---
title: "Self-Management Program Lowers HbA1c, eGFR, SBP, LDL-C in High-Risk DKD Patients with Type 2 Diabe"
id: "plos-one-11-effectiveness-of-a-self-management-intervention-on-clinical-outcomes-among"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-11-effectiveness-of-a-self-management-intervention-on-clinical-outcomes-among"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360475"
published_at: "2026-10-12T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Self-Management Program Lowers HbA1c, eGFR, SBP, LDL-C in High-Risk DKD Patients with Type 2 Diabe
## Provenance & Clinical Metadata
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- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360475)
- **Published At:** 2026-10-12T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- A prospective, two-arm, assessor-blinded randomized controlled trial tested a 12-week structured self-management program plus 12-week follow-up versus usual care in 134 patients with **type 2 diabetes** identified as high risk for **Diabetic Kidney Disease** (DKD) using a validated risk model. - Participants were recruited at a tertiary hospital in Shandong Province, China, randomized 1:1 and assessed at baseline (T0), 12 weeks (T1) and 24 weeks (T2). - Primary clinical outcomes measured from medical records included **HbA1c**, urinary albumin-to-creatinine ratio (**UACR**), estimated glomerular filtration rate (**eGFR**), systolic blood pressure (SBP), LDL-C, BMI and waist circumference. - The intervention comprised six scripted video sessions based on the Health Belief Model covering DKD knowledge, diet, activity, medication management and self-monitoring, delivered over six weeks with a strengthening phase and monthly contact during 12-week follow-up. - Generalized estimating equations under an intention-to-treat framework analyzed repeated measures with sensitivity analyses using multiple imputation; results were robust across primary and sensitivity analyses. - Investigators reported statistically significant improvements in **HbA1c**, **eGFR**, SBP and LDL-C in the intervention group compared with control; no between-group differences were found for **UACR**, BMI or waist circumference. - Attrition occurred (11.94% at T1 overall; 6.71% at T2 overall); investigators reported no baseline differences between retained and lost participants (p > 0.05), suggesting data missing at random. - Trial limitations documented by the study team include single-center recruitment, short-term follow-up (24 weeks), and inability to blind intervention deliverers and analysts due to intervention nature.
## Clinical Analysis & Structured Key Points
Effectiveness of a self-management intervention on clinical outcomes among patients with type 2 diabetes at high risk for Diabetic Kidney Disease: A Randomised Controlled Trial | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Background Diabetic kidney disease (DKD) is a common complication of type 2 diabetes mellitus (T2DM) and a leading cause of end-stage renal disease, posing substantial health and economic burdens. Early self-management interventions targeting modifiable risk factors are crucial to prevent or slow DKD progression. However, most studies focus on later stages of DKD, leaving a gap in early prevention for high risk of DKD individuals. Objective To evaluate the effectiveness of a structured self-management intervention on clinical outcomes among T2DM patients at high risk of DKD. Design A prospective, two-arm, randomized controlled trial. Setting A tertiary hospital in Shandong Province, China. Participants 134 patients with T2DM who were classified as being at high risk of DKD using a validated DKD risk prediction model. Methods 134 participants were randomized 1:1 to the intervention or control group. The intervention group received a 12-week a structured self-management program and 12-week follow-up. The control group received usual care. Clinical outcomes included hemoglobin A1c (HbA1c), urinary albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), body mass index (BMI), waist circumference (WC) were collected at baseline (T0), after the 12-week intervention (T1), and at 24 weeks (T2). Generalized estimating equations and intention-to-treat analyses were performed. Sensitivity analyses using multiple imputation were conducted. Results All participants completed the intervention phase. During the follow-up visits period, 7 participants in the intervention group and 9 participants in the control group were lost to follow-up at T1 (attrition rate: 11.94%), while 5 participants in the intervention group and 4 participants in the control group were lost to follow-up at T2 (attrition rate: 6.71%). No significant differences in baseline sociodemographic or clinical indicators between participants retained and those lost to follow-up (p > 0.05), suggesting data were missing at random. Baseline comparisons between the intervention and control groups revealed no significant differences in demographic or clinical indicators (p > 0.05). Compared with the control group, the intervention group demonstrated significant improvements in HbA1c, eGFR, SBP, and LDL-C, while no significant differences were observed for UACR, BMI, or WC. Sensitivity analyses yielded consistent results. Conclusions The self-management intervention effectively improved HbA1c, eGFR, SBP and LDL-C among T2DM patients at high risk of DKD. These findings suggest that integrating structured self-management into routine diabetes care may help improve modifiable risk factors and support early DKD prevention. Registration China Clinical Trial Registry (Chi CTR2400092745 ; registered on 22 November 2024) Citation: Chu D, Liu B, Soh KL, Saidi HI, Japar S (2026) Effectiveness of a self-management intervention on clinical outcomes among patients with type 2 diabetes at high risk for Diabetic Kidney Disease: A Randomised Controlled Trial. PLoS One 21(10): e0360475. https://doi.org/10.1371/journal.pone.0360475 Editor: Diego Moriconi, University of Pisa, ITALY Received: December 21, 2025; Accepted: August 25, 2026; Published: October 12, 2026 Copyright: © 2026 Chu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. 