Continuous glucose monitoring helps reduce hypoglycemia and improve control in insulin treated diabetic patients managed in primary care

Continuous glucose monitoring helps reduce hypoglycemia and improve control in insulin treated diabetic patients managed in primary care | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Continuous glucose monitoring helps reduce hypoglycemia and improve control in insulin treated diabetic patients managed in primary care Ching Keung Wong, Lap Kin Chiang, Ka Ming Ho, Xiao Rui Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7226274/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objectives: To determine the efficacy of Continuous Glucose Monitoring (CGM) in reducing hypoglycemia and improving glycemic control in Type 2 diabetes mellitus (T2DM) patients treated with insulin in primary care. Design: Prospective Cohort study Subjects: T2DM patients treated with insulin and had been regularly followed up at primary care clinics from the Hospital Authority from January 2022 to December 2023. Participants were divided into either the CGM group (Cohort group) and the non-CGM group (Control group). Main Outcome Measures: The primary outcome: change in HbA1c levels over a 12-months period and the changes in the frequency of hypoglycemic episodes per month. Secondary outcome: changes in other clinical parameters including blood pressure control, lipid control and biochemical profiles. Results: The study included 59 patients in the CGM group and 60 patients in the non-CGM group. The CGM group exhibited a statistically significant reduction in HbA1c level between-groups with mean difference of 0.95±0.21 (p=0.006). The incidence of hypoglycemic episodes markedly decreased in the CGM group too, witha mean difference of 4.69±1.45 (p=<0.001) between the groups. Conclusion: CGM significantly improved glycemic control and reduced hypoglycemic episodes in T2DM patients managed in primary care setting. Family physicians should actively consider incorporating CGM into the comprehensive diabetes assessment for enhanced diabetes management. Continuous Glucose Monitoring Type 2 Diabetes Mellitus Hypoglycemia primary care Figures Figure 1 Introduction Background: Type 2 Diabetes mellitus is a prevalent disease in primary care settings. Proper glucose control has been shown to diminish microvascular and macrovascular complications of diabetes. To achieve optimal glycemic control, advanced T2DM patients will frequently require insulin therapy as the main stay of treatment. Managing insulin-treated T2DM patients can be quite challenging. Physicians must balance between achieving optimal glycemic control via proactive medication titrations while simultaneously mitigating the substantial risk of hypoglycemia. [1] The most widely used measure of glycaemia, HbA1c level, is straightforward to assess and plays a definitive role in enhancing treatment efficacy. Nonetheless, HbA1c possesses significant limitations that should be considered in clinical practice, including its inability to reflect the glycemic variability, and its accuracy would be compromised by the presence of haemoglobinopathy, among other factors. [2] Continuous glucose monitoring is a technique for incessantly tracking glucose levels day and night, utilizing a small sensor inserted beneath the skin to measure interstitial fluid glucose levels. CGM has emerged as an essential tool for mitigating hypoglycemia and improving glucose control, providing real time feedback on blood glucose levels. By assessing the overall glycemic control and glycemic patterns, identifying glucose fluctuation, detecting hypoglycemic episodes, and incorporating into lifestyle management and motivational support, CGM demonstrated a reduction in both hypoglycemia and HbA1c levels patients with type 1 diabetes [3,4,5,6] and enhanced patient engagement and motivation, thereby improving their self-management skills. Despite the extensive use of CGM in specialist settings, there is little data regarding its efficacy in managing patients with T2DM in primary care [8]. This study aims to investigate the efficacy of CGM in mitigating hypoglycemia and enhancing glycemic control in patients with T2DM receiving care in primary clinics. Objectives of the study: To compare the difference in glycemic control (change in HbA1c level) and the reduction in frequency of hypoglycemic episodes between CGM group and non-CGM groups. Research hypothesis: There is a significant difference in glycemic control, as measured by HbA1c levels, between the CGM group receiving CGM as a motivational tool for DM self-management and the control group receiving usual care with encouragement for self-monitoring of blood glucose (SMBG) and self-monitoring of hypoglycemic episodes. Methods Study design: prospective cohort study Subjects: Participants who fulfilled the following inclusion criteria were included in the study: T2DM patients, aged 18-65 years, who had been regularly followed up at primary care clinics in the Kowloon Central Cluster (KCC) of the Hospital Authority from 01/01/2022 to 31/12/2023 and had received insulin treatment for a minimum of 60 days and had experienced any of the following symptoms since their previous follow-up visit: With hypoglycemia symptoms ≥ 2 times per week or With Self blood glucose monitoring (SMBG) ≤ 3.9mmol/L for > 2 episodes/week or With the history of severe hypoglycemia requiring hospital admission. The assignment to CGM group or routine care group is on a voluntary basis. Those who agreed to receive CGM services were included into the CGM group; those who preferred the routine care i.e. to continue SMBG for monitoring were included in the non-CGM group. Exclusion criteria: prior usage of CGM device before the case recruitment period, patients with serious or unstable medical or psychological disorders and those unable to adhere to research requirements. Patients unable to provide informed consent will also be excluded. The CGM group Participants in the CGM group will be provided with a CGM device (Freestyle Libre) for a duration of 14 days, along with training on its usage. Participants will gain access to a mobile application that enables real-time viewing of glucose data (average glucose, glucose management indicator [estimated HbA1c], glucose variability, time in ranges) in real-time and provides alarms for hypoglycemic events. The patients will be urged to use the CGM device as a motivational tool to engage in self-management behaviors. Several studies indicated that CGM was useful in modifying a patient’s diet and exercise habits and could induce better glycemic control than SMBG for patients with type 2 diabetes.[8,9] The CGM report includes pattern and variability (the amplitude, frequency and duration of glucose fluctuations) of the sensor glucose readings, the percentage of time the sensor glucose readings exceed 10.0 mmol/L, fall below 3.9 mmol/L, or remain within the Target Glucose Range ( 3.9- 10 mmol/L), low glucose events ( glucose reading is lower than 3.9 mmol/L for longer than 15 minutes) and the estimated A1c level ( A1C levels based on average glucose measured using CGM values ) . The non-CGM group Participants in the non-CGM group will be encouraged to perform SMBG as usual throughout the study period. They will have same follow-up arrangement as that of CGM group. All participants will also receive education on hypoglycemia, including self-monitoring and self-management of hypoglycemic episodes. This study was a prospective cohort study and did not involve a health care intervention requiring trial registration, as per BMC Primary Care’s editorial policies. Ethical approval was obtained from Research Ethics Committee (Kowloon Central/Kowloon East), Ref: KC/KE-23-0096/ER3, and all applicable ethical guidelines were followed. Outcomes: The primary outcomes are the change in HbA1c levels from baseline to the end of the study and the difference in the frequency of hypoglycemic events per month encountered by participants during the study period. Hypoglycemic events will be defined as any episode in which the participant’s glucose level drops below 3.9 mmol/L. The secondary outcomes include alteration in systolic blood pressure (SBP), diastolic blood pressure (DBP), Low -density lipoprotein (LDL) levels, body mass index (BMI), estimated glomerular filtration rate (eGFR), fasting glucose and urine albumin- to-creatinine ratio (uACR) from baseline to the end of the study. Sample size calculations A sample size of 47 in each group based on the HbA1c means change of CGM group -0.5% with a standard deviation of 0.9, and HbA1c mean change of non CGM group at 0% with a standard deviation of 0.8 [13], is required to achieve 80% power and a significant level of 0.05 using a two-sided independent-sample test of difference of HbA1c levels. Assuming a 20% dropout rate, the estimated sample size is 57 in each group. Statistical analysis Descriptive statistics was presented by N (%) for categorical variables. Continuous variables were presented by Mean±SD. To compare any significant difference between groups, independent-samples t test was used. For categorical data, Pearson Chi- square test was used to determine the statistically significant in different groups. Regarding to the analysis of outcomes, the within-group comparison was analysed by paired-t test, while between-group comparison was analysed by one-way ANCOVA, adjusted for the baseline score. