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目的探讨分析基于云智能系统对T2DM患者院外血糖管理的应用效果,为社区血糖管理提供新模式。方法选取T2DM患者120例,采用随机数字表法将其分为云智能组和对照(Con)组,每组各60例。云智能组首次接受云智能终端使用的培训,并全程使用云智能系统。Con组每月到糖尿病中心进行门诊常规诊疗;并于第25~48周接受云智能终端使用的培训和干预。分别于第24、48周血糖控制指标比较。结果两组第24周、48周时上述指标与初始血糖比较均呈下降趋势,特别是云智能组各时间点比较,差异有统计学意义[FPG(9.6±2.7)vs(5.4±0.9)vs(4.7±0.8)mmol/L,2hPG(15.4±3.1)vs(7.8±1.2)vs(7.1±0.9)mmol/L,HbA_1c(9.7±2.6)%vs(6.4±1.1)%vs(5.3±0.9)%,P<0.05];第24周时,云智能组FPG、2hPG、HbA_1c均优于Con组[(5.4±0.9)vs(7.6±1.8)mmol/L,(7.8±1.2)vs(10.5±2.1)mmol/L,(6.4±1.1)%vs(8.3±1.7)%,P<0.05];第48周时,两组上述指标比较,差异无统计学意义[(4.7±0.8)vs(5.0±1.4)mmol/L,(7.1±0.9)vs(7.4±1.1)mmol/L,P>0.05],而云智能组HbA_1c低于Con组[(5.3±0.9)%vs(6.5±1.2)%,P<0.05];云智能组第1和第2阶段平均每月血糖监测次数均高于Con组[(45.3±3.7)vs(35.7±2.8)次/月,(25.4±3.3)vs(18.5±2.5)次/月,P<0.05)]。BMI云智能组第1阶段BMI低于Con组[(24.6±2.7)vs(27.8±3.4)kg/m~2,P<0.05]。结论基于云智能系统的糖尿病管理模式有助于对T2DM患者血糖水平进行即时掌握和管理,可有效提高血糖控制效果。
Objective To explore the application effect of cloud-based intelligent system on blood glucose management in T2DM patients and to provide a new model for community blood glucose management. Methods One hundred and twenty patients with T2DM were selected and divided into two groups according to the random number table method, 60 cases in each group. For the first time, the cloud intelligent group has been trained in the use of cloud intelligent terminals and has used cloud intelligent systems throughout. Con group routinely went to the Diabetes Center for outpatient treatment routinely every month and received training and interventions on cloud-based intelligent terminals from Weeks 25-48. Respectively, in the first 24,48 weeks of blood glucose control index comparison. Results Compared with the initial blood glucose, the above indexes showed a decreasing trend at the 24th and 48th week in both groups (P <0.05), especially in the Smart Group at each time point (FPG (9.6 ± 2.7) vs (5.4 ± 0.9) vs (4.7 ± 0.8) mmol / L, 2hPG (15.4 ± 3.1) vs (7.8 ± 1.2) vs (7.1 ± 0.9) mmol / L and HbA 1c (9.7 ± 2.6)% vs (6.4 ± 1.1)% vs ), P <0.05]. At week 24, FPG, 2hPG and HbA_1c in the cloud-smart group were significantly higher than those in the Con group [(5.4 ± 0.9 vs 7.6 ± 1.8) mmol / ± 2.1) mmol / L, (6.4 ± 1.1)% vs (8.3 ± 1.7)%, P <0.05]. There was no significant difference between the two groups at week 48 [(4.7 ± 0.8) vs 5.0 ± 1.4 mmol / L, 7.1 ± 0.9 vs 7.4 ± 1.1 mmol / L, P> 0.05], while the HbA_1c in the cloud-smart group was lower than that in the Con group [(5.3 ± 0.9)% vs %, P <0.05]. The average number of monthly blood glucose monitoring in the first and second phases of the cloud-intelligence group was significantly higher than that in the Con group (45.3 ± 3.7 vs 35.7 ± 2.8) / (25.4 ± 3.3) vs 18.5 ± 2.5) times / month, P <0.05)]. The BMI in stage 1 of BMI cloud intelligence group was lower than that in Con group [(24.6 ± 2.7) vs (27.8 ± 3.4) kg / m ~ 2, P <0.05]. Conclusion The diabetes management model based on cloud intelligence system can help to grasp and manage the blood glucose level in patients with T2DM immediately, which can effectively improve the effect of blood sugar control.