Introduction
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease caused by insufficient insulin secretion or reduced insulin efficiency, accounting for approximately 90-95% of all diabetes cases worldwide [1]. According to the International Diabetes Federation, over 537 million people currently suffer from diabetes globally, and this number is projected to reach 643 million by 2030 [2]. In China, diabetes has become a significant public health issue, with recent studies showing an estimated prevalence of approximately 11% among adults, equating to over 140 million individuals affected [3].
For patients with T2DM, maintaining optimal blood glucose levels, specifically an HbA1c level of ≤ 7%, is a primary goal in clinical care. Studies have demonstrated that effective blood glucose control can substantially reduce the risk of diabetes-related complications (by 53-63%) and mortality (by 46%) [4]. However, successful blood glucose management largely depends on patients’ adherence to healthy lifestyle practices, such as regular exercise, balanced diet, and proper medication usage [5]. Many patients, however, find it challenging to sustain these behaviors in daily life, making ongoing supervision and follow-up essential in diabetes management.
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With advancements in telemedicine, remote nursing interventions have emerged as a convenient, efficient, and cost-effective approach to diabetes care, and have been widely adopted in many countries [6]. Remote nursing includes a variety of methods such as phone calls, mobile applications, WeChat, SMS, internet platforms, and smart nursing systems [7], Among these, telephone intervention has become one of the most commonly used tools for nurses to provide remote care to diabetes patients due to its accessibility and interactive benefits [8].
Although several studies have explored the effects of telephone interventions by various healthcare professionals (such as dietitians, physicians, multidisciplinary teams, and psychologists) on diabetes management [9‐11], there remains a lack of evidence-based data regarding the impact of nurse-led telephone interventions on HbA1c levels in T2DM patients. Additionally, there is no consensus on key elements of telephone follow-up, including optimal frequency, duration per call, content of follow-ups, and interval between follow-ups, which are crucial for effective blood glucose control. Clarifying the influence of these factors on glycemic outcomes would help to inform more targeted follow-up protocols in clinical practice.
This study aims to conduct a meta-analysis to systematically evaluate the effects of nurse-led telephone interventions on HbA1c levels in T2DM patients. Through subgroup analyses, we aim to identify the optimal duration, content, frequency, and interval of telephone follow-ups. This will provide clinical nurses with effective follow-up protocols, enhance the quality of remote nursing care, and contribute evidence-based strategies for blood glucose management in T2DM patients.
Methods
This review strictly follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and is registered on the PROSPERO platform (Registration Number: CRD42024578866).
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Inclusion and exclusion criteria
To ensure the comprehensiveness of this meta-analysis, we made every effort to include all eligible studies that met the inclusion criteria. Comprehensive database searches were conducted across PubMed, Web of Science, Cochrane Library, Embase, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database (VIP). Additionally, we manually screened the reference lists of relevant articles to identify additional studies and contacted corresponding authors when necessary to obtain further data or clarifications. These efforts were undertaken to minimize the risk of omitting relevant studies and to ensure that the analysis provides a complete and representative evaluation of the available evidence.
Inclusion criteria: (1) Participants: Adult patients with a confirmed diagnosis of T2DM. (2) Intervention: The experimental group received nurse-led telephone follow-ups in addition to standard care, with a detailed follow-up protocol; the control group received standard care. (3) Outcome measure: The primary outcome was HbA1c levels before and after the intervention, a reliable indicator of average blood glucose levels over the preceding 2–3 months in diabetic patients [12]. (4) Study design: Randomized controlled trials (RCTs). Exclusion criteria: (1) Interventions not led by nurses (e.g., interventions by physician teams or psychologists); (2) Inaccessible full texts or studies where data could not be extracted; (3) Conference abstracts, study protocols, or reviews; (4) Non-English and non-Chinese literature: To ensure the comprehensiveness and regional applicability of this study, we included Chinese literature. The inclusion of these studies was based on China’s extensive research background and wide implementation experience in nurse-led follow-up interventions. All Chinese studies were translated into English by the research team and assessed according to standardized quality evaluation criteria to ensure the reliability and transparency of the data. (5) Duplicate publications.
