A review on diabetes patient lifestyle management using mobile application

Basar, Abul; Alvi, Hassan Nomani; Bokul, Gazi Nowrin; Khan, Mohammad Shahriar; Anowar, Farzana; Huda, Mohammad Nurul; Mamun, Khondaker A.

doi:10.1109/iccitechn.2015.7488100

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…The function of this features is to control and calculate calories and physical activity, along with diagnosing based on physical symptoms and laboratory result. The features were developed based on research that revealed about the patient preference of existing diabetes mellitus applications, such as food, physical activity, tracking glucose, and some of the important features in diabetes mellitus self-management like saving patient's record and calculating calories based on weight and height [2] There are three levels of users in this expert system registered user, guest, and administrator. Registered user can input their recent data of laboratory result and physical activity, calculate their calories, and save their diagnose result.…”

Section: Resultsmentioning

confidence: 99%

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The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

2020

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Diabetes Mellitus (DM) is a degenerative disease that has many causative factors. DM patients cannot recover completely but they can maintain the stability of their glucose level by following a healthy lifestyle. The goal of this research is to make an integrated system for people to promote a healthy lifestyle. This system also allows people to monitor the changes in their daily health conditions regularly. Main features in this system diagnose, treatment, guidelines for a healthy lifestyle, including a feature for counting and saving daily calorie consumption and physical activity. The system using the backward inference method to get the conclusion from a set of rules in the knowledge base. The verification using a query, validation with using recall and precision, and usability testing using a questionnaire, was conducted to test the performance of the system. The result of the testing shows that the system gives a good response to all queries given and the user also satisfied with the system. The recall value is 1 with the precision is 0.658. This precision value also promotes the inference process used in the system, which every user will suspect has DM when they start to use the diagnose feature.

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Section: Resultsmentioning

confidence: 99%

“…The system is developed using PHP and MySQL for programming language and database respectively. The system was developed to give selfmanagement for diabetic patients [2]…”

Section: System Developmentmentioning

confidence: 99%

The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

2020

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“…A major wellness concern and pandemic disease with a high incidence in both emerging and industrialized nations, diabetes impacts people all over the globe [24], [25]. As per the World Health Organization, diabetes is a chronic disorder with a variety of causes.…”

Section: A Diabetes Mellitusmentioning

confidence: 99%

A Comprehensive Review of Modern Methods to Improve Diabetes Self-Care Management Systems

Alsayed,

Ismail,

Hasan

et al. 2023

IJACSA

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Diabetes mellitus has become a global epidemic, with an increasing number of individuals affected by this chronic metabolic disorder. Effective management of diabetes requires a comprehensive self-care approach, which encompasses various aspects like monitoring blood glucose levels, adherence to medication, modifications in lifestyle, and regular healthcare monitoring. Innovative techniques for bettering diabetic self-care management have been developed recently as a result of developments in technology and healthcare systems. This comprehensive review examines the modern methods that have emerged to enhance diabetes self-care management systems. The review focuses on the integration of technology, Behavioural Change Techniques (BCTs), behavioural health theories such as Transtheoretical Model (TTM), the Health Belief Model (HBM), Theory of Reasoned Action/Planned Behaviour (TPB), Social Cognitive Theory (SCT) techniques to promote optimal diabetes care outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 standards were followed in this research's documentation. The Systematic Literature Review (SLR) period, which covered 2009 to 2020, was used to acquire the most recent complete review. Overall, the SLR results show that self-care interventions have a favourable impact on behaviours modification, the encouragement of good lifestyle habits, the lowering of blood glucose scales, and the accomplishment of significant weight loss. According to the review's findings, treatments for diabetic self-management that included behavioural health theories and BCTs in their creation tended to be more successful. In order to assist academics and practitioners with the creation of future applications, the restriction and future direction were finally defined. After recognising the potential for combining BCT methodologies and theories, it creates self-management interventions. Depending on these recognised cutting-edge mechanisms, the current SLR can assist application developers create a model to construct efficient self-care interventions for diabetes.

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“…In references [2,6], the authors developed a meal recommender system using the knowledge-based approach where the recommended meal will meet the users' needs based on knowledge of the required nutrients obtained from the user's details. They implemented their system using Genetic algorithm where a random number of meals were selected, and a fitness function calculated using the difference between calories in meals and calories required by the user.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Type 2 diabetes (formerly called noninsulin-dependent, or adult-onset) results from the body's ineffective use of insulin. Type 2 diabetes comprises what the majority of people with diabetes around the world have and is mostly the result of excess body weight and physical inactivity [1,[5][6][7][8]. According to the World Health Organization (WHO), to help prevent type 2 diabetes and its complications, people should achieve and maintain healthy body weight; be physically active-at least 30 minutes of regular, moderate-intensity activity on most days.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Diabetes Management System Using Machine Learning

Sowah

Bampoe-Addo

Armoo

et al. 2020

International Journal of Telemedicine and Applications

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This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (with k=5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.

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A review on diabetes patient lifestyle management using mobile application

Cited by 14 publications

References 18 publications

The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

A Comprehensive Review of Modern Methods to Improve Diabetes Self-Care Management Systems

Design and Development of Diabetes Management System Using Machine Learning

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