Introduction

Diabetes is among the most prevalent chronic diseases globally. In 2021, an estimated 536 million adults worldwide were living with diabetes, a figure projected to increase to 784 million by 2045,1 in China, more than 140 million of nearly 1.08 billion adults currently have type 2 diabetes.2 Glycemic control is a key determinant of prognosis among patients with type 2 diabetes. In non-pregnant adults, optimal glycemic control is defined as glycated hemoglobin (HbA1c) < 7% in the absence of significant hypoglycemia.3 Despite ongoing advances in diabetes treatment, the global proportion of patients achieving glycemic targets remains only 42.8%,4 and the corresponding rate among Chinese patients is 49.2%, indicating persistently suboptimal control.5

Poor glycemic control increases the risk of blindness, end-stage renal disease, cardiovascular disease, and lower-extremity vascular disease in patients with type 2 diabetes mellitus.6 According to the International Diabetes Federation (IDF), approximately 6.7 million adults died from diabetes and its complications in 2021. Owing to its large population, China accounted for up to 1.4 million deaths attributable to diabetes and its complications.2 Diabetes-related health care expenditure was estimated to reach US$165.3 billion in 2021. The large population living with diabetes, together with the substantial financial burden of the disease, has placed considerable pressure on the health care system.2 Therefore, identifying factors associated with glycemic control is important for optimizing the management of type 2 diabetes and its complications. Previous studies have shown that glycemic control may be influenced by sociodemographic characteristics, disease duration, treatment modality, self-efficacy, self-management behaviors, and family or social support.7,8 However, many existing studies have examined these factors separately or from a single dimension, and relatively few have integrated them within a systematic theoretical framework.

The PRECEDE–PROCEED planning model, also known as PPM, provides a structured framework for assessing health-related behaviors and their determinants.9 The model consists of two major components, PRECEDE and PROCEED, and includes nine phases characterized by dynamic, continuous, cyclical, and progressive processes.10 The PRECEDE component of the PRECEDE–PROCEED model provides a structured framework for comprehensive assessment across five domains: social, epidemiological, behavioral and environmental, educational and ecological, and administrative and policy assessments. Within this framework, factors influencing health outcomes are categorized as predisposing, reinforcing, and enabling factors. This classification facilitates the identification of modifiable determinants of glycemic control and provides a theoretical basis for designing multilevel interventions. Compared with studies that examine isolated associated factors, the PRECEDE–PROCEED model enables a more systematic understanding of factors associated with HbA1c control and supports the development of targeted strategies through subsequent policy, regulatory, and environmental actions. However, evidence remains limited regarding the application of this model to investigate determinants of glycemic control among patients with type 2 diabetes mellitus (T2DM).

Based on the PRECEDE framework, we hypothesized that older age, longer diabetes duration, smoking history, lower self-efficacy, poorer self-management behaviors, and lower social support would be associated with suboptimal glycemic control. In alignment with China’s Medium- and Long-Term Plan for the Prevention and Treatment of Chronic Diseases (2017–2025),11 which emphasizes risk factor control and the creation of supportive health environments, this study aimed to assess the current status of glycemic control and its associated factors among patients with T2DM using the PRECEDE–PROCEED model. The findings may provide evidence to inform the development of scientific, effective, and multilevel glycemic management programs.

MethodsDesign

This was a cross-sectional observational study guided by the assessment components of the PRECEDE-PROCEED Model (PPM). The PPM was used as a theoretical framework to guide variable selection and classification rather than to implement an intervention. This study followed the steps of the PRECEDE–PROCEED Model (PPM) to examine glycemic control and related factors among patients with type 2 diabetes mellitus. The first step, social assessment, involves assessing the target population to identify key health-related issues and inform the context for subsequent health planning. In this study, social assessment was used to describe the demographic and health-related characteristics of patients with type 2 diabetes mellitus. The second step, epidemiological assessment, involves identifying measurable and health-related outcomes. In this study, HbA1c was identified as the main health outcome reflecting glycemic control. The third step, behavioral and environmental assessment, focuses on identifying relevant behavioral and environmental factors. For the first three steps, a general information questionnaire will be administered to 220 patients with type 2 diabetes mellitus. The fourth step, educational and ecological assessment, examines predisposing, reinforcing, and enabling factors based on the theoretical framework of the PPM, as shown in Figure 1. In this study, the Chronic Disease Management Self-Efficacy Scale, Type 2 Diabetes Self-Management Behavior Scale, and Social Support Rating Scale will be used to assess these factors and examine their associations with HbA1c levels among patients with type 2 diabetes mellitus.