Literature search strategy
A comprehensive search was conducted in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP, CBM, Wan fang Database for RCTs investigating the effects of nurse-led telephone interventions on HbA1c control in patients with type 2 diabetes. The search period extended from the inception of each database to February 2024. This timeframe was chosen to ensure that all relevant studies, from the earliest available literature on nurse-led telephone interventions to the most recent findings, were included. Nurse-led telephone follow-ups have gained prominence in diabetes care over the past several decades, and this search period comprehensively captures the evolution of this intervention. We used a combination of subject terms and free terms. Boolean operators were employed to refine searches. The search terms included “telehealth,” “telemedicine,” “tele-referral,” “virtual medicine,” “virtual nursing,” “telecare,” “tele intensive care,” “mobile health,” “digital health,” “remote consultation,” “mobile phone,” “telephone,” “smartphone,” “e-mail,” and terms for diabetes such as “Type 2 Diabetes,” “Diabetes Mellitus, Type II,” “Diabetes Mellitus, Noninsulin-Dependent,” “Type 2 Diabetes,” “NIDDM,” and “T2DM.” The detailed search strategy for each database is provided in Appendix Table 1.
Table 1
Basic characteristics of the studies
Author | Year | Country | Sample (n) | Intervention | Duration | Content | Frequency | ||
|---|---|---|---|---|---|---|---|---|---|
T | C | T | C | ||||||
Zhu et al. [23] | 2018 | China | 49 | 49 | Standard Care + Telephone | Standard Care | 2 | ④ | 3 |
Liu et al. [24] | 2021 | China | 60 | 60 | Standard Care + Telephone | Standard Care | 6 | ② | 12 |
Liu et al. [25] | 2013 | China | 100 | 100 | Standard Care + Telephone | Standard Care | 3 | ① | 3 |
Wang et al. [26] | 2018 | China | 62 | 61 | Standard Care + Telephone | Standard Care | 12 | ③ | 12 |
Odnoletkova et al. [22] | 2016 | Belgium | 240 | 246 | Standard Care + Telephone | Standard Care | 18 | ② | 5 |
Blackberry et al. [14] | 2013 | Australia | 221 | 219 | Standard Care + Telephone | Standard Care | 15 | ③ | 8 |
De Vasconcelos et al. [16] | 2018 | Brazil | 16 | 15 | Standard Care + Telephone | Standard Care | 6 | ③ | 12 |
Esmaeilpour-Bandbonil et al. [17] | 2021 | Iran | 28 | 32 | Standard Care + Telephone | Standard Care | 3 | ④ | 8 |
Kim and Oh et al. [18] | 2003 | Korea | 20 | 16 | Standard Care + Telephone | Standard Care | 3 | ④ | 16 |
Kim et al. [19] | 2005 | Korea | 15 | 10 | Standard Care + Telephone | Standard Care | 3 | ④ | 16 |
Nesari et al. [20] | 2010 | Iran | 30 | 30 | Standard Care + Telephone | Standard Care | 3 | ④ | 16 |
Odnoletkova and Goderis et al. [21] | 2014 | Belgium | 287 | 287 | Standard Care + Telephone | Standard Care | 18 | ② | 16 |
Brown-Deacon et al. [15] | 2017 | USA | 20 | 21 | Standard Care + Telephone | Standard Care | 3 | ① | 3 |
Table 2
Subgroup analysis of the effects of telephone intervention on overall HbA1c levels
Subgroup Variables | Category | Number of Studies | N (T/C) | MD (95%Cl) | Heterogeneity Test | Two-Tailed Test | |||
|---|---|---|---|---|---|---|---|---|---|
x2 | P | I2(%) | Z | P | |||||
Follow-Up Frequency | 3–5 | 4 | 409/416 | -0.28[-0.64,0.07] | 14.04 | 0.003 | 79 | 1.57 | 0.12 |
8 | 2 | 249/251 | -0.36[-0.99,0.27] | 7.15 | 0.007 | 86 | 1.13 | 0.26 | |
12 | 3 | 138/136 | -0.87[-1.28,-0.46] | 4.94 | 0.08 | 60 | 4.13 | < 0.001 | |
16 | 4 | 352/343 | -0.92[-1.71,-0.12] | 27.25 | < 0.001 | 89 | 2.26 | 0.02 | |
Interval Duration | 1 week | 4 | 165/156 | -0.93[-1.68,-0.17] | 24.99 | < 0.01 | 88 | 2.40 | 0.02 |
2 weeks | 5 | 411/415 | -0.46[-1.00,0.09] | 28.96 | < 0.01 | 86 | 1.65 | 0.10 | |
