Burden of disability in type 2 diabetes mellitus and the moderating effects of physical activity

The Role of Tele-Exercise for People with Type 2 Diabetes: A Scoping Review

BACKGROUND

Supervised exercise interventions tend to be more effective than unsupervised exercises or physical activity advice alone. However, people with type 2 diabetes may find it difficult to attend supervised exercise interventions due to several obstacles. Tele-exercise, or utilizing technology to deliver home-based exercise, might be a solution.

OBJECTIVE

This scoping review aimed to explore clinical trials investigating the impact of tele-exercise interventions in individuals with type 2 diabetes Methods: Four electronic databases were searched for the period up to January 2024 for clinical trials investigating the impact of tele-exercise on health-related outcomes in adults with type 2 diabetes.

RESULTS

Seven trials involving 460 individuals with type 2 diabetes met the inclusion criteria. In these trials, combined aerobic and resistance exercise programs were the main types delivered remotely. To deliver such programs, both synchronous (n = 4) and asynchronous (n = 3) delivery modes were adopted. Regardless of the delivery mode, all tele-exercise interventions led to improvements in various factors related to type 2 diabetes and its complications, including glycemic control, blood lipids, body composition, functional capacity, muscle strength, and quality of life. The improvements were also found to be as effective as those of supervised exercise.

CONCLUSIONS

Tele-exercise interventions seem to be feasible and as effective as supervised exercise interventions in terms of improving glycemic control, blood lipids, functional capacity, muscle strength, body composition, and quality of life for people with type 2 diabetes.

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell's concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744-0.792) and 0.745 (95% CI, 0.669-0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30-54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.