A Mobile-Based Intervention for Glycemic Control in Patients With Type 2 Diabetes: Retrospective, Propensity Score-Matched Cohort Study

Validation of the Chinese version of the diabetes health profile to predict the impact of mobile health education on quality of life in type 2 diabetes patients

Purpose

The Diabetes Health Profile (DHP18), initially created in the United Kingdom, currently lacks a Chinese version. This study endeavors to authenticate the Chinese adaptation of the DHP18 and assess the influence of mobile health (mHealth) education intervention on the quality of life of individuals living with diabetes.

Patients and methods

The study included 470 Type 2 diabetes Mellitus (T2DM) patients (204 men, 266 women), spanning an age range of 19-79 years, with an average age of 54 ± 12.40 years. Data analysis employed Jamovie and Mplus software. Moreover, test-retest reliability was evaluated in 52 hospitalized T2DM patients through two repeated measurements taken 4 weeks apart.

Results

The Chinese version DHP18 scale exhibited high reliability, evidenced by a Cronbach's alpha of 0.88, and coefficient of test-retest reliability of 0.84. Individual subscales also demonstrated strong reliability, ranging from 0.76 to 0.84, with test-retest reliability spanning from 0.71 to 0.74. Confirmatory Factor Analysis (CFA) employing a three-factor structure (χ2 = 294.69, GFI = 0.92, TLI = 0.91, RMSEA = 0.05, SRMR = 0.06) validated the scale's construct validity. Notably, there was a statistically significant difference (p < 0.05) in the quality of life between Type 2 diabetes patients using mHealth education intervention and those without mHealth education intervention. Mediation analysis revealed that Appraisal of Diabetes (ADS) and Self-Management Efficacy (SED) mediated the effects of Psychological Distress (PD) and Behavior Adherence (BA) on quality of life, both significant direct and indirect effects (p < 0.001). In addition, Dietary Abstinence (DE) displayed significant overall impact (β = -0.13, p < 0.001) and indirect influence (β = -0.10, p < 0.01) on diabetic patients' quality of life, though lacking a significant direct effect (β = -0.03, p = 0.38).

Conclusion

The Chinese version of the Diabetes Health Profile Scale meets stringent psychometric standards and stands as an appropriate measurement tool for Chinese T2DM patients, maintaining comparable results to the original scale's structure. The mHealth education intervention yielded a notably positive impact on the quality of life among T2DM patients. Mediation analysis revealed that the three dimensions of the DHP were mediated by Appraisal of Diabetes and Diabetes Self-Management Efficacy, partially mediated by Psychological Distress and Behavior Adherence, and fully mediated by Dietary Abstinence, providing insight into the positive effects of the mHealth model on the quality of life of diabetic patients.

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study

BACKGROUND

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control.

OBJECTIVE

To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support.

METHODS

In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels.

RESULTS

Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12 weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses.

CONCLUSIONS

The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes.

TRIAL REGISTRATION

Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

Portrait for Type 2 Diabetes with Goal-Achieved HbA1c Using Digital Diabetes Care Model: A Real-World 12-Month Study from China

Background

Our previous study demonstrated that digital diabetes care model (DDCM) created by multidisciplinary care team (MDCT) can improve glycemic control for patients with diabetes than usual care. Therefore, we aimed to explore long-term glycemic control with DDCM and influencing factors in type 2 diabetic cohort, in order to make a portrait for diabetes with goal-achieved HbA1c in clinics.

Methods

A total of 1198 outpatients with type 2 diabetes using DDCM for at least 12 months were recruited as a cohort. Medical records and specific DDCM indexes were collected. The influencing factors for glycemic control were explored by multivariate logistic regression analysis, followed by an internal and external validation.

Results

A total of 887 patients were finally included. HbA1c target-achieving rate was increased from 39.83% at baseline to 71.79% after 3-month follow-up. A shorter duration of diabetes, more frequent self-monitoring of blood glucose, lower HbA1c level at baseline, and less frequent emergency out-of-hospital follow-ups were influencing factors for HbA1c <7% at 12-month follow-up. AUC of the prediction model was 0.790, with a sensitivity of 69.7% and specificity of 76.1%. Internal and external validation in patients using the DDCM monitored by MDCT indicated that the DDCM was robust (AUC =0.783 and 0.723, respectively).

