esthetics, and information), and a total quality score was obtained from the weighted average of the 4 subscales. Scores for the 4 objective subscales were determined by the mean score of each of its individual questions. The perceived impact and subjective quality of acT1ve for each participant were calculated by averaging the scores of their related questions, but were not considered in the total quality score. All scores have a maximal possible value of 5, and they are presented as medians, IQRs, and ranges. Results The main themes arising from the interview analysis were “increased knowledge,” “increased confidence to exercise,” and “suitability” for people who were less engaged in exercise. The uMARS scores for acT1ve were high (out of 5) for its total quality (median 4.3, IQR 4.2-4.6), engagement (median 3.9, IQR 3.6-4.2), functionality (median 4.8, IQR 4.5-4.8), information (median 4.6, IQR 4.5-4.8), esthetics (median 4.3, IQR 4.0-4.7), subjective quality (median 4.0, IQR 3.8-4.2), and perceived impact (median 4.3, IQR 3.6-4.5). Conclusions The acT1ve app is functional and acceptable, with a high user satisfaction. The efficacy and safety of this app will be tested in a randomized controlled trial in the next phase of this study. Trial Registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001414101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378373

Improved practicality of insulin measurements by using urine as a source: relationship between serum and urine measurements as a function of dietary intake (Preprint)

2019 ◽  
Author(s):  
Shilpa Tejpal ◽  
Narinder Sanghera ◽  
Vijayalaxmi Manoharan ◽  
Thomas M Barber ◽  
Louise Halder ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Blood Glucose ◽  
Food Intake ◽  
Dietary Intake ◽  
Mobile Health ◽  
Urine Samples ◽  
Metabolic Effects ◽  
Blood Insulin ◽  
Laboratory Conditions ◽  
Low Carbohydrate

BACKGROUND Obesity, insulin resistance and diabetes are taking epidemic proportions and novel approaches to addressing this world-wide public health challenge are required. The only molecular parameter that is currently routinely measured in this domain is blood glucose in patients with diabetes. However, measuring insulin concentrations would be more informative of the metabolic state of any person and applicable to people with obesity at varying levels of insulin resistance, including those with normal blood glucose levels. We recently demonstrated with 52 participants dieting the utility of determining insulin concentrations in urine as a molecular feedback mechanism. OBJECTIVE Our ultimate goal is to replace invasive blood insulin measurements with a more non-invasive approach. Towards this goal, we here demonstrate the use of a mobile health application to record diet and anthropometric data together with the measurement of insulin in urine and in blood, in controlled laboratory conditions and in the field. METHODS Five females aged 40-50 years were recruited and studied under laboratory conditions. Two of these were also studied in their work/home environment. The participants recorded events such as food intake, urine volume and exercise to the mobile health platform available as webinterface personalhealth.warwick.ac.uk and as mobile applications through the apple and google play stores. Urine samples were collected while varying dietary intake (low-carbohydrate, normal and ketogenic diets) and timing of food intake. Urine insulin values were measured by a highly sensitive immunosandwich electrochemiluminescence assay, which features 5 orders of magnitude in dynamic range and a fM detection limit. RESULTS We show that blood insulin and urine insulin values are linearly dependent, with urine concentrations being 3 times lower than the corresponding concentrations in serum. Characteristic urine insulin profiles were obtained by varying diet and participant and were found to be highly reproducible for the same diet/participant combination. As expected, concentrations of insulin were overall higher under normal diet conditions as compared to low-carbohydrate or ketogenic diet conditions. CONCLUSIONS This research demonstrates a practical and accurate approach to measure insulin in urine without the need for pre-processing of urine samples. The approach is applicable to a broad range of insulin concentrations as found under a variety of dieting conditions and inter-personal differences. Potential applications are improved diabetes care, and diet adherence monitoring, useful not only for clinicians but also for individuals who can thus obtain personalized metabolic feedback to food intake choices. This may empower individuals to visualize the metabolic effects of nutritional interventions with the quantitative biomarker insulin. CLINICALTRIAL NA

scholarly journals A Novel Mobile Health App to Educate and Empower Young People With Type 1 Diabetes to Exercise Safely: Prospective Single-Arm Mixed Methods Pilot Study (Preprint)

2021 ◽  
Author(s):  
Vinutha B Shetty ◽  
Wayne H K Soon ◽  
Alison G Roberts ◽  
Leanne Fried ◽  
Heather C Roby ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Young People ◽  
Mobile Health ◽  
Quality Score ◽  
Total Quality ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Perceived Impact

BACKGROUND Empowering young people with type 1 diabetes (T1D) to manage their blood glucose levels during exercise is a complex challenge faced by health care professionals due to the unpredictable nature of exercise and its effect on blood glucose levels. Mobile health (mHealth) apps would be useful as a decision-support aid to effectively contextualize a blood glucose result and take appropriate action to optimize glucose levels during and after exercise. A novel mHealth app acT1ve was recently developed, based on expert consensus exercise guidelines, to provide real-time support for young people with T1D during exercise. OBJECTIVE Our aim was to pilot acT1ve in a free-living setting to assess its acceptability and functionality, and gather feedback on the user experience before testing it in a larger clinical trial. METHODS A prospective single-arm mixed method design was used. Ten participants with T1D (mean age 17.7 years, SD 4.2 years; mean HbA<sub>1c</sub>, 54 mmol/mol, SD 5.5 mmol/mol [7.1%, SD 0.5%]) had acT1ve installed on their phones, and were asked to use the app to guide their exercise management for 6 weeks. At the end of 6 weeks, participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS). All semistructured interviews were transcribed. Thematic analysis was conducted whereby interview transcripts were independently analyzed by 2 researchers to uncover important and relevant themes. The uMARS was scored for 4 quality subscales (engagement, functionality, esthetics, and information), and a total quality score was obtained from the weighted average of the 4 subscales. Scores for the 4 objective subscales were determined by the mean score of each of its individual questions. The perceived impact and subjective quality of acT1ve for each participant were calculated by averaging the scores of their related questions, but were not considered in the total quality score. All scores have a maximal possible value of 5, and they are presented as medians, IQRs, and ranges. RESULTS The main themes arising from the interview analysis were “increased knowledge,” “increased confidence to exercise,” and “suitability” for people who were less engaged in exercise. The uMARS scores for acT1ve were high (out of 5) for its total quality (median 4.3, IQR 4.2-4.6), engagement (median 3.9, IQR 3.6-4.2), functionality (median 4.8, IQR 4.5-4.8), information (median 4.6, IQR 4.5-4.8), esthetics (median 4.3, IQR 4.0-4.7), subjective quality (median 4.0, IQR 3.8-4.2), and perceived impact (median 4.3, IQR 3.6-4.5). CONCLUSIONS The acT1ve app is functional and acceptable, with a high user satisfaction. The efficacy and safety of this app will be tested in a randomized controlled trial in the next phase of this study. CLINICALTRIAL Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001414101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378373

scholarly journals Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Syed Hasib Akhter Faruqui ◽  
Yan Du ◽  
Rajitha Meka ◽  
Adel Alaeddini ◽  
Chengdong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Glucose ◽  
Glucose Level ◽  
Mobile Health ◽  
Learning Model ◽  
Health Data ◽  
Glucose Levels ◽  
Health Lifestyle ◽  
Deep Learning Model

BACKGROUND Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM. OBJECTIVE The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before. METHODS We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data. RESULTS The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values. CONCLUSIONS Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

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