1) Inclusion Criteria: a) Participants aged ≥ 18 years; b) Populations diagnosed with diabetes or not yet diagnosed with diabetes; c) Diabetes patients who are receiving or not receiving treatment; d) Observational studies.

2) Exclusion Criteria: a) Individuals who cannot understand or refuse to use continuous glucose monitoring; b) Less than 70% usage time of continuous glucose monitoring; c) Participants with impaired decision-making capacity or without decision-making capacity; d) Participants who have died or whose continuous glucose monitoring data is missing.

Computer searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library, with a search period covering from the establishment of each database until March 2025. The search employed English subject terms combined with advanced search strategies. The search query was: Advanced search ((((((Continuous dynamic blood glucose monitoring, influence factor) OR (Interstitial fluid blood glucose, influence factor)) AND ((Continuous dynamic blood glucose monitoring, management) OR (Interstitial fluid blood glucose, management) OR (Freestyle Libre, influence factor) OR (Freestyle Libre, management)))))). Subject term search: “Continuous dynamic blood glucose monitoring” [Mesh] AND (influence factor, management). Search results yielded a total of 526 articles, which were included in the meta-analysis. The search process is illustrated in Figure 1 .

We retrieved a total of 526 articles from four electronic databases. Using EndNeto software, we excluded 425 ineligible articles. For the remaining 101 articles, we entered information such as the title, author, and publication year, and the system automatically identified and excluded 14 duplicate studies. Finally, we conducted an in-depth review of the remaining 87 articles. Among these: 17 were animal experiments, 31 were studies involving participants under 18 years old, 14 articles were missing, and 15 were reviews. Ultimately, 11 studies met our requirements and were included for analysis.

The research team for this study consisted of four members. During the retrieval and analysis of articles, three researchers selected articles and extracted data based on inclusion and exclusion criteria. They then cross-verified the selections. If any contentious issues arose, the other three researchers were consulted to resolve them. Articles were preliminarily included by reviewing their titles and abstracts. After identifying and excluding duplicate articles, each article was then read in detail to finalize the selection of appropriate articles. Data were extracted from these chosen articles and included information such as: author, country, publication year, study type, sample size, statistical tests (M ± SD), and outcome indicators. This information was compiled into a summary table, such as Table 1 . The primary outcome indicator was MARD (Mean Absolute Relative Difference); the smaller the value, the more accurately continuous glucose monitoring reflects glucose levels [7] . Secondary outcome indicators included Time in Range (TIR) %, Glycemic Variability (CG-MBY), Precision Absolute Relative Difference (PARD), etc. as detailed in Tables 1-4 .

Continued

Green : Low-risk bias; Yellow : Medium-risk bias; Red : High-risk bias; Black : Extremely high risk bias; Circle : No information.

All included studies used consistent measurement tools, enabling a meta-analysis of combined quantitative data. The mean and standard deviation of the scores on the humanistic care ability scale in each study were summarized using Stata SE.14, and presented using weighted mean difference (WMD) effect sizes and 95% confidence intervals (CI). The Cochrane Q test and I² statistics were utilized with I² values of 25%, 50%, and 75%, indicating low, moderate, and high heterogeneity, respectively [20] . When I² > 50%, and p < 0.05, a random-effects model was used.

Results, as shown in Figure 2 , indicate that among the 11 studies that could affect continuous glucose monitoring (CGM) outcomes, the analysis produced χ²: 3438.01, I² = 99.7%, P = 0.266 > 0.05. There were no significant differences in the effects of influencing factors between the groups.

As shown in Figure 3 , indicate that among the 11 studies that could affect continuous glucose monitoring (CGM) outcomes, the analysis yielded χ²: 2046.12, I² = 99.8%, P = 0.269 > 0.05. There were no significant differences in the effects of influencing factors between the groups.

As a result, for example: Figure 4 , among the 11 studies that might affect the results of continuous dynamic glucose monitoring (CGM), the analysis results showed that χ²: 30.7, I² = 93.8%, P = 0.413 > 0.05. There is no difference in the effect of each influencing factor between groups.

As shown in Figure 4 , indicate that among the 11 studies that could affect continuous glucose monitoring (CGM) outcomes, the analysis yielded χ²: 0.62, I² = 0.0%, P = 0.432 > 0.05 (see in Figure 5 ). There were no significant differences in the effects of influencing factors between the groups.

With continuous technological advancements, the methods for continuous glucose monitoring (CGM) have been consistently updated and developed in numerous studies. These advancements have reduced interfering factors, improved monitoring signals and sensitivity, and even allowed for stable and accurate blood glucose readings in critically ill patients experiencing hypoxia. In recent years, many manufacturers have been committed to product improvements, calibration, algorithms, and technological updates, making CGM more precise and reliable [21] - [27] . This has facilitated easier self-management of blood glucose for patients, reducing pain and anxiety and increasing patient compliance with self-management. The stability of CGM provides clinical value for healthcare providers monitoring blood glucose in different patients, improving work efficiency and effectively preventing hypoglycemia [28] . It also offers healthcare professionals a reliable basis for clinically guiding patients in dietary and medication management [29] .

Diabetes is a leading cause of end-stage renal failure. Concerns have been raised about whether continuous glucose monitoring (CGM) results might be inaccurate in hemodialysis patients. However, analysis indicates that CGM accuracy is not impacted in these patients. In hemodialysis patients, CGM can effectively prevent adverse events such as hypoglycemia [30] . It also aids in guiding insulin medication during dialysis, providing valuable insights. CGM can be a significant benefit for blood glucose management in hemodialysis patients [31] [32] .

Regarding exercise, there is some debate about whether it interferes with continuous glucose monitoring (CGM) results. Some studies suggest that exercise may impact CGM results, but this remains controversial [33] [34] . From the analysis, exercise does not appear to significantly affect the accuracy of the results. This could be related to product performance, as research indicates that the Dexcom G6 product shows better accuracy during exercise [35] .