Multimedia Platform-based Home Care Management for Elderly Patients with Diabetes

Summary

This retrospective cohort study shows that a multimedia platform-based home care model significantly improves glycemic and lipid control, is associated with a lower incidence of cardiovascular complications and reduced frailty, and enhances self-management ability and quality of life in older adults with type 2 diabetes.

Abstract

With the acceleration of population aging, poor blood glucose control and elevated cardiovascular complication risk among older adults with diabetes mellitus (DM) have emerged as significant public health challenges. This study conducted a retrospective, nonrandomized cohort analysis to compare clinical outcomes between two groups of older adults with type 2 diabetes mellitus (T2DM) stratified by treatment period and to investigate the association between a multimedia platform-based home care model and glycemic control, cardiovascular outcomes, and other patient-related outcomes.

A total of 167 older adults with DM were included (82 in the observation group and 85 in the control group). The control group received routine outpatient follow-up between January and August 2024, while the observation group received a 6-month comprehensive intervention via a hospital-developed WeChat mini-program after August 2024. After the 6-month intervention period, significant between-group differences in glycemic and lipid control were observed, with the observation group showing greater improvements from baseline (P < 0.05). A lower incidence of cardiovascular complications during the follow-up period was also observed in the observation group (P < 0.05). Additionally, the proportion of frailty in the observation group decreased from 45.12% to 20.73% after the intervention, and improvements in self-management capability and quality of life (assessed at 6 months post-intervention) were observed in this group.

These observed between-group differences may be partially explained by the behavioral reinforcement, personalized intervention strategies, and multi-risk factor management integrated into the multimedia platform model. Despite limitations including a single-center design and nonrandomized grouping, this model shows preliminary promise for optimizing home-based chronic disease management for older adults with diabetes. Further large-scale, randomized controlled studies are needed to validate these findings and explore the integration of intelligent devices to expand their clinical utility.

Introduction

With the acceleration of global population aging, diabetes mellitus (DM) in the elderly has become one of the major chronic diseases threatening public health¹. Statistics indicate that the prevalence of DM among individuals aged 60 and above reaches as high as 30%, often accompanied by cardiovascular complications (e.g., hypertension, coronary heart disease), significantly increasing disability and mortality rates^2,3. Effective blood glucose management is central to delaying the progression of complications. However, traditional outpatient follow-up models struggle to meet the long-term care needs of elderly patients due to challenges such as mobility limitations, cognitive decline, and uneven distribution of medical resources^4,5. In recent years, multimedia platforms (e.g., smartphone apps, remote monitoring devices, and video follow-up systems) have emerged as a new pathway for home care due to their convenience, real-time performance, and interactivity, becoming a research hotspot in chronic disease management⁶. Currently, multiple clinical studies on multimedia-based home care for DM have demonstrated positive effects in enhancing patients' self-management capabilities and improving blood glucose control^7,8. However, existing studies predominantly focus on all age groups or single indicators⁹, with insufficient long-term observation on the specific needs of elderly populations (such as complex comorbidities and varying levels of digital literacy) and the protective effects against cardiovascular complications. Besides, some studies only evaluate short-term intervention outcomes, lacking follow-up on the incidence of cardiovascular events¹⁰. The specific application models of multimedia platforms and their suitability for elderly patients require further validation. Therefore, there is an urgent need to develop personalized multimedia intervention programs tailored to the characteristics of elderly DM patients, considering their physiological and psychological states, technology acceptance, and family support.

This study aimed to systematically investigate the impact of multimedia platform-based home care on glycemic control and the incidence of cardiovascular complications in elderly patients with DM, and to further explore its effects on frailty status, self-management ability, and quality of life. The innovative aspects of this study include: (1) Designing a multimedia care program tailored to the characteristics of the elderly population, encompassing multidimensional content such as blood glucose monitoring guidance, medication reminders, dietary and exercise interventions, and psychological support; (2) Observing the incidence of cardiovascular complications with a 6-month intervention and complete follow-up, addressing the limitation of short-term outcome evaluation in most existing studies. The findings were intended to provide evidence-based support for home care of elderly patients with DM, thereby advancing the integration of multimedia technology with chronic disease management, optimizing health management strategies for elderly patients, reducing cardiovascular complication risks, and enhancing quality of life.

