Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients' CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient-reported meals. The results showed poor disagreement between HCP- and patient-reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study.
Keywords: Meal detection; continuous glucose monitoring; type 2 diabetes.