unique dexcom g4 platinum cgm 5 digit transmitter identification number  (Dexcom Inc)

Journal: Frontiers in Nutrition

Article Title: Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

doi: 10.3389/fnut.2023.1308348

Figure Lengend Snippet: Characteristics of included publications.

Article Snippet: Hoyos et al. ( ) [supplemented with information from Aleppo et al. ( )] , Data from a study assessing the reliability of CGM measurements were used to compare two scenarios: one scenario with the original meal events announced by the participants and one with the meal events generated automatically by the super-twisting-based meal detector introduced in Faccioli et al. ( ). “An unsupervised clustering algorithm based on Fuzzy C-Means was applied to classify event-to-event segments of CGM data. Events defining data partitioning were automatically generated based on: (1) an automatic meal detection algorithm (for day periods) and (2) time of day (for night periods).” (p. 576) , 44 adults , T1DM , 26-week study; only participants with an average of 3 to 5 reported meals per day were considered , , Free-living data with CGM measurements, insulin pump recordings, etc. , Results (M ± SD) for automatically detected meals: Number of clusters (c*) = 8.09 ± 1.67; Fukuyama-Sugeno index (V FS ) = −16,893 ± 5,838; Compactness (V com ) = 0.236 ± 0.063; Variance (V var ) = 966 ± 653.4; Time in range = 45.2 ± 15% , CGM: Dexcom G4 Platinum; Sampling time = 5 min.

Techniques: Sampling, Comparison, Activity Assay, Plasmid Preparation, Standard Deviation, Generated