Self-Management of Diabetes Mellitus with Remote Monitoring

Mushcab, Hayat; Kernohan, William George; Wallace, Jonathan; Harper, Roy; Martin, Suzanne

doi:10.4018/ijehmc.2017010104

Cited by 11 publications

(6 citation statements)

References 13 publications

(19 reference statements)

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“…Remote home monitoring services have previously been implemented in the UK for chronic conditions 2,3 , and during the first wave of the pandemic these services were set-up and piloted for COVID-19 patients in England 4 . These services were subsequently rolled out nationally, known as 'COVID Oximetry at home (CO@h)' or 'Virtual Wards' for COVID-19 patients who had been discharged early from hospital.…”

Section: Introductionmentioning

confidence: 99%

Examining disparities relating to service reach and patient engagement with COVID-19 remote home monitoring services in England: a mixed methods rapid evaluation

Crellin

Herlitz

Sidhu

et al. 2022

Preprint

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Background: The adoption of remote methods of care has been accelerated by the COVID-19 pandemic, but concerns exist relating to the potential impact on health disparities. This evaluation explores the implementation of COVID-19 remote home monitoring services across England, focussing on patients experiences and engagement with the service. Methods: The study was a rapid, multi-site, mixed methods evaluation. Data were collected between January and June 2021. We conducted qualitative interviews with staff service leads, and patients and carers receiving the service. We conducted quantitative surveys with staff delivering the service, and patients and carers receiving the service across 28 sites in England, UK. Qualitative data were analysed using thematic analysis and quantitative data were analysed using univariate and multivariate methods. Findings: Many sites designed their service to be inclusive to the needs of their local population. Strategies included widening eligibility criteria, prioritising vulnerable groups, and creating referral pathways. Many sites also adapted their services according to patient needs, including providing information in different languages or more accessible formats, offering translation services, offering non-digital options, or providing face-to-face assessments. Despite these adaptions, disparities were reported across patient groups (e.g. age, health status, ethnicity, level of education) in their experience of and engagement with the service. Interpretation: Services must determine how best to design and implement remote monitoring services to be of value to all populations. National guidance should play a role in supporting services to best serve the needs of their populations, and patients and staff must play an active role in service design.

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Section: Introductionmentioning

confidence: 99%

Examining disparities relating to service reach and patient engagement with COVID-19 remote home monitoring services in England: a mixed methods rapid evaluation

Crellin

Herlitz

Sidhu

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…1 These estimates and projections mark Diabetes as an epidemic and a global burden on healthcare. 3 Diabetes is a chronic condition characterized by high glucose levels in the blood due to either shortage in the body's production of insulin or its inability to sufficiently use it (sometimes both). 2,4 Daily insulin therapy is essential for people with type 1 diabetes (T1D) which constitutes 5-10% of all people with diabetes.…”

Section: Introductionmentioning

confidence: 99%

Tawazon- An Intensive Lifestyle Diabetes Prevention Program: The Saudi Arabian Pilot

Mushcab¹,

Alsharif²,

Nazeer³

et al. 2021

JMDH

Self Cite

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Background Lifestyle modification interventions help prevent or postpone Type 2 Diabetes and its complications with diet modification and increasing physical activity. This is translational research of Tawazon-DPP in which we evaluate the efficacy of the intensive lifestyle modification program. Methods Tawazon was piloted in 2 phases over 6 and 9 months at a Tertiary Hospital. Phase I was conducted in 2017 in one district facility with 47 participants while Phase II was conducted in 2018 in 4 district facilities with 247 participants. Data are collected retrospectively in a before-and-after study design. The program included adults with 25 kg/m 2 BMI and HbA1c ranging between 5.7 and 6.4 mmol/L, while excluding participants with comorbidities, such as cardiovascular conditions, musculoskeletal injuries, and pregnancy. Results We used descriptive analysis as well as Wilcoxon rank sign test and McNemar-Bowker for before and after data comparisons. Phase I exhibited a mean age of 43±7 years and statistically significant improvement in HbA1c (0.3 mmol/L), weight (3.3 kg), BMI (1 kg/m 2 ), triglyceride (30.4 mg/dL) and LDL (14.67 mg/dL). Phase II exhibited a mean age of 45±9 years with 70% above 40 years old. Significant improvement was also found in HbA1c (0.2 mmol/L), weight (5.1 kg), and BMI (1.8 kg/m 2 ); the lipid profile in the second phase showed better results with significant improvement in total cholesterol (7.4 mg/dL), triglyceride (20.9 mg/dL), LDL (8.8 mg/dL), and (0.9 mg/dL) increase in HDL. Conclusion This pilot has taken lifestyle interventions to real-life and it led to the favored and desired outcome. It showed that long-term guided support can help patients make the needed changes in their diet, habits, and physical activity, thus, prevent or delay the onset of Type 2 diabetes.

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“…In this section, review of recent literature related to diabetes patient classification has been carried out. Researches in [2] proposed a machine learning methodology to differentiate between patients affected with diabetes and patients not affected with diabetes based on the feature vector obtained through the diabetes patient's dataset. The method has been applied towards the real world data and evaluated.…”

Section: Related Workmentioning

confidence: 99%

Application of Deep Neural Network for Diabetes Classification and Prediction

Santhakumar¹

2019

IJRASET

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Diabetes mellitus is one of the major non-communicable diseases which have great impact on human life today. The statistics show that one in two adults with diabetes is undiagnosed, and in future there is a chance of one in 10 adults will have diabetes. By applying computational analytics on medical analytic data, the massive amount of data generated by the healthcare systems, that will be used to create medical intelligence which is drive medical prediction and forecasting. Developing medical intelligence out of the medical data available will create healthcare system to be patient centered and that will reduce medical cost and hospital readmission. Our work proposes a Neural System model for classifying patients with diabetes based on data sets with diabetic patients. The Pima Indians Diabetes Dataset is been used. In order to set the features vector, Diabetes Pedigree Function is used with multiple premises. The Neuro modeling is implemented using MATLAB Toolbox. The performances of the algorithm are analyzed in terms of specificity, precision and sensitivity. The proposed classification algorithm using comparison of SVM, Naive Bayes and Decision Tree are trained and tested on Indians Diabetes Database, proving maximum accuracy for training and testing data to make the banking and healthcare decision.

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Self-Management of Diabetes Mellitus with Remote Monitoring

Cited by 11 publications

References 13 publications

Examining disparities relating to service reach and patient engagement with COVID-19 remote home monitoring services in England: a mixed methods rapid evaluation

Examining disparities relating to service reach and patient engagement with COVID-19 remote home monitoring services in England: a mixed methods rapid evaluation

Tawazon- An Intensive Lifestyle Diabetes Prevention Program: The Saudi Arabian Pilot

Application of Deep Neural Network for Diabetes Classification and Prediction

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