Protocol

Study participants
Elderly patients with DM who visited the Department of Medical Affairs of Tian Shui Wulin Street Community Health Service Center, Hangzhou, from January 2024 to February 2025 were enrolled in the study, totaling 167 cases (82 in the observation group and 85 in the control group). Inclusion criteria: Age ≥ 60 years; meeting the diagnostic criteria for T2DM¹¹; presenting with at least one cardiovascular risk factor (hypertension, dyslipidemia, obesity, or family history of premature cardiovascular disease); being conscious, with basic communication skills, and able to operate multimedia platforms independently or with family assistance. Exclusion criteria: Type 1 DM or special types of DM; serious hepatic or renal impairment (ALT/AST > 3x the upper limit of normal, Cr > 177 µmol/L); psychiatric disorders or cognitive impairment preventing cooperation; ≥3 hospitalizations within the past 6 months or scheduled for surgical intervention. The study has been approved by the Ethics Committee of the hospital, and all the participants have signed the informed consent form.

Grouping method
A retrospective cohort analysis was conducted, dividing patients into two groups based on whether they received "multimedia platform-based home care." The observation group included patients who sought medical care after August 2024, voluntarily participated in the hospital's "Home Management Multimedia Platform for DM" project, and received standardized home care for 6 months The control group consisted of patients who sought medical attention from January to August 2024, received only routine outpatient follow-up (every 3 months), and did not use the multimedia platform. Patients admitted between January and August 2024 had completed their routine outpatient follow-up procedures before the official launch of the multimedia platform program in August 2024 and thus were not eligible for enrollment in the intervention group. The two patient groups showed no statistically significant differences in age, gender, disease duration, baseline blood glucose levels, or cardiovascular risk factors (P > 0.05), indicating comparable baseline characteristics (see Table 1).

Intervention
Routine outpatient follow-up: Outpatient visits were scheduled once every 3 months, including blood glucose testing, medication adjustments, and distribution of printed health manuals. No remote monitoring or video follow-ups were conducted.

Multimedia platform-based home care: A WeChat mini-program (APP) independently developed by the hospital was used (https://weixin.ngarihealth.com/weixin/wx/mp/wx87e933682e320dea/index.html?pageModule=secHomepage). The mini-program included multiple functional modules to support home-based diabetes management. The data monitoring module enabled automatic synchronization of fasting and 2 h postprandial blood glucose levels, as well as blood pressure measurements, from Bluetooth-enabled glucometers. Abnormal values (e.g., blood glucose >13.9 mmol/L or <3.9 mmol/L) triggered real-time alerts through application notifications and SMS messages to healthcare providers.

A medication management module provided individualized medication schedules, including drug names, dosages, and administration frequencies, with automated reminders delivered 15 min before dosing using voice and vibration alerts. A complication self-assessment module incorporated structured flowcharts to support screening for diabetic retinopathy, peripheral neuropathy, and foot ulcers, using images and short videos, along with monthly AI-assisted voice-guided assessments. An emergency contact module allowed patients to quickly connect with family members, community physicians, and specialist nurses, including access to one-touch video consultation.

Health education content was delivered twice weekly (Monday and Thursday) through the application, using brief text (≤300 words) combined with images or short videos (≤3 min). Content included practical topics such as dietary guidance (e.g., seasonal glycemic control recipes) and simple exercise demonstrations (e.g., seated lower-limb exercises). In addition, monthly live online sessions were conducted by endocrinologists and nutritionists.

For glycemic management, patients were instructed to upload blood glucose measurements twice daily (fasting and postprandial). Nursing staff reviewed the data and provided feedback within 48 h to adjust individualized glycemic targets (fasting 7.0–8.0 mmol/L; postprandial <10.0 mmol/L), with emphasis on safety in elderly patients. Pharmacists provided online consultations to evaluate potential drug interactions, particularly in patients receiving five or more medications.

Dietary management followed a structured “3 + 3” model, consisting of three main meals with controlled portions of staples and three low-glycemic snacks. A visual “blood glucose control plate” model (vegetables: protein: staples = 2:1:1) was provided weekly. The intervention was conducted over a 6-month period in both groups.

