The Value of Measuring Diabetes Burnout

Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors

Diabetes mellitus is a chronic disease that affects over 500 million people worldwide, necessitating personalized health management programs for effective long-term control. Among the various biomarkers, glycated hemoglobin (HbA1c) is a crucial indicator for monitoring long-term blood glucose levels and assessing diabetes progression. This study introduces an innovative approach to diabetes management by integrating a mobile application and machine learning. We designed and implemented an intelligent application capable of collecting comprehensive data from diabetic patients, creating a novel diabetes dataset named DiabMini with 127 features of 88 instances, including medical information, personal information, and detailed nutrient intake and lifestyle. Leveraging the DiabMini, we focused the analysis on HbA1c dynamics due to their clinical significance in tracking diabetes progression. We developed a stacking model combining eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Extra Trees (ET), and K-Nearest Neighbors (KNN) to explore the impact of various influencing factors on HbA1c dynamics, which achieved a classification accuracy of 94.23%. Additionally, we applied SHapley Additive exPlanations (SHAP) to visualize the contributions of risk factors to HbA1c dynamics, thus clarifying the differential impacts of these factors on diabetes progression. In conclusion, this study demonstrates the potential of integrating mobile health applications with machine learning to enhance personalized diabetes management.

Identification of psychological constructs for a positive psychology intervention to assist with the adjustment to closed loop technology among adolescents living with type 1 diabetes

Aim

Adolescents have been identified as the group who struggle most with successful adjustment to closed loop technology. This study aims to identify the psychological constructs that should form part of a positive psychology intervention to assist with the adjustment to closed loop technology among adolescents living with type 1 diabetes.

Method

Qualitative document analysis was employed to integrate findings from two documents: a published ongoing intervention study and a recent phenomenological study by the authors. Reflexive thematic analysis was used to identify themes from the documents.

Findings

The following themes were identified as important psychological constructs that aid adjustment: the importance of knowledge and education; the process of positive adjustment to closed loop technology; a positive outlook; and building a relationship with diabetes.

Conclusion

Interventions are needed to assist adolescents in their adjustment to closed loop technology. The psychological constructs identified served as a starting point in designing an effective, evidence-based intervention grounded in data and theory. Knowledge and education, responsibility, identity, positive affect, gratitude, support, and trust are psychological constructs that need to be included in an intervention program.

Psychosocial comorbidities of diabetes during the COVID-19 pandemic in Iran

BACKGROUND

The psychosocial impacts of the COVID-19 pandemic are mainly focused on the general population, while pandemics do not impact the mental health of the entire population uniformly, especially vulnerable populations with underlying health conditions. This study aimed to investigate diabetes psychosocial comorbidities among Iranians with type 1 diabetes (T1D) during the COVID-19 pandemic. This study aimed to investigate diabetes psychosocial comorbidities among Iranians with type 1 diabetes (T1D) during the COVID-19 pandemic.

MATERIALS AND METHODS

This was a cross-sectional study of 212 adults with T1D in different cities in Iran. Study participants completed an online survey in April-June 2020. The survey collected self-reported data on diabetes psychosocial comorbidities (i.e. diabetes burnout, diabetes distress, and depressive symptoms). Demographic and COVID-19 data before and during the pandemic were also collected. Responses were analyzed using ordinary least squares and logistic regression methods.

RESULTS

Around 17.5% reported being tested for COVID-19 virus, 8% were diagnosed positive, 10.8% were hospitalized, and 92.9% followed precaution recommendations during the pandemic. Participants had high levels of diabetes distress (57.1%), depressive symptoms (60.8%), and diabetes burnout (mean score = 3.1 out of 5). During the pandemic, trouble paying for the very basic needs was a consistent factor increasing the risk of diabetes distress, diabetes burnout, and depressive symptoms. Lack of access to diabetes care was only associated with diabetes burnout, while diabetes hospitalization/emergency department (ED) visit was associated with diabetes distress. Existing diabetes disparities before the pandemic were also associated with higher scores of diabetes psychosocial comorbidities [accessing diabetes supplies and medications (P < 0.0001) and places for physical exercise (P < 0.0333)].

CONCLUSION

The negative impact of the COVID-19-related changes on individuals with diabetes, as one of the most vulnerable populations, must be recognized alongside the physical, financial, and societal impact on all those affected. Psychological interventions should be implemented urgently in Iran, especially for those with such characteristics.

