Research consultancy in Uganda
“A Deep Learning Model for self-management, adherence and Education among Type 1 Diabetes Patients”
CHAPTER ONE
Introduction
This chapter presents the background of the study, the problem statement, purpose, objectives of the study, research questions, study scope, justification of the study, significance.
For 2,000 years’ diabetes has been recognized as a devastating and deadly disease (Mahaffey, 2019), In the first century A.D. a Greek physician, Aretaeus, described the destructive nature of the affliction, which he named “diabetes” from the Greek word for siphon (Mahaffey, 2019).
Physicians in ancient times, like Aretaeus, recognized the symptoms of diabetes but were powerless to treat it effectively (Petersmann et al., 2019). In the 17thcentury a London physician, Dr. Thomas Willis, determined whether his patients had diabetes or not by sampling their urine. If it had a sweet taste he would diagnose them with diabetes mellitus-honeyed diabetes. This method of monitoring blood sugars went largely unchanged until the 20th century (Kato et al., 2019).Before the discovery of the insulin little could be done for patients suffering from diabetes. Low calorie diets prolonged their lives but left them weak and near starvation (Yuan et al., 2019), But in 1921, doctors in Canada treated patients dying of diabetes with insulin and managed to drop high blood sugars to normal levels. Since then, medical breakthroughs have continued to prolong and ease the life of people with diabetes (Lowe et al., 2019). In the ’50s, it was discovered that there were two types of diabetes: “insulin sensitive” (type I) and insulin insensitive (type II). Two thousand years have passed since Aretaeus spoke of diabetes as the mysterious sickness. It has been a long and arduous process of discovery, as generations of physicians and scientists have added their collective knowledge to finding a cure (Sattar et al., 2019). It was from this wealth of knowledge that the discovery of insulin emerged in a small laboratory in Canada. Since then, medical innovations have continued to make life easier for people with diabetes (Pivari et al. 2019).
Global diabetes cases continue to soar from 529 million to 1.3 billion by 2050 (Ong et al., 2023). Prevalence has been rising more rapidly in low- and middle-income countries especially in Asia and Africa than in high-income countries. In Africa, there are currently 24 million adult diabetics; by 2045, that number is expected to increase by 129% to 55 million (WHO analysis, 2024). The incidence of type 1 diabetes varies from 1.5 to 10.1 per 100 000 depending on the nation and age group analyzed, according to estimates from various sub-Saharan African countries (Katte et al., 2023). According to estimates from the International Diabetes Federation (IDF), around 25,000 African children and adolescents under the age of twenty-one had diabetes in 2019.(Katte et al., 2023). According to the International Diabetes Foundation (IDF), 716,000 persons in Uganda were predicted to have diabetes by 2021; of those, 89% were not aware of their condition or on medication (WHO, 2023).
There has been a significant evolution in system modeling and intelligence since the introduction of early deep learning models. Deep learning methods rapidly emerged and expanded their applications across various scientific and engineering domains (Micikevicius et al., 2022). Health informatics, energy, urban informatics, safety, security, hydrological systems modeling, economics, bioinformatics, and computational mechanics were among the first areas to leverage deep learning techniques. State-of-the-art surveys on data-driven methods and machine learning algorithms indicate that deep learning, along with ensemble and hybrid machine learning methods, represents the future of data science. Further comparative studies show that deep learning models and hybrid machine learning models often outperform conventional machine learning models (Sharma, & Guleria, 2022).
Deep learning methods are rapidly evolving to achieve higher performance. The literature includes numerous review papers on the advancing algorithms in specific application domains such as renewable energy forecasting, cardiovascular image analysis, super-resolution imaging, radiology, 3D sensed data classification, multimedia analytics, sentiment classification, text detection, transportation systems, activity recognition in radar, hyperspectral analysis, medical ultrasound analysis, image cytometry, and Apache Spark (Singh et al., 2022).