Resource requirements for the intervention
The implementation of the multimedia platform intervention required one full-time project manager, two specialized diabetes nurses, one part-time clinical pharmacist, one part-time endocrinologist, and one part-time nutritionist. The WeChat mini-program was developed and maintained by the hospital's information technology department, with a one-time development cost of approximately 15,000 USD and an annual maintenance cost of 2,000 USD.

Data source
The data were retrospectively extracted from the hospital electronic medical record system (HIS), multimedia platform backend database, and follow-up records.

Intervention adherence metrics
Pre-specified platform usage indicators were used to assess adherence to the multimedia platform intervention, with standardized definitions as follows: 1. Monthly platform login frequency: The total number of successful patient logins to the WeChat mini-program per calendar month during the 6-month intervention period, reported as the mean value across the full 6-month intervention. 2. Valid blood glucose upload rate: The percentage of valid blood glucose readings uploaded by patients relative to the total required readings during the intervention period. Per the intervention protocol, patients were required to upload 2 valid readings per day (1 fasting, 1 2 h postprandial), totaling 360 readings over the 6-month intervention. Valid readings were defined as values within the physiological measurement range of the study's standardized Bluetooth glucometer, with clear fasting/postprandial time labeling, excluding duplicate or invalid test results. 3. Video follow-up participation rate: The percentage of planned online video-based interactions attended by patients during the intervention period, including the monthly live expert Q&A sessions (6 total sessions over 6 months) and scheduled video follow-up consultations with the diabetes care team.

Outcome measures
Blood glucose and lipid parameters, including HbA1c, FPG, 2hPG, TC, TG, LDL-C, and HDL-C, were recorded before the intervention and at 3 and 6 months. Cardiovascular complications (e.g., heart failure, angina pectoris) occurring during the intervention were statistically documented. All cardiovascular events were adjudicated independently by two board-certified cardiologists unaware of group assignments. Discrepancies in event classification were resolved by consensus with a third senior cardiologist. Patient frailty status before and after intervention was recorded using the Fried frailty phenotype¹²: 1. Unintentional weight loss: ≥4.5 kg (or ≥5% of body weight) loss in the previous 12 months; 2. Exhaustion: self-reported positive response to either of two items from the Center for Epidemiologic Studies Depression Scale (CES-D); 3. Weakness: decreased grip strength, stratified by gender and body mass index (BMI) per the original phenotype thresholds; 4. Slowness: decreased walking speed over a 4 m walk, stratified by sex and height per the original phenotype thresholds; 5. Low physical activity: sex-specific low weekly energy expenditure, defined as <383 kcal/week for men and <270 kcal/week for women. Meeting ≥3 of the 5 criteria was defined as "frailty"; meeting 1–2 criteria was defined as "pre-frailty"; and meeting 0 criteria was defined as "non-frailty." At 6 months, patients completed the Summary of Diabetes Self-Care Activities Scale (SDSCA)¹³, which assesses five domains: blood glucose testing, diet, exercise, medications, and foot care. Higher scores indicate better self-management ability. Concurrently, patients were required to complete the Short Form 36 Health Survey (SF-36)¹⁴. There are eight dimensions: Physical Functioning (PF), Role-Physical (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role-Emotional (RE), Mental Health (MH); higher scores indicate a better quality of life.

Statistical analysis
Enumeration data were recorded as [n (%)] and compared using the chi-square test. Measurement data were first assessed for distribution (Shapiro-Wilk test); normally distributed data were recorded as (x̄ ± s) and compared using independent samples t-tests (between groups) and paired t-tests (within groups); non-normally distributed data were recorded as [M (P25, P75)] and compared using the Mann-Whitney U tests (between groups) and Wilcoxon tests (within groups). The comparison at multiple time points was conducted using the repeated strategy analysis of variance and the Bonferroni intra-group test. P < 0.05 was considered statistically significant.

Results

Multimedia platform usage
The following data reflect adherence to the multimedia platform intervention in the observation group and are implementation indicators rather than clinical outcome measures. Statistical analysis revealed that the frequency of platform logins in the observation group was (21.50±5.63) times per month, with both blood glucose upload rate (89.67%) and video follow-up participation rate (92.58%) reaching approximately 90%, confirming effective utilization of the platform by pati...

Discussion

The results of this retrospective cohort study showed that elderly patients with type 2 diabetes who received multimedia platform-based home care had significantly better glycemic and lipid control, a lower incidence of cardiovascular events during the 6-month observation period, and improved frailty status, compared with those who received routine outpatient follow-up. This suggests that the model may have potential integrated benefits across multiple dimensions of health outcomes.