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk. NY, USA computer software). A p-value of < 0.05 was considered statistically significant. Table 1. Baseline characteristics and demographics of patients included in the study Overall (N=1 19 ) Mean±SD / N (%) CGM group (N=5 9 ) Mean±SD / N (%) Non-CGM group (N= 60 ) Mean±SD / N (%) p-value Age (years) 57.08±6.71 56.29±7.70 57.87±5.52 0.202 Gender-male 66 (55.5%) 35 (59.3%) 31 (51.7%) 0.401 Ethnicity-Chinese 97 (81.5%) 46 (78.0%) 51 (85.0%) 0.323 HT 90 (75.6%) 46 (78.0%) 44 (73.3%) 0.556 Hyperlipidaemia 104 (87.4%) 50 (84.7%) 54 (90.0%) 0.388 Obesity 76 (63.9%) 36 (61.0%) 40 (66.7%) 0.521 HbA1c (%) 8.03±1.65 8.72±1.85 7.35±1.05 *<0.001 Hypoglycemic event per month 8.03±8.31 9.84±9.76 6.25±6.16 *0.019 Fasting glucose (mmol/L) 6.01±3.74 7.87±4.18 4.19±1.99 *<0.001 SBP (mmHg) 125.20±11.32 123.49±11.34 126.88±11.15 0.102 DBP (mmHg) 71.79±10.38 73.97±10.13 69.65±10.25 *0.023 LDL (mmol/L) 2.03±0.62 2.02±0.65 2.04±0.59 0.851 Table 2. Comparison of different parameters within and between the CGM and Non-CGM groups CGM group (N=59) Non CGM group (N=60) Pre (Mean±SD) Post (Mean±SD) Post-pre difference (d1) (Mean±SD) p-value Pre (Mean±SD) Post (Mean±SD) Post-pre difference (d2) (Mean±SD) p-value d1-d2 difference (Mean±SD) p-value (Between-group) HbA1c (%) 8.72±1.85 8.29±1.53 -0.43±1.38 *0.021 7.35±1.05 7.86±1.51 0.53±0.79 *<0.001 -0.95±0.21 *0.006 hypoglycaemic event (pre month) 9.84±9.76 0.36±1.18 -9.48±9.85 *<0.001 6.25±6.16 1.47±2.37 -4.78±5.16 *<0.001 -4.69±1.45 *<0.001 eGFR (ml/min/1.73m 2 ) 87.68±18.52 84.19±19.70 -3.49±10.27 *0.011 85.85±16.99 84.27±17.76 -1.58±8.86 0.163 -1.91±1.74 0.313 Fasting glucose (mmol/L) 7.87±4.18 7.58±3.20 -0.29±4.15 0.598 4.19±1.99 5.99±2.48 1.81±3.10 *<0.001 -2.09±0.67 0.263 SBP (mmHg) 123.49±11.34 127.36±14.43 3.86±15.07 0.054 126.88±11.15 129.13±11.40 2.25±9.79 *0.080 1.61±2.33 0.992 DBP (mmHg) 73.97±10.13 72.10±9.96 -1.86±9.60 0.141 69.65±10.25 69.77±7.81 0.12±10.21 0.930 -1.98±1.82 0.709 LDL (mmol/L) 2.02±0.65 1.97±0.52 -0.04±0.72 0.639 2.04±0.59 1.92±0.39 -0.12±0.57 0.102 0.08±0.12 0.449 BMI ( kg/m 2 ) 26.68±4.38 26.84±4.44 0.17±1.35 0.347 26.78±4.40 26.74±4.38 -0.04±0.86 0.713 0.21±0.21 0.324 Urine albumin-to- creatinine ratio ( mg/mmol ) 9.42±30.57 11.16±36.90 1.75±11.46 0.247 17.5±69.52 16.62±67.00 0.88±9.90 0.496 2.62±1.96 0.205 Table 3. Comparisons of glycemic control rate between the CGM and non-CGM group Study group (N=59) Control group (N=60) Pre (%) Post (%) Post-pre difference (d1) (%) p-value Pre (%) Post (%) Post-pre difference (d2) (%) p-value Between-group difference (%) p-value (Between -group) HbA1c<7% 8 (13.6%) 9 (15.3%) 1.7% 1.000 26 (43.3%) 20 (33.3%) -10.0% 0.180 11.7% *0.006 *Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test. Table 4. Comparison of BP control rate between the CGM and non-CGM group Study group (N=59) Control group (N=60) Pre (%) Post (%) Post-pre difference (d1) (%) p-value Pre (%) Post (%) Post-pre difference (d2) (%) p-value Between-group difference (%) p-value (Between-group) SBP<130 mmHg 44 (74.6%) 37 (62.7%) -11.9% 0.189 37 (61.7%) 32 (53.3%) -8.4% 0.267 3.5% 0.527 DBP<80 mmHg 43 (72.9%) 46 (78.0%) 5.1% 0.581 50 (83.3%) 56 (93.3%) 10.0% 0.109 4.9% 0.309 *Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test. Table 5. Comparison of lipid control rate between the CGM and non-CGM group. Study group (N=59) Control group (N=60) Pre (%) Post (%) Post-pre difference (d1) (%) p-value Pre (%) Post (%) Post-pre difference (d2) (%) p-value Between-group difference (%) p-value (Between-group) LDL<2.6 mmol/L 52 (88.1%) 56 (94.9%) 6.8% 0.289 50 (83.3%) 56 (93.3%) 10.0% 0.146 3.2% 0.528 *Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test. Results The study flow was illustrated in Figure 1 . Overall, 119 patients, including 59 participants from the CGM group and 60 participants from non-CGM group were recruited and included in the final analysis. Their mean age was 57.08±6.71 years, while 66 (55.5%) of them were male and 97 (81.5%) were Chinese patients. Baseline characteristics and demographics of the participants were presented in Table 1 . There were no significant differences in baseline data including age, gender-male, ethnicity-Chinese and comorbidities like hypertension, hyperlipidaemia and obesity. The baseline HbA1c level, the frequency of hypoglycaemic event and diastolic blood pressure between groups are statistically significant. In the Table 2 , the mean HbA1c in CGM group decreased from 8.72±1.85% to 8.29±1.53% (mean difference -0.43±1.38, p=0.021), while the mean HbA1c in non-CGM group increased from 7.35±1.05% to 7.86±1.51% (mean difference 0.53±0.79, p<0.001). The reduction in HbA1c between the CGM group and the non-CGM group was statistically significant (difference -0.95±0.21, p=0.006). Regarding hypoglycemic episodes per month, the mean number of hypoglycemic events in the CGM group decreased significantly from 9.84±9.76 to 0.36±1.18 (mean difference -9.48±9.85, p<0.001), while the mean number of hypoglycemic events in the non-CGM group decreased from 6.25±6.16 to 1.47±2.37 (mean difference -4.78±5.16, p<0.001). The reduction in hypoglycemic events was statistically significantly greater in the CGM group compared to the non-CGM group (difference -4.69±1.45, p<0.001). There were no significant differences in eGFR (difference -1.91±1.74, p=0.313), fasting glucose (difference -2.09±0.67, p=0.263), systolic blood pressure (difference 1.61±2.33, p=0.992), diastolic blood pressure (difference -1.98±1.82, p=0.709), LDL level (difference 0.08±0.12, p=0.449), BMI (difference 0.21±0.21, p=0.324) or urine albumin-to-creatinine ratio (difference 2.62±1.96, p=0.205) by comparing CGM and non-CGM groups ( Table 2 ). In the Table 3 , the proportion of patients achieving satisfactory glycemic control (A1c <7%) showed no statistically significant improvement within either group: In the study group, the rate increased slightly from 13.6% (pre) to 15.3% (post) (post-pre difference: 1.7%, p = 1.000). In the control group, the rate decreased from 43.3% (pre) to 33.3% (post) (post-pre difference: -10.0%, p = 0.180). Between-group analysis showed a significant difference in the post-pre change in glycemic control. (11.7%, p = 0.006). Between-groups, the differences in satisfactory blood pressure nor satisfactory lipid control rates were not statistically significant. ( Table 4 and Table 5) Discussion The study is to evaluate the efficacy of CGM as a motivational tool for diabetic management in T2DM patients on insulin therapy. The findings showed significant improvements in diabetic control and a substantial reduction in hypoglycemic episodes for patients using CGM compared to those receiving usual care with SMBG. The CGM group had a substantial decrease in HbA1c levels compared to the non-CGM group (-0.95 ± 0.21, p = 0.006). CGM is seen as a motivational tool for patients and a resource for physicians to facilitate therapeutic adjustment. This may enhance glycemic control. Additional research on CGM demonstrated its efficacy in enhancing glycemic control among diabetes patients by lifestyle modification without treatment modification in primary care and secondary care [ 7 , 15 ]. A comparable local study indicates a significant reduction in HbA1c level by comparing between CGM group and SMBG groups in type II diabetic patients [ 14 ]. In randomized controlled trials (RCTs) and meta-analyses, some reported no significant difference, while majority indicated greater HbA1c reductions with CGM group than SMBG group. [ 16 ] Some differences are likely due to varying average baseline HbA1c between studies; in general, greater HbA1c reductions were seen in studies with higher baseline HbA1c, with smaller reductions seen with lower baseline HbA1c. [ 16 ] This study revealed a substantial reduction in hypoglycemia episodes in the CGM group compared to the non-CGM group (difference 4.69 ± 1.45, p < 0.001), alongside a noteworthy decrease in HbA1c within the CGM group. The CGM group can sustain an acceptable HbA1c rate. A study indicated that the median hypoglycemic event rate decreased by 30% in the CGM group, whereas the rate was nearly unchanged in the control group [ 6 ]. Hypoglycemia is a significant risk for patients on insulin therapy, potentially resulting in severe health consequences and reducing the quality of life. The CGM group gained from real-time alarms and continuous glucose trend data, which enhanced prevention and management of hypoglycemic episodes. This enhancement underscores the significance of CGM in augmenting patient safety and alleviating the incidence of hypoglycemia. At the baseline, the CGM group demonstrated a higher fasting glucose level (7.81 ± 4.18 vs 4.19 ± 1.99, P < 0.001), a higher HbA1c level (8.72 ± 1.85 vs 7.35 ± 1.05, P < 0.001) and a higher frequency of hypoglycemic events (9.84 ± 9.76 vs 6.25 ± 6.16, P = 0.019) compared to the non-CGM group. The potential causes are non-randomized allocation and selection bias. For instance, patients with more unstable or challenging diabetes may have been preferentially assigned to the CGM group, resulting in higher baseline rates of fasting glucose level, higher HbA1c level and higher