Causes of diabetes
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. Currently, 24 million adults are living with diabetes in Africa with the figure projected to rise by 129% to 55 million by 2045. Estimates from some sub-Saharan African countries have found that the incidence of type 1 diabetes varies from 1.5 to 10.1 per 100 000 depending on the country and age group studied (Katte et al., 2023). In 2019, the International Diabetes Federation (IDF) estimated that about 25,000 children and adolescents aged <20 years have diabetes in Africa (Katte et al., 2023). From the International Diabetes Foundation (IDF), by 2021, an estimated 716,000 adults in Uganda had diabetes with about 89% of Ugandans with diabetes neither on medication nor aware of their status(WHO, 2023). Type I Diabetes (T1D) in Uganda is growing at 10.6% each year compared with 3.7% for Type 2 Diabetes and therefore presents to the health system with difficult to treat complications(Type 1 Diabetes Index, 2024).
Causes of diabetes
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 (Sawyer et al., 2022). Type 1 Diabetes (T1D) is an autoimmune condition that causes the beta cells in the pancreas that produce insulin to be destroyed (Lucier and Weinstock, 2023). Insulin pump therapy, several daily insulin injections, or the use of an automated insulin delivery system are necessary for T1D patients to maintain their insulin levels for this is a chronic metabolic disorder characterized by the presence of elevated levels of blood glucose, which can result into serious damage to the heart, blood vessels, eyes, kidneys, and nerves (Lucier and Weinstock, 2023). This disorder requires patients to perform critical self-management tasks multiple times per day (Campbell et al., 2018). 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.
Causes of diabetes
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).
Causes of diabetes
To address the limitations of human recall and bias in health behavior research, ecological momentary assessment (EMA) methods have been developed and successfully utilized in a range of health conditions such as diabetes, heart diseases and hypertension (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 methods provide a more proximal, and often more accurate, technology-mediated method to monitor and assess the contexts, subjective experiences, and processes that surround health decisions in daily life (Zhang et al., 2022). In particular, EMA methods that provide more relevant and frequent observations per person and generates rich data to assess correlates of health behavior more accurately and identify novel correlates for intervention such as continuous glucose monitoring (CGM) have been developed for therapeutic benefits in diabetes management. The usage of real-time CGM systems has been demonstrated to reduce the number of severe hypoglycemic events for T1D subjects with multiple daily injection (MDI). As a wearable device that automatically measures glucose levels with a fixed frequency (e.g., five minutes), CGM can be combined with an insulin pump as sensor-augmented therapy or an artificial pancreas for closed-loop glycemic control (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.
Causes of diabetes
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). Accurate glucose prediction is, therefore, a useful tool to enable proactive interventions and timely medication administration and education to enhance T1D self-management. . .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.
Causes of diabetes
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. At the master’s level, a research I conducted on Type 2 Diabetes showed that health technologies are largely feasible and acceptable. 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 methodologies have been employed in various diabetes-related tasks, from screening to blood glucose classification. With Deep learning, hierarchical layers to process input data, making it especially powerful for tasks such as image analysis and natural language processing (Tahir and Farhan, 2023).