Conclusion

Our findings made a portrait for T2DM with goal-achieved HbA1c in our DDCM. It is important to recognize associated factors for health providers to make personalized intervention in clinical practice.

Design and Development of a Mobile-Based Self-Care Application for Patients with Type 2 Diabetes

INTRODUCTION

Mobile-based applications play a leading role in changes in life-style, improve medication adherence, and provide a unique opportunity to aid patients with type 2 diabetes mellitus (T2DM) elevate their healthcare level. Therefore, we aim to design and develop a mobile-based self-care application for patients with T2DM.

METHODS

The present study was an applied and developmental study to design and develop a mobile-based self-care application for people living with T2DM conducted in 2020. The design and development of the T2DM self-care application were done in 2 main phases of determining the key features and capabilities, and design and development of the T2DM self-care mobile app.

RESULTS

We identified the main model and a set of capabilities and features for the T2DM self-care application. By content analysis on 32 different applications and a previous study by the author, 18 features were extracted for the T2DM self-care mobile app. JAVA programming languages were used to design T2DM applications. Moreover, because of the cost-effectiveness, the Android operating system (AOS) was selected as a platform, and because of the widespread use of smartphones; these phones were chosen as the format of T2DM self-care application.

CONCLUSIONS

In this study, we design and develop a mobile-based self-care application for patients with type 2 diabetes that shows potential in solving the shortcomings of mobile apps for diabetes care. By utilizing the T2DM self-care mobile app we are able to deploy a self-care application with a wide range of functionality such as text messaging, blood glucose monitoring, insulin dose suggestions, educational messaging, metabolic management, pedometer counts, and reporting. Future studies are needed to develop self-care applications for a different type of diabetes with different functions of diabetes care.

Update on Approaches to Improve Delivery and Quality of Care for People with Diabetes

To translate improvements in diabetes management into improved outcomes, it is essential to improve care delivery. To help guide clinicians and health organizations in their efforts to achieve these improvements, this article briefly describes key components underpinning effective diabetes care and six categories of innovations in approaches to improve diabetes care delivery: (1) team-based clinical care; (2) cross-specialty collaboration/integration; (3) virtual clinical care/telehealth; (4) use of community health workers (CHWs) and trained peers to provide pro-active self-management support; (5) incorporating screening for and addressing social determinants of health into clinical practice; and (6) cross-sectoral clinic/community partnerships.

Analysis of Effectiveness and Psychological Techniques Implemented in mHealth Solutions for Middle-Aged and Elderly Adults with Type 2 Diabetes: A Narrative Review of the Literature

BACKGROUND

in diabetes, multiple mHealth solutions were produced and implemented for self-management behaviors. However, little research on the effectiveness of psychological techniques implemented within these mHealth solutions was carried out, and even less with the elderly population where technological barriers might exist. Reliable evidence generated through a comprehensive evaluation of mHealth interventions may accelerate its growth for successful long-term implementation and to help to experience mHealth benefits in an enhanced way in all ages.

OBJECTIVE

this study aimed to review mHealth solutions for diabetes self-management in older adults (adherence to treatments and glycemic control) by analyzing the effectiveness of specific psychological techniques implemented.

METHODS

a narrative review was conducted following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. PubMed (Medline) and American Psychological Association (APA) PsycInfo databases were searched for published papers that addressed eHealth solutions' effectiveness for diabetes self-management. Studies in English, Spanish, and/or German of any design were screened, with no time constraints regarding the year of publication. A qualitative analysis of the selected papers was conducted in several steps.