Diabetes is a chronic disease that affects how somes’ body processes blood sugar (glucose), which is the primary source of energy for thecells (Foretz, Guigas, &Viollet, 2019), Insulin is a hormone produced by the pancreas that regulates blood sugar levels, and in people with diabetes, the body either doesn’t produce enough insulin or doesn’t use it effectively (Glovaci et al., 2019), resulting in high blood sugar levels, there are several types of diabetes, including; Type 1 diabetes: This type of diabetes occurs when the immune system attacks and destroys the cells in the pancreas that produce insulin. Type 1 diabetes is usually diagnosed in children and young adults, and people with this type of diabetes need to take insulin injections or use an insulin pump to manage their blood sugar levels; Type 2 diabetes: This type of diabetes occurs when the body becomes resistant to insulin or doesn’t produce enough insulin to maintain normal blood sugar levels. Type 2 diabetes is the most common type of diabetes and is usually diagnosed in adults, although it is increasingly being diagnosed in children and adolescents; Gestational diabetes: This type of diabetes occurs during pregnancy and usually goes away after the baby is born (Zelniker et al., 2019), However, women who have had gestational diabetes are at increased risk of developing type 2 diabetes later in life.Other types of diabetes: There are several other rare types of diabetes, including monogenic diabetes and cystic fibrosis-related diabetes (Kenny, & Abel, 2019).Diabetes can cause a variety of health problems if left untreated, including heart disease, kidney disease, nerve damage, and blindness. However, with proper management, people with diabetes can live long, healthy lives. Treatment usually involves a combination of medication, lifestyle changes (such as a healthy diet and regular exercise), and monitoring blood sugar levels (Cappon et al., 2019).
In Uganda, the annual growth rate of Type 1 Diabetes (T1D) is 10.6%, while Type 2 Diabetes grows at a rate of 3.7%. As a result, T1D patients often present to the health system with challenging-to-treat comorbidities(Type 1 Diabetes Index, 2024). For young people with diabetes, living successfully with Type 1Diabetes (T1D) is particularly hard due to many potential psychosocial and contextual barriers to self-management and adherence (Sawyer et al., 2022).
Scholars state that individuals with type 1 diabetes (T1D) must maintain their insulin levels through insulin pump therapy, multiple daily insulin injections, or the use of an automated insulin delivery system. T1D is a chronic metabolic disorder marked by elevated blood glucose levels, which can cause serious damage to the kidneys, heart, blood vessels, eye , nerves, and kidneys (Lucier and Weinstock, 2023). This condition requires patients to perform critical self- management tasks multiple times per day through the Two key self-management tasks in Type 1 Diabetes are frequent monitoring of blood glucose (BG) and administering insulin (Campbell et al., 2018). These tasks help manage glycemic control to avoid or delay serious short- and long-term consequences, such as retinopathy, neuropathy, and mortality. Mealtimes are a critical time for diabetes self-management (Mulvaney et al., 2019). The management of glucose levels and education is necessary to prevent TID related complications (Campbell et al., 2018). Achieving these glucose targets and awareness is imperative to reduce an increased risk of hypoglycaemia due to the limitations of subcutaneous insulin delivery; hence, diabetes self-management is a critical element of care. Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve self-management skills is an essential way to help patients enhance their metabolic control and quality of life.
Artificial intelligence is the science of making machines that can think like humans (Li et al., 2020, 2022). This AI technology enable such as Medical Image Analysis, Virtual Assistants, Predictive Analytics, Chatbots, Automated Administrative Tasks and Wearable Devices and Sensors have made significant progress in transforming available genetic data and clinical information into valuable knowledge (Li et al., 2020, 2022) . AI also helps to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management (Li et al., 2020). To identify problems related to self-management, patients, caregivers, and clinicians rely on blood glucose and insulin administration data from devices along with a patient recall of behavioural, emotional, and/or contextual events that could pose barriers to self-management (Zhang et al., 2022) . However, utilizing retrospective memory or recall for events that are days or weeks in the past has been identified as generally unreliable and potentially biased. Unreliable recall of events in diabetes problem-solving could result in modifications to the insulin regimen that are not based on reliable information (Sawyer et al., 2022).