1. Background The 10th edition of the IDF Diabetes Atlas confirms that Diabetes Mellitus (DM) is one of the fastest-growing global health emergencies of the 21st century, it is estimated that 537 million people with diabetes in 2021, this number is projected to reach 643 million by 2030 and 783 million by 2045 [ 1 , 2 ]. The rising prevalence has a significant implications for Diabetic Kidney Disease (DKD). DKD is one of the most common microvascular complication and it is the leading cause of end-stage renal disease (ESRD) [ 3 , 4 ]. Current studies reported 20.0% to 40.0% of diabetic patients develop DKD [ 5 , 6 ]. González-Pérez et al. [ 7 ] reported the all-cause mortality rate for DKD is 50.3 per 1000 person-years, with cardiovascular disease (CVD) mortality rate at 8.0, and ESRD-related mortality rate at 6.9, posing a severe threat to the lives of DKD individuals. Clinically, DKD is primarily evaluated using the urinary albumin-to-creatinine ratio (UACR) and the estimated glomerular filtration rate (eGFR). UACR levels ≥30 mg/g are considered indicative of microalbuminuria, representing the early stage of overt renal damage. eGFR provides an objective measure of renal function and can be used to monitor changes over time [ 5 , 8 ]. Previous studies have explored various risk factors associated with DKD progression in patients with T2DM, including sociodemographic factors, lifestyle factors and clinical indicators [ 9 – 14 ]. These factors can be categorized as non-modifiable and modifiable factors. Non-modifiable factors, such as age, gender, genetic susceptibility and modifiable factors, including glycemia, hypertension, dyslipidemia and obesity, which can be regulated through lifestyle modification and self-management. Poor glycemic control induces glomerular hyperfiltration, oxidative stress, and renal inflammation; elevated blood pressure increases intraglomerular pressure and accelerates proteinuria and renal function decline; dyslipidemia promotes lipid accumulation and endothelial dysfunction; while excess body weight, particularly central fat, exacerbates insulin resistance and systemic inflammation, further aggravating renal damage, these metabolic together forming key biological pathways driving DKD progresses [ 15 – 17 ]. Considering the severity and far-reaching impact of DKD, clinical guideline for the prevention and treatment of DKD emphasize early screening, early diagnosis, early treatment, and integrated comprehensive intervention [ 18 ]. In particular, strict control of hyperglycemia and hypertension has been recognized as a cornerstone in delaying the progression of DKD [ 19 ]. Therefore, self-management is particularly important, as it enables patients to actively participate in controlling these modifiable risk factors. Self-management refers to an individual’s ability to take an active role in their own care and well-being, making informed decisions and adopting behaviors that promote health and manage chronic conditions. By actively participating in self-management, individuals can better control their own health, improve their quality of life, and reduce the risk of disease. American Diabetes Association Professional Practice Committee [ 20 ] emphasizes the crucial role of self-management education for diabetic patients in establishing positive health behaviors and maintain mental well-being. Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group [ 21 ] also recommend that a structured self-management educational program be implemented for care of people with diabetes and chronic kidney disease. Patients who are skilled in self-management and actively participating in disease management can reduce modifiable risk factors, thereby have improved health outcomes. Over the past years, diabetes self-management interventions have been shown to be effective and cost-effective in promotion and facilitation of self-management, with improvements in patients’ knowledge, skills, and motivation leading to improved biomedical, glycemic control, behavioral, and psychosocial outcomes [ 22 – 24 ]. Currently, self-management interventions for DKD patients primarily focus on individuals with established disease, particularly those at the microalbuminuria or dialysis stages. However, given that renal damage is often irreversible, early intervention is particularly meaningful. Despite this, evidence on structured self-management interventions targeting patients at high risk of DKD remains limited, and such individuals are often overlooked in current clinical practice. By using a DKD risk prediction model [ 25 ], high risk individuals among T2DM patients can be identified for early intervention. This approach aims to enhance patients’ self-management behaviors and improve clinical indicators that serve as risk factors for the development of DKD, ultimately delay or mitigate DKD progression and complications. Objective To evaluate the effectiveness of a structured self-management intervention on clinical outcomes among T2DM patients at high risk of DKD. 2. Methods 2.1 Study design and setting The study design is a prospective, two-arm, assessor-blinded, randomized controlled trial. It has also been registered in the China Clinical Trial Registry on 22 November 2024, with registration number ChiCTR2400092745. The study is conducted at a tertiary hospital located in Shandong Province, China. 