RESULTS

this review found 38 studies setting up and analyzing mHealth solutions for older adults. Most research showed improvements in HbA1c, self-management behaviors, and medication adherence in T2DM patients post intervention. However, different mid-to-long term effects were found across studies, specifically concerning the maintenance and adherence to healthy behaviors. The most employed psychological framework was CBT, including techniques such as self-monitoring of outcome behaviors (mostly targeting glycemia measurements and healthy habits as physical activity and/or diet), tailored motivational feedback from medical staff, and psychoeducation or health coaches. The most successful mHealth intervention combined the feature of tailored feedback messages, interactive communication with healthcare professionals, and multifaceted functions.

CONCLUSIONS

there is a lack of elaborate and detailed information in the literature regarding the factors considered in the design and development of mHealth solutions used as interventions for T2DM self-management in the elderly. Documentation and inclusion of such vital information will foster a transparent and shared decision-making process that will ultimately lead to the development of useful and user-friendly self-management apps that can enhance the quality of life for diabetes patients. Further research adapting mHealth solutions to older adults' sensory deficits is necessary.

Cost-Effectiveness Analysis of a Mobile-Based Intervention for Patients with Type 2 Diabetes Mellitus

OBJECTIVE

The aim of this study was to evaluate the cost effectiveness of a mobile-based intervention for patients with type 2 diabetes mellitus (T2DM) and compare it with the usual management mode.

METHOD

A total of 215 patients with T2DM in a tertiary-care hospital specific to diabetes were selected as the study population. This study was conducted from January 1, 2019 to January 1, 2020. Of the 215 patients, 130 were randomly assigned to the mHealth group and 85 were assigned to the usual care group. IBM SPSS 25.0 software was used for descriptive statistics, t tests, chi-square tests, and correlation analyses. Haemoglobin A1c (HbA1c) was the effectiveness parameter adopted. Cost-effectiveness analyses were performed, and incremental cost-effectiveness ratios (ICERs) were calculated.

RESULTS

Of the 215 patients with T2DM, the proportion of male patients was 66.0%. The mean age of the patients was 47.2 (SD 9.95). Differences in baseline information were not statistically significant between the two groups (P > 0.05). At the 3-, 6-, and 12-month follow-ups, the mHealth group reported higher control rates of HbA1c than the usual care group, 67.9% versus 46.2% (P < 0.001), 72.4% versus 45.4% (P < 0.001), and 74.6% versus 47.1% (P < 0.001), respectively. The value of HbA1c was positively related to total patient cost, material fee, Western medicine fee, and hospitalization expenses (P < 0.05), with correlation coefficients of 0.202, 0.200, 0.172, and 0.183, respectively. The costs of the mHealth group and usual care group were CNY¥ 1169.76 and CNY¥ 1775.44 per patient/year, respectively. The incremental cost of the mHealth intervention was CNY¥ -605.68 per patient/year. The ICER was CNY¥ -22.02 per patient/year.

CONCLUSION

Compared with the usual care mode, the mHealth management model for patients with T2DM improved the control rate of HbA1c, and the mHealth management mode had better cost effectiveness.

Identifying features of a mobile-based application for self-care of people living with T2DM

INTRODUCTION

Diabetes self-care requires support to empower patients to improve self-monitoring and maintain the necessary self-care behaviors. We aimed to identify features of a mobile-based application as a technology-based device for self-care of people living with T2DM.

METHOD

This study was conducted in two main phases in 2020. In the first phase, a literature review study was performed to identify the data elements and technical features of the T2DM self-care application. In the second phase, using the information obtained from the review of similar articles, a questionnaire was designed to validate identified requirements. The statistical population of the present study consisted of 22 endocrinologists and metabolic specialists.

RESULTS

Identification of 55 data elements and technical features for mobile-based self-care application for people with T2DM, and according to the statistical population, 15data elements for demographic requirements, 16 data elements for clinical requirements, and 17 features for the technical capability of this app were selected.

CONCLUSION

Blood sugar monitoring, exercise, nutrition, weight monitoring, and educational capabilities were the most highlighted technical features of the T2DM self-care application. Software designers can use these requirements to design a self-care app for people with type-2 diabetes that can help manage and improve patients' health status.