Half a month | 2 | 111/110 | -1.29[-2.43,-0.15] | 5.27 | 0.02 | 81 | 2.22 | 0.03 | |
≥ 1month | 2 | 461/465 | -0.09[-0.24,0.06] | 0.06 | 0.80 | 0 | 1.12 | 0.26 | |
Follow-Up Duration | 10–15 min | 3 | 190/193 | -0.54[-1.02,-0.06] | 26.00 | < 0.01 | 92 | 2.21 | 0.03 |
20–25 min | 3 | 65/56 | -1.23[-1.63,-0.83] | 1.08 | 0.58 | 0 | 5.98 | < 0.001 | |
30 min | 2 | 307/308 | -0.09[-0.25,0.06] | 0.34 | 0.56 | 0 | 1.17 | 0.24 | |
Follow-Up Content | ① | 2 | 120/121 | -0.15[-0.30,-0.01] | 0.50 | 0.48 | 0 | 2.07 | 0.04 |
② | 3 | 587/593 | -0.42[-0.87,0.04] | 24.53 | < 0.01 | 92 | 1.80 | 0.07 | |
③ | 3 | 299/295 | -0.37[-0.99,0.25] | 19.94 | < 0.01 | 90 | 1.18 | 0.24 | |
④ | 5 | 142/137 | -1.17[-1.60,-0.75] | 8.23 | 0.08 | 51 | 5.42 | < 0.01 | |
Literature screening and data extraction
Two researchers (Y.C. and T.Z.) with backgrounds in evidence-based medicine independently screened the literature and extracted data. In cases of disagreement, a third researcher (X.K.) was consulted to resolve conflicts. All identified studies were imported into EndNote, and duplicates were removed. Titles and abstracts were screened for initial selection, followed by a full-text review for final inclusion. Extracted data included first author, publication year, country, sample size, intervention details, duration of intervention (in months), and outcome measures.
Quality assessment of included studies
Two researchers (Y.C. and T.Z.) trained in evidence-based medicine independently assessed the quality of each study using the Cochrane Handbook’s criteria for RCTs [13]. Quality indicators included random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessors, data integrity, selective outcome reporting, and other sources of bias. Each criterion was assessed as follows, in accordance with the guidelines provided by the Cochrane Handbook for Systematic Reviews of Interventions: Low risk: The study provided adequate methods or information to meet the criterion, with minimal risk of bias. Unclear risk: The study provided insufficient details to fully assess the criterion, or the reported methods lacked transparency. High risk: The study reported methods that clearly introduced bias or failed to meet the criterion.
Studies were categorized into three grades based on the combined assessment of all seven criteria, following the Cochrane Handbook: Grade A (High Quality): A study was rated as Grade A only if all seven criteria were assessed as “low risk.”Grade B (Moderate Quality): A study was rated as Grade B if it had a majority (≥ 4) of “low risk” ratings but included one or more “unclear risk” ratings and no more than one “high risk” rating. Grade C (Low Quality): A study was rated as Grade C if fewer than four criteria were assessed as “low risk” or if two or more criteria were assessed as “high risk. After quality appraisal, studies rated as Grade A or Grade B were included in the meta-analysis. Grade C studies were excluded due to their high risk of bias and potential to compromise the validity of the results.
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Statistical analysis
Meta-analysis was conducted using RevMan 5.4 software. Heterogeneity was first assessed; if P > 0.1 and I² < 50%, a fixed-effect model was applied, indicating acceptable heterogeneity. If P ≤ 0.1 and I² > 50%, a random-effect model was applied, and potential sources of heterogeneity were further explored through subgroup analysis. Sensitivity analysis was used to test the robustness of the results, and publication bias was assessed through funnel plot analysis. A significance level of α = 0.05 was applied, with P < 0.05 considered statistically significant.