The Role of Habit Formation and Automaticity in Diabetes Self-Management: Current Evidence and Future Applications

PURPOSE OF REVIEW

Diabetes is a chronic condition that requires consistent self-management for optimal health outcomes. People with diabetes are prone to burnout, cognitive burden, and sub-optimal performance of self-management tasks. Interventions that focus on habit formation have the potential to increase engagement by facilitating automaticity of self-management task performance. The purpose of this review is to (1) clarify the conceptualizations of habit formation and behavioral automaticity in the context of health behavior interventions, (2) review the evidence of habit in relation to behaviors relevant to diabetes self-management, and (3) discuss opportunities for incorporating habit formation and automaticity into diabetes self-management interventions.

RECENT FINDINGS

Modern habit research describes a habit as a behavior that results over time from an automatic mental process. Automatic behaviors are experienced as cue-dependent, goal-independent, unconscious, and efficient. Habit formation requires context-dependent repetition to form cue-behavior associations. Results of diabetes habit studies are mixed. Observational studies have shown positive associations between habit strength and target self-management behaviors such as taking medication and monitoring blood glucose, as well as glycemic outcomes such as HbA1c. However, intervention studies conducted in similar populations have not demonstrated a significant benefit of habit-forming interventions compared to controls, possibly due to varying techniques used to promote habit formation. Automaticity of self-management behaviors has the potential to minimize the burden associated with performance of self-management tasks and ultimately improve outcomes for people with diabetes. Future studies should focus on refining interventions focused on context-dependent repetition to promote habit formation and better measurement of habit automaticity in diabetes self-management.

Individuals with Type 2 Diabetes Mellitus Tend to Select Low-Carbohydrate, Low-Calorie Food Menus at Home on Diet Application

(1) Background: From the perspective of patient-centered care, it is important for medical professionals involved in diabetes care to know the role of choice behavior when individuals with type 2 diabetes mellitus select their meals at home. In Japan, online meal management applications are widely used to help individuals to prepare healthy, colorful, and tasty meals. (2) Objective: To assess menu selection from an online diet management application in individuals with type 2 diabetes mellitus over a period of 24 months. (3) Method: The saved data of the selected food menus on the online diet management application were analyzed. We identified specific nutritional groups of the food menus, called nutritional clusters, by clustering the multidimensional data of the nutrients after de-dimensioning them. Then, we analyzed the constitutional nutrients of each nutritional cluster with the highest and lowest frequencies of selection by the users of the application. (4) Results: In all, 9674 food menus made by 3164 people were included in the analysis, and 12 nutritional clusters were identified. Low-carbohydrate and low-calorie food clusters showed the highest selection frequency. The average caloric value of 149.7 kcal and average carbohydrate ratio of 47% in the cluster with the highest selection frequency were significantly lower than the average caloric value of 435.2 kcal and carbohydrate ratio of 63% in the cluster with the lowest selection frequency (p < 0.001, respectively). (5) Conclusion: Individuals with type 2 diabetes in this population preferred to select lower-carbohydrate and lower-calorie food menus at home using online diet management applications. To improve sustained self-management and quality of life, medical professionals may consider incorporating preferred dietary behaviors into medical management of type 2 diabetes mellitus.

Potential next-generation medications for self-administered platforms

The Coronavirus Disease (COVID-19) pandemic has reshaped clinical chronic disease management. Patients reduced the number of physical clinic visits for regular follow-up care because of the pandemic. However, in developing countries, the scattered healthcare system hindered accessibility to clinical consultation, and poorly controlled chronic diseases resulted in numerous complications. Furthermore, the longer patients suffered from the chronic disease being treated, the more physical and psychological stress they experienced. “Diabetes Burnout,” as an example, is a term to describe the phenomenon of psychological reluctance in long-term glycemic control. A comprehensive, patient-centered, and automatic drug administration and delivery model may reduce patient stress and increase compliance. Potential next-generation medication platforms, consisting of internal regulation and external interaction, may conduct autonomous dose adjustment and continuous selfmonitoring with the assistance of artificial intelligence, telemedicine, and wireless technologies. Internal regulation forms a closed-loop system in which drug administration is optimized in an implanted drug-releasing device according to a patient's physiopathological response. The other feature, external interaction, creates an ecosystem among patients, healthcare providers, and pharmaceutical researchers to monitor and adjust post-market therapeutic efficacy and safety. These platforms may provide a solution for self-medication and self-care for a wide variety of patients but may be life-changing for patients who live in developing countries where the healthcare system is scattered, as they could effectively remove healthcare barriers. As the technology matures, these self-administrated platforms may become more available and increasingly affordable, offering considerable impact to health and wellness efforts worldwide.