Figure 1 Visual Depiction of the Precede-Proceed Model.

Setting and Participants

This cross-sectional study was conducted in the Department of Endocrinology and Metabolism of a tertiary-level hospital in Nanchang City, Jiangxi Province. After obtaining ethics approval on April 13, 2023, 220 patients with type 2 diabetes mellitus who met the inclusion and exclusion criteria were recruited using simple random sampling.

A simple random sampling method was used to recruit participants. During the study period, a list of patients with type 2 diabetes who met the preliminary eligibility criteria was generated from the inpatient registration system. Each eligible patient was assigned a unique identification number. Random numbers were then generated using a computer-based Excel random number function, and patients were selected accordingly. Patients who met the inclusion criteria and provided informed consent were invited to complete the questionnaire. If a selected patient declined participation or was later found to meet the exclusion criteria, the next randomly selected eligible patient was approached until the required sample size was reached.

The inclusion criteria were as follows: (a) meeting the diagnostic criteria specified in the China Guidelines for the Prevention and Treatment of Type 2 Diabetes Mellitus (2020 edition), with a clinician-confirmed diagnosis of type 2 diabetes mellitus for at least 6 months; (b) age ≥18 years; (c) being conscious, free of cognitive impairment, and able to cooperate with the study procedures; and (d) providing written informed consent. The exclusion criteria were as follows: (a) comorbid severe cardiovascular disease, renal disease, or malignant tumors; (b) pregnancy or lactation; (c) discontinuation of treatment or transfer to another hospital or department during the study; and (d) withdrawal from the study or failure to complete the study procedures according to the established protocol.

The sample size was determined according to the main statistical analyses of this cross-sectional study. Since HbA1c was treated as a continuous outcome variable and correlation analysis was one of the core analyses, the sample size was estimated using Fisher’s z transformation for correlation analysis:n1=2+3, where for a two-sided significance level of , for 80% statistical power, and represents the minimum meaningful correlation coefficient. Assuming a small but meaningful correlation coefficient of , the required sample size was 194. After allowing for a 10% rate of invalid or incomplete data, the adjusted required sample size was approximately 216. Therefore, the final sample size of 220 was sufficient. For multiple linear regression, 13 independent variables were included. According to Green’s rule of thumb, n≥50+8m, the minimum required sample size was 50+ 8×13=154. The final sample size of 220 exceeded this requirement and was therefore considered adequate for regression analysis.

Data Collection and MeasuresGeneral Demographic Characteristics

The general information questionnaire was developed by the researchers based on the study objectives and content. It comprised two sections: sociodemographic information, including sex, age, marital status, education level, occupation, medical expense coverage, and monthly household income; and disease-related information, including treatment modality, diabetes-related complications, family medical history, smoking history, disease duration, HbA1c, blood pressure, sources of diabetes-related knowledge, and use of continuous glucose monitoring (CGM).

HbA1c was the primary outcome variable of this study. HbA1c values were obtained from the patients’ most recent laboratory test results recorded in the hospital information system/electronic medical records during the study period. To ensure temporal consistency with the questionnaire survey, HbA1c results measured within three months before questionnaire completion were used. If more than one HbA1c result was available during this period, the result closest to the date of questionnaire completion was selected.