Ecological momentary assessment (EMA) techniques have been created and effectively applied in a variety of health disorders such as diabetes, heart illnesses, and hypertension in order to solve the limits of human recall and bias in health behaviour research (Zhang et al., 2022). In contrast to traditional assessment methods, EMA utilizes more frequent and in-vivo ambulatory assessment of factors that impact health behaviors and decision-making (Zhang et al., 2022). EMA techniques offer a more direct and frequently more precise technology-mediated way to track and evaluate the situations, personal experiences, and decision-making processes that surround health decisions in day-to-day living (Zhang et al., 2022). Specifically, EMA techniques that yield richer data and more frequent and relevant observations per individual have been developed for therapeutic benefits in diabetes management. These techniques help identify novel correlates for interventions like continuous glucose monitoring (CGM) and improve the accuracy of health behaviour correlation assessments. (Friedman et al., 2023). Smartphone apps to log daily events and calculate bolus insulin are increasingly being adopted to successfully reduce the daily burden associated with T1D self-management (Friedman et al., 2023). Other wearables, such as wristbands, have been used in recent literature to estimate physical activity for T1D subjects (Zhu et al., 2022). However, there are currently no integrated platforms synchronized with wearable technologies and apps for decision making and clinical efficacy(Zhu et al., 2022). The use of these technologies continue to yield a substantial amount of granular data and have boosted machine learning-based algorithms.
Despite their exceptional performance across a growing range of problems, deep learning models that excel on average often make systematic errors, struggling with semantically coherent subsets of data. A landmark example is the Gender Shades study, which revealed that vision models for gender recognition have disproportionately high error rates for images of black women. AI systems have also demonstrated poor performance for marginalized groups in areas such as object recognition, speech recognition, mortality prediction, and recruiting tools (Alanazi et al., 2022), Other systematic errors can be harder to anticipate. For instance, medical imaging classifiers may be sensitive to changes in imaging hardware; essay scoring software might give high scores to long, poorly written essays; and visual question-answering systems can fail when questions are rephrased. Recognizing and mitigating these errors is crucial to avoid designing systems that exhibit discriminatory or systematically unreliable behavior (Wang et al., 2022).
These challenges have led the community to develop better tools for testing model performance, establish clearer standards for reporting model biases, and create various methods for training more equitable or robust models. Even when it is difficult or impossible to make repairs, identifying and flagging edge cases where systems fail can help expert users work around an algorithm’s flaws (Saba et al., 2022).
However, these methods require practitioners to recognize and label well-defined groups in their datasets ahead of time, which inevitably overlooks semantically related sets of inputs not identified in advance. While it is essential for practitioners to explicitly assess model performance on sensitive subpopulations, it is extremely challenging to anticipate all types of inputs on which models might systematically fail, such as specific age groups, poses, backgrounds, or lighting conditions (Gupta, & Agrawal, 2022).
