Authors are from: Sydney School of Veterinary Science, The University of Sydney, Camden, New South Wales 2570, Australia ”

Import data, run library

library(readxl)
library(tidyverse)
library(lme4)
library(broom.mixed)
library(performance)
library(see)
library(patchwork)
library(knitr)
library(janitor)

data <- read_excel("data_fluid_app.xlsx")
data2 <- read_excel("data_fluid_app.xlsx", 
    sheet = "import2")
quant <- read_excel("data_fluid_app.xlsx", 
    sheet = "other data", col_types = c("text", 
        "text", "text", "text", "text", "text", 
        "text", "text", "text", "text", "text", 
        "text", "text", "text", "text", "skip", 
        "skip", "skip", "skip"))

data <- data %>% arrange(id)
data2 <- data2 %>% arrange(id)
quant <- quant %>% arrange(id)

Check data for incomplete surveys

#id "R_1IfKGBEdaAjQZ7K" did not fill in some sections. Will delete

data <- data %>% filter(id != "R_1IfKGBEdaAjQZ7K")
data2 <- data2 %>% filter(id != "R_1IfKGBEdaAjQZ7K")
quant <- quant %>% filter(id != "R_1IfKGBEdaAjQZ7K")
quant <- quant %>% select(-apps_used,-preference,-attitude,-feedback)
quant$age <- quant$age %>% as.numeric()