Chronic Disease Management Self-Efficacy Scale

This scale was developed by the Stanford Patient Education Research Center to assess self-management abilities and behaviors among patients with chronic diseases.12 It includes two dimensions-Symptom Management Self-Efficacy and Disease Co-Management Self-Efficacy-and consists of six items. Items 1–4 assess patients’ confidence in managing fatigue, pain, emotional distress, and other health problems, whereas items 5–6 assess perceived control over self-care behaviors and medication use. Each item is rated on a 10-point scale, ranging from 1 (“not at all confident”) to 10 (“completely confident”), with higher scores indicating greater self-efficacy. The Cronbach’s alpha coefficient for this scale is 0.91.13,14

Type 2 Diabetes Self-Management Behavior Scale

The scale was developed by Wang et al,15 to assess self-management behaviors among patients with type 2 diabetes mellitus. It comprises 26 items across six dimensions: diet, exercise, medication use, blood glucose monitoring, foot care, and management of hyperglycemia and hypoglycemia. Each item is rated on a 5-point Likert scale, ranging from 1 (“never”) to 5 (“always”), with total scores ranging from 26 to 130. Higher scores indicate better self-management behaviors. According to the scoring criteria, scores of 0–60 indicate a low level of self-management, scores of 60–85 indicate a moderate level, and scores above 85 indicate a good level. Metric analyses demonstrated excellent scale reliability, with Cronbach’s ranging from 0.82 to 0.88 across subscales, indicating strong internal consistency. Furthermore, test–retest reliability coefficients of 0.92–0.96 confirmed superior temporal stability.16

Social Support Rating Scale

The scale was developed by Xiao Shuiyuan et al17 It consists of 10 items across three dimensions: subjective support, objective support, and utilization of social support. Items 1–4 and 8–10 are single-choice items scored on a 4-point scale, with options 1, 2, 3, and 4 corresponding to 1–4 points, respectively. Items 5–7 are multiple-choice items. Item 5 includes four subitems-A, B, C, and D-which are scored separately and summed, with each subitem assigned 1–4 points according to the level of support. Items 6 and 7 are scored based on the presence or absence of support sources. A total score of 20 was used as the cut-off point: scores <20 indicated low social support, scores of 20–30 indicated moderate social support, and scores of 30–40 indicated satisfactory social support. Higher scores reflect greater perceived social support. The test-retest reliability of the scale was 0.92, and the Cronbach’s alpha for each item ranged from 0.89 to 0.94.18

The internal consistency reliability of the instruments was assessed in the present sample using Cronbach’s alpha coefficients. The Cronbach’s alpha coefficients for the Chronic Disease Management Self-Efficacy Scale, the Type 2 Diabetes Self-Management Behavior Scale, and the Social Support Rating Scale were 0.970, 0.965, and 0.677, respectively. The standardized Cronbach’s alpha coefficient of the Social Support Rating Scale was 0.775. The relatively lower unstandardized Cronbach’s alpha of the Social Support Rating Scale may be attributable to its multidimensional structure, which includes subjective support, objective support, and social support utilization, as well as the heterogeneous scoring formats of several items. After item standardization, the internal consistency of the Social Support Rating Scale was acceptable.

Ethical Considerations

The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University (2nd NCU 2023 - 04 - 13).

Data Analysis

Excel was used to establish the database, and the data were exported to SPSS 26.0 for statistical analysis. All questionnaires were reviewed for completeness at the time of collection. Participants were asked to complete any unintentionally omitted items before submission. Therefore, no missing questionnaire data were present in the final dataset. Cases with incomplete clinical data, including missing HbA1c values, were excluded from the analysis. HbA1c was analyzed as a continuous dependent variable because it provides a continuous clinical measure of glycemic control. No predefined glycemic-control groups were established, as dichotomizing HbA1c may lead to information loss and reduced statistical power. Continuous variables are presented as mean ± standard deviation; the t-test was used for normally distributed data, and the rank-sum test was used for non-normally distributed data. Categorical variables are presented as frequencies and percentages, and the chi-square test was used for group comparisons. Spearman correlation analysis was used to examine the associations of HbA1c with self-efficacy, self-management behaviors, and social support scores. Multivariable linear regression was used to identify factors associated with glycemic control status in patients with type 2 diabetes. Multicollinearity among independent variables in the multivariate linear regression model was assessed using the variance inflation factor. A VIF value less than 5 was considered to indicate the absence of serious multicollinearity. Two-tailed hypothesis tests with P value less than 0.05 were considered statistically significant.