Results
Literature search results
A total of 2,194 studies were retrieved from eight databases, including PubMed, Cochrane Library, CNKI, etc., with the search period from the establishment of the database to February 2024. After removing 693 duplicate records, the remaining 1,501 studies were screened for eligibility based on titles and abstracts. Following this screening, 1,448 studies were excluded. Subsequently, 53 full-text articles were evaluated in detail, and finally, 13 studies [14‐26] were included for analysis. This screening process was conducted independently by two researchers (Y.C. and T.Z.) trained in evidence-based medicine. Any discrepancies during the screening were resolved through discussion or, when necessary, by consulting a third researcher (X.K.). The literature screening process is shown in Fig. 1.
Fig. 1
Literature screening flowchart. Notes: CNKI: China National Knowledge Infrastructure, VIP: China Science and Technology Journal Database, CBM: China Biology Medicine
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Basic characteristics of the included studies
Among the 13 studies included, 9 were published in English [14‐22] and 4 in Chinese [23‐26],, involving a total of 2,294 patients with T2DM. The control groups in all studies received standard care, while the experimental groups implemented nurse-led telephone follow-up interventions. The studies were conducted in various countries, including China (4 studies), South Korea (2 studies), Belgium (2 studies), Iran (2 studies), Australia (1 study), Brazil (1 study), and the United States (1 study). The follow-up duration ranged from 2 to 18 months, with intervention frequencies ranging from 3 to 16 times. Intervention content varied across studies and encompassed four main categories: ① Medication and lifestyle guidance, focusing on medication adherence and general health behaviors; ② Medication, diet, and exercise, providing detailed dietary planning, physical activity guidance, and medication management; ③ Medication and lifestyle, with an emphasis on medication adherence and lifestyle adjustments; ④ Comprehensive management, including medication, diet, exercise, and blood glucose monitoring. As shown in Table 1, studies with shorter follow-up durations (e.g., 2–3 months) typically emphasized immediate interventions such as medication adherence, while those with longer durations (e.g., 16 months) often incorporated comprehensive diabetes management. The specific characteristics of the included studies, including intervention content, duration, and frequency, are detailed in Table 1.
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Quality assessment of the included studies
The quality of the studies was assessed based on random sequence generation, allocation concealment, blinding, data completeness, selective outcome reporting, and other biases. The results indicated that 3 studies were rated as Grade A (high quality) and 10 studies were rated as Grade B (moderate quality). The results of the quality assessment are presented in Supplementary File - Appendix 2.
Meta-analysis results
Overall effect test
Thirteen studies reported the effects of nurse-led telephone interventions on HbA1c in T2DM patients. All studies measured HbA1c using venous blood samples, and the mean difference (MD) was used to combine effect sizes. Due to substantial heterogeneity in the results (P < 0.01, Tau²= 0.15, I² = 87%), a random-effects model was used for analysis. The results indicated that nurse-led telephone interventions significantly improved HbA1c levels in T2DM patients (MD = -0.59, 95% CI = [-0.85, -0.34], P < 0.00001). The overall effect forest plot is shown in Fig. 2.
Fig. 2
Forest Plot of the overall effect of nurse-led telephone intervention on HbA1c
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Subgroup analysis
Table 2 presents the results of the subgroup analysis, which was conducted based on the following factors: follow-up frequency (3–5 times, 8 times, 12 times, 16 times), the interval between two follow-ups (1 week, 2 weeks, half a month, ≥ 1 month), the duration of a single follow-up (10–15 min, 20–25 min, 30 min), and intervention content (medication, blood glucose monitoring; medication, diet, exercise; medication, lifestyle, disease management; medication, diet, exercise, blood glucose monitoring). The results are as follows:
Follow-up frequency
Four studies [15, 22, 23, 25] had 3–5 follow-ups; Two studies [14, 17] had 8 follow-ups; Three studies [16, 24, 26] had 12 follow-ups; Four studies [18‐21] had 16 follow-ups. Among these, 16 follow-ups showed the most significant improvement in HbA1c (MD = -0.92, 95% CI = [-1.71, -0.12], I² = 87%, P = 0.02), followed by 12 follow-ups (MD = -0.87, 95% CI = [-1.28, -0.46], I² = 60%, P < 0.001). Although a downward trend in HbA1c was observed after 3–5 follow-ups (MD = -0.28, 95% CI = [-0.64, 0.07], I² = 79%, P = 0.12) or 8 follow-ups (MD = -0.36, 95% CI = [-0.99, 0.27], I² = 86%, P = 0.26), the improvement was not statistically significant compared to the control group.
