宁波市2型糖尿病患者移动应用辅助血糖自我监测:12个月回顾性队列研究

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Xujia Ma, Kaushik Chattopadhyay, Miao Xu, Li Li, Jialin Li
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引用次数: 0

摘要

背景:在包括中国在内的临床实践指南中,推荐自我监测血糖(SMBG),作为患者教育、自我管理和赋权的一部分。随着技术的进步,电信技术现已用于医疗保健领域的远程监测。移动应用程序已经成为2型糖尿病(T2DM)患者监测SMBG的实用工具。然而,这种方法在现实世界中的长期有效性需要进一步探索。目的:本研究旨在确定移动应用辅助SMBG在中国宁波标准治疗之外改善T2DM患者12个月血糖控制的有效性。方法:在这项回顾性队列研究中,纳入了2019年9月1日至2022年6月30日在宁波国家代谢管理中心首次登记并接受标准化糖尿病管理的成年2型糖尿病患者。该研究比较了两组:选择手机应用辅助SMBG的组和没有选择SMBG的组。基于相似的基线特征,倾向评分匹配将移动应用程序辅助的SMBG组与对照组进行匹配。在12个月的随访中比较血糖控制相关结果。使用线性和逻辑回归模型分别估计平均差异和优势比(ORs)以及95% ci,并对基线特征进行调整。结果:共纳入160例患者,每组80例。在移动应用辅助SMBG组中,血糖监测的中位数(IQR)频率为每周0(0-2)次,28%(22/80)的人每周至少监测两次血糖,应用程序使用频率为每周1(0-3)次,40%(32/80)的人每周至少登录两次。在12个月时,手机应用辅助的SMBG组与对照组的血糖控制结果无统计学差异。具体而言,结果显示:(1)空腹血糖和糖化血红蛋白水平无显著差异(平均差异为-0.17 mmol/L, 95% CI为-0.85至0.51 mmol/L; P= 0.62和-0.12%,95% CI为-0.58%至0.33%;P= 0.59)和(2)达到或维持空腹血糖的患者比例结论:在中国宁波的T2DM患者的现实世界队列中,移动应用程序辅助的SMBG在12个月时血糖控制没有统计学意义上的显著改善。这表明,在资源充足的环境中,单独的标准护理可能相对有效。然而,进一步改进的机会仍然存在。缺乏观察到的益处可能是由于与过程相关的问题,例如对干预的次优参与。解决这些挑战应该是未来研究的重点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mobile App-Assisted Self-Monitoring of Blood Glucose in Type 2 Diabetes in Ningbo, China: 12-Month Retrospective Cohort Study.

Mobile App-Assisted Self-Monitoring of Blood Glucose in Type 2 Diabetes in Ningbo, China: 12-Month Retrospective Cohort Study.

Mobile App-Assisted Self-Monitoring of Blood Glucose in Type 2 Diabetes in Ningbo, China: 12-Month Retrospective Cohort Study.

Background: Self-monitoring of blood glucose (SMBG) is recommended in clinical practice guidelines, including those in China, as part of patient education, self-management, and empowerment. With technological advancements, telecommunication technologies are now used for telemonitoring in health care. Mobile apps have become a practical tool for SMBG among patients with type 2 diabetes mellitus (T2DM). However, the long-term effectiveness of this approach in real-world practice requires further exploration.

Objective: The study aims to determine the effectiveness of mobile app-assisted SMBG in improving glycemic control in patients with T2DM at 12 months, in addition to standard care, in Ningbo, China.

Methods: In this retrospective cohort study, adults with T2DM who registered at the National Metabolic Management Center, Ningbo, for the first time between September 1, 2019, and June 30, 2022, and received standardized diabetes management were included. The study compared 2 groups: those who opted for mobile app-assisted SMBG and those who did not. Propensity score matching matched the mobile app-assisted SMBG group with the control group based on similar baseline characteristics. Glycemic control-related outcomes were compared at 12-month follow-up. Linear and logistic regression models were used to estimate mean differences and odds ratios (ORs) along with 95% CIs, respectively, and adjustments were made for baseline characteristics.

Results: A total of 160 patients (80 in each group) were included in the study. In the mobile app-assisted SMBG group, the median (IQR) frequency of blood glucose monitoring was 0 (0-2) times per week, with 28% (22/80) monitoring their blood glucose at least twice per week, and the app usage frequency was 1 (0-3) time per week, with 40% (32/80) logging in at least twice per week. There were no statistically significant differences observed between the mobile app-assisted SMBG group and the control group in glycemic control outcomes at 12 months. Specifically, the results showed no significant difference in (1) fasting blood glucose and glycosylated hemoglobin levels (mean difference -0.17 mmol/L, 95% CI -0.85 to 0.51 mmol/L; P=.62 and -0.12%, 95% CI -0.58% to 0.33%; P=.59, respectively) and (2) the proportion of patients achieving or maintaining fasting blood glucose at <7 mmol/L and glycosylated hemoglobin at <7% (OR 0.89, 95% CI 0.46-1.73; P=.74 and OR 0.91, 95% CI 0.44-1.88; P=.79, respectively).

Conclusions: In a real-world cohort of patients with T2DM in Ningbo, China, mobile app-assisted SMBG did not lead to statistically significant improvements in glycemic control at 12 months. This suggests that in a well-resourced setting, standard care alone may be relatively effective. However, opportunities for further improvement remain. The lack of observed benefit may be due to process-related issues, such as suboptimal engagement with the intervention. Addressing these challenges should be a focus of future research.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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