frequency of hypoglycemic event in the CGM group. A higher baseline HbA1c in the CGM group may facilitate the observation of reductions in HbA1c levels over time, while a greater baseline frequency of hypoglycemia may influence the detection of reductions in hypoglycemic occurrences. The disparities complicate the attribution of the observed changes exclusively to the CGM. A one-way ANCOVA was conducted to account for baseline differences in the between-group comparison. There is an increase in fasting glucose level, an increase in HbA1c level, and a decrease in satisfactory HbA1c rate in non-CGM group at the end of study. The subsequent are the potential explanations. First, to prevent hypoglycemic episodes, physicians may implement less strict glycemic control in treatment plans and patients may have less proactive in modifying their daily activities and insulin regimens. Second, the non-CGM group depended on SMBG, potentially overlooking glucose fluctuations between measurements (such as postprandial spikes or nocturnal hyperglycemia), which may have resulted in fewer or less precise adjustments, contributing to suboptimal glucose control and elevated HbA1c levels. Third, SMBG generally fails to record overnight glucose levels. Unrecognized nocturnal hyperglycemia can result in prolonged increased blood glucose, hence contributes to an increase in HbA1c level. Finally, patients in non-CGM group may have adopted strategies to prevent hypoglycemia, such as increasing carbohydrate intake before bedtime or decreasing insulin dosages at night. These strategies result in increased fasting glucose levels the following morning and higher HbA1c level over time. A significant aspect of this study was the utilization of CGM as a motivational tool for enhancing patient engagement in self-management. The significant improvements in glycemic control and reduction in hypoglycemic episodes in the CGM group highlight the potential of CGM to empower patients. Access to real-time glucose data enhances patients’ awareness and comprehension of their glucose patterns, resulting in more proactive diabetes care [ 10 , 11 , 12 ]. This conclusion corroborates the research indicating that patient engagement and empowerment are essential for effective diabetes management. The findings of this study possess significant clinical implications. CGM technology may be regarded as a significant enhancement in diabetes care, especially for T2DM patients susceptible to hypoglycemia. The decrease in hypoglycemia incidents and improved diabetes management linked to CGM utilization may result in fewer emergency visits and hospitalizations, hence lowering healthcare expenses. Limitations: There are several limitations of this study. Firstly, the study was conducted in public primary care clinics, therefore the findings of this study may not be generalized to private setting or specialist setting. Secondly, as the grouping of T2DM to CGM group or routine care group was on voluntary basis and was not randomized, selection bias may exist. This was reflected by their difference in HbA1c level, frequency of hypoglycemic episodes etc. at the baseline. However, the inter and the intra group analysis has clearly demonstrated the efficacy of CGM on the glycemic control and reduction of hypoglycemic episodes between the groups. Thirdly, the study duration was relatively short (12 months), therefore longer-term studies are needed to evaluate the sustained impact of CGM on glycemic control and hypoglycemia. A more ideal outcome could be achieved by conducting a repeat CGM at the 12-months for CGM group. A direct comparison of the CGM data at baseline and at 12 months may yield more favorable outcomes. Regretfully, we were unable to provide the second CGM to CGM group due to insufficient resources. Future Directions: Future research could focus on cohort study with larger sample size, balanced baseline data of HbA1c level and the episodes of hypoglycemic events. By comparing the serial CGM data like time in range, estimated HbA1c, time above range and time below range could help understand how the CGM reduce the glucose fluctuation that leads to better clinical outcome. Besides, CGM could become a new tool for the development of new anti-diabetic medications. Furthermore, exploring the cost-effectiveness of CGM and its impact on patient-reported outcomes, such as quality of life and satisfaction, would provide valuable insights for development of new CGM devices. In addition, investigating the integration of CGM with other digital health tools and personalized diabetes management programs could further enhance the effectiveness of CGM in clinical practice. Finally, employing AI technology to analyze CGM data may offer immediate feedback to patients for adjustments in diet and exercise. Conclusion In summary, CGM serves as an effective tool for T2DM patients in improving glycemic control and reducing the hypoglycemic incidents in primary care settings. The interpretation of the CGM data has become a crucial skill for physicians to deliver comprehensive care for diabetic patients. Family physicians should actively consider incorporating CGM into comprehensive diabetes assessment for enhanced diabetes management. Declarations Ethics approval and consent to participate : The research was conducted in accordance with the Declaration of Helsinki and ICH GCP. Ethics approval: Research Ethics Committee (Kowloon Central/Kowloon East), Ref: KC/KE-23-0096/ER3 Consent for publication : All authors consent for publication in BMC Primary Care. Availability of data and materials ： uploaded to Supplementary material Competing interests ： The authors declare no competing interests. No financial or non-financial conflicts of interest exist that could influence the study’s design, conduct, or reporting. Funding ： HKCFP research Fellowship 2023 Authors' contributions ： Wong Ching Keung secured funding for the study, designed the study protocol, conducted data collection, performed statistical analyses and contributed to the results. Wong Ching Keung, Chiang Lap Kin and Chen Xiao Rui contributed to discussion sections. Chen Xiao Rui supervised the research team. Acknowledgements Chinese University of Hong Kong The Jockey Club School of Public Health and Primary Care Kwong Wah Hospital Clinical Research Centre for biostatistics consultation service All clinical staff participating in the study Research assistant: Edward Choi References Huang, B. K. (2022). Hypoglycemia unawareness identified by continuous glucose monitoring system is frequent in outpatients with type 2 diabetes without receiving intensive therapeutic interventions. Diabetology & Metabolic Syndrome, 14 (180). Little, R. R., & Roberts, W. L. A. (2009). Review of variant hemoglobins interfering with hemoglobin A1c measurement. Journal of Diabetes Science and Technology, 3 (446-451). Radermecker, R. P. (2010). Continuous glucose monitoring reduces both hypoglycemia and HbA1c in hypoglycemia-prone type 1 diabetic patients treated with a portable pump. Diabetes & Metabolism, 36 (409-413). Beck, R. W. (2017). Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: The DIAMOND randomized clinical trial. JAMA, 317 (4), 371-378. Rodbard, D. (2017). Continuous glucose monitoring: A review of recent studies demonstrating improved glycemic outcomes. Diabetes Technology & Therapeutics, 19 (S3). Riddlesworth, T. (2017). Hypoglycemic event frequency and the effect of continuous glucose monitoring in adults with type 1 diabetes using multiple daily insulin injections. Diabetes Therapy, 8 (947-951). Simonson, G. D. (2021). Effect of professional CGM (pCGM) on glucose management in type 2 diabetes patients in primary care. Journal of Diabetes Science and Technology, 15 (3), 539-545. Yoo, H. J., An, H. G., Park, S. Y., et al. (2008). Use of a real-time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes. Diabetes Research and Clinical Practice, 82 (73-79). https://doi.org/10.1016/j.diabres.2008.06.015 Kitazawa, M., & Takeda, Y. (2024). Lifestyle intervention with smartphone app and isCGM for people at high risk of type 2 diabetes: Randomized trial. Journal of Clinical Endocrinology & Metabolism, 109 (4), 1060-1070. https://doi.org/10.1210/clinem/dgad639 Ehrhardt, N. (2020). Continuous glucose monitoring as a behavior modification tool. Clinical Diabetes, 38 (2). Engler, S., Fields, S., Leach, W., et al. (2022). Real-time continuous glucose monitoring as a behavioral intervention tool for T2D: A systematic review. Journal of Technology in Behavioral Science, 7 (252-263). https://doi.org/10.1007/s41347-022-00247-5 Vallis, M. (2023). How continuous glucose monitoring can motivate self-management: Can motivation follow behavior? Canadian Journal of Diabetes, 47 (5), 435-444. https://doi.org/10.1016/j.jcjd.2023.04.001 Laffel, L. M., Kanapka, L. G., Beck, R. W., et al. (2020). Effect of continuous glucose monitoring on glycemic control in adolescents and young adults with type 1 diabetes: A randomized clinical trial. JAMA, 323 (23), 2388-2396. https://doi.org/10.1001/jama.2020.6940 Wong, T. W. (2023). Use of personal continuous glucose monitoring (CGM) with support in people with type 1 and 2 diabetes treated with insulin in the outpatient clinic: A single-center retrospective cohort study. Clinical Diabetology, 12 (2). Kim, S. K., Kim, H. J., Kim, T., et al. (2014). Effectiveness of 3-day continuous glucose monitoring for improving glucose control in type 2 diabetic patients in clinical practice. Diabetes & Metabolism Journal, 38 (6), 449-455. https://doi.org/10.4093/dmj.2014.38.6.449 Oser, T. K., Litchman, M. L., Allen, N. A., et al. (2021). Personal continuous glucose monitoring use among adults with type 2 diabetes: Clinical efficacy and economic impacts. Current Diabetes Reports, 21 (49). https://doi.org/10.1007/s11892-021-01408-1 Additional Declarations No competing interests reported. Supplementary Files CGMgroupdataset.xlsx nonCGMgroupdataset.