Abstract
This study investigates the impact of a timing theory-based case management model on inpatient management efficiency, self-management capacity, and complication rates in patients with type 2 diabetes mellitus (T2DM). We retrospectively analyzed 120 inpatients with T2DM admitted between December 2023 and March 2024. Patients were divided into 2 groups based on the type of nursing care they received: an observation group (n = 60), which received a timing theory–based case management model, and a control group (n = 60), which received routine nursing care. The timing theory–based model structured nursing interventions into 5 progressive stages – diagnosis, stabilization, preparation, implementation, and adaptation – designed to align with patients’ physical and psychological needs throughout the disease course. Primary outcomes included length of hospital stay, HbA1c target achievement rate, diabetes self-management questionnaire scores, psychological status (depression-anxiety-stress scale), and incidence of complications. Compared with the control group, the observation group had a shorter average hospital stay (8.56 ± 3.45 vs 10.34 ± 3.84 days, P < .001) and a 16.67% higher rate of achieving HbA1c goals (66.67% vs 50.00%, P = .041). Total diabetes self-management questionnaire scores (40.70 ± 3.50 vs 33.00 ± 3.80) and subscale scores – such as medication adherence and dietary control – were significantly better in the observation group (all P < .001). Post-intervention, scores for depression (10.46 ± 3.84 vs 15.51 ± 3.93) and anxiety (9.78 ± 3.73 vs 13.37 ± 3.96) were notably lower (both P < .001). The overall complication rate decreased by 26.66% (16.67% vs 43.33%, P = .007), while complete treatment adherence improved by 13.3% (50.0% vs 36.7%, P = .013). A timing theory-based case management model can significantly shorten hospital stays, enhance self-management and psychological well-being, and reduce the risk of complications in T2DM patients. These findings provide practical evidence for comprehensive diabetes care and offer new perspectives for long-term T2DM management.
Keywords: case management, self-management, timing theory, type 2 diabetes mellitus
1. Introduction
With rapid socioeconomic development and an aging population, the incidence of type 2 diabetes mellitus (T2DM) has surged worldwide, posing a serious challenge to healthcare systems.[1] Recent epidemiological data in China indicate that the prevalence of adult diabetes has reached 12.8% and continues to climb.[2] Given the chronic and complex nature of diabetes, standardized treatment and comprehensive long-term management are crucial. However, surveys show that the current awareness (36.5%), treatment (32.2%), and control (49.2%) rates among patients remain relatively low.[3] One key factor is the prevailing reliance on group-based, fragmented approaches that lack both systematic oversight and individualized care, resulting in insufficient disease knowledge and poor self-management. Thus, enhancing patients’ disease awareness and self-management capabilities has become a pressing issue for clinical practice and community health.
Currently, patients with T2DM commonly face a range of unmet individualized needs in nursing practice. Studies have shown that most hospitalized T2DM patients primarily focus on blood glucose control, while often neglecting critical aspects such as disease education, emotional adjustment, and the development of self-management skills. In parallel, the management of chronic diseases is shifting toward multidisciplinary collaboration and continuity of care, with patient expectations evolving from basic disease treatment to comprehensive, long-term management and psychological support. However, in clinical settings, nurses often struggle to formulate targeted interventions that align with the patient’s evolving psychological and physiological status across different stages of the disease, resulting in inconsistent care outcomes. Given this situation, it is both urgent and necessary to introduce nursing theories characterized by phased and dynamic management strategies.
Proposed by Cameron, the timing theory suggests that patients move through 5 critical stages – diagnosis, stabilization, preparation, implementation, and adaptation – throughout the course of their illness.[4] This theory emphasizes aligning interventions with patients’ psychological needs and physical conditions at each stage to improve care outcomes.[5] Meanwhile, case management models have gained considerable attention in chronic disease care, especially in the long-term management of diabetes.[6] Integrating timing theory into case management can more precisely meet patient needs at different stages of disease progression, thus improving glycemic control and self-care.[7]
This retrospective study aimed to evaluate the effectiveness of a timing theory-based case management model in T2DM patients compared with routine care. By examining hospital stay, self-management ability, psychological status, and complication rates, the study further explores how multidisciplinary collaboration and information-based follow-up may influence HbA1c target achievement and adherence.[8,9] The findings offer new perspectives and practical evidence for advancing diabetes nursing and long-term patient management.