Data without .xlsx files can input data using the code below

data <- structure(list(id = c("FS_1F4ndmfv4FRhMl7", "FS_1F4ndmfv4FRhMl7", 
"FS_2aQanMGUwpJZDYl", "FS_2aQanMGUwpJZDYl", "R_06gon5h75y2OtZD", 
"R_06gon5h75y2OtZD", "R_10YNVdHuP1M7BLY", "R_10YNVdHuP1M7BLY", 
"R_1E585h59C3GfybR", "R_1E585h59C3GfybR", "R_1FlfqH1Mt6W4OV4", 
"R_1FlfqH1Mt6W4OV4", "R_1GDeDAdiJnVB0s2", "R_1GDeDAdiJnVB0s2", 
"R_1hG9g6mIxGn8ZWj", "R_1hG9g6mIxGn8ZWj", "R_1IKw2OhleiYRL4q", 
"R_1IKw2OhleiYRL4q", "R_1IMcAojZOzeJuvF", "R_1IMcAojZOzeJuvF", 
"R_1IMYbcaCx7KeA38", "R_1IMYbcaCx7KeA38", "R_1kXIx2HhCumDkmq", 
"R_1kXIx2HhCumDkmq", "R_1l04SIKet4JM5oK", "R_1l04SIKet4JM5oK", 
"R_1LLqU4C3umOUznu", "R_1LLqU4C3umOUznu", "R_1llydHpgYrmJIMS", 
"R_1llydHpgYrmJIMS", "R_1mVMgqxtCtSwBXO", "R_1mVMgqxtCtSwBXO", 
"R_1P8gxZvb4H0Shv9", "R_1P8gxZvb4H0Shv9", "R_1qaiQQyr8bu2Dnh", 
"R_1qaiQQyr8bu2Dnh", "R_1Qz719yHd5BQBFv", "R_1Qz719yHd5BQBFv", 
"R_22RcWofEoI56dsf", "R_22RcWofEoI56dsf", "R_29azxBaN1eR6tn9", 
"R_29azxBaN1eR6tn9", "R_29c9bWPlbniXPhg", "R_29c9bWPlbniXPhg", 
"R_2ClhxDDaB38uJd2", "R_2ClhxDDaB38uJd2", "R_2coWnUVOwj7YBN4", 
"R_2coWnUVOwj7YBN4", "R_2dQ26mYjJVtDDmw", "R_2dQ26mYjJVtDDmw", 
"R_2EHnrhzveVCptoc", "R_2EHnrhzveVCptoc", "R_2eXNk9BXMzAp5g2", 
"R_2eXNk9BXMzAp5g2", "R_2f7C3tT9Jk2UJEP", "R_2f7C3tT9Jk2UJEP", 
"R_2qsM6jMLPpUDkUF", "R_2qsM6jMLPpUDkUF", "R_2rHO6Flyml9FP5p", 
"R_2rHO6Flyml9FP5p", "R_2yjWe8cVqJm89zO", "R_2yjWe8cVqJm89zO", 
"R_30ipYcmaXJLh732", "R_30ipYcmaXJLh732", "R_31mUdlu6JrQ0uw8", 
"R_31mUdlu6JrQ0uw8", "R_3CMVIvsrKobFDCA", "R_3CMVIvsrKobFDCA", 
"R_3dRGOnsGmQcGnLh", "R_3dRGOnsGmQcGnLh", "R_3g2czbQSlDvx8JP", 
"R_3g2czbQSlDvx8JP", "R_3illfbJFkdNqkGM", "R_3illfbJFkdNqkGM", 
"R_3kKVLswnnIpQiFE", "R_3kKVLswnnIpQiFE", "R_3LhEYMu6J9UMzja", 
"R_3LhEYMu6J9UMzja", "R_3TWNY0AZLcA7S7v", "R_3TWNY0AZLcA7S7v", 
"R_6PfXWjedMivcSZ3", "R_6PfXWjedMivcSZ3", "R_8oVjn5iCjrNPjlT", 
"R_8oVjn5iCjrNPjlT", "R_9XZ9sme0CvR8wKt", "R_9XZ9sme0CvR8wKt", 
"R_AGSkDnfSNkFxg7T", "R_AGSkDnfSNkFxg7T", "R_bI3SnTlrBKH6Xx7", 
"R_bI3SnTlrBKH6Xx7", "R_bm7GkgUVCQpM9Mt", "R_bm7GkgUVCQpM9Mt", 
"R_bx6Yr5nMbIfTTtn", "R_bx6Yr5nMbIfTTtn", "R_C9duWufyN6qomIh", 
"R_C9duWufyN6qomIh", "R_DwQX8mRiBs84YGR", "R_DwQX8mRiBs84YGR", 
"R_pu7nnDIsRnwFhp7", "R_pu7nnDIsRnwFhp7", "R_RrkyKiRuWP7rTIR", 
"R_RrkyKiRuWP7rTIR", "R_SAUEuXWSTQWNFeh", "R_SAUEuXWSTQWNFeh", 
"R_WwiQDYmMooTAA2l", "R_WwiQDYmMooTAA2l", "R_Z2BJEtTVJ4rVOqR", 
