PMID- 26424241
OWN - NLM
STAT- MEDLINE
DCOM- 20160824
LR  - 20221207
IS  - 1932-2968 (Electronic)
IS  - 1932-2968 (Linking)
VI  - 9
IP  - 6
DP  - 2015 Sep 30
TI  - Development of a Smartphone Application to Capture Carbohydrate, Lipid, and 
      Protein Contents of Daily Food: Need for Integration in Artificial Pancreas for 
      Patients With Type 1 Diabetes?
PG  - 1170-4
LID - 10.1177/1932296815607861 [doi]
AB  - BACKGROUND: Meal lipids (LIP) and proteins (PRO) may influence the effect of 
      insulin doses based on carbohydrate (CHO) counting in patients with type 1 
      diabetes (T1D). We developed a smartphone application for CHO, LIP, and PRO 
      counting in daily food and assessed its usability in real-life conditions and 
      potential usefulness. METHODS: Ten T1D patients used the android application for 
      1 week to collect their food intakes. Data included meal composition, premeal and 
      2-hour postmeal blood glucose, corrections for hypo- or hyperglycemia after 
      meals, and time for entering meals in the application. Meal insulin doses were 
      based on patients' CHO counting (application in blinded mode). Linear mixed 
      models were used to assess the statistical differences. RESULTS: In all, 187 
      meals were analyzed. Average computed CHO amount was 74.37 +/- 31.78 grams; LIP 
      amount: 20.26 +/- 14.28 grams and PRO amount: 25.68 +/- 16.68 grams. Average CHO, 
      LIP, and PRO contents were significantly different between breakfast and 
      lunch/dinner. The average time for meal entry in the application moved from 3-4 
      minutes to 2.5 minutes during the week. No significant impact of LIP and PRO was 
      found on available blood glucose values. CONCLUSION: Our study shows CHO, LIP, 
      and PRO intakes can be easily captured by an application on smartphone for meal 
      entry used by T1D patients. Although LIP and PRO meal contents did not influence 
      glucose levels when insulin doses were based on CHO in this pilot study, this 
      application could be used for further investigation of this topic, including in 
      closed-loop conditions.
CI  - (c) 2015 Diabetes Technology Society.
FAU - Diouri, Omar
AU  - Diouri O
AD  - Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of 
      Montpellier, Montpellier, France.
FAU - Place, Jerome
AU  - Place J
AD  - Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of 
      Montpellier, Montpellier, France.
FAU - Traverso, Magali
AU  - Traverso M
AD  - Montpellier University Hospital, Department of Endocrinology, Diabetes, 
      Nutrition, Montpellier, France.
FAU - Georgescu, Vera
AU  - Georgescu V
AD  - Montpellier University Hospital, Department of Medical Information, Unit of 
      Clinical and Epidemiological Research, Montpellier, France.
FAU - Picot, Marie-Christine
AU  - Picot MC
AD  - Montpellier University Hospital, Department of Medical Information, Unit of 
      Clinical and Epidemiological Research, Montpellier, France INSERM Clinical 
      Investigation Centre 1411, Montpellier, France.
FAU - Renard, Eric
AU  - Renard E
AD  - Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of 
      Montpellier, Montpellier, France Montpellier University Hospital, Department of 
      Endocrinology, Diabetes, Nutrition, Montpellier, France Montpellier University 
      Hospital, Department of Medical Information, Unit of Clinical and Epidemiological 
      Research, Montpellier, France e-renard@chu-montpellier.fr.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
DEP - 20150930
PL  - United States
TA  - J Diabetes Sci Technol
JT  - Journal of diabetes science and technology
JID - 101306166
RN  - 0 (Biomarkers)
RN  - 0 (Blood Glucose)
RN  - 0 (Dietary Carbohydrates)
RN  - 0 (Dietary Fats)
RN  - 0 (Dietary Proteins)
RN  - 0 (Glycated Hemoglobin A)
RN  - 0 (Hypoglycemic Agents)
RN  - 0 (Insulin)
RN  - 0 (hemoglobin A1c protein, human)
SB  - IM
MH  - Adult
MH  - Biomarkers/blood
MH  - Blood Glucose/drug effects/metabolism
MH  - Diabetes Mellitus, Type 1/blood/diagnosis/*diet therapy
MH  - *Diet, Diabetic
MH  - Dietary Carbohydrates/*administration & dosage
MH  - Dietary Fats/*administration & dosage
MH  - Dietary Proteins/*administration & dosage
MH  - Equipment Design
MH  - Female
MH  - Glycated Hemoglobin/metabolism
MH  - Humans
MH  - Hypoglycemic Agents/administration & dosage
MH  - Insulin/administration & dosage
MH  - Linear Models
MH  - Male
MH  - Middle Aged
MH  - *Mobile Applications
MH  - *Pancreas, Artificial
MH  - Pilot Projects
MH  - *Smartphone
MH  - Software Design
MH  - Software Validation
MH  - Time Factors
MH  - Treatment Outcome
MH  - Young Adult
PMC - PMC4667322
OTO - NOTNLM
OT  - carbohydrates
OT  - glucose control
OT  - lipids
OT  - proteins
OT  - smartphone application
OT  - type 1 diabetes
COIS- Declaration of Conflicting Interests: The author(s) declared no potential 
      conflicts of interest with respect to the research, authorship, and/or 
      publication of this article.
EDAT- 2015/10/02 06:00
MHDA- 2016/08/25 06:00
PMCR- 2016/10/30
CRDT- 2015/10/02 06:00
PHST- 2015/10/02 06:00 [entrez]
PHST- 2015/10/02 06:00 [pubmed]
PHST- 2016/08/25 06:00 [medline]
PHST- 2016/10/30 00:00 [pmc-release]
AID - 1932296815607861 [pii]
AID - 10.1177_1932296815607861 [pii]
AID - 10.1177/1932296815607861 [doi]
PST - epublish
SO  - J Diabetes Sci Technol. 2015 Sep 30;9(6):1170-4. doi: 10.1177/1932296815607861.