2. Materials and methods
2.1. Study design
This study was approved by the Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University. This study adopted a retrospective cohort design, selecting 120 patients with T2DM who were admitted to our hospital’s Department of Endocrinology from December 2023 to March 2024. According to the different nursing methods, patients were assigned to either an observation group or a control group, each consisting of 60 individuals. Based on existing nursing records and follow-up data, we compared a timing theory-based case management model with routine care to evaluate their respective effects on clinical outcomes. As a retrospective study, all data were derived from previously recorded patient information, and the institutional ethics committee granted a waiver of additional informed consent. The study strictly adhered to the Declaration of Helsinki and relevant national ethical regulations, ensuring patient privacy and confidentiality.
2.2. Inclusion and exclusion criteria
2.2.1. Inclusion criteria
Meeting the 1999 WHO diagnostic criteria for diabetes, with a confirmed T2DM diagnosis and HbA1c > 7.0%; age 18 to 65 years at admission; complete medical and nursing documentation available, including length of hospital stay, in-hospital blood glucose monitoring, and self-management data; at least 3 months of post-discharge follow-up records; signed informed consent at admission.
2.2.2. Exclusion criteria
Severe cardiovascular or cerebrovascular events or malignancy that precluded standard diabetes management; incomplete medical records preventing reliable data retrieval; severe mental or cognitive impairment; concurrent participation in clinical trials that could affect glycemic control or interfere with the intervention; transfer to a non-endocrinology department or discharge against medical advice without complete records.
2.3. Study protocol
2.3.1. Control group
Patients in the control group received routine in-hospital diabetes care, including standard blood glucose monitoring, basic diabetes education, and guidance on diet and exercise to prevent hypoglycemia. After discharge, a designated ward nurse conducted monthly telephone follow-ups to assess glycemic control, insulin use, and daily management. Outpatient visits were arranged at 1, 3, 6, 9, and 12 months post-discharge, during which relevant indicators were recorded.
2.3.2. Observation group
On top of routine care, the observation group was managed through a timing theory-based case management model. A multidisciplinary team – consisting of a case manager, endocrinologist, diabetes nurse, nutritionist, and psychologist – handled patient screening, evaluation, intervention design, and regular follow-up. According to timing theory, patient management was divided into 5 stages:
Diagnosis stage (within 24 hours of admission): Prioritize psychological support and patient assessment, establish trust, and clarify needs.
Stabilization stage: As blood glucose levels stabilize, develop personalized care and education plans.
Preparation stage: Strengthen structured health education, ensuring that patients acquire fundamental knowledge and skills for post-discharge self-management.
Implementation stage (within 1 month post-discharge): Monitor adherence and glucose levels through an online “Case Communication” platform, WeChat groups, and telephone follow-ups, correcting deviations promptly.
Adaptation stage: Through regular clinic appointments and online monitoring (at 1, 3, 6, 9, and 12 months post-discharge), reinforce and adjust self-management strategies, encouraging continued follow-up and appropriate exercise.
2.4. Outcome measures
2.4.1. Clinical indicators
Four parameters were used to assess the effects of each intervention: HbA1c achievement rate, length of hospital stay, time to stabilize postprandial glucose, and time required for discharge education. These measures help evaluate how differing nursing models affect hospitalization duration and discharge preparation efficiency.
2.4.2. Self-management capacity
Self-management capacity was gauged using the Chinese version of the diabetes self-management questionnaire (DSMQ). This instrument contains 16 items across 5 dimensions: medication adherence (2 items), blood glucose control (3 items), dietary control (4 items), physical activity (3 items), and follow-up (4 items). Each item is scored on a 4-point Likert scale (0 = “strongly disagree,” 3 = “strongly agree”) for a total of 48 possible points. Higher scores indicate stronger self-management, and between-group comparisons highlight the impact of a timing theory-based case management model on patient self-care and disease management.
2.4.3. Psychological status (DASS)
Depression-anxiety-stress scale (DASS) scores were obtained from nursing documentation to compare emotional status before and after intervention, thereby evaluating the effectiveness of different nursing models in alleviating negative emotions and promoting mental health.
2.4.4. Coping style (MCMQ)
The medical coping modes questionnaire, completed during hospitalization and follow-up, provided insight into patients’ coping mechanisms – facing, avoidance, and resignation. This measure offers a basis for assessing whether the timing theory approach fosters more positive coping behaviors.