Follow-up interval
Four studies [18‐20, 25] had a follow-up interval of 1 week; Five studies [15‐17, 21, 24] had a follow-up interval of 2 weeks; Two studies [23, 26] had a follow-up interval of half a month; Two studies had a follow-up interval of ≥ 1 month [14, 22]. The best improvement in HbA1c was observed with a half-month follow-up interval (MD = -1.29, 95% CI = [-2.43, -0.15], I² = 81%, P = 0.03), followed by 1 week (MD = -0.93, 95% CI = [-1.68, -0.17], I² = 88%, P = 0.02). Studies with a follow-up interval of ≥ 1 month did not show statistically significant improvement (MD = -0.09, 95% CI = [-0.24, 0.06], I² = 0, P = 0.26).
Follow-up duration
Three studies [17, 25, 26] had a follow-up duration of 10–15 min; Three studies [18‐20] had a follow-up duration of 20–25 min; Two studies [21, 22] had a follow-up duration of 30 min; Five studies [14‐16, 23, 24] did not report the follow-up duration. The best improvement in HbA1c was seen in the 20–25 min follow-up duration (MD = -1.23, 95% CI = [-1.63, -0.83], I² = 0, P < 0.001), followed by 10–15 min (MD = -0.54, 95% CI = [-1.02, -0.06, I² = 92%, P = 0.03). A follow-up duration of 30 min did not show a statistically significant improvement in HbA1c (MD = -0.09, 95% CI = [-0.25, 0.06], I² = 0, P = 0.24).
Intervention content
Two studies [15, 25] included medication and blood glucose monitoring; Three studies [21, 22, 24] included medication, diet, and exercise; Three studies [14, 16, 26] included medication, lifestyle, and disease management; Five studies [17‐20, 23] included medication, diet, exercise, and blood glucose monitoring. The best improvement in HbA1c was found in studies that included medication, diet, exercise, and blood glucose monitoring (MD = -1.17, 95% CI = [-1.60, -0.75], I² = 51%, P < 0.01).
Sensitivity analysis and publication Bias
A sensitivity analysis was conducted by removing each study one at a time, and no significant change in the effect size was observed, indicating that the meta-analysis results are stable and reliable. Publication bias was assessed using a funnel plot (Fig. 3), Egger’s Test, and Begg’s Test. The funnel plot showed slight asymmetry, suggesting potential publication bias. However, Egger’s Test results (p = 0.108) and Begg’s Test results (p = 0.199) indicated no statistically significant publication bias. These findings suggest that the overall impact of publication bias on the meta-analysis results is likely minimal, though caution is warranted when interpreting the findings.
Fig. 3
Funnel plot for assessing publication bias
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Discussion
This meta-analysis demonstrates that nurse-led telephone interventions significantly improve HbA1c levels in patients with T2DM. These interventions are particularly valuable for underserved or rural populations, as they address geographical and temporal barriers to accessing professional healthcare services. Our findings highlight that follow-up protocols with specific characteristics—such as at least 12 sessions, conducted weekly or every half month, each lasting 10–25 min, and focusing on medication, diet, exercise, and glucose monitoring—are effective in improving HbA1c outcomes. Among these, protocols involving 16 follow-ups with half-month intervals and session durations of 20–25 min achieved the most significant HbA1c reductions, indicating the importance of optimizing follow-up frequency and intensity to maximize therapeutic benefits.