ResultsDemographic and Disease-Related Characteristics of Participants

A total of 220 patients with type 2 diabetes mellitus were included in this study, with a mean age of 57.90 years (SD = 14.28) and a mean disease duration of 8.76 years (SD = 7.52). Among the participants, 25.0% had achieved the glycemic control target (HbA1c < 7%), and 41.4% had HbA1c levels below 8%. The main sources of diabetes-related knowledge were doctors and nurses (78.6%), television and radio (31.8%), family members (30.9%), the internet (30.0%), books and newspapers (25.9%), bulletin boards (18.6%), friends (16.4%), follow-up systems (2.3%), and no information source (4.5%).Participants’ sociodemographic and disease-related characteristics, together with the results of the univariate analysis, are presented in Table 1. The univariate analysis indicated significant differences in glycemic control across age, sex, healthcare payment method, disease duration, diabetes complications, education level, monthly household income, treatment modality, smoking status, and alcohol consumption among patients with type 2 diabetes.

Table 1 Results of Univariate Analysis of Demographic and Sociologic Information, Disease-Related Information, and HbA1c Levels (n=220)

Descriptive Statistics and Correlations for Self-Efficacy, Self-Management Behaviors, and Social Support

Descriptive statistics for chronic disease management self-efficacy, self-management behaviors, and social support among patients with type 2 diabetes are presented in Table 2. The mean self-efficacy score was 35.79 ± 10.96, with higher scores indicating greater self-efficacy. The mean self-management behavior score was 75.06 ± 19.44, suggesting a moderate level of self-management. The mean scores for the dietary control, movement, medication, blood glucose monitoring, and foot care dimensions were 16.58 ± 5.35, 13.56 ± 4.36, 7.31 ± 3.66, 11.98 ± 4.69, and 13.65 ± 4.67, respectively. The mean score for the hyperglycemia and hypoglycemia management dimension was 11.95 ± 4.24. The mean social support score was 34.29 ± 8.48, indicating a relatively high level of perceived social support among patients with type 2 diabetes.

Table 2 Descriptive Analysis of Self-Efficacy, Self-Management Behavior and Social Support Scores

The correlations of HbA1c with self-efficacy, self-management behaviors, and social support among patients with type 2 diabetes are shown in Table 3. Spearman correlation analysis showed that HbA1c was negatively associated with self-efficacy, self-management behaviors, and social support scores (all P < 0.05). Self-efficacy was positively associated with social support (P < 0.05). No significant associations were observed between self-efficacy and self-management behaviors or between self-management behaviors and social support (both P > 0.05).

Table 3 Correlations Between HbA1c and Self-Efficacy, Self-Management Behavior, and Social Support Scores

Results of Multiple Linear Regression Analysis

Table 4 presents the multiple linear regression analysis with HbA1c as the dependent variable and variables showing significant differences in the univariate and correlation analyses as independent variables. The results of the multiple linear regression analysis showed that age (β=−0.189, P<0.001), disease duration (β=0.148, P=0.006), smoking history (β=0.128, P=0.047), self-efficacy (β=−0.220, P<0.001), self-management behavior (β=−0.350, P<0.001), and social support (β=−0.326, P<0.001) were significantly associated with HbA1c, and these factors jointly accounted for 44.4% of the variance.

Table 4 Multiple Linear Regression Model with HbA1c as the Dependent Variable and Multiple Independent Variables

Discussion

Guided by the PPM model, which provides a multidimensional framework for examining factors related to health outcomes, this study investigated the prevalence of poor glycemic control and its associated factors among patients with type 2 diabetes mellitus. In this study, 75% of patients exhibited poor glycemic control, a proportion comparable to those reported among patients with diabetes in Ghana (70%)19 and Malaysia (76%).20,21 Moreover, age, disease duration, smoking history, self-efficacy, self-management behaviors, and social support were significantly associated with poor glycemic control, collectively accounting for 44.4% of the variance in glycemic control.