Furthermore, Previous literature explored several classic machine-learning approaches for the prediction of glucose levels or glycemic events using prediction horizons between 15- and 60-min (Zhu et al., 2022). However, the performance of physiological and rule-based prediction models is still limited by the influence of various external factors and high inter and intra-subject variability on glucose dynamics (Zhu et al., 2022) . It is well-accepted that self-management is essential to reduce the risks of chronic complications in diabetes patients (Li et al., 2020, 2022). Diabetes education is a system-wide and individualized way to ensure the effectiveness of patient self-management (Li et al., 2020, 2022). Considering the variety of individual needs, goals, and life experiences, patients should be educated with fine-tuned knowledge and skills that fit their situation. Mobile health (mhealth) and Electronic health (eHealth) tools have been embraced in chronic disease management (Mugabirwe et al., 2021). In fact, health technology with different models, frameworks and e-health tools such as such as PositiveLinks Interventions (Mugabirwe et al., 2021) and short message service (SMS) texting(Asiimwe et al., 2011; Siedner et al., 2012) have been embraced to help in the self-management of non-communicable diseases. Moreover, by 2017, more than 318,000 mobile health applications were available to consumers worldwide with Diabetes apps accounted for 16% of the total number of disease-specific apps available to consumers (Alkawaldeh, Choi and Jacelon, 2020). These Diabetes apps vary in the functions they provide, including tracking blood glucose measurements, nutrition database and carbohydrate tracking, physical activity and weight tracking, sharing data with clinicians or peers, social support, messaging, and reminders (Alkawaldeh, Choi and Jacelon, 2020). However, most of these technologies address type 2 diabetes, and there fewer literatures in low resource countries like Uganda especially on TID self-management and educative messages using health technologies.
AI techniques have been used for a number of diabetes-related activities, including blood glucose classification and screening. Deep learning is particularly effective for applications like image analysis and natural language processing because it uses hierarchical layers to interpret input data.
(Tahir and Farhan, 2023). This research therefore proposes a deep learning model that is able to synchronize the real-time data from TID patients’ data to improve decision support towards enabling timely self-management and educative messages of TID.
Diabetes mellitus, particularly Type 2 Diabetes Mellitus (T2DM), is a significant and escalating non-communicable disease in Uganda and across sub-Saharan Africa, contributing to a substantial disease burden among older adults. T2DM patients face a doubled risk of coronary heart disease and have poor prognoses, with many cases going undiagnosed and untreated. This silent killer necessitates active prevention and management strategies to curb its rising prevalence.
In Uganda, the situation is further compounded for Type 1 Diabetes (T1D) patients, who face severe challenges in managing their condition (Bulemela et al., 2023). Access to insulin and necessary medical supplies has deteriorated, resulting in decreased insulin injection frequency, blood glucose monitoring, and meal frequency, which complicates adherence and effective management. The lifelong commitment required for insulin therapy involves meticulous monitoring and frequent administration of insulin, which is mentally and physically demanding (Ashique et al., 2024). These challenges lead to heightened stress, anxiety, and a decline in quality of life, especially in home settings with limited medical support.
Current diabetes interventions, such as continuous glucose monitoring and digital health applications, are often inaccessible to patients in low-resource settings due to their complexity and cost. These gaps in accessibility and the lack of tailored self-management support exacerbate the difficulties faced by individuals managing diabetes (Surlari et al., 2023). Furthermore, disparities in healthcare resources in low- and middle-income nations intensify these challenges, making it imperative to develop solutions that empower patients and their caregivers with enhanced tools for remote management (Ashique et al., 2024).
Therefore, this study aims to design a Deep Learning Model for self-management of Type 1 Diabetes. The model will enhance remote management, adherence, and education of individuals dealing with T1D through a robust system that integrates wearable-generated data. By addressing the specific needs and challenges of T1D patients in Uganda and similar low-resource settings, the model seeks to improve glycemic control, reduce the risk of complications, and ultimately enhance the quality of life for these individuals.
Diabetes is one of the top non-communicable diseases in Africa, contributing to the increasing disease burden among the old adults. In Uganda, diabetes mellitus is a silent major non communicable disease and the T2DM is a major and growing problem in the country (Gatimu et al., 2016). Individuals with T2DM have a two times higher danger of acquiring coronary illness than whatever remains of the populace, and their prognosis is very poor (Tuomilehto et al., 1997). T2DM today, is found in nearly all population and epidemiological suggestion suggests that without active prevention and management agendas, the prevalence will remain to upsurge worldwide (Unwin et al., 2009). In Gatimu et al.2010, work it was stated that in sub-Saharan Africa, 21.5 million individuals are living with diabetes prompting roughly a large portion of a million diabetes-related passing’s in 2013 (IDF, 2013). Diabetes mellitus is one of the still most important non-communicable disease and the T2DM is a key rising problem in the country. T2DM is a silent killer as several of the cases go undiagnosed (de-Graft Aikins, 2007).