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Our findings align with prior studies demonstrating the benefits of remote interventions in diabetes management. For instance, de Vasconcelos et al. [16] found that increasing the number of remote interventions significantly improved blood glucose levels, consistent with our finding that higher follow-up frequencies (≥ 12 sessions) lead to better glycemic control. Specifically, our results showed that 12 follow-ups reduced HbA1c by -0.87%, while 16 follow-ups yielded an even greater reduction of -0.92%. These improvements emphasize the importance of reinforcing self-management behaviors through consistent, structured interventions. In contrast, follow-up frequencies of 3–5 or 8 sessions did not yield significant HbA1c improvement compared to the control group, suggesting that fewer follow-ups may be insufficient to sustain behavioral changes. Consequently, we recommend that nurses conduct at least 12 follow-ups for effective HbA1c management in clinical practice.
While 16 follow-ups demonstrated the most significant improvement in HbA1c levels in this study, diabetes management is a lifelong process that requires continuous support beyond the intensive follow-up phase. After completing 16 follow-ups, transitioning to less intensive schedules, such as monthly or quarterly follow-ups, may help maintain the glycemic improvements achieved during the initial phase while reducing the burden on healthcare systems and patients. Additionally, integrating alternative approaches, such as community-based interventions, mobile health applications, or remote glucose monitoring technologies, may provide sustained support for long-term diabetes management. Future research should explore the efficacy and feasibility of these strategies, as well as their impact on glycemic control and other clinical outcomes.
Similarly, intervention duration significantly impacted outcomes; sessions lasting 20–25 min achieved the greatest reduction in HbA1c levels. In contrast, shorter sessions (10–15 min) and longer sessions (> 30 min) were less effective, likely due to insufficient engagement or patient fatigue. This finding aligns with Li et al. [27], who reported that intervention duration can impact patient outcomes, such as blood pressure management. Thus, we recommend maintaining telephone session durations between 10 and 25 min to ensure sessions are neither too brief to be effective nor too lengthy to cause disengagement. The results also indicate that follow-up intervals of 1 week or half a month are most effective in lowering HbA1c levels, while intervals longer than one month do not provide significant improvements compared to control groups. Shorter intervals may facilitate consistent monitoring and adjustment of patient behaviors, while excessively long intervals may reduce patient adherence due to limited oversight, and overly frequent contacts may lead to patient fatigue. Therefore, we suggest an optimal follow-up interval of 1 week or half a month to balance engagement and adherence. Moreover, comprehensive diabetes management encompasses diet, medication, exercise, glucose monitoring, and diabetes education [28], which aligns with the most effective follow-up content identified in our study. This finding suggests that addressing these core management aspects in telephone interventions can facilitate improved HbA1c outcomes.
While earlier meta-analyses included diverse healthcare professionals, our study focuses exclusively on nurse-led, telephone-based interventions, enhancing its relevance to real-world clinical settings where nurses often play a primary role in patient follow-up [29]. Research indicates that education and support provided by nurses enhance patients’ self-management abilities [30]. The International Diabetes Federation recommends regular health education for all diabetic patients, which is crucial for effective glycemic control [28]. Asante et al. [31] found that patients receiving nurse-led telephone interventions showed better adherence to dietary, exercise, glucose monitoring, and foot care practices compared to those receiving standard care. Furthermore, previous meta-analyses suggested that remote interventions by various healthcare professionals effectively reduced blood glucose and systolic blood pressure in T2DM patients [10], consistent with our findings.
It is important to note that this study exhibits a certain degree of heterogeneity, which likely stems from multiple factors, including patient baseline characteristics, comorbidities, baseline HbA1c levels, follow-up protocols, and regional variability. For instance, baseline HbA1c levels varied significantly across studies, ranging from 5.24% in Xiaomei’s study to 11.06% in Brown-Deacon et al., potentially contributing to variability in intervention effects. Patients with higher baseline HbA1c levels may experience greater improvements due to regression to the mean, while those with near-normal levels may have less room for improvement. Additionally, differences in comorbidities, such as hypertension or cardiovascular disease, may have influenced patient responses to the interventions, adding to the heterogeneity. Variations in follow-up protocols, including frequency, duration, and content, were also notable. For example, studies that implemented more frequent or comprehensive follow-ups showed greater HbA1c improvements, indicating that standardizing follow-up protocols could reduce variability. Furthermore, regional and cultural differences, such as disparities in healthcare systems and access to resources, likely contributed to the variability in the results.
