1
|
Gillingham MB, Li Z, Beck RW, Calhoun P, Castle JR, Clements M, Dassau E, Doyle FJ, Gal RL, Jacobs P, Patton SR, Rickels MR, Riddell M, Martin CK. Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App. Diabetes Technol Ther 2021; 23:85-94. [PMID: 32833544 PMCID: PMC7868577 DOI: 10.1089/dia.2020.0357] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
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.
Collapse
Affiliation(s)
- Melanie B. Gillingham
- Oregon Health and Sciences University, Portland, Oregon, USA
- Address correspondence to: Melanie B. Gillingham, PhD, RD, Department of Molecular and Medical Genetics, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mailcode L103, Portland, OR 97239, USA
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, Florida, USA
| | | | - Mark Clements
- Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Robin L. Gal
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Jacobs
- Oregon Health and Sciences University, Portland, Oregon, USA
| | | | - Michael R. Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| |
Collapse
|