2.4.5. Treatment adherence and complications
Adherence was classified as full, partial, or nonadherence (with respect to diet, medication, and follow-up), according to medical and follow-up records. The incidence of acute and chronic diabetes complications – such as hypoglycemia, ketoacidosis, cardiovascular events, and diabetic nephropathy – was documented based on ICD-11 diagnostic standards.
2.5. Data collection
Two experienced researchers independently gathered and verified medical and follow-up records. Any discrepancies were reviewed by a third researcher to ensure data accuracy. Cases lacking required inpatient or follow-up data were excluded. All personal identifiers were removed to maintain patient privacy and data security. All included patient records had complete data for the primary and secondary outcomes. Cases with missing values in core indicators were excluded during the initial screening process to ensure data integrity and reduce bias in the final analysis. Follow-up assessments for complications were conducted at 12 months after discharge.
2.6. Statistical analysis
Data analysis was performed using SPSS 26.0 (IBM Corp., Armonk). Continuous variables are presented as mean ± standard deviation (x ± s), and between-group differences were tested using independent-samples t tests. Categorical data are expressed as frequencies and percentages, analyzed by chi-square (χ²) tests. For time-series indicators across multiple follow-up points, repeated-measures ANOVA or nonparametric tests were applied. A 2-tailed P < .05 was considered statistically significant.
3. Results
3.1. Baseline characteristics
A total of 120 patients with T2DM were included in this study. No statistically significant differences were observed between the observation and control groups in age, sex, BMI, disease duration, or comorbidities (all P > .05). The mean age in each group was 56.20 ± 7.82 versus 55.60 ± 8.15 years (P = .618), with 50.0% versus 46.7% males (P = .718). Similarly, distributions of hypertension, hyperlipidemia, and family history did not differ significantly (Table 1), suggesting comparable baselines.
Table 1.
Comparison of baseline characteristics.
| Variable | Observation (n = 60) | Control (n = 60) | t/ χ² | P |
|---|---|---|---|---|
| Age (yr) | 56.20 ± 7.82 | 55.60 ± 8.15 | 0.501 | .618 |
| Sex (male/female, n, %) | 30/30 (50.0/50.0) | 28/32 (46.7/53.3) | 0.130 | .718 |
| BMI (kg/m²) | 25.50 ± 2.30 | 25.20 ± 2.60 | 0.570 | .57 |
| Disease course (yr) | 9.00 ± 5.20 | 8.40 ± 4.60 | 0.782 | .437 |
| Family history (yes, %) | 18 (30.0) | 21 (35.0) | 0.383 | .537 |
| Hypertension (yes, %) | 18 (30.0) | 17 (28.3) | 0.040 | .842 |
| Hyperlipidemia (yes, %) | 20 (33.3) | 19 (31.7) | 0.038 | .86 |
| Diabetes (yes, %) | 22 (36.7) | 20 (33.3) | 0.152 | .699 |
3.2. Self-management ability (DSMQ)
The observation group showed significantly higher total and subscale DSMQ scores than the control group (all P < .001). Notably, medication adherence (6.00 ± 1.20 vs 4.50 ± 1.10), blood glucose control (9.00 ± 1.00 vs 7.00 ± 1.10), and dietary management (11.00 ± 1.30 vs 9.50 ± 1.20) exhibited the most pronounced improvements. The observation group’s total DSMQ score reached 40.70 ± 3.50, reflecting a 23.3% increase compared to the control group (Table 2, Fig. 1).
Table 2.
Comparison of self‑management ability (DSMQ).
| Variable | Observation (n = 60) | Control (n = 60) | t | P |
|---|---|---|---|---|
| Medication adherence | 6.00 ± 1.20 | 4.50 ± 1.10 | 7.501 | <.001 |
| Blood glucose control | 9.00 ± 1.00 | 7.00 ± 1.10 | 8.002 | <.001 |
| Dietary control | 11.00 ± 1.30 | 9.50 ± 1.20 | 9.032 | <.001 |
| Physical exercise | 7.00 ± 1.00 | 6.00 ± 1.00 | 6.556 | <.001 |
| Follow-up | 7.70 ± 1.10 | 6.00 ± 1.00 | 7.208 | <.001 |
| Total score | 40.70 ± 3.50 | 33.00 ± 3.80 | 10.051 | <.001 |
DSMQ = diabetes self-management questionnaire.
Figure 1.
Comparison of self-management ability.