In Uganda, Type 1 Diabetes patient’s access to insulin syringes has significantly worsened, and Insulin injection frequency, blood glucose monitoring and meal frequency significantly decreased causing adherence and management challenges (Sseguya et al., 2023). Controlled and self-managed Type 1 Diabetes to reduce the risks of chronic complications in diabetes patients through self-management, adherence and education in an individualized way to ensure the effectiveness of patient is very key (Maina, Pienaar and Reid, 2023). Considering the variety of individual needs, goals, and life experiences, patients should be educated with fine-tuned knowledge and skills that fit their situation.
Patients with type 1 diabetes must follow a number of treatment guidelines, including food, insulin dosage schedules, and blood glucose monitoring, among others, since these all improve the course of the condition (Kyokunzire and Matovu, 2018). However, the lifelong commitment of Type 1 diabetes patients to insulin therapy is very complex, because individuals must meticulously monitor their blood glucose levels and administer insulin multiple times a day to maintain glycemic control (Janež et al., 2020; Zhu et al., 2022). This routine, though essential, can be mentally and physically demanding, and lead to heightened stress and anxiety levels, affecting their quality of life. This becomes even more burdensome especially in home settings where access to immediate medical support may be limited (Janež et al., 2020; Zhu et al., 2022). These challenges contribute to the persistent escalation of diabetes-related complications and mortality, as well as the lack knowledge, and encounter obstacles related to timely decision-making support.
Current diabetes interventions, including continuous glucose monitoring, digital health apps, and AI-driven recommendations, face gaps in accessibility to patients in low resource setting, because many need resources and are complex in nature, and barely sustainable (Kumbara et al., 2023), as they do not address self-management, adherence and inadequate knowledge regarding management of TID. Furthermore, disparities in healthcare resources, especially in low- and middle-income nations, intensify these challenges. Limited access to advanced monitoring technologies and specialized medical care exacerbates the difficulties faced by individuals managing diabetes, making it imperative to develop solutions that empower them and their caregivers with enhanced tools for remote management. This study, therefore, aims to design a Deep Learning Model for self-management for Type 1 Diabetes to enhance remote management, adherence and education of individuals dealing with type 1 diabetes through a robust model that integrates wearable-generated data. This technology will enable patients self-manage, adhere to their medication and therapy as well as have access to educative information ultimately improving the overall outcomes and quality of life for those affected by type 1 diabetes.
Main Objective
The study aims at contributing towards improving Type 1 Diabetes (T1D) self-management by designing a deep learning model embedded into an application for self-management, adherence and education among TID patients.
Specific Objectives
- To analyze deep learning models in order to find out their strength and weaknesses during self-management of type 1 diabetes and inform the design of deep learning model.
- To design and develop a deep learning model incorporated into an app for self-management, adherence and education among Type 1 Diabetes patients.
- To assess the preliminary impact of the model on self-management, adherence, and knowledge among Type 1 Diabetes patients using the UTAUT model, it is important to delve deeper into the concepts of adherence and knowledge in the context of diabetes management.
Research Question
- Type 1 diabetes (T1D) is a chronic condition characterized by the autoimmune destruction of insulin-producing beta cells in the pancreas, leading to lifelong dependency on exogenous insulin. Managing T1D is complex and requires continuous monitoring of blood glucose levels, carbohydrate intake, and physical activity, along with regular insulin administration. This intricate balancing act poses significant challenges for patients, often leading to both acute and chronic complications if not managed properly. Consequently, effective management strategies are crucial to maintaining glycemic control and preventing complications, therefore it is worth examining What are the strengths and weaknesses of deep learning models in management of type 1 diabetes?