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 16 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 27 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7226274","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508740762,"identity":"d558002b-51b0-4633-946a-c8e91575c6c2","order_by":0,"name":"Ching Keung Wong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFACNoYDH3hsEqC8AwbEaGF8OEMmjTQtzMY8NodJ0GJw/FiaNE/O+Tz+BuZjH78w3DEmrOVM2jHJOWduF0scYEueLcPwzIygFrMb7G0Sb3tuJzYc4DFmlmA4bEOcFt5/5xLnk6CF7bAhD8+BxA1ALYwfGA4Tdpj9mbTEhzN4khM3HmZLZmYwOEzY+5LtxwyAUWmXOO9482HGHxWHDRsI6oEDZiDiISYiUQDjD1J1jIJRMApGwYgAAL+lP9+aK99cAAAAAElFTkSuQmCC","orcid":"","institution":"Kowloon Central Cluster, Hospital Authority","correspondingAuthor":true,"prefix":"","firstName":"Ching","middleName":"Keung","lastName":"Wong","suffix":""},{"id":508740763,"identity":"611b0dbf-da43-4daf-a034-13908666b296","order_by":1,"name":"Lap Kin Chiang","email":"","orcid":"","institution":"Kowloon Central Cluster, Hospital Authority","correspondingAuthor":false,"prefix":"","firstName":"Lap","middleName":"Kin","lastName":"Chiang","suffix":""},{"id":508740764,"identity":"b03a355a-c85b-49a5-b234-224254d9afe9","order_by":2,"name":"Ka Ming Ho","email":"","orcid":"","institution":"Kowloon Central Cluster, Hospital Authority","correspondingAuthor":false,"prefix":"","firstName":"Ka","middleName":"Ming","lastName":"Ho","suffix":""},{"id":508740765,"identity":"c2a699ca-ed2d-4a31-810b-b365c0f69a3c","order_by":3,"name":"Xiao Rui Chen","email":"","orcid":"","institution":"Kowloon Central Cluster, Hospital Authority","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"Rui","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-27 12:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7226274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7226274/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90792123,"identity":"158408b8-e35e-485d-9dc7-eac5d2e56559","added_by":"auto","created_at":"2025-09-08 08:25:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48261,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of case recruitment of CGM and non-CGM group\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7226274/v1/b79fbc54dc5ead913fa020de.png"},{"id":90794116,"identity":"bd8f65cc-c45e-4ce6-8121-9023e0387222","added_by":"auto","created_at":"2025-09-08 08:41:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":860068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7226274/v1/41925f22-a348-4901-b610-9e0fbaf2c037.pdf"},{"id":90792128,"identity":"a65cc7c0-6e6c-45a0-9532-414136fae929","added_by":"auto","created_at":"2025-09-08 08:25:19","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17835,"visible":true,"origin":"","legend":"","description":"","filename":"CGMgroupdataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7226274/v1/234047989e872533d60f48b1.xlsx"},{"id":90792896,"identity":"5edb5e3f-93e0-43e9-81c0-2b40a38ecb8e","added_by":"auto","created_at":"2025-09-08 08:33:20","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17416,"visible":true,"origin":"","legend":"","description":"","filename":"nonCGMgroupdataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7226274/v1/1bb271af38a4e295e5c7b5b2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Continuous glucose monitoring helps reduce hypoglycemia and improve control in insulin treated diabetic patients managed in primary care","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Type 2 Diabetes mellitus is a prevalent disease in primary care settings.\u0026nbsp;Proper glucose control has been shown to diminish microvascular and macrovascular complications of diabetes. To achieve optimal glycemic control, advanced T2DM patients will frequently require insulin therapy as the main stay of treatment.\u0026nbsp;Managing insulin-treated T2DM patients can be quite challenging. Physicians must balance between achieving optimal glycemic control via proactive medication titrations while simultaneously mitigating the substantial risk of hypoglycemia.\u0026nbsp;[1] The most widely used measure of glycaemia, HbA1c\u0026nbsp;level,\u0026nbsp;is straightforward to assess\u0026nbsp;and\u0026nbsp;plays a definitive role\u0026nbsp;in\u0026nbsp;enhancing treatment efficacy.\u0026nbsp;Nonetheless, HbA1c\u0026nbsp;possesses\u0026nbsp;significant\u0026nbsp;limitations that should be\u0026nbsp;considered\u0026nbsp;in clinical practice,\u0026nbsp;including\u0026nbsp;its\u0026nbsp;inability to\u0026nbsp;reflect the glycemic variability, and its accuracy would be\u0026nbsp;compromised\u0026nbsp;by the presence of haemoglobinopathy, among other factors. [2]\u003c/p\u003e\n\u003cp\u003eContinuous glucose monitoring is a technique for\u0026nbsp;incessantly\u0026nbsp;tracking glucose levels day and night,\u0026nbsp;utilizing\u0026nbsp;a small sensor inserted beneath the skin to measure interstitial fluid glucose levels. CGM has emerged as an essential tool for mitigating hypoglycemia and improving glucose control, providing real time feedback on blood glucose levels.\u003c/p\u003e\n\u003cp\u003eBy assessing the overall glycemic control and glycemic patterns, identifying glucose fluctuation, detecting hypoglycemic episodes, and incorporating into lifestyle management and motivational support, CGM demonstrated a reduction in both hypoglycemia and HbA1c levels patients with type 1 diabetes [3,4,5,6] and enhanced patient engagement and motivation, thereby improving their self-management skills. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the extensive use of CGM\u0026nbsp;in specialist settings,\u0026nbsp;there is little data regarding its efficacy in managing patients with T2DM in primary care [8].\u0026nbsp;This study aims to investigate the efficacy of CGM in mitigating hypoglycemia and enhancing glycemic control in patients with T2DM receiving care in primary clinics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives of the study:\u0026nbsp;\u003c/strong\u003eTo compare the difference in glycemic control (change in HbA1c level) and the reduction in frequency of hypoglycemic episodes between CGM group and non-CGM groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch hypothesis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is a significant difference in glycemic control, as measured by HbA1c levels, between the CGM group receiving CGM as a motivational tool for DM self-management and the control group receiving usual care with encouragement for self-monitoring of blood glucose (SMBG) and self-monitoring of hypoglycemic episodes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design:\u0026nbsp;\u003c/strong\u003eprospective cohort study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants who fulfilled the following inclusion criteria were included in the study: T2DM patients, aged 18-65 years, who had been regularly followed up at primary care clinics in the Kowloon Central Cluster (KCC) of the Hospital Authority from 01/01/2022 to 31/12/2023 and had received insulin treatment for a minimum of 60 days and had experienced any of the following symptoms since their previous follow-up visit:\u0026nbsp;\u003c/p\u003e\n\u003col start=\"1\" type=\"a\"\u003e\n \u003cli\u003eWith hypoglycemia symptoms \u0026ge; 2 times per week or\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWith Self blood glucose monitoring (SMBG) \u0026le; 3.9mmol/L for \u0026gt; 2 episodes/week or\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWith the history of severe hypoglycemia requiring hospital admission.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe assignment to CGM group or routine care group is on a voluntary basis. Those who agreed to receive CGM services were included into the CGM group; those who preferred the routine care i.e. to continue SMBG for monitoring were included in the non-CGM group.\u003c/p\u003e\n\u003cp\u003eExclusion criteria: prior usage of CGM device before the case recruitment period, patients with serious or unstable medical or psychological disorders and those unable to adhere to research requirements. Patients unable to provide informed consent will also be excluded.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CGM group\u003c/p\u003e\n\u003cp\u003eParticipants in the CGM group will be provided with a CGM device (Freestyle Libre) for a duration of 14 days, along with training on its usage. Participants will gain access to a mobile application that enables real-time viewing of glucose data (average glucose, glucose management indicator [estimated HbA1c], glucose variability, time in ranges) in real-time and provides alarms for hypoglycemic events.