3.3. Clinical indicators
The observation group had a significantly higher HbA1c achievement rate than the control group (66.67% vs 50.00%, P = .041). Time required for discharge education (1.96 ± 1.03 vs 4.08 ± 1.14 days) and average hospital stay (8.56 ± 3.45 vs 10.34 ± 3.84 days) were both markedly shorter in the observation group (both P < .001). There was no statistically significant difference in postprandial blood glucose stabilization time between groups (P > .05; Table 3, Fig. 2).
Table 3.
Comparison of clinical indicators.
| Variable | Observation (n = 60) | Control (n = 60) | t/ χ² | P |
|---|---|---|---|---|
| HbA1c achievement rate | 40 (66.67) | 30 (50.00) | 4.201 | .041 |
| Discharge education time (d) | 1.96 ± 1.03 | 4.08 ± 1.14 | 10.69 | <.001 |
| Mean hospital stay (d) | 8.56 ± 3.45 | 10.34 ± 3.84 | 3.673 | <.001 |
| Postprandial BG stabilization time (h) | 25.95 ± 11.73 | 26.36 ± 10.18 | 0.205 | .839 |
Figure 2.
Comparison of clinical indicators. (A) Discharge education time (d); (B) mean hospital stay (d); (C) postprandial BG stabilization time (h).
3.4. DASS scores
Before the intervention, there were no significant differences in depression, anxiety, or stress scores between groups (all P > .05). After the intervention, the observation group showed markedly lower scores than the control group (all P < .001), suggesting a positive effect on alleviating patients’ negative emotions (Table 4).
Table 4.
Comparison of DASS scores (mean ± SD).
| Depression | Anxiety | Stress | |
|---|---|---|---|
| Before care | |||
| Observation (n = 60) | 16.96 ± 2.13 | 16.36 ± 2.98 | 16.93 ± 2.68 |
| Control (n = 60) | 17.41 ± 2.16 | 16.25 ± 3.16 | 17.08 ± 2.51 |
| t | 1.149 | 0.196 | 1.846 |
| P | .253 | .845 | .067 |
| After care | |||
| Observation (n = 60) | 10.46 ± 3.84 | 9.78 ± 3.73 | 11.03 ± 3.18 |
| Control (n = 60) | 15.51 ± 3.93 | 13.37 ± 3.96 | 14.17 ± 2.83 |
| t | 7.119 | 6.438 | 9.472 |
| P | <.001 | <.001 | <.001 |
DASS = depression-anxiety-stress scale.
3.5. Coping styles
No significant difference in coping styles (facing, avoidance, and yielding) was observed between groups at baseline (all P > .05). Post-intervention, the observation group scored significantly higher on facing (19.02 ± 3.72 vs 13.17 ± 3.84, P < .001) and significantly lower on avoidance and yielding than the control group (both P < .001; Table 5).
Table 5.
Comparison of coping style scores (mean ± SD).
| Face (points) | Avoidance (points) | Yield (points) | |
|---|---|---|---|
| Before care | |||
| Observation (n = 60) | 23.23 ± 4.66 | 8.75 ± 2.10 | 8.66 ± 2.05 |
| Control (n = 60) | 23.20 ± 4.61 | 8.80 ± 2.15 | 8.61 ± 2.00 |
| t | 0.035 | 0.129 | 0.135 |
| P | .972 | .898 | .893 |
| After care | |||
| Observation (n = 60) | 19.02 ± 3.72 | 11.17 ± 2.63 | 11.15 ± 1.74 |
| Control (n = 60) | 13.17 ± 3.84 | 16.21 ± 2.85 | 16.84 ± 2.81 |
| t | 8.476 | 10.067 | 13.335 |
| P | <.001 | <.001 | <.001 |
3.6. Treatment adherence
The observation group had a higher rate of complete adherence (50.0% vs 36.7%) and a lower rate of nonadherence (16.7% vs 33.3%) compared with the control group (χ² = 8.678, P = .013; Table 6).
Table 6.
Comparison of medical compliance behaviors (n, %).
| Group | Complete compliance | Partial compliance | Noncompliance | χ² | P |
|---|---|---|---|---|---|
| Observation (60) | 30 (50.0) | 20 (33.3) | 10 (16.7) | 8.678 | .013 |
| Control (60) | 22 (36.7) | 18 (30.0) | 20 (33.3) |
3.7. Complications
The overall complication rate in the observation group was 16.67%, significantly lower than the 43.33% observed in the control group (χ² = 7.501, P = .007), suggesting that a timing theory-based case management approach effectively reduces the risk of diabetes-related complications (Table 7).
