Kongdee Rujiravee, Parsia Bijan, Thabit Hood, Harper Simon
Department of Computer Science, University of Manchester, Manchester, UK.
Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Digit Health. 2025 May 6;11:20552076251332580. doi: 10.1177/20552076251332580. eCollection 2025 Jan-Dec.
Current glucose monitoring user interfaces (UIs) are problematic for people with Type 1 Diabetes Mellitus (T1DM) in maintaining recommended blood glucose levels effectively. However, there is a lack of in-depth investigation into this problem when these individuals interpret and make real-time decisions based on the glucose monitoring devices they use daily.
We aim to investigate problems associated with glucose monitoring UIs by observing users' interpretation and decision-making while reading their Continuous Glucose Monitoring (CGM), Flash Glucose Monitoring (Flash) or Self-monitoring of Blood Glucose (SMBG).
A mixed-method study was conducted. The Think Aloud protocol was used to capture participants' decision-making process while reading various device UIs. Their responses were evaluated using standard clinical guidance to assess their accuracy. Additionally, a survey was distributed to gather their perceptions of self-management practices.
Twenty-seven participants (17 patients and 10 carers) were recruited. Interpretation accuracy averaged 380% 111% for CGM, 395% 88% for Flash, and 333% 78% for SMBG group. Treatment action accuracy was 215% 156% for CGM, 212% 140% for Flash, and 180% 132% for SMBG group. Despite this, 750% of all participants expressed very high confidence in their self-management.
Interpreting and making decisions using glucose monitoring UIs remains significantly challenging for people with T1DM despite their self-perceived performance. Improving such UIs is crucial to reduce misinterpretation and help these individuals make better treatment decisions without relying on their potentially inaccurate interpretations.
目前的血糖监测用户界面(UI)对于1型糖尿病(T1DM)患者有效维持推荐的血糖水平存在问题。然而,当这些个体基于他们日常使用的血糖监测设备进行解读并做出实时决策时,对于这个问题缺乏深入的调查。
我们旨在通过观察用户在阅读其连续血糖监测(CGM)、动态血糖监测(Flash)或自我血糖监测(SMBG)时的解读和决策,来调查与血糖监测用户界面相关的问题。
进行了一项混合方法研究。采用出声思考协议来捕捉参与者在阅读各种设备用户界面时的决策过程。使用标准临床指南评估他们的回答,以评估其准确性。此外,还分发了一份调查问卷,以收集他们对自我管理实践的看法。
招募了27名参与者(17名患者和10名护理人员)。CGM组的解读准确率平均为380%±111%,Flash组为395%±88%,SMBG组为333%±78%。治疗行动准确率CGM组为215%±156%,Flash组为212%±140%,SMBG组为180%±132%。尽管如此,所有参与者中有75.0%表示对他们的自我管理非常有信心。
对于T1DM患者来说,尽管他们自我感觉表现良好,但使用血糖监测用户界面进行解读和决策仍然极具挑战性。改进此类用户界面对于减少误解并帮助这些个体在不依赖其可能不准确的解读的情况下做出更好的治疗决策至关重要。