Comprehensive Analysis of the Real Lifestyles of T1D Patients for the Purpose of Designing a Personalized Counselor for Prandial Insulin Dosing
Language English Country Switzerland Media electronic
Document type Journal Article
Grant support
No.15-25710A (P08 panel)
The Czech Health Research Council
PubMed
31126048
PubMed Central
PMC6567095
DOI
10.3390/nu11051148
PII: nu11051148
Knihovny.cz E-resources
- Keywords
- carbohydrate counting, diet, mobile application, obesity, overweight, postprandial glycaemia, prandial insulin bolus, type 1 diabetes,
- MeSH
- Biomarkers blood MeSH
- Time Factors MeSH
- Exercise MeSH
- Diabetes Mellitus, Type 1 blood diagnosis drug therapy MeSH
- Dietary Carbohydrates administration & dosage MeSH
- Adult MeSH
- Energy Metabolism MeSH
- Hypoglycemic Agents administration & dosage adverse effects MeSH
- Insulin administration & dosage adverse effects MeSH
- Insulin Infusion Systems MeSH
- Blood Glucose drug effects metabolism MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Mobile Applications * MeSH
- Cell Phone * MeSH
- Proof of Concept Study MeSH
- Pilot Projects MeSH
- Postprandial Period * MeSH
- Blood Glucose Self-Monitoring MeSH
- Feeding Behavior MeSH
- Drug Dosage Calculations * MeSH
- Treatment Outcome MeSH
- Life Style * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Biomarkers MeSH
- Dietary Carbohydrates MeSH
- Hypoglycemic Agents MeSH
- Insulin MeSH
- Blood Glucose MeSH
Post-prandial hyperglycemia is still a challenging issue in intensified insulin therapy. Data of 35 T1D patients during a four-week period were analyzed: RT-CGM (real time continuous glucose monitoring) record, insulin doses, diet (including meal photos), energy expenditure, and other relevant conditions. Patients made significant errors in carbohydrate counting (in 56% of cooked and 44% of noncooked meals), which resulted in inadequate insulin doses. Subsequently, a mobile application was programmed to provide individualized advice on prandial insulin dose. When using the application, a patient chooses only the type of categorized situation (e.g., meals with other relevant data) without carbohydrates counting. The application significantly improved postprandial glycemia as normoglycemia was reached in 95/105 testing sessions. Other important findings of the study include: A high intake of saturated fat (median: 162% of recommended intake); a low intake of fiber and vitamin C (median: 42% and 37%, respectively, of recommended intake); an increase in overweight/obesity status (according to body fat measurement), especially in women (median of body fat: 30%); and low physical activity (in 16/35 patients). The proposed individualized approach without carbohydrate counting may help reach postprandial normoglycemia but it is necessary to pay attention to the lifestyle habits of T1D patients too.
See more in PubMed
Skrha J., Sumnik Z., Pelikanova T., Kvapil M. Recommendation for management of the type 1 Diabetes mellitus. DMEV. 2016;19:156–159. (In Czech)
Kovatchev BP. Metrics for glycaemic control—from HbA1c to continuous glucose monitoring. Nat Rev Endocrinol. 2017;13:425–436. doi: 10.1038/nrendo.2017.3. PubMed DOI
Schmidt S., Nørgaard K. Bolus calculators. J. Diabetes Sci. Technol. 2014;8:1035–1041. doi: 10.1177/1932296814532906. PubMed DOI PMC
Kopecky M., Kikalova K., Charamza J. The secular trend in body height and weight in the adult population in the Czech Republic. Čas.Lék.čes. 2016;155:357–364. (In Czech) PubMed
Neu A., Lange K., Barrett T., Cameron F., Dorchy H., Hoey H., Jarosz-Chobot P., Mortensen H.B., Robert J.J., Robertson K., et al. Hvidoere Study Group Classifying insulin regimens—difficulties and proposal for comprehensive new definitions. Pediatr. Diabetes. 2015;16:402–406. doi: 10.1111/pedi.12275. PubMed DOI
Meade L.T., Rushton W.E. Accuracy of Carbohydrate Counting in Adults. Clin. Diabetes. 2016;34:142–147. doi: 10.2337/diaclin.34.3.142. PubMed DOI PMC
Gurnani M., Pais V., Cordeiro K., Steele S., Chen S., Hamilton J.K. One potato, two potato,… assessing carbohydrate counting accuracy in adolescents with type 1 diabetes. Pediatr. Diabetes. 2018;19:1302–1308. doi: 10.1111/pedi.12717. PubMed DOI
Uchendu C., Blake H. Effectiveness of cognitive-behavioural therapy on glycaemic control and psychological outcomes in adults with diabetes mellitus: A systematic review and meta-analysis of randomized controlled trials. Diabet Med. 2017;34:328–339. doi: 10.1111/dme.13195. PubMed DOI
Bally L., Dehais J., Nakas C.T., Anthimopoulos M., Laimer M., Rhyner D., Rosenberg G., Zueger T., Diem P., Mougiakakou S., et al. Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes: A Randomized Prospective Pilot Study. Diabetes Care. 2017;40:e6–e7. doi: 10.2337/dc16-2173. PubMed DOI
Vasiloglou M.F., Mougiakakou S., Aubry E., Bokelmann A., Fricker R., Gomes F., Guntermann C., Meyer A., Studerus D., Stanga Z. A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians. Nutrients. 2018;10 doi: 10.3390/nu10060741. PubMed DOI PMC
Lee M., Gatton T.M., Lee K.K. A monitoring and advisory system for diabetes patient management using a rule-based method and KNN. Sensors. 2010;10:3934–3953. doi: 10.3390/s100403934. PubMed DOI PMC
Contreras I., Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J. Med. Internet. Res. 2018;20:e10775. doi: 10.2196/10775. PubMed DOI PMC
Boiroux D., Aradóttir T.B., Nørgaard K., Poulsen N.K., Madsen H., Jørgensen J.B. An Adaptive Nonlinear Basal-Bolus Calculator for Patients with Type 1 Diabetes. J. Diabetes Sci. Technol. 2017;11:29–36. doi: 10.1177/1932296816666295. PubMed DOI PMC
Pesl P., Herrero P., Reddy M., Xenou M., Oliver N., Johnston D., Toumazou C., Georgiou P. An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results. IEEE J. Biomed. Health Inform. 2016;20:11–17. doi: 10.1109/JBHI.2015.2464088. PubMed DOI
Cappon G., Vettoretti M., Marturano F., Facchinetti A., Sparacino G. A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring. J. Diabetes Sci. Technol. 2018;12:265–272. doi: 10.1177/1932296818759558. PubMed DOI PMC
Vettoretti M., Cappon G., Acciaroli G., Facchinetti A., Sparacino G. Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications. J. Diabetes Sci. Technol. 2018;12:1064–1071. doi: 10.1177/1932296818774078. PubMed DOI PMC
Katz M.L., Mehta S., Nansel T., Quinn H., Lipsky L.M., Laffel L.M. Associations of nutrient intake with glycemic control in youth with type 1 diabetes: Differences by insulin regimen. Diabetes Technol. Ther. 2014;16:512–518. doi: 10.1089/dia.2013.0389. PubMed DOI PMC
Merger S.R., Kerner W., Stadler M., Zeyfang A., Jehle P., Müller-Korbsch M., Holl R.W., DPV Initiative. German BMBF Competence Network Diabetes mellitus Prevalence and comorbidities of double diabetes. Diabetes Res. Clin. Pract. 2016;119:48–56. doi: 10.1016/j.diabres.2016.06.003. PubMed DOI
