MyJournals.org - Science - IJERPH, Vol. 20, Pages 5887: Self-Management of Diabetes and Associated Factors among Patients Seeking Chronic Care in Tshwane, South Africa: A Facility-Based Study (International Journal of Environmental Research and Public Health)
Background: The rising prevalence of type 2 diabetes in Australia is a public health concern, contributing to significant disease burden and economic costs. Text-message programs have been shown to improve health outcomes for people with type 2 diabetes, however they remain underutilized, and no evidence exists on their cost-effectiveness or costs of scale up to a population level in Australia. This study aimed to determine the cost-effectiveness and cost-utility of a 6-month text-message intervention (DTEXT) to improve glycated hemoglobin (HbA1c) and self-management behaviors for Australian adults with type 2 diabetes. Methods: A within-trial economic evaluation was conducted on the DTEXT randomized controlled trial. Incremental cost-effectiveness ratios (ICERs) were determined per 11 mmol/mol (1%) reduced HbA1c and per quality adjusted life year (QALY) gained, compared to usual care. Cost-effectiveness acceptability curves (CEAC) determined the probability of the intervention being c
The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 betwe
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