"R_Z2BJEtTVJ4rVOqR", "R_ZwY8Rfs5r6csG9X", "R_ZwY8Rfs5r6csG9X"
), group = c("control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app"), case = c("3w", 
"5d", "5d", "3w", "3w", "5d", "3w", "5d", "5d", "3w", "3w", "5d", 
"5d", "3w", "3w", "5d", "5d", "3w", "3w", "5d", "3w", "5d", "5d", 
"3w", "3w", "5d", "3w", "5d", "3w", "5d", "5d", "3w", "5d", "3w", 
"3w", "5d", "3w", "5d", "5d", "3w", "5d", "3w", "3w", "5d", "3w", 
"5d", "5d", "3w", "5d", "3w", "5d", "3w", "3w", "5d", "3w", "5d", 
"3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "5d", 
"3w", "3w", "5d", "5d", "3w", "5d", "3w", "3w", "5d", "5d", "3w", 
"5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", 
"3w", "3w", "5d", "5d", "3w", "3w", "5d", "3w", "5d", "3w", "5d", 
"5d", "3w", "3w", "5d", "3w", "5d", "5d", "3w", "3w", "5d"), 
    deficit = c(0, 1, 1, 2, 1, 2, 2, 2, 0, 2, 1, 2, 0, 2, 2, 
    2, 2, 2, 0, 2, 0, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 
    1, 2, 0, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 0, 2, 2, 1, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 1, 
    2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 0, 2, 2, 0, 
    0, 2, 1, 0, 1, 2, 2, 0, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2), 
    losses = c(1, 1, 0, 1, 2, 0, 2, 2, 0, 2, 2, 2, 1, 2, 1, 2, 
    1, 1, 2, 2, 0, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 2, 
    2, 2, 2, 1, 2, 1, 2, 1, 2, 0, 1, 1, 2, 1, 2, 1, 2, 0, 2, 
    2, 2, 1, 2, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 1, 0, 0, 0, 0, 
    0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 0, 1, 
    0, 0, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2), mainten = c(0, 
    2, 1, 0, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 
    2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 2, 
    2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 
    2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2), base = c(0, 2, 
    2, 0, 2, 2, 1, 2, 2, 2, 2, 2, 0, 2, 1, 2, 1, 2, 1, 2, 0, 
    0, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 
    1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 
    2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 0, 2, 2, 1, 2, 1, 2, 2, 
    1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 
    2, 0, 2, 0, 2, 2, 2, 2, 2, 1, 1, 1, 2)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -110L))

