Department of Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Inner Analytics Inc, Toronto, ON, Canada.
JMIR Mhealth Uhealth. 2020 Oct 28;8(10):e22074. doi: 10.2196/22074.
Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.
Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy.
Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA level was also assessed.
Use of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study.
Our results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.
ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142.
碳水化合物计数是糖尿病管理的重要组成部分，但它具有挑战性，通常不准确，并且可能成为优化糖尿病管理的障碍。iSpy 是一款新颖的移动应用程序，利用机器学习通过图像识别食物，旨在帮助 1 型糖尿病青少年进行碳水化合物计数。
我们旨在测试该应用程序的可用性及其对碳水化合物计数准确性的潜在影响。
共对 16 名年龄在 8.5-17 岁的 1 型糖尿病患者进行了 3 个周期的迭代可用性测试。参与者提供了一部移动设备，并要求他们在使用 iSpy 应用程序功能时大声思考，完成任务。记录错误，评估可接受性，并在各周期内进行改进和重新测试。随后，在一项有 22 名 iSpy 用户和 22 名常规护理对照者的青少年 10-17 岁的试点随机对照试验中评估了 iSpy。主要结局是 3 个月内碳水化合物计数能力的变化。次要结局包括参与度和可接受性水平。还评估了 HbA 水平的变化。
使用 iSpy 与改善碳水化合物计数准确性相关（每餐总克数，P=.008），减少个体计数错误大于 10 g 的频率（P=.047），降低 HbA 水平（P=.03）。定性访谈和可接受性评分均为阳性。没有发现重大技术挑战。此外，在研究结束时，21 名 iSpy 参与者中的 43%（9/21）仍在参与，至少每两周使用一次。
我们的结果提供了一种新型碳水化合物计数应用程序有效性和高可接受性的证据，支持为 1 型糖尿病青少年的糖尿病护理推进数字健康应用程序。需要进一步的测试，但 iSpy 可能是传统糖尿病管理的有用补充。
ClinicalTrials.gov NCT04354142；https://clinicaltrials.gov/ct2/show/NCT04354142。
