Leung Angela Ym, Xu Xin Yi, Chau Pui Hing, Yu Yee Tak Esther, Cheung Mike Kt, Wong Carlos Kh, Fong Daniel Yt, Wong Janet Yh, Lam Cindy Lk
Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China (Hong Kong).
School of Nursing, The University of Hong Kong, Hong Kong, China (Hong Kong).
JMIR Mhealth Uhealth. 2018 May 24;6(5):e10662. doi: 10.2196/10662.
To decrease the burden of diabetes in society, early screening of undiagnosed diabetes and prediabetes is needed. Integrating a diabetes risk score into a mobile app would provide a useful platform to enable people to self-assess their risk of diabetes with ease.
The objectives of this study were to (1) assess the profile of Diabetes Risk Score mobile app users, (2) determine the optimal cutoff value of the Finnish Diabetes Risk Score to identify undiagnosed diabetes and prediabetes in the Chinese population, (3) estimate users' chance of developing diabetes within 2 years of using the app, and (4) investigate high-risk app users' lifestyle behavior changes after ascertaining their risk level from the app.
We conducted this 2-phase study among adults via mobile app and online survey from August 2014 to December 2016. Phase 1 adopted a cross-sectional design, with a descriptive analysis of the app users' profile. We used a Cohen kappa score to show the agreement between the risk level (as shown in the app) and glycated hemoglobin test results. We used sensitivity, specificity, and area under the curve to determine the optimal cutoff value of the diabetes risk score in this population. Phase 2 was a prospective cohort study. We used a logistic regression model to estimate the chance of developing diabetes after using the app. Paired t tests compared high-risk app users' lifestyle changes.
A total of 13,289 people used the app in phase 1a. After data cleaning, we considered 4549 of these as valid data. Most users were male, and 1811 (39.81%) had tertiary education or above. Among them, 188 (10.4%) users agreed to attend the health assessment in phase 1b. We recommend the optimal value of the diabetes risk score for identifying persons with undiagnosed diabetes and prediabetes to be 9, with an area under the receiver operating characteristic curve of 0.67 (95% CI 0.60-0.74), sensitivity of 0.70 (95% CI 0.58-0.80), and specificity of 0.57 (95% CI 0.47-0.66). At the 2-year follow-up, people in the high-risk group had a higher chance of developing diabetes (odds ratio 4.59, P=.048) than the low-risk group. The high-risk app users improved their daily intake of vegetables (baseline: mean 0.76, SD 0.43; follow-up: mean 0.93, SD 0.26; t=-3.77, P<.001) and daily exercise (baseline: mean 0.40, SD 0.49; follow-up: mean 0.54, SD 0.50; t=-2.08, P=.04).
The Diabetes Risk Score app has been shown to be a feasible and reliable tool to identify persons with undiagnosed diabetes and prediabetes and to predict diabetes incidence in 2 years. The app can also encourage high-risk people to modify dietary habits and reduce sedentary lifestyle.
为减轻社会中糖尿病的负担，需要对未诊断的糖尿病和糖尿病前期进行早期筛查。将糖尿病风险评分整合到移动应用程序中，将提供一个有用的平台，使人们能够轻松地自我评估患糖尿病的风险。
本研究的目的是：（1）评估糖尿病风险评分移动应用程序用户的概况；（2）确定芬兰糖尿病风险评分在中国人群中识别未诊断的糖尿病和糖尿病前期的最佳临界值；（3）估计用户在使用该应用程序2年内患糖尿病的几率；（4）在通过该应用程序确定其风险水平后，调查高风险应用程序用户的生活方式行为变化。
我们在2014年8月至2016年12月期间，通过移动应用程序和在线调查对成年人进行了这项两阶段研究。第一阶段采用横断面设计，对应用程序用户的概况进行描述性分析。我们使用Cohen kappa评分来显示风险水平（如应用程序中所示）与糖化血红蛋白测试结果之间的一致性。我们使用敏感性、特异性和曲线下面积来确定该人群中糖尿病风险评分的最佳临界值。第二阶段是一项前瞻性队列研究。我们使用逻辑回归模型来估计使用该应用程序后患糖尿病的几率。配对t检验比较了高风险应用程序用户的生活方式变化。
在第一阶段a中，共有13289人使用了该应用程序。经过数据清理后，我们将其中的4549人视为有效数据。大多数用户为男性，1811人（39.81%）拥有大专及以上学历。其中，188名（10.4%）用户同意参加第一阶段b中的健康评估。我们建议将用于识别未诊断的糖尿病和糖尿病前期患者的糖尿病风险评分的最佳值设定为9，其受试者工作特征曲线下面积为0.67（95%CI 0.60-0.74），敏感性为0.70（95%CI 0.58-0.80），特异性为0.57（95%CI 0.47-0.66）。在2年的随访中，高风险组的人患糖尿病的几率（优势比4.59，P=0.048）高于低风险组。高风险应用程序用户增加了蔬菜的每日摄入量（基线：平均值0.76，标准差0.43；随访：平均值0.93，标准差0.26；t=-3.77，P<0.001）和每日运动量（基线：平均值0.40，标准差0.49；随访：平均值0.54，标准差0.50；t=-2.08，P=0.04）。
糖尿病风险评分应用程序已被证明是一种可行且可靠的工具，可用于识别未诊断的糖尿病和糖尿病前期患者，并预测2年内的糖尿病发病率。该应用程序还可以鼓励高风险人群改变饮食习惯，减少久坐的生活方式。
