Toschi Elena, Fisher Lawrence, Wolpert Howard, Love Michael, Dunn Timothy, Hayter Gary
1 Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA.
2 Department of Family and Community Medicine, University of California, San Francisco, CA, USA.
J Diabetes Sci Technol. 2018 Nov;12(6):1143-1151. doi: 10.1177/1932296818791534. Epub 2018 Jul 31.
The goal of this uncontrolled pilot study was to assess the feasibility of a self-care management mobile app, called Sugar Sleuth, which incorporates the FreeStyle Libre™ glucose sensor, to help clinicians and people with type 1 diabetes (PWD) identify and mitigate self-care behaviors that contribute to glucose variability.
PWDs with a baseline A1c between 7.5 and 9.0% used the mobile app for 14 weeks. The app prompted the PWD to enter the suspected cause of detected glycemic excursions, and to record food and insulin information. PWDs met with clinicians to collaboratively review data, identify challenges, and devise a specific self-care plan. Outcome measures included a single glycemic outcome score (SGOS) and attitude rating scales to better understand how participant attitudes could affect glycemic outcome.
Thirty enrolled PWDs had a mean age of 55 ± 2.6 years, and a mean diabetes duration of 32 ± 2.9 years. A significant average reduction in A1c of 0.5 ± 0.07% ( P < .01) and in mean daily carbohydrate intake of 43 ± 21 grams ( P = .05) was found. No statistically significant change in glycemic metrics, body weight, or total daily insulin dose was found. A significant negative association occurred between SGOS and "hypoglycemia tolerance" ( P = .04), and a positive correlation occurred that approached significance with "motivation to change behavior" ( P = .06).
These findings suggest that this mobile app system, in conjunction with CGM, provides a useful platform for helping clinicians and adults with T1D improve self-management skills to improve glycemic control.
这项非对照性试点研究的目的是评估一款名为“Sugar Sleuth”的自我护理管理移动应用程序的可行性，该应用程序整合了FreeStyle Libre™葡萄糖传感器，以帮助临床医生和1型糖尿病患者（PWD）识别并减轻导致血糖波动的自我护理行为。
基线糖化血红蛋白（A1c）在7.5%至9.0%之间的1型糖尿病患者使用该移动应用程序14周。该应用程序提示患者输入检测到的血糖波动的疑似原因，并记录食物和胰岛素信息。患者与临床医生会面，共同审查数据、识别挑战并制定具体的自我护理计划。结果指标包括单一血糖结果评分（SGOS）和态度评定量表，以更好地了解参与者的态度如何影响血糖结果。
30名登记的1型糖尿病患者的平均年龄为55±2.6岁，平均糖尿病病程为32±2.9年。发现糖化血红蛋白平均显著降低0.5±0.07%（P<.01），平均每日碳水化合物摄入量显著降低43±21克（P=.05）。未发现血糖指标、体重或每日总胰岛素剂量有统计学显著变化。SGOS与“低血糖耐受性”之间存在显著负相关（P=.04），与“改变行为的动机”之间存在接近显著的正相关（P=.06）。
这些发现表明，这款移动应用程序系统与连续血糖监测（CGM）相结合，为帮助临床医生和1型糖尿病成年人提高自我管理技能以改善血糖控制提供了一个有用的平台。
