The bw package implements the Dynamic Weight Change model from Hall et al. for adults and the Children dynamic weight model from Hall et al for children.
To install the latest version please run the following code:
if (!require(devtools)){install.packages("devtools")}
devtools::install_github("INSP-RH/bw")The main function to estimate weight change is adult_weight. It takes as input body weight (kg), height (m), age (yrs), sex (either "male" or "female") either as column vectors from a database or as individual level. In addition it takes a day-by-day matrix for change in energy intake from baseline (kcal/day) and another matrix for change in sodium (mg/day).
#Individual's parameters
bw <- 80
ht <- 1.8
age <- 32
sex <- "female"
#Matrix of energy intake change for 100 days
deltaEI <- rep(-100, 365)
#Function of sodium intake
deltaNA <- rep(-10, 365)
#Estimate weight change.
wtrajectory <- adult_weight(bw, ht, age, sex, deltaEI, deltaNA)The variable wtrajectory is a list containing matrices of all the modelled variables:
names(wtrajectory)## [1] "Time" "Age"
## [3] "Adaptive_Thermogenesis" "Extracellular_Fluid"
## [5] "Glycogen" "Fat_Mass"
## [7] "Lean_Mass" "Body_Weight"
## [9] "Body_Mass_Index" "BMI_Category"
## [11] "Energy_Intake" "Correct_Values"
These can be plotted with model_plot:
model_plot(wtrajectory)
The model can also be used for computing weight change for several individuals at a time:
#Antropometric data
weights <- c(45, 67, 58, 92, 81)
heights <- c(1.30, 1.73, 1.77, 1.92, 1.73)
ages <- c(45, 23, 66, 44, 23)
sexes <- c("male", "female", "female", "male", "male")
#Matrix of energy consumption reduction:
EIchange <- rbind(rep(-100, 365), rep(-200, 365), rep(-200, 365),
rep(-123, 365), rep(-50, 365))
#Returns a weight change matrix and other matrices
model_weight <- adult_weight(weights, heights, ages, sexes,
EIchange)
model_plot(model_weight)
Average for included variables can be done:
model_mean(model_weight)## time variable group mean SE_mean Lower_CI_mean
## 1 1 Age 1 40.20000 8.046117 24.42990
## 2 1 Adaptive_Thermogenesis 1 0.00000 0.000000 0.00000
## 3 1 Extracellular_Fluid 1 17.68290 2.445504 12.88980
## 4 1 Glycogen 1 0.50000 0.000000 0.50000
## 5 1 Fat_Mass 1 16.11919 1.634683 12.91527
## 6 1 Lean_Mass 1 32.94791 4.340489 24.44071
## Upper_CI_mean variance SE_variance Lower_CI_variance Upper_CI_variance
## 1 55.97010 323.70000 148.838516 31.981870 615.41813
## 2 0.00000 0.00000 0.000000 0.000000 0.00000
## 3 22.47600 29.90246 16.471194 -2.380486 62.18541
## 4 0.50000 0.00000 0.000000 0.000000 0.00000
## 5 19.32311 13.36094 5.819498 1.954934 24.76695
## 6 41.45512 94.19923 31.078690 33.286112 155.11234
BMI prevalence can be computed:
adult_bmi(model_weight)## Day Group BMI_Category Mean 2.5 % 97.5 %
## 1 1 1 Normal 0.6 0.11990883 1.0800912
## 2 1 1 Pre-Obese 0.4 -0.08009117 0.8800912
## 3 2 1 Mild Thinness 0.2 -0.19199280 0.5919928
## 4 2 1 Normal 0.4 -0.08009117 0.8800912
## 5 2 1 Pre-Obese 0.4 -0.08009117 0.8800912
## 6 2 1 Mild Thinness 0.2 -0.19199280 0.5919928
The function child_weight estimates weight change the same way as adult_weight. It takes as input age (yrs), sex ("male" or "female"), fat free mass (kg), and fat mass (kg). The functions model_mean and model_plot can be also com A function of energy intake (kcals/yr) for all the years can be inputed.
#Antropometric data
FatFree <- c(32, 17.2, 18.8, 20, 24.1)
Fat <- c(4.30, 2.02, 3.07, 1.12, 2.93)
ages <- c(10, 6.2, 5.4, 4, 4.1)
sexes <- c("male", "female", "female", "male", "male")
#Returns a weight change matrix and other matrices
model_weight <- child_weight(ages, sexes, Fat, FatFree)
#Plot
model_plot(model_weight)
Mean by sex:
model_mean(model_weight, group = sexes)## time variable group mean SE_mean Lower_CI_mean
## 1 1 Age female 5.800000 0.3162278 5.180205
## 2 1 Age male 6.033333 1.8107191 2.484389
## 3 1 Fat_Free_Mass female 2.545000 0.4150489 1.731519
## 4 1 Fat_Free_Mass male 2.783333 0.8406732 1.135644
## 5 1 Fat_Mass female 18.000000 0.6324555 16.760410
## 6 1 Fat_Mass male 25.366667 3.2146943 19.065982
## Upper_CI_mean variance SE_variance Lower_CI_variance Upper_CI_variance
## 1 6.419795 0.320000 5.488310e-16 0.3200000 0.320000
## 2 9.582278 11.803333 5.387460e+00 1.2441059 22.362561
## 3 3.358481 0.551250 0.000000e+00 0.5512500 0.551250
## 4 4.431023 2.544233 1.161278e+00 0.2681696 4.820297
## 5 19.239590 1.280000 0.000000e+00 1.2800000 1.280000
## 6 31.667352 37.203333 1.698092e+01 3.9213403 70.485326
Additional information on usage can be found on the package's vignette:
browseVignettes("bw")