Digital technology and its effects on knowledge improvement for diabetes management: An integrative review

Improving the management of type 2 diabetes in China using a multifaceted digital health intervention in primary health care: the SMARTDiabetes cluster randomised controlled trial

Background

There is limited evidence, mainly from high-income countries, that digital health interventions improve type 2 diabetes (T2DM) care. Large-scale implementation studies are lacking.

Methods

A multifaceted digital health intervention comprising: (1) a self-management application ('app') for patients and lay 'family health promotors' (FHPs); and (2) clinical decision support for primary care doctors was evaluated in an open-label, parallel, cluster randomized controlled trial in 80 communities (serviced by a primary care facility for >1000 residents) in Hebei Province, China. People >40 years with T2DM and a glycated haemoglobin (HbA1c) ≥7% were recruited (∼25/community). After baseline assessment, community clusters were randomly assigned to intervention or control groups (1:1) via a web-based system, stratified by locality (rural/urban). Control arm clusters received usual care without access to the digital health application or family health promoters. The primary outcome was at the participant level defined as the proportion with ≥2 "ABC" risk factor targets achieved (HbA1c < 7.0%, blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L) at 24 months.

Findings

A total of 2072 people were recruited from the 80 community clusters (40 urban and 40 rural), with 1872 (90.3%) assessed at 24 months. In the intervention arm, patients used FHPs for support more in rural than urban communities (252 (48.6%) rural vs 92 (21.5%) urban, p < 0.0001). The mean monthly proportion of active app users was 46.4% (SD 7.8%) with no significant difference between urban and rural usage rates. The intervention was associated with improved ABC control rates (339 [35.9%] intervention vs 276 [29.9%] usual care; RR 1.20, 95% CI 1.02-1.40; p = 0.025), with significant heterogeneity by geography (rural 220 [42.6%] vs 158 [31.0%]; urban 119 [27.9%] vs 118 [28.6%]; p = 0.022 for interaction). Risk factor reductions were mainly driven by improved glycaemic control (mean HbA1C difference -0.33%, 95% CI -0.48 to -0.17; p = 0.00025 and mean fasting plasma glucose difference -0.58 mmol, 95% CI -0.89 to -0.27; p = 0.00013). There were no changes in blood pressure and LDL-cholesterol levels.

Interpretation

A multifaceted digital health intervention improved T2DM risk factor control rates, particularly in rural communities where there may be stronger relationships between patients and doctors and greater family member support.

Funding

National Health and Medical Research CouncilGlobal Alliance for Chronic Diseases (ID 1094712).

Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review

INTRODUCTION

Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.

METHOD

A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.

RESULTS

The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).

CONCLUSION

The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.

Use of Online Communities among People with Type 2 Diabetes: A Scoping Review

PURPOSE OF REVIEW

People with diabetes require continuous self-monitoring and face numerous decisions in their day-to-day lives. Therefore, on many occasions, they need more support than that provided by health professionals. In this context, peer support in online diabetes communities could be a useful tool. The purpose of the review is to describe, analyze and synthesize the available evidence on the use and health out-comes of online communities for people with type 2 diabetes mellitus. A scoping review was conducted in accordance with the Joanna Briggs Institute guidelines. Searches were performed PubMed, Web of Science, CINHAL, Scopus and Cochrane databases.

RECENT FINDINGS

From 1821 identified documents, 6 articles were included. These studies explored the characteristics of diabetes online communities and the population features. Besides, the results were classified according to whether they were clinical, psychosocial, or addressed people's experiences with the online community. The analysis underscores their value in facilitating communication, improving diabetes management, and enhancing psychosocial well-being. Future investigations should prioritize longitudinal assessments to elucidate the sustained impact of community engagement and optimize user participation for enhanced patient outcomes. The growing relevance of new technologies has led to a significant number of individuals with chronic illnesses seeking peer support. Online health communities have emerged as virtual spaces where individuals with shared health interests interact and form relationships. Within these digital spaces, individuals can engage in peer interaction, observe behaviors, and mutually benefit, potentially leading to improved attitudes toward the disease.

Longitudinal Relationship between Cognitive Function and Health-Related Quality of Life among Middle-Aged and Older Patients with Diabetes in China: Digital Usage Behavior Differences

BACKGROUND

Cognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million.

METHODS

Cohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship.

RESULTS

The results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior.

CONCLUSIONS

Unidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function.