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe patients will be urged to use the CGM device as a motivational tool to engage in self-management behaviors. Several studies indicated that CGM was useful in modifying a patient\u0026rsquo;s diet and exercise habits and could induce better glycemic control than SMBG for patients with type 2 diabetes.[8,9]\u003c/p\u003e\n\u003cp\u003eThe CGM report includes pattern and variability (the amplitude, frequency and duration of glucose fluctuations) of the sensor glucose readings, the percentage of time the sensor glucose readings exceed 10.0 mmol/L, fall below 3.9 mmol/L, or remain within the Target Glucose Range ( 3.9- 10 mmol/L), low glucose events ( glucose reading is lower than 3.9 mmol/L for longer than 15 minutes) and the \u003cstrong\u003eestimated A1c level\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003c/strong\u003eA1C levels based on average glucose measured using CGM values\u003cstrong\u003e)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-CGM group\u003c/p\u003e\n\u003cp\u003eParticipants in the non-CGM group will be encouraged to perform SMBG as usual\u0026nbsp;throughout the study period.\u0026nbsp;They will have same follow-up arrangement as that of CGM group.\u0026nbsp;All participants will also receive education on hypoglycemia,\u0026nbsp;including self-monitoring and self-management of hypoglycemic episodes.\u003c/p\u003e\n\u003cp\u003eThis study was a prospective cohort study and did not involve a health care intervention requiring trial registration, as per BMC Primary Care\u0026rsquo;s editorial policies. Ethical approval was obtained from Research Ethics Committee (Kowloon Central/Kowloon East), Ref: KC/KE-23-0096/ER3, and all applicable ethical guidelines were followed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes:\u003c/strong\u003e The primary outcomes are the change in HbA1c levels from baseline to the end of the study and the difference in the frequency of hypoglycemic events per month encountered by participants during the study period. Hypoglycemic events will be defined as any episode in which the participant\u0026rsquo;s glucose level drops below 3.9 mmol/L. The secondary outcomes include alteration in systolic blood pressure (SBP), diastolic blood pressure (DBP), Low -density lipoprotein (LDL) levels, body mass index (BMI), estimated glomerular filtration rate (eGFR), fasting glucose and urine albumin- to-creatinine ratio (uACR) from baseline to the end of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sample size of 47 in each group based on the HbA1c means change of CGM group -0.5%\u0026nbsp;with a standard deviation\u0026nbsp;of 0.9, and HbA1c mean change of non CGM group at 0%\u0026nbsp;with a standard deviation\u0026nbsp;of 0.8 [13], is required to achieve 80% power and a significant level of 0.05 using a two-sided independent-sample test of difference of HbA1c levels. Assuming a 20% dropout rate, the estimated sample size is 57 in each group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics was presented by N (%) for categorical variables. Continuous variables were presented by Mean\u0026plusmn;SD.\u003c/p\u003e\n\u003cp\u003eTo compare any significant difference between groups, independent-samples t test was used. For categorical data, Pearson Chi- square test was used to determine the statistically significant in different groups.\u003c/p\u003e\n\u003cp\u003eRegarding to the analysis of outcomes, the within-group comparison was analysed by paired-t test, while between-group comparison was analysed by one-way ANCOVA, adjusted for the baseline score.\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk. NY, USA computer software). A p-value of \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 1. Baseline characteristics and demographics of patients included in the study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=1\u003c/strong\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD / N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGM group (N=5\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD / N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-CGM group (N=\u003c/strong\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD / N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e57.08\u0026plusmn;6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e56.29\u0026plusmn;7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e57.87\u0026plusmn;5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGender-male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e66 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e35 (59.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e31 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eEthnicity-Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e97 (81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e46 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e51 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e90 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e46 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e44 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHyperlipidaemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e104 (87.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e50 (84.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e54 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e76 (63.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e36 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e40 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8.03\u0026plusmn;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.72\u0026plusmn;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e7.35\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHypoglycemic event per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8.03\u0026plusmn;8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9.84\u0026plusmn;9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e6.25\u0026plusmn;6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e*0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eFasting glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6.01\u0026plusmn;3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.87\u0026plusmn;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e4.19\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e125.20\u0026plusmn;11.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e123.49\u0026plusmn;11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e126.88\u0026plusmn;11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e71.79\u0026plusmn;10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73.97\u0026plusmn;10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e69.65\u0026plusmn;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e*0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2.03\u0026plusmn;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.02\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e2.04\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Comparison of different parameters within and between the CGM and Non-CGM groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"765\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGM group (N=59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon CGM group (N=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePre\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ePost (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePost-pre difference (d1) (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePre\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePost (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePost-pre difference (d2) (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003ed1-d2 difference (Mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003ep-value (Between-group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8.72\u0026plusmn;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.29\u0026plusmn;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.43\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.35\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.86\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.95\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e*0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehypoglycaemic event (pre month)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9.84\u0026plusmn;9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.36\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-9.48\u0026plusmn;9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.25\u0026plusmn;6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.47\u0026plusmn;2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-4.78\u0026plusmn;5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-4.69\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e87.68\u0026plusmn;18.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e84.19\u0026plusmn;19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.49\u0026plusmn;10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e85.85\u0026plusmn;16.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e84.27\u0026plusmn;17.