Table 7.
Comparison of complications.
| Complication type | Observation (n = 60) | Control (n = 60) | χ² | P |
|---|---|---|---|---|
| Hypoglycemia | 2 (3.33) | 5 (8.33) | – | – |
| Diabetic ketoacidosis | 1 (1.67) | 3 (5.00) | – | – |
| Hyperosmolar hyperglycemic state | 0 (0.00) | 2 (3.33) | – | – |
| Cardiovascular/cerebrovascular events | 2 (3.33) | 4 (6.67) | – | – |
| Neuropathy | 3 (5.00) | 5 (8.33) | – | – |
| Diabetic nephropathy | 1 (1.67) | 3 (5.00) | – | – |
| Retinopathy | 1 (1.67) | 2 (3.33) | – | – |
| Diabetic foot | 0 (0.00) | 2 (3.33) | – | – |
| Total | 10 (16.67) | 26 (43.33) | 7.501 | .007 |
4. Discussion
T2DM is one of the most prevalent chronic metabolic diseases, both in China and globally, with an incidence that continues to rise owing to population aging and lifestyle changes.[10] Because of its long disease course and high risk of complications, T2DM imposes a substantial burden on patients and families,[11] and it also presents formidable challenges to healthcare resources and socioeconomic development,[12] In recent years, multidisciplinary collaboration, information technology, and individualized nursing approaches have been increasingly employed in chronic disease management. By providing tailored interventions, these strategies aim to enhance patients’ self-management and treatment adherence, ultimately delaying the onset or progression of complications.[13–15] The timing theory-based case management model is a structured, stage-specific nursing approach that aligns care delivery with the evolving psychological and physiological needs of patients during the course of illness. Based on Cameron’s timing theory, the model divides the care process into 5 stages – diagnosis, stabilization, preparation, implementation, and adaptation – each of which corresponds to a critical period in the patient’s disease experience. By tailoring interventions to each stage, this model enhances patient engagement, optimizes education delivery, and strengthens self-management and psychological support. In the context of chronic disease care, particularly for T2DM, such a dynamic and patient-centered model offers significant potential to improve treatment outcomes and continuity of care.
In the present retrospective cohort study, implementation of a timing theory-based case management model – when compared with routine care – significantly reduced hospital stay and discharge education time, while notably improving self-management scores across all dimensions. Moreover, depression, anxiety, and stress indicators showed greater improvement in the observation group, accompanied by lower complication rates and better treatment adherence. These findings suggest that a phased and personalized nursing intervention can effectively strengthen disease control and mitigate risks during the transition from inpatient to outpatient care. In contrast, routine care alone may partially stabilize blood glucose but tends to be less robust in psychological support, comprehensive patient education, and continuity of care.
Aligned with previous research, our results reinforce the positive role of case management in long-term T2DM care[16] and support the feasibility of applying timing theory in chronic disease management.[17] Literature indicates that addressing patients’ psychological and physical needs at each disease stage can significantly improve adherence and disease understanding, thereby reducing complication rates.[18] However, the absence of a significant between-group difference in short-term glycemic targets suggests that factors such as the duration of observation, specific intervention focus, and baseline patient characteristics may also be influential. Although case management emphasizes whole-process, individualized care, short-term glycemic control often depends on multiple elements – such as medication, diet, and exercise – and may require more extended follow-up or additional indicators for thorough evaluation.
This study has several limitations. First, it was a single-center retrospective study with a relatively small sample size and limited follow-up period, making it difficult to comprehensively assess long-term complication prevention. Second, baseline variations and external confounders could not be fully eliminated. Lastly, we did not analyze the detailed effects or costs associated with online platforms and other specific intervention components. Future multi-center, large-scale prospective studies are warranted to validate the generalizability of this model and refine intervention strategies, ultimately offering safer, more efficient, and personalized comprehensive care for patients with diabetes.
5. Conclusion
This retrospective cohort study found that timing theory-based case management significantly reduced hospital stay, improved self-management and treatment adherence, and lowered the risk of complications during the transition from inpatient to outpatient care in T2DM patients. While further multi-center, long-term studies are needed to validate these findings, the model has demonstrated strong potential for application and offers valuable insights for the future of chronic disease management and personalized care.
Author contributions
Conceptualization: Yaling Tang, Li Liu.
Data curation: Yaling Tang, Li Liu, Hang Sun.
Formal analysis: Yaling Tang, Weiwei Xu, Hang Sun.