Social Assessment

The present findings suggest that disease duration and age are associated with glycemic control in patients with type 2 diabetes mellitus. Longer diabetes duration was associated with poorer glycemic control, consistent with findings in Moroccans reported by Ahmed et al,22 This may reflect the progressive nature of type 2 diabetes mellitus, in which pancreatic β-cell function declines over time, accompanied by increased insulin resistance and reduced insulin secretion, thereby making glycemic control more difficult.23 In addition, Tian et al,24 reported that the prevalence of chronic microvascular and macrovascular complications in patients with type 2 diabetes increased from 71.2% in 2012 to 84.6% in 2015. A prolonged disease course may be associated with a higher complication burden, which in turn may relate to lower medication adherence and poorer quality of life, both of which are linked to glycemic control. Increased complications may also be associated with lower medication adherence and poorer quality of life, potentially creating a negative cycle with poor glycemic control.

The present study found that younger age was associated with poorer glycemic control, which is inconsistent with previous reports.25 Browne et al,26 reported that, among individuals aged 18–39 years, those with type 2 diabetes exhibited poorer self-care behaviors, lower physical activity, less healthy dietary patterns, and less standardized insulin injection practices than those with type 1 diabetes and older adults with type 2 diabetes. This pattern may be related to competing developmental priorities in younger adulthood, such as career development, intimate relationships, and family formation, which may be associated with lower adherence to self-management behaviors.27 In addition, self-stigma in younger adults with type 2 diabetes has been reported to be closely associated with glycemic control. Uchigata et al,28 suggested that the lifelong demands of diabetes self-management may be associated with a greater risk of self-stigma in younger individuals. Feelings of being different or discriminated against may also contribute to avoidance of insulin injections in the workplace because of concerns about coworkers’ judgment or exclusion. Such concerns may be associated with irregular medication use and poorer glycemic control.29

Behavioral and Environmental Assessment

Smoking was identified as a behavioral and environmental factor associated with glycemic control in patients with type 2 diabetes mellitus, and was significantly associated with poor glycemic control, consistent with the findings of Odai et al.30 Although the biological mechanisms underlying the association between smoking and poor glycemic control remain incompletely understood, nicotine has been reported to stimulate the secretion of several insulin-antagonistic hormones, including glucocorticoids, growth hormone, and pituitary pressor hormones, and may also impair insulin function and inhibit insulin secretion through toxic effects on pancreatic β-cells.31 The observed association between smoking and poor glycemic control suggests that smoking-related education and cessation support should be strengthened in clinical practice to improve patients’ awareness of the adverse implications of smoking for glycemic status.

Predisposing Factors

Predisposing factors refer to factors that facilitate patients’ adoption of necessary health-related behaviors; in the present study, these factors were reflected by self-efficacy. The concept of self-efficacy originates from social cognitive theory and refers to an individual’s belief in their ability to perform the specific behaviors required to achieve a desired goal.32 In the present study, self-efficacy was negatively associated with poor glycemic control, consistent with the findings of Parichat et al.33 Disease knowledge and self-efficacy are key prerequisites for effective self-management and may mediate the achievement of favorable clinical outcomes, which may help explain why higher self-efficacy was associated with better glycemic control.34 In addition, self-efficacy was positively associated with social support, in line with Tariq et al, who reported a bidirectional motivational relationship among social support, self-efficacy, and glycemic control.35 One possible explanation is that patients with higher self-efficacy may be more likely to seek support from healthcare providers and family members to obtain disease-related knowledge, while family involvement in monitoring disease needs and self-management behaviors may further reinforce self-efficacy, a pattern that may be associated with better glycemic control.36

Enabling Factor

Self-management, defined as active engagement in self-care behaviors to improve health behavior and well-being, is central to glycemic management in type 2 diabetes mellitus. In the present study, glycemic control was significantly associated with self-management level, consistent with previous reports.37 This association may be related to the role of diet, physical activity, and medication management in glycemic management among patients with type 2 diabetes mellitus. However, no significant association was observed between self-management and either self-efficacy or social support, which differs from previous studies.38,39 This discrepancy may be related to differences in study populations, as earlier studies mainly included younger adults with type 2 diabetes, whereas the present sample was predominantly composed of middle-aged and older patients. In addition, Rodrigo et al,39 included other psychosocial variables, such as depression and family functioning, which may also have contributed to the differences between their findings and those of the present study.