- Self-management is paramount in the treatment of Type 1 Diabetes, as it empowers patients to take control of their condition through informed decision-making and proactive health behaviors. Successful self-management involves regular blood glucose monitoring, insulin dosage adjustments, dietary planning, and physical activity regulation. Education on these aspects is essential, enabling patients to understand how different factors influence their blood glucose levels and how to respond to fluctuations. Adherence to these self-management practices can significantly reduce the risk of complications and improve overall quality of life for individuals with Type 1 Diabetes, therefore How can a deep learning model incorporated into an app for self-management, adherence and education among Type 1 Diabetes patients be designed and developed?
The advent of technology, particularly deep learning models, has opened new avenues for enhancing T1D management. Deep learning models excel at analyzing large datasets and identifying patterns, making them suitable for predicting blood glucose levels, optimizing insulin dosing, and providing personalized recommendations. These models can process continuous glucose monitoring (CGM) data, dietary intake, and physical activity information to offer real-time insights and predictive analytics, thus aiding in more precise and timely decision-making, what is the preliminary impact of the model on self-management, adherence and knowledge among Type 1 Diabetes patients?
Significance of the study
Diabetes has been a recognized medical challenge for over 2,000 years, with its destructive nature first described by the Greek physician Aretaeus in the first century A.D. Despite significant advancements in diabetes treatment and management, it remains a leading cause of morbidity and mortality globally, particularly in low- and middle-income countries (Mahaffey, 2019; Petersmann et al., 2019). The discovery of insulin in 1921 marked a major breakthrough, transforming diabetes management and enabling many patients to live longer and healthier lives (Yuan et al., 2019; Lowe et al., 2019). However, the burden of diabetes continues to escalate, with global cases projected to rise from 529 million to 1.3 billion by 2050, disproportionately affecting regions such as Africa and Asia (Ong et al., 2023; WHO, 2024).
In Uganda, the diabetes epidemic is particularly concerning, with a significant increase in both Type 1 and Type 2 Diabetes cases. The annual growth rate of Type 1 Diabetes (T1D) in Uganda is 10.6%, leading to severe complications and comorbidities due to inadequate self-management and healthcare infrastructure (Type 1 Diabetes Index, 2024). The challenges faced by T1D patients in Uganda are exacerbated by limited access to insulin, essential medical supplies, and effective diabetes education, resulting in poor disease management and a decline in quality of life (Ashique et al., 2024).
Recent advancements in artificial intelligence (AI) and deep learning offer promising solutions to improve diabetes management. Deep learning techniques, particularly those involving real-time data integration from wearable devices, have shown potential in enhancing decision support systems, enabling timely self-management, and providing personalized educative messages for diabetes patients (Li et al., 2020; Zhang et al., 2022). These technologies can overcome the limitations of traditional diabetes management approaches, which often rely on unreliable patient recall and retrospective data analysis.
This study aims to develop a deep learning model specifically designed for the self-management of Type 1 Diabetes in Uganda. By leveraging real-time data from wearable devices, this model seeks to enhance glycemic control, adherence, and patient education. The proposed model addresses the unique challenges faced by T1D patients in low-resource settings, offering a cost-effective and accessible solution to improve diabetes management and reduce the risk of complications.
The significance of this study lies in its potential to transform diabetes care in Uganda and similar low-resource settings. By providing a robust, data-driven decision support system, the deep learning model can empower patients with the tools and knowledge needed for effective self-management. This, in turn, can lead to improved health outcomes, reduced healthcare costs, and a better quality of life for individuals living with Type 1 Diabetes. Furthermore, this research contributes to the broader field of health informatics by demonstrating the practical applications of AI and deep learning in chronic disease management, paving the way for future innovations in healthcare technology.
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