Investigation: Weiwei Xu, Hang Sun.
Methodology: Yaling Tang, Weiwei Xu, Hang Sun.
Validation: Hang Sun.
Visualization: Yaling Tang, Yunqiu Luo, Hang Sun.
Writing – original draft: Yaling Tang, Li Liu, Yunqiu Luo, Hang Sun.
Writing – review & editing: Yaling Tang, Hang Sun.
Abbreviations:
- DASS
- depression-anxiety-stress scale
- DSMQ
- diabetes self-management questionnaire
- T2DM
- type 2 diabetes mellitus.
Nursing Scientific Research Project of the Second Affiliated Hospital of Chongqing Medical University (HL2023-13).
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Tang Y, Liu L, Xu W, Luo Y, Sun H. Applying timing theory to nursing case management of type 2 diabetes mellitus: A clinical retrospective study. Medicine 2025;104:35(e43832).
Contributor Information
Yaling Tang, Email: 178239725712@163.com.
Li Liu, Email: Liuli279801400@163.com.
Weiwei Xu, Email: Xuweiwei2544082972@163.com.
Yunqiu Luo, Email: Luoyunqiu249953317@163.com.
References
- [1].Cho NH, Shaw JE, Karuranga S, et al. IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–81. [DOI] [PubMed] [Google Scholar]
- [2].Chinese Diabetes Society. Guidelines for the prevention and treatment of type 2 diabetes in China (2020 edition) [in Chinese]. Chin J Diabetes. 2021;13:315–409. [Google Scholar]
- [3].Xu Y, Wang L, He J, et al. ; 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA. 2013;310:948–59. [DOI] [PubMed] [Google Scholar]
- [4].Cameron JI, Gignac MA. “Timing it right”: a conceptual framework for addressing the support needs of family caregivers to stroke survivors from the hospital to the home. Patient Educ Couns. 2008;70:305–14. [DOI] [PubMed] [Google Scholar]
- [5].Ma JJ, Zhang XH, Wang L. Application progress of timing theory in chronic disease nursing [in Chinese]. Chin J Nurs. 2020;55:280–4. [Google Scholar]
- [6].Vrijhoef HJM, Berbee R, Wagner EH, et al. Quality of integrated chronic care measured by patient survey: Identification, selection and application of most appropriate instruments. Health Policy. 2009;91:293–304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Cameron JI, Naglie G, Green TL, et al. A feasibility study of the timing it right stroke family support program. Clin Rehabil. 2016;30:454–63. [DOI] [PubMed] [Google Scholar]
- [8].Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM. Self-management education for adults with type 2 diabetes: a meta-analysis of the effect on glycemic control. Diabetes Care. 2001;25:1159–71. [DOI] [PubMed] [Google Scholar]
- [9].Powers MA, Bardsley J, Cypress M, et al. Diabetes self-management education and support in type 2 diabetes: a joint position statement of the ADA, AADE, and AND. Diabetes Care. 2020;43:1636–49. [DOI] [PubMed] [Google Scholar]
- [10].Saeedi P, Petersohn I, Salpea P, et al. ; IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas. Diabetes Res Clin Pract. 2019;157:107843. [DOI] [PubMed] [Google Scholar]
- [11].Einarson TR, Acs A, Ludwig C, Panton UH. Economic burden of cardiovascular disease in type 2 diabetes: a systematic review. Value Health. 2018;21:881–90. [DOI] [PubMed] [Google Scholar]
- [12].Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88–98. [DOI] [PubMed] [Google Scholar]
- [13].Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA. 2002;288:1775–9. [DOI] [PubMed] [Google Scholar]
- [14].Lu J, Wang C, Shen Y, et al. Effects of mobile health intervention on self-management in patients with type 2 diabetes: a randomized controlled trial. JMIR Mhealth Uhealth. 2020;8:e17787. [Google Scholar]
- [15].Davies MJ, D’Alessio DA, Fradkin J, et al. Management of hyperglycemia in type 2 diabetes: a consensus report by the ADA and EASD. Diabetes Care. 2018;41:2669–701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Holt RI, de Groot M, Golden SH. Diabetes and depression. Curr Diab Rep. 2014;14:491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Gerstein HC, Miller ME, Byington RP, et al. ; Action to Control Cardiovascular Risk in Diabetes Study Group. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358:2545–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes: a patient-centered approach. Diabetes Care. 2015;38:140–9. [DOI] [PubMed] [Google Scholar]