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.58\u0026plusmn;8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.91\u0026plusmn;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting glucose (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.87\u0026plusmn;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e7.58\u0026plusmn;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.29\u0026plusmn;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4.19\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5.99\u0026plusmn;2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.81\u0026plusmn;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-2.09\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e123.49\u0026plusmn;11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e127.36\u0026plusmn;14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3.86\u0026plusmn;15.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e126.88\u0026plusmn;11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e129.13\u0026plusmn;11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.25\u0026plusmn;9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e*0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.61\u0026plusmn;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e73.97\u0026plusmn;10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e72.10\u0026plusmn;9.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.86\u0026plusmn;9.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e69.65\u0026plusmn;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e69.77\u0026plusmn;7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.12\u0026plusmn;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.98\u0026plusmn;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.02\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.97\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.04\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.04\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.92\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.12\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (\u003c/strong\u003e\u003cstrong\u003ekg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26.68\u0026plusmn;4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e26.84\u0026plusmn;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.17\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26.78\u0026plusmn;4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26.74\u0026plusmn;4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.04\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine albumin-to- creatinine ratio (\u003c/strong\u003e\u003cstrong\u003emg/mmol\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9.42\u0026plusmn;30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e11.16\u0026plusmn;36.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.75\u0026plusmn;11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e17.5\u0026plusmn;69.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16.62\u0026plusmn;67.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.88\u0026plusmn;9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.62\u0026plusmn;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Comparisons of glycemic control rate between the CGM and non-CGM group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eStudy group (N=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eControl group (N=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d1) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d2) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBetween-group difference (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ep-value (Between\u003c/p\u003e\n \u003cp\u003e-group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eHbA1c\u0026lt;7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e26 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e20 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e11.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e*0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test.\u003c/p\u003e\n\u003cp\u003eTable 4. Comparison of BP control rate between the CGM and non-CGM group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eStudy group (N=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eControl group (N=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d1) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d2) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBetween-group difference (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003ep-value (Between-group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSBP\u0026lt;130\u0026nbsp;mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e44 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e37 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e37 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e32 (53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eDBP\u0026lt;80\u0026nbsp;mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e43 (72.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e46 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e50 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e56 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test.\u003c/p\u003e\n\u003cp\u003eTable 5. Comparison of lipid control rate between the CGM and non-CGM group.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eStudy group (N=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eControl group (N=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d1) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ePost (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePost-pre difference (d2) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBetween-group difference (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003ep-value (Between-group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLDL\u0026lt;2.6\u0026nbsp;mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e52 (88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e56 (94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e50 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e56 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant difference. Within-group comparison was analyzed by McNemar test. Between-group comparison was analyzed by difference in two proportion test.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study flow was illustrated in \u003cstrong\u003eFigure 1\u003c/strong\u003e. Overall, 119 patients, including 59 participants from the CGM group and 60 participants from non-CGM group were recruited and included in the final analysis. Their mean age was 57.08\u0026plusmn;6.71 years, while 66 (55.5%) of them were male and 97 (81.5%) were Chinese patients. Baseline characteristics and demographics of the participants were presented in\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e. There were no significant differences in baseline data including age, gender-male, ethnicity-Chinese and comorbidities like hypertension, hyperlipidaemia and obesity. The baseline HbA1c level, the frequency of hypoglycaemic event and diastolic blood pressure between groups are statistically significant.\u003c/p\u003e\n\u003cp\u003eIn the\u003cstrong\u003e\u0026nbsp;Table 2\u003c/strong\u003e, the mean HbA1c in CGM group decreased from 8.72\u0026plusmn;1.85% to 8.29\u0026plusmn;1.53% (mean difference -0.43\u0026plusmn;1.38, p=0.021), while the mean HbA1c in non-CGM group increased from 7.35\u0026plusmn;1.05% to 7.86\u0026plusmn;1.51% (mean difference 0.53\u0026plusmn;0.79, p\u0026lt;0.001). The reduction in HbA1c between the CGM group and the non-CGM group was statistically significant (difference -0.95\u0026plusmn;0.21, p=0.006). Regarding hypoglycemic episodes per month, the mean number of hypoglycemic events in the CGM group decreased significantly from 9.84\u0026plusmn;9.76 to 0.36\u0026plusmn;1.18 (mean difference -9.48\u0026plusmn;9.85, p\u0026lt;0.001), while the mean number of hypoglycemic events in the non-CGM group decreased from 6.25\u0026plusmn;6.16 to 1.47\u0026plusmn;2.37 (mean difference -4.78\u0026plusmn;5.16, p\u0026lt;0.001). The reduction in hypoglycemic events was statistically significantly greater in the CGM group compared to the non-CGM group (difference -4.69\u0026plusmn;1.45, p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in eGFR (difference -1.91\u0026plusmn;1.74, p=0.313), fasting glucose (difference -2.09\u0026plusmn;0.67, p=0.263), systolic blood pressure (difference 1.61\u0026plusmn;2.33, p=0.992), diastolic blood pressure (difference -1.98\u0026plusmn;1.82, p=0.709), LDL level (difference 0.08\u0026plusmn;0.12, p=0.449), BMI (difference 0.21\u0026plusmn;0.21, p=0.324) or urine albumin-to-creatinine ratio (difference 2.62\u0026plusmn;1.96, p=0.205) by comparing CGM and non-CGM groups (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn the \u003cstrong\u003eTable 3\u003c/strong\u003e, the proportion of patients achieving satisfactory glycemic control (A1c \u0026lt;7%) showed no statistically significant improvement within either group:\u003c/p\u003e\n\u003cp\u003eIn the study group, the rate increased slightly from 13.6% (pre) to 15.3% (post) (post-pre difference: 1.7%, p = 1.000). In the control group, the rate decreased from 43.3% (pre) to 33.3% (post) (post-pre difference: -10.0%, p = 0.180). Between-group analysis showed a significant difference in the post-pre change in glycemic control. (11.7%, p = 0.006).