Specifical...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Automatic Biochemical Analyzer	Roche Diagnostics (Shanghai) Co., Ltd.	cobas c 702	Used for batch detection of core biochemical indicators in the study, including Fasting Plasma Glucose (FPG), 2-hour Postprandial Plasma Glucose (2hPG), Total Cholesterol (TC), Triglyceride (TG), Low-Density Lipoprotein Cholesterol (LDL-C), and High-Density Lipoprotein Cholesterol (HDL-C). Its detection accuracy meets the clinical laboratory standardization requirements, and provides core detection data for the study outcome indicators.
Blood Glucose (Glucose) Detection Reagent	Roche Diagnostics (Shanghai) Co., Ltd.	Matching Biochemical Reagent for cobas c 702	Used in conjunction with the automatic biochemical analyzer for the accurate quantitative detection of glucose concentration in serum/plasma. It provides detection data for the core blood glucose indicators of FPG and 2hPG, and the inter-batch difference meets the clinical laboratory quality control standards.
Bluetooth Glucometer	Roche Diagnostics (Shanghai) Co., Ltd.	Accu-Chek Guide	Core home testing device for the study intervention, used for the detection of patients' fasting and 2-hour postprandial blood glucose. It supports automatic Bluetooth data synchronization to the study-specific WeChat Mini Program, can identify abnormal blood glucose values (>13.9 mmol/L or <3.9 mmol/L) and trigger alerts, and provides standardized data support for home-based dynamic blood glucose monitoring in elderly patients with diabetes.
Disposable EDTA-K2 Anticoagulant Blood Collection Tube	Becton, Dickinson and Company (BD), BD Medical Devices (Shanghai) Co., Ltd.	2mL Anticoagulant Type	Used for collecting patients' whole blood samples. The EDTA-K2 anticoagulant can stabilize blood cell morphology, is compatible with the detection of Glycated Hemoglobin (HbA1c), and complies with the clinical blood sample collection specifications.
Disposable Intravenous Blood Collection Needle	Becton, Dickinson and Company (BD), BD Medical Devices (Shanghai) Co., Ltd.	21G	Used in conjunction with vacuum blood collection tubes for the collection of patients' venous blood samples. It has a sterile disposable design, the specification is suitable for adult intravenous blood collection procedures, which reduces the risk of sample hemolysis and ensures the accuracy of test results.
Disposable Vacuum Coagulation-Promoting Blood Collection Tube	Becton, Dickinson and Company (BD), BD Medical Devices (Shanghai) Co., Ltd.	3mL Conventional Coagulation-Promoting Type	Used for collecting patients' venous blood samples. The coagulant can accelerate blood coagulation, and the separated serum is compatible with the detection of biochemical indicators such as blood glucose and blood lipids. The disposable sterile design ensures the safety and standardization of sample collection.
Electronic Hand Dynamometer	Guangdong Xiangshan Weighing Apparatus Group Co., Ltd.	EH101	Used for the accurate measurement of grip strength indicators in the Fried frailty phenotype assessment. It can quantitatively record the maximum hand grip strength, and judge the decline of muscle strength stratified by gender and BMI, fully complying with the standardized operation requirements for frailty assessment in this study.
Glycated Hemoglobin (HbA1c) Detection Reagent	Bio-Rad Laboratories, Inc. (USA)	Matching Reagent for Variant II Turbo	Used in conjunction with the glycated hemoglobin analyzer to detect HbA1c in whole blood samples by HPLC. It is used to evaluate the long-term blood glucose control level of patients, and is a special detection reagent for the core blood glucose outcome indicators of this study.
Handheld Laser Rangefinder	Deli Group Co., Ltd.	DL4168	Used to accurately calibrate the standard distance of the 4-meter walking test, eliminate manual measurement errors, and ensure the consistency and accuracy of walking speed measurement in the Fried frailty phenotype assessment.
High-Density Lipoprotein Cholesterol (HDL-C) Detection Reagent	Roche Diagnostics (Shanghai) Co., Ltd.	Matching Biochemical Reagent for cobas c 702	Used in conjunction with the automatic biochemical analyzer for the quantitative detection of HDL-C concentration in serum, to assist in the assessment of patients' lipid protective factors and cardiovascular disease risk.
