Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App

• Main Authors: Beck, R.W (Author), Calhoun, P. (Author), Castle, J.R (Author), Clements, M. (Author), Dassau, E. (Author), Doyle, F.J (Author), Gal, R.L (Author), Gillingham, M.B (Author), Jacobs, P. (Author), Li, Z. (Author), Martin, C.K (Author), Patton, S.R (Author), Rickels, M.R (Author), Riddell, M. (Author)
• Format: Article
• Language: English
• Published: Mary Ann Liebert Inc. 2021
• Subjects: adolescent; Adolescent; adult; Adult; aged; Aged; Article; Blood Glucose; blood glucose monitoring; body weight change; carbohydrate; carbohydrate analysis; carbohydrate diet; Carbohydrate estimation; clinical article; Diabetes Mellitus, Type 1; Dietary Carbohydrates; energy expenditure; female; Female; glucose blood level; glycemic load; human; Humans; insulin; Insulin; insulin dependent diabetes mellitus; insulin treatment; low fat diet; macronutrient; Macronutrients; male; Male; meal; Meals; middle aged; Middle Aged; mobile application; Mobile Applications; nutrient content; Nutrients; photography; Photography; Postprandial Period; postprandial state; priority journal; protein diet; protein restriction; Remote food photography method; young adult; Young Adult
• Online Access: View Fulltext in Publisher

 Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method (RFPM). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered. © 2021, Mary Ann Liebert, Inc., publishers.