Reinforcing Factors

Enabling factors encompass the resources and skills required for behavior change, whereas reinforcing factors increase the likelihood that such behaviors will be maintained. In the present study, social support functioned as both an enabling and a reinforcing factor. This may reflect its role as an important psychosocial resource that provides not only practical assistance but also emotional support and understanding, thereby helping patients with type 2 diabetes cope more effectively with the demands of self-management and maintain adherence-related behaviors.40 In addition, accumulating evidence highlights the supportive role of healthcare providers in diabetes management and education, while peer support has also received increasing attention in recent years.41 Because most self-management activities among patients with type 2 diabetes take place in the home setting, the contribution of family networks to glycemic management should not be overlooked. Therefore, future interventions may benefit from strengthening the collective efficacy of self-management by enhancing family members’ capacity to provide support and share caregiving responsibilities, while also improving the use of social support to facilitate the adoption and maintenance of healthy behaviors.42

Limitations

The limitations of this study should be acknowledged. First, this was a cross-sectional survey; therefore, causal relationships among the study variables could not be established, although the study was guided by a sound theoretical framework. Future longitudinal studies are needed to further verify the associations among the variables of interest. Second, participants were recruited only from Jiangxi Province, which may limit the generalizability of the findings to other regions. Third, although the overall sample size was sufficient for the main analyses, some subgroup categories were relatively small, which may have reduced the stability of subgroup estimates; therefore, these findings should be interpreted cautiously and verified in larger, more balanced samples. Finally, some data were collected through patient self-report, which may have introduced recall or reporting bias. Despite these limitations, this study comprehensively considered internal and external factors associated with glycemic control in patients with type 2 diabetes mellitus under the guidance of the PRECEDE–PROCEED model, and may provide useful evidence for developing future implementation programs based on the PROCEED component.

Conclusion

Guided by the PRECEDE–PROCEED model, this study found that age, disease duration, smoking history, self-efficacy, self-management behavior, and social support were associated with HbA1c control among patients with type 2 diabetes mellitus. Age and disease duration were classified as social diagnostic factors, smoking history as a behavioral and environmental diagnostic factor, self-efficacy as a predisposing factor, self-management behavior as an enabling factor, and social support as both an enabling and reinforcing factor. These findings may inform the development of individualized and targeted glycemic management interventions in future studies.

Data Sharing Statement

The datasets generated during the current study are available from the corresponding author on reasonable request.

Ethical Standard Statement

The research program has been approved by the Ethics Committee of the second affiliated Hospital of Nanchang University ((2nd NCU 2023 - 04 - 13). All methods were carried out in accordance with the Declaration of Helsinki ethical guidelines.

Informed consent was obtained from study participants, including consent to publish of the findings as a paper.

Acknowledgment

We are grateful to all subjects who participated in this study.

Author Contributions

Juan Tu: Conceptualization, Methodology, Investigation, Data curation, Writing - original draft, and Writing - review & editing. Guoxin Lin: Methodology, Investigation, Data curation, Formal analysis, Writing - original draft, and Writing - review & editing. Feng Zhu: Investigation, Resources, Data curation, and Writing - review & editing. Feipeng Xu: Investigation, Resources, Validation, and Writing - review & editing. Yuan Gao: Formal analysis, Visualization, and Writing - review & editing. Qiulan Gan: Investigation, Data curation, Validation, and Writing - review & editing. Yali Chen: Investigation, Resources, Validation, and Writing - review & editing. Xiaofang Hu: Conceptualization, Supervision, Project administration, and Writing - review & editing. Rui Tang: Conceptualization, Supervision, Project administration, and Writing - review & editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by the Jiangxi provincial science and technology department Key R&D program (20202BBGL73075) and the Putian Science and Technology Plan Project (Grant number [2024SY001]).

Disclosure

The authors declare that there are no conflicts of interest in this study.

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