\u003c/p\u003e\n\u003cp\u003eBetween-groups, the differences in satisfactory blood pressure nor satisfactory lipid control rates were not statistically significant. (\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTable 5)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study is to evaluate the efficacy of CGM as a motivational tool for diabetic management in T2DM patients on insulin therapy. The findings showed significant improvements in diabetic control and a substantial reduction in hypoglycemic episodes for patients using CGM compared to those receiving usual care with SMBG.\u003c/p\u003e\u003cp\u003eThe CGM group had a substantial decrease in HbA1c levels compared to the non-CGM group (-0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.006). CGM is seen as a motivational tool for patients and a resource for physicians to facilitate therapeutic adjustment. This may enhance glycemic control. Additional research on CGM demonstrated its efficacy in enhancing glycemic control among diabetes patients by lifestyle modification without treatment modification in primary care and secondary care [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A comparable local study indicates a significant reduction in HbA1c level by comparing between CGM group and SMBG groups in type II diabetic patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In randomized controlled trials (RCTs) and meta-analyses, some reported no significant difference, while majority indicated greater HbA1c reductions with CGM group than SMBG group. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Some differences are likely due to varying average baseline HbA1c between studies; in general, greater HbA1c reductions were seen in studies with higher baseline HbA1c, with smaller reductions seen with lower baseline HbA1c. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis study revealed a substantial reduction in hypoglycemia episodes in the CGM group compared to the non-CGM group (difference 4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside a noteworthy decrease in HbA1c within the CGM group. The CGM group can sustain an acceptable HbA1c rate. A study indicated that the median hypoglycemic event rate decreased by 30% in the CGM group, whereas the rate was nearly unchanged in the control group [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Hypoglycemia is a significant risk for patients on insulin therapy, potentially resulting in severe health consequences and reducing the quality of life. The CGM group gained from real-time alarms and continuous glucose trend data, which enhanced prevention and management of hypoglycemic episodes. This enhancement underscores the significance of CGM in augmenting patient safety and alleviating the incidence of hypoglycemia.\u003c/p\u003e\u003cp\u003eAt the baseline, the CGM group demonstrated a higher fasting glucose level (7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18 vs 4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a higher HbA1c level (8.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 vs 7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a higher frequency of hypoglycemic events (9.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76 vs 6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16, P\u0026thinsp;=\u0026thinsp;0.019) compared to the non-CGM group. The potential causes are non-randomized allocation and selection bias. For instance, patients with more unstable or challenging diabetes may have been preferentially assigned to the CGM group, resulting in higher baseline rates of fasting glucose level, higher HbA1c level and higher frequency of hypoglycemic event in the CGM group. A higher baseline HbA1c in the CGM group may facilitate the observation of reductions in HbA1c levels over time, while a greater baseline frequency of hypoglycemia may influence the detection of reductions in hypoglycemic occurrences. The disparities complicate the attribution of the observed changes exclusively to the CGM. A one-way ANCOVA was conducted to account for baseline differences in the between-group comparison.\u003c/p\u003e\u003cp\u003eThere is an increase in fasting glucose level, an increase in HbA1c level, and a decrease in satisfactory HbA1c rate in non-CGM group at the end of study. The subsequent are the potential explanations. First, to prevent hypoglycemic episodes, physicians may implement less strict glycemic control in treatment plans and patients may have less proactive in modifying their daily activities and insulin regimens. Second, the non-CGM group depended on SMBG, potentially overlooking glucose fluctuations between measurements (such as postprandial spikes or nocturnal hyperglycemia), which may have resulted in fewer or less precise adjustments, contributing to suboptimal glucose control and elevated HbA1c levels. Third, SMBG generally fails to record overnight glucose levels. Unrecognized nocturnal hyperglycemia can result in prolonged increased blood glucose, hence contributes to an increase in HbA1c level. Finally, patients in non-CGM group may have adopted strategies to prevent hypoglycemia, such as increasing carbohydrate intake before bedtime or decreasing insulin dosages at night. These strategies result in increased fasting glucose levels the following morning and higher HbA1c level over time.\u003c/p\u003e\u003cp\u003eA significant aspect of this study was the utilization of CGM as a motivational tool for enhancing patient engagement in self-management. The significant improvements in glycemic control and reduction in hypoglycemic episodes in the CGM group highlight the potential of CGM to empower patients. Access to real-time glucose data enhances patients\u0026rsquo; awareness and comprehension of their glucose patterns, resulting in more proactive diabetes care [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This conclusion corroborates the research indicating that patient engagement and empowerment are essential for effective diabetes management.\u003c/p\u003e\u003cp\u003eThe findings of this study possess significant clinical implications. CGM technology may be regarded as a significant enhancement in diabetes care, especially for T2DM patients susceptible to hypoglycemia. The decrease in hypoglycemia incidents and improved diabetes management linked to CGM utilization may result in fewer emergency visits and hospitalizations, hence lowering healthcare expenses.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eThere are several limitations of this study. Firstly, the study was conducted in public primary care clinics, therefore the findings of this study may not be generalized to private setting or specialist setting. Secondly, as the grouping of T2DM to CGM group or routine care group was on voluntary basis and was not randomized, selection bias may exist. This was reflected by their difference in HbA1c level, frequency of hypoglycemic episodes etc. at the baseline. However, the inter and the intra group analysis has clearly demonstrated the efficacy of CGM on the glycemic control and reduction of hypoglycemic episodes between the groups. Thirdly, the study duration was relatively short (12 months), therefore longer-term studies are needed to evaluate the sustained impact of CGM on glycemic control and hypoglycemia. A more ideal outcome could be achieved by conducting a repeat CGM at the 12-months for CGM group. A direct comparison of the CGM data at baseline and at 12 months may yield more favorable outcomes. Regretfully, we were unable to provide the second CGM to CGM group due to insufficient resources.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFuture Directions:\u003c/h3\u003e\n\u003cp\u003eFuture research could focus on cohort study with larger sample size, balanced baseline data of HbA1c level and the episodes of hypoglycemic events. By comparing the serial CGM data like time in range, estimated HbA1c, time above range and time below range could help understand how the CGM reduce the glucose fluctuation that leads to better clinical outcome. Besides, CGM could become a new tool for the development of new anti-diabetic medications. Furthermore, exploring the cost-effectiveness of CGM and its impact on patient-reported outcomes, such as quality of life and satisfaction, would provide valuable insights for development of new CGM devices. In addition, investigating the integration of CGM with other digital health tools and personalized diabetes management programs could further enhance the effectiveness of CGM in clinical practice. Finally, employing AI technology to analyze CGM data may offer immediate feedback to patients for adjustments in diet and exercise.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, CGM serves as an effective tool for T2DM patients in improving glycemic control and reducing the hypoglycemic incidents in primary care settings. The interpretation of the CGM data has become a crucial skill for physicians to deliver comprehensive care for diabetic patients. Family physicians should actively consider incorporating CGM into comprehensive diabetes assessment for enhanced diabetes management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted in accordance with the Declaration of Helsinki and ICH GCP.