Abstract
BackgroundCarbohydrate 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.ObjectiveOur objective was to test the app's usability and potential impact on carbohydrate counting accuracy.MethodsIterative 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 HbA1c level was also assessed.ResultsUse 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 HbA1c 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.ConclusionsOur 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.Trial RegistrationClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142
Highlights
MethodsType 1 diabetes is among the most common chronic diseases of childhood, and its incidence is rising [1]
Insulin administration remains the cornerstone of type 1 diabetes management, but its optimal dosing is often complicated by the need to count carbohydrates [2,3,4]
In one study [5] focused on parents of children with type 1 diabetes, more accurate parental carbohydrate counting was associated with 0.8% lower hemoglobin A1c (HbA1c) values in their children
Summary
MethodsType 1 diabetes is among the most common chronic diseases of childhood, and its incidence is rising [1]. The management of diabetes in youth is complex and impacted by numerous factors including numeracy skills, education, socioeconomic status, family dynamics, engagement with treatment regimens, and use of technologies such as pumps and continuous sensors. Among these factors, insulin administration remains the cornerstone of type 1 diabetes management, but its optimal dosing is often complicated by the need to count carbohydrates [2,3,4]. In one study [5] focused on parents of children with type 1 diabetes, more accurate parental carbohydrate counting was associated with 0.8% lower HbA1c values in their children. 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
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