2.2 Inclusion Criteria and Exclusion Criteria Patients were screened according to predefined inclusion and exclusion criteria. Inclusion criteria were as follows: (a) Patients meet the World Health Organization diagnostic criteria for T2DM; (b) Patients at high risk for DKD (DKD risk scores ≥16). (c) Age 39–75 years old; (d) Patients can use WeChat; (e) Voluntarily join the study and sign the informed consent form. Exclusion Criteria were (a) Patients with DKD; (b) Those who have mental disorders, cognitive impairment, etc. and are unable to cooperate with the researcher; (c) Other secondary glomerular diseases caused by primary renal disease and hypertension, renal arteriosclerosis; (d) There have been recent factors affecting urinary protein such as diabetic ketoacidosis, acute nephritis, and urinary tract infection. 2.3 Sample Size The sample size was calculated based on expected changes in HbA1c reported in a previous intervention study [ 26 ], with a baseline of 7.79 ± 1.82 and post-intervention mean of 7.06 ± 0.89. The calculation was performed using the standard formula n = 2σ 2 (Z 1 ₋ α/2 + Z 1 ₋ β) 2 / (µ 1 − µ 2 ) 2 . Based on a two-sided significance level of 0.05 and a power of 80%, the required sample size was estimated to be 60 participants per group. Allowing for a 10% dropout rate, the total sample size was adjusted to 134 participants. 2.4 Recruitment, randomization and blinding The recruitment poster was placed at the nurses’ station to recruit participants. Potential participants who were interested in the study were evaluated by the nursing manager to determine whether they met the inclusion criteria. Those who met the criteria and provided written informed consent were enrolled in the study. Randomization was conducted using a pre-generated sequence of 134 unique random integers corresponding to the enrollment sequence numbers. This sequence was prepared by an independent individual using an online random number generator. Each random number was placed in a kraft paper envelope labeled on the outside with its corresponding enrollment number. Eligible participants received the envelope matching their enrollment sequence, and the number inside the envelope determined group allocation. Odd numbers were assigned to the intervention group, while even numbers were assigned to the control group. Participant recruitment was conducted from 2 December 2024–28 February 2025. This study was a single-blind randomized controlled trial. Blinding was not feasible for the researchers delivering the intervention or for the data analysts due to the nature of the intervention. To reduce potential bias, outcome assessors and participants were kept unaware of group assignments. During data collection, participants were identified using their enrollment numbers rather than their allocation. 2.5 Intervention Methods 2.5.1 Control group. Upon admission, the physician evaluates the patient’s condition through a detailed inquiry and relevant examinations, develops a treatment plan, and issues medical orders. Medical orders typically include intravenous injection therapy, medications, and health education. Health education is delivered verbally by the primary nurse and covers basic diabetes knowledge (types, common complications, normal glucose levels), recognition and management of hyperglycemia and hypoglycemia, dietary advice, reducing alcohol intake, and encouraging safe moderate physical activity. For patients using insulin, medication instructions and injection techniques are also provided. 2.5.2 Intervention group. 2.5.2.1 Intervention Team: The intervention team consists of one endocrinologist, one nursing manager, two diabetes-specialized nurses, and one nursing researcher. The endocrinologist released the recruitment information, contacted participants, and provided overall study supervision. The nursing manager screened and enrolled eligible patients and distributed sealed envelopes that indicated the group allocation. The two diabetes-specialized nurses were responsible for data collection, while the nursing researcher implemented the intervention and conducted the statistical analysis. All members received standardize training before the enrollment. 