![]() No major technical challenges were identified. Qualitative interviews and acceptability scale scores were positive. 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 1c levels (P=.03). Change in HbA 1c level was also assessed. Secondary outcomes included levels of engagement and acceptability. Primary outcome was change in carbohydrate counting ability over 3 months. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. ![]() Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. ![]() Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy. ![]() 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. 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. ![]()
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