\u003c/p\u003e\n\u003cp\u003eEthics approval: Research Ethics Committee (Kowloon Central/Kowloon East), Ref: KC/KE-23-0096/ER3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e All authors consent for publication in \u003cem\u003eBMC Primary Care.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003euploaded to Supplementary material\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare no competing interests. No financial or non-financial conflicts of interest exist that could influence the study\u0026rsquo;s design, conduct, or reporting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eHKCFP research Fellowship 2023\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWong Ching Keung secured funding for the study, designed the study protocol, conducted data collection, performed statistical analyses and contributed to the results. Wong Ching Keung, Chiang Lap Kin and Chen Xiao Rui contributed to discussion sections. Chen Xiao Rui supervised the research team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eChinese University of Hong Kong The Jockey Club School of Public Health and Primary Care\u003c/li\u003e\n \u003cli\u003eKwong Wah Hospital Clinical Research Centre for biostatistics consultation service\u003c/li\u003e\n \u003cli\u003eAll clinical staff participating in the study\u003c/li\u003e\n \u003cli\u003eResearch assistant: Edward Choi\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang, B. K. (2022). Hypoglycemia unawareness identified by continuous glucose monitoring system is frequent in outpatients with type 2 diabetes without receiving intensive therapeutic interventions. \u003cem\u003eDiabetology \u0026amp; Metabolic Syndrome, 14\u003c/em\u003e(180). \u003c/li\u003e\n\u003cli\u003eLittle, R. R., \u0026amp; Roberts, W. L. A. (2009). Review of variant hemoglobins interfering with hemoglobin A1c measurement. \u003cem\u003eJournal of Diabetes Science and Technology, 3\u003c/em\u003e(446-451). \u003c/li\u003e\n\u003cli\u003eRadermecker, R. P. (2010). Continuous glucose monitoring reduces both hypoglycemia and HbA1c in hypoglycemia-prone type 1 diabetic patients treated with a portable pump. \u003cem\u003eDiabetes \u0026amp; Metabolism, 36\u003c/em\u003e(409-413). \u003c/li\u003e\n\u003cli\u003eBeck, R. W. (2017). Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: The DIAMOND randomized clinical trial. \u003cem\u003eJAMA, 317\u003c/em\u003e(4), 371-378.\u003c/li\u003e\n\u003cli\u003eRodbard, D. (2017). Continuous glucose monitoring: A review of recent studies demonstrating improved glycemic outcomes. \u003cem\u003eDiabetes Technology \u0026amp; Therapeutics, 19\u003c/em\u003e(S3).\u003c/li\u003e\n\u003cli\u003eRiddlesworth, T. (2017). Hypoglycemic event frequency and the effect of continuous glucose monitoring in adults with type 1 diabetes using multiple daily insulin injections. \u003cem\u003eDiabetes Therapy, 8\u003c/em\u003e(947-951). \u003c/li\u003e\n\u003cli\u003eSimonson, G. D. (2021). Effect of professional CGM (pCGM) on glucose management in type 2 diabetes patients in primary care. \u003cem\u003eJournal of Diabetes Science and Technology, 15\u003c/em\u003e(3), 539-545. \u003c/li\u003e\n\u003cli\u003eYoo, H. J., An, H. G., Park, S. Y., et al. (2008). Use of a real-time continuous glucose monitoring system as a motivational device for poorly controlled type 2 diabetes. \u003cem\u003eDiabetes Research and Clinical Practice, 82\u003c/em\u003e(73-79). https://doi.org/10.1016/j.diabres.2008.06.015\u003c/li\u003e\n\u003cli\u003eKitazawa, M., \u0026amp; Takeda, Y. (2024). Lifestyle intervention with smartphone app and isCGM for people at high risk of type 2 diabetes: Randomized trial. \u003cem\u003eJournal of Clinical Endocrinology \u0026amp; Metabolism, 109\u003c/em\u003e(4), 1060-1070. https://doi.org/10.1210/clinem/dgad639\u003c/li\u003e\n\u003cli\u003eEhrhardt, N. (2020). Continuous glucose monitoring as a behavior modification tool. \u003cem\u003eClinical Diabetes, 38\u003c/em\u003e(2). \u003c/li\u003e\n\u003cli\u003eEngler, S., Fields, S., Leach, W., et al. (2022). Real-time continuous glucose monitoring as a behavioral intervention tool for T2D: A systematic review. \u003cem\u003eJournal of Technology in Behavioral Science, 7\u003c/em\u003e(252-263). https://doi.org/10.1007/s41347-022-00247-5\u003c/li\u003e\n\u003cli\u003eVallis, M. (2023). How continuous glucose monitoring can motivate self-management: Can motivation follow behavior? \u003cem\u003eCanadian Journal of Diabetes, 47\u003c/em\u003e(5), 435-444. https://doi.org/10.1016/j.jcjd.2023.04.001\u003c/li\u003e\n\u003cli\u003eLaffel, L. M., Kanapka, L. G., Beck, R. W., et al. (2020). Effect of continuous glucose monitoring on glycemic control in adolescents and young adults with type 1 diabetes: A randomized clinical trial. \u003cem\u003eJAMA, 323\u003c/em\u003e(23), 2388-2396. https://doi.org/10.1001/jama.2020.6940\u003c/li\u003e\n\u003cli\u003eWong, T. W. (2023). Use of personal continuous glucose monitoring (CGM) with support in people with type 1 and 2 diabetes treated with insulin in the outpatient clinic: A single-center retrospective cohort study. \u003cem\u003eClinical Diabetology, 12\u003c/em\u003e(2).\u003c/li\u003e\n\u003cli\u003eKim, S. K., Kim, H. J., Kim, T., et al. (2014). Effectiveness of 3-day continuous glucose monitoring for improving glucose control in type 2 diabetic patients in clinical practice. \u003cem\u003eDiabetes \u0026amp; Metabolism Journal, 38\u003c/em\u003e(6), 449-455. https://doi.org/10.4093/dmj.2014.38.6.449\u003c/li\u003e\n\u003cli\u003eOser, T. K., Litchman, M. L., Allen, N. A., et al. (2021). Personal continuous glucose monitoring use among adults with type 2 diabetes: Clinical efficacy and economic impacts. \u003cem\u003eCurrent Diabetes Reports, 21\u003c/em\u003e(49). https://doi.org/10.1007/s11892-021-01408-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Continuous Glucose Monitoring, Type 2 Diabetes Mellitus, Hypoglycemia, primary care","lastPublishedDoi":"10.21203/rs.3.rs-7226274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7226274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To determine the efficacy of Continuous Glucose Monitoring (CGM) in reducing hypoglycemia and improving glycemic control in Type 2 diabetes mellitus (T2DM) patients treated with insulin in primary care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign: \u003c/strong\u003eProspective Cohort study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects:\u003c/strong\u003e T2DM patients treated with insulin and had been regularly followed up at primary care clinics from the Hospital Authority from January 2022 to December 2023. Participants were divided into either the CGM group (Cohort group) and the non-CGM group (Control group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Outcome Measures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome: change in HbA1c levels over a 12-months period and the changes in the frequency of hypoglycemic episodes per month.\u003c/p\u003e\n\u003cp\u003eSecondary outcome: changes in other clinical parameters including blood pressure control, lipid control and biochemical profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study included 59 patients in the CGM group and 60 patients in the non-CGM group. The CGM group exhibited a statistically significant reduction in HbA1c level between-groups with mean difference of 0.95±0.21 (p=0.006). The incidence of hypoglycemic episodes markedly decreased in the CGM group too, witha mean difference of 4.69±1.45 (p=\u0026lt;0.001) between the groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e CGM significantly improved glycemic control and reduced hypoglycemic episodes in T2DM patients managed in primary care setting. Family physicians should actively consider incorporating CGM into the comprehensive diabetes assessment for enhanced diabetes management.\u003c/p\u003e","manuscriptTitle":"Continuous glucose monitoring helps reduce hypoglycemia and improve control in insulin treated diabetic patients managed in primary care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:25:14","doi":"10.21203/rs.3.rs-7226274/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-10-16T18:39:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"270899595046375136058235144809710575836","date":"2025-10-06T19:54:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156165091702169883334255804388244649333","date":"2025-09-30T11:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332977970003806314163164969430599882928","date":"2025-09-11T10:26:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T14:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196163815462955586254818276079420540629","date":"2025-08-29T12:48:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T16:37:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-26T18:57:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2025-08-12T14:00:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3139bc73-88e7-4bec-b4c6-8fa933f95210","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T08:25:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 08:25:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7226274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7226274","identity":"rs-7226274","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}