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The function mad produces GVP values in a tibble object.

Usage

gvp(data)

Arguments

data

DataFrame object with column names "id", "time", and "gl"

Value

A tibble object with two columns: subject id and corresponding GVP value.

Details

A tibble object with 1 row for each subject, a column for subject id and a column for GVP values is returned. NA glucose values are omitted from the calculation of the GVP.

GVP is calculated by dividing the total length of the line of the glucose trace by the length of a perfectly flat trace. The formula for this is \(sqrt(diff^2+dt0^2)/(n*dt0)\), where diff is the change in Glucose measurements from one reading to the next, dt0 is the time gap between measurements and n is the number of glucose readings

References

Peyser et al. (2017) Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data. Diabetes Technol Ther 20(1):6–16, doi:10.1089/dia.2017.0187 .

Author

David Buchanan, Mary Martin

Examples


data(example_data_1_subject)
gvp(example_data_1_subject)
#> # A tibble: 1 × 2
#>   id          GVP
#>   <fct>     <dbl>
#> 1 Subject 1  16.8

data(example_data_5_subject)
gvp(example_data_5_subject)
#> # A tibble: 5 × 2
#>   id          GVP
#>   <fct>     <dbl>
#> 1 Subject 1  16.8
#> 2 Subject 2  20.5
#> 3 Subject 3  25.6
#> 4 Subject 4  17.1
#> 5 Subject 5  38.3