High-Precision Electronic Stopwatch	Shenzhen Tianfu Electronics Co., Ltd.	PC894	Used for timing measurement of 4-meter walking speed in the Fried frailty phenotype assessment, with a timing accuracy of 0.01 seconds. It provides accurate timing data for the judgment of frailty indicators related to slowed walking speed.
Hospital Information System (HIS)	Winning Health Technology Group Co., Ltd.	Winning HIS V5.0	The core data source for patients' baseline data, clinical test results, and diagnosis and treatment records in this study. It can completely trace the full-cycle diagnosis and treatment information of patients, and ensure the authenticity, integrity and traceability of the study data.
HPLC Glycated Hemoglobin Analyzer	Bio-Rad Laboratories, Inc. (USA)	Variant II Turbo	Adopts High Performance Liquid Chromatography (HPLC) to detect Glycated Hemoglobin A1c (HbA1c). It is the gold standard equipment for evaluating the average blood glucose control level of diabetic patients in the past 2-3 months, and provides standardized and traceable test results for the blood glucose control outcomes of this study.
Low-Density Lipoprotein Cholesterol (LDL-C) Detection Reagent	Roche Diagnostics (Shanghai) Co., Ltd.	Matching Biochemical Reagent for cobas c 702	Used in conjunction with the automatic biochemical analyzer for the quantitative detection of LDL-C concentration in serum. It is the core lipid indicator detection reagent for the risk assessment of cardiovascular complications in this study.
Medical Electronic Body Weight Scale	Huawei Device Co., Ltd.	AH100	Used to accurately measure patients' body weight and calculate Body Mass Index (BMI). It is the core detection tool for the unintentional weight loss indicator in the Fried frailty phenotype, and also provides basic data for patients' body weight management and personalized dietary intervention.
Medical Low-Speed Centrifuge	Hunan Xiangyi Laboratory Instrument Development Co., Ltd.	TDZ5-WS	Used for the pretreatment of venous blood samples. It separates serum/plasma through standardized centrifugation to provide qualified samples for subsequent biochemical indicator testing such as blood glucose and blood lipids, and is compatible with the routine centrifugation parameter requirements for clinical laboratory testing.
SPSS Statistical Analysis Software	IBM Corporation (USA)	SPSS 26.0	Used for the statistical analysis of the full set of clinical data in this study. It can perform a variety of statistical methods including normality test, independent samples t-test, paired t-test, chi-square test, Mann-Whitney U test, and repeated measures analysis of variance, with P<0.05 set as the threshold for statistical significance.
Total Cholesterol (TC) Detection Reagent	Roche Diagnostics (Shanghai) Co., Ltd.	Matching Biochemical Reagent for cobas c 702	Used in conjunction with the automatic biochemical analyzer for the quantitative detection of total cholesterol concentration in serum, and provides core data for the assessment of patients' lipid metabolism status.
Triglyceride (TG) Detection Reagent	Roche Diagnostics (Shanghai) Co., Ltd.	Matching Biochemical Reagent for cobas c 702	Used in conjunction with the automatic biochemical analyzer for the quantitative detection of triglyceride concentration in serum, to assist in the assessment of patients' abnormal lipid metabolism and cardiovascular disease risk.
Upper Arm Bluetooth Electronic Sphygmomanometer	Omron Healthcare (China) Co., Ltd.	HEM-7136T	Used in conjunction with the blood glucose monitoring module for automated home blood pressure testing of patients. It supports Bluetooth data synchronization to the multimedia management platform, and assists in the continuous monitoring and comprehensive management of patients' cardiovascular risk factors.
WeChat Mini Program for Home-Based Diabetes Management	Independently Developed by the Information Technology Department of the Study Hospital	https://weixin.ngarihealth.com/weixin/wx/mp/wx87e933682e320dea/index.html?pageModule=secHomepage	The core carrier of the intervention measures in this study, which includes six core modules: data monitoring, medication management, complication self-assessment, health knowledge push, expert live Q&A, and emergency contact. It realizes the full-process closed-loop management of home-based care for elderly diabetic patients, and supports automatic synchronization of blood glucose and blood pressure data as well as real-time alerts for abnormal values.