2.5.2.2 Intervention Content: Participants in the intervention group received a structured self-management program covering five core topics: (1) DKD knowledge, (2) diet management, (3) physical activity, (4) medication management, and (5) self-monitoring. The content was developed based on current evidence and major clinical guidelines for diabetes and chronic kidney disease, ensuring consistency with best practices and enhancing methodological rigor [ 20 , 21 , 27 – 29 ]. There is often a significant gap between knowledge and action, and merely providing individuals with health knowledge is insufficient to promote behavior change. While many individuals may understand the correct practices, they often struggle to implement them in their daily lives. The Health Belief Model (HBM) addresses this gap by identifying the key factors that influence behavior. It offers a scientific framework and systematic approach to designing more effective interventions [ 30 ]. These five topics are structured based on the theoretical framework of the HBM, which aims to increase participants’ perceived severity and susceptibility to DKD, clarify the benefits of behavioral change, and strengthen self-efficacy to support sustainable self-management behaviors’ The intervention was delivered through six video-based sessions produced by the researcher, with scripts developed strictly according to authoritative guidelines and aligned with the HBM framework. The six sessions are summarized in Table 1 . Download: PNG larger image TIFF original image Table 1. Overview of the Self-Management Intervention Sessions. https://doi.org/10.1371/journal.pone.0360475.t001 2.5.3 Intervention periods. The intervention comprises two periods: a six-week initial learning phase and a subsequent strengthening phase. The initial learning phase included six weekly sessions, while the strengthening phase focused on consolidating the knowledge and skills acquired earlier. After completing the intervention, participants in the intervention group underwent a 12-week follow-up. During this period, researchers conducted monthly telephone or WeChat calls to monitor adherence, support self-management practices, and encourage the maintenance of healthy behaviors. At the end of follow-up, participants were notified by phone to schedule a return visit. 2.6 Outcomes Sociodemographic data: Patients’ sociodemographic characteristics were collected using a self-designed questionnaire. The variables included age, gender, ethnicity, marital status, educational level, monthly family income, residential status, family history of diabetes, duration of diabetes, treatment method, work intensity, history of hypertension, frequency of blood glucose monitoring. And clinical outcomes: Clinical outcomes were obtained from patients’ medical records. These included hemoglobin A1c (HbA1c), urinary albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), body mass index (BMI), waist circumference (WC). 2.7 Data Collection Two researchers, who were not involved in group assignments, collected and recorded participants’ data using enrollment sequence numbers for identification. Data were collected at three time points: baseline (T0, after enrollment), post-intervention (T1, 12 weeks after enrollment), and at the end of follow-up (T2, 24 weeks after enrollment). Laboratory indicators were recorded from medical reports by the researchers. 2.8 Data Analysis Data were analyzed using SPSS 29.0. Normality of continuous variables was assessed with the Shapiro–Wilk test. Normally distributed variables are presented as mean ± standard deviation, while non-normal variables are expressed as median (P25, P75). Categorical variables are reported as frequencies and percentages. Baseline comparisons between groups were performed using independent t tests for normally distributed continuous variables, Mann–Whitney U tests for non-normal variables, and chi-square or Fisher’s exact tests for categorical variables. To evaluate the effectiveness of the intervention over time, generalized estimating equations (GEE) were applied to analyze repeated-measures data and to account for within-subject correlations. The models included group, time, and the group × time interaction term to examine differential changes between the intervention and control groups. An exchangeable working correlation structure was applied, and robust standard errors were used. A normal distribution with an identity link function was specified. For skewed variables, log-transformation was performed prior to analysis, and the estimated coefficients were back-transformed for interpretation. All analyses followed the Intention-to-Treat (ITT) principle, whereby all participants were analyzed according to their originally assigned groups, regardless of adherence or dropout. To assess the robustness of the findings to missing data assumptions, sensitivity analyses were performed using multiple imputation with 10 imputed datasets. The results were pooled using Rubin’s rules in R and compared with those from the primary GEE analyses based on the original data. A two-tailed p-value of < 0.05 was considered statistically significant. 2.9 Ethical Consideration The research was approved by the Medical Ethics Committee of Zaozhuang Central Hospital of Shandong Guoxin Yiyang Group on 18 October 2024, with approval number 2024−015. Written informed consent was obtained from all participants prior to enrollment. Participants were fully informed about the purpose, procedures, potential benefits and risks of the study. They were also informed of their right to withdraw from the study at any time without any consequences. 3. Results 3.1 Participants flow As illustrated in Fig 1 , a total number of 317 patients were screened during recruitment. Of these, 134 eligible individuals provided written informed consent and were enrolled. 134 participants were randomly assigned to the interve
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