While subgroup analyses helped to identify some potential sources of heterogeneity, residual variability remains, which highlights the limitations of this meta-analysis. Differences in study quality and sample size may have further contributed to the observed heterogeneity, as smaller studies tend to introduce greater variability. Additionally, the variability in healthcare access and cultural norms across regions may limit the generalizability of the findings. Future research should focus on standardizing follow-up protocols, conducting larger, high-quality randomized controlled trials, and exploring contextual factors such as patient adherence and socioeconomic status to address these sources of heterogeneity. Despite these limitations, the consistent trends in HbA1c improvement observed across the included studies provide robust evidence supporting the effectiveness of nurse-led telephone interventions.
Implications
This study provides significant implications at the clinical, educational, organizational, and research levels. Clinically, the findings offer evidence-based guidance for optimizing nurse-led telephone follow-ups to improve glycemic control in T2DM patients, particularly for those in rural or underserved areas. Educationally, the study highlights the need to incorporate structured diabetes education and follow-up training into nursing curricula and professional development programs, ensuring nurses are equipped with the skills to deliver effective remote interventions. At the organizational level, the study underscores the importance of allocating sufficient resources and establishing standardized protocols for nurse-led follow-ups, balancing nursing workloads with patient outcomes. From a research perspective, the findings emphasize the need for further high-quality randomized controlled trials to refine follow-up protocols and investigate their applicability to diverse patient populations with varying comorbidities and baseline conditions.
Strengths and limitations
This study has several strengths. First, it is the first meta-analysis to focus exclusively on nurse-led, telephone-based interventions, which provides clinically relevant evidence for real-world settings where nurses often play a primary role in patient management. Second, the inclusion of subgroup analyses allowed for a more detailed investigation of key factors influencing intervention effectiveness, such as follow-up frequency, duration, and content, offering specific, actionable recommendations for optimizing follow-up protocols. Third, the study included a broad range of populations from diverse geographic regions, improving the generalizability of findings to various healthcare settings. Lastly, rigorous quality assessment and sensitivity analyses ensured the stability and reliability of the results, minimizing the impact of potential biases.
However, this study also has several limitations. First, the included studies varied in quality, underscoring the need for further high-quality randomized controlled trials to strengthen the robustness of these findings. Second, the study exhibited some heterogeneity, likely stemming from differences in patient baseline characteristics, comorbidities, and initial HbA1c levels across studies. Additionally, variations in follow-up protocols, including duration, content, and frequency, may have contributed to outcome differences. Future research should focus on developing more tailored follow-up protocols that consider patients’ comorbidities, disease duration, and baseline conditions to enhance intervention effectiveness. Third, while the inclusion of Chinese literature improves the comprehensiveness of this meta-analysis, it may introduce potential language bias, as studies in other non-English languages were not included. To address this, future meta-analyses should aim to include studies from other non-English languages to provide a more balanced and inclusive analysis. Lastly, although subgroup analyses and sensitivity tests were conducted to explore heterogeneity, some residual variability remains unexplained. Further research is needed to standardize intervention protocols and explore contextual factors such as patient adherence and regional healthcare infrastructure to reduce this variability.
Conclusion
This meta-analysis demonstrates that nurse-led telephone interventions significantly improve HbA1c levels in patients with T2DM. The most effective protocol includes 16 follow-ups at half-month intervals, addressing key areas such as diet, medication, exercise, and glucose monitoring. Healthcare providers are encouraged to use this evidence-based protocol as a foundation to design tailored follow-up services that meet the unique needs of their patient populations. Future research should prioritize evaluating the long-term sustainability of glycemic improvements achieved through these interventions. Additionally, the integration of alternative approaches, such as mobile health technologies, should be explored to enhance accessibility and efficiency. Developing personalized follow-up protocols that account for patient characteristics, including comorbidities and socioeconomic factors, will further refine nurse-led telephone interventions and expand their applicability across diverse healthcare settings.
Acknowledgements
None.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
All participants provided written informed consent for publication of their data.
Competing interests
The authors declare no competing interests.
Clinical trial number
Not applicable.
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