data2 <- structure(list(id = c("FS_1F4ndmfv4FRhMl7", "FS_1F4ndmfv4FRhMl7", 
"FS_2aQanMGUwpJZDYl", "FS_2aQanMGUwpJZDYl", "R_06gon5h75y2OtZD", 
"R_06gon5h75y2OtZD", "R_10YNVdHuP1M7BLY", "R_10YNVdHuP1M7BLY", 
"R_1E585h59C3GfybR", "R_1E585h59C3GfybR", "R_1FlfqH1Mt6W4OV4", 
"R_1FlfqH1Mt6W4OV4", "R_1GDeDAdiJnVB0s2", "R_1GDeDAdiJnVB0s2", 
"R_1hG9g6mIxGn8ZWj", "R_1hG9g6mIxGn8ZWj", "R_1IKw2OhleiYRL4q", 
"R_1IKw2OhleiYRL4q", "R_1IMcAojZOzeJuvF", "R_1IMcAojZOzeJuvF", 
"R_1IMYbcaCx7KeA38", "R_1IMYbcaCx7KeA38", "R_1kXIx2HhCumDkmq", 
"R_1kXIx2HhCumDkmq", "R_1l04SIKet4JM5oK", "R_1l04SIKet4JM5oK", 
"R_1LLqU4C3umOUznu", "R_1LLqU4C3umOUznu", "R_1llydHpgYrmJIMS", 
"R_1llydHpgYrmJIMS", "R_1mVMgqxtCtSwBXO", "R_1mVMgqxtCtSwBXO", 
"R_1P8gxZvb4H0Shv9", "R_1P8gxZvb4H0Shv9", "R_1qaiQQyr8bu2Dnh", 
"R_1qaiQQyr8bu2Dnh", "R_1Qz719yHd5BQBFv", "R_1Qz719yHd5BQBFv", 
"R_22RcWofEoI56dsf", "R_22RcWofEoI56dsf", "R_29azxBaN1eR6tn9", 
"R_29azxBaN1eR6tn9", "R_29c9bWPlbniXPhg", "R_29c9bWPlbniXPhg", 
"R_2ClhxDDaB38uJd2", "R_2ClhxDDaB38uJd2", "R_2coWnUVOwj7YBN4", 
"R_2coWnUVOwj7YBN4", "R_2dQ26mYjJVtDDmw", "R_2dQ26mYjJVtDDmw", 
"R_2EHnrhzveVCptoc", "R_2EHnrhzveVCptoc", "R_2eXNk9BXMzAp5g2", 
"R_2eXNk9BXMzAp5g2", "R_2f7C3tT9Jk2UJEP", "R_2f7C3tT9Jk2UJEP", 
"R_2qsM6jMLPpUDkUF", "R_2qsM6jMLPpUDkUF", "R_2rHO6Flyml9FP5p", 
"R_2rHO6Flyml9FP5p", "R_2yjWe8cVqJm89zO", "R_2yjWe8cVqJm89zO", 
"R_30ipYcmaXJLh732", "R_30ipYcmaXJLh732", "R_31mUdlu6JrQ0uw8", 
"R_31mUdlu6JrQ0uw8", "R_3CMVIvsrKobFDCA", "R_3CMVIvsrKobFDCA", 
"R_3dRGOnsGmQcGnLh", "R_3dRGOnsGmQcGnLh", "R_3g2czbQSlDvx8JP", 
"R_3g2czbQSlDvx8JP", "R_3illfbJFkdNqkGM", "R_3illfbJFkdNqkGM", 
"R_3kKVLswnnIpQiFE", "R_3kKVLswnnIpQiFE", "R_3LhEYMu6J9UMzja", 
"R_3LhEYMu6J9UMzja", "R_3TWNY0AZLcA7S7v", "R_3TWNY0AZLcA7S7v", 
"R_6PfXWjedMivcSZ3", "R_6PfXWjedMivcSZ3", "R_8oVjn5iCjrNPjlT", 
"R_8oVjn5iCjrNPjlT", "R_9XZ9sme0CvR8wKt", "R_9XZ9sme0CvR8wKt", 
"R_AGSkDnfSNkFxg7T", "R_AGSkDnfSNkFxg7T", "R_bI3SnTlrBKH6Xx7", 
"R_bI3SnTlrBKH6Xx7", "R_bm7GkgUVCQpM9Mt", "R_bm7GkgUVCQpM9Mt", 
"R_bx6Yr5nMbIfTTtn", "R_bx6Yr5nMbIfTTtn", "R_C9duWufyN6qomIh", 
"R_C9duWufyN6qomIh", "R_DwQX8mRiBs84YGR", "R_DwQX8mRiBs84YGR", 
"R_pu7nnDIsRnwFhp7", "R_pu7nnDIsRnwFhp7", "R_RrkyKiRuWP7rTIR", 
"R_RrkyKiRuWP7rTIR", "R_SAUEuXWSTQWNFeh", "R_SAUEuXWSTQWNFeh", 
"R_WwiQDYmMooTAA2l", "R_WwiQDYmMooTAA2l", "R_Z2BJEtTVJ4rVOqR", 
"R_Z2BJEtTVJ4rVOqR", "R_ZwY8Rfs5r6csG9X", "R_ZwY8Rfs5r6csG9X"
), group = c("control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app", "control", 
"app", "control", "app", "control", "app", "control", "app", 
"control", "app", "control", "app", "control", "app"), case = c("3w", 
"5d", "5d", "3w", "3w", "5d", "3w", "5d", "5d", "3w", "3w", "5d", 
"5d", "3w", "3w", "5d", "5d", "3w", "3w", "5d", "3w", "5d", "5d", 
"3w", "3w", "5d", "3w", "5d", "3w", "5d", "5d", "3w", "5d", "3w", 
"3w", "5d", "3w", "5d", "5d", "3w", "3w", "5d", "3w", "5d", "3w", 
"5d", "5d", "3w", "5d", "3w", "5d", "3w", "3w", "5d", "3w", "5d", 
"3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "5d", 
"3w", "3w", "5d", "5d", "3w", "5d", "3w", "3w", "5d", "5d", "3w", 
"5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", "3w", "5d", 
"3w", "3w", "5d", "5d", "3w", "3w", "5d", "3w", "5d", "3w", "5d", 
"5d", "3w", "3w", "5d", "3w", "5d", "5d", "3w", "3w", "5d"), 
    total = c(4, 3, 2, 2, 2, 5, 4, 4, 2, 3, 3, 2, 2, 4, 4, 6, 
    4, 6, 3, 6, 3, 2, 3, 4, 2, 3, 4, 4, 4, 4, 4, 4, 3, 1, 4, 
    5, 2, 4, 4, 6, 2, 6, 2, 5, 1, 1, 3, 6, 4, 6, 1, 4, 6, 5, 
    5, 3, 2, 3, 2, 6, 4, 4, 4, 4, 2, 6, 5, 5, 3, 4, 2, 4, 1, 
    3, 4, 3, 1, 3, 4, 3, 5, 5, 1, 4, 3, 4, 4, 2, 4, 5, 4, 4, 
    1, 1, 5, 0, 4, 5, 1, 6, 2, 3, 4, 4, 3, 4, 4, 3, 3, 5), deficit = c(1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 
    0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), volume = c(0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 
    1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
    1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 
    0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 
    0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 
    1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), alk = c(1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
    0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 
    1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), bicarb = c(1, 0, 
    0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 
    0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 
    0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 
    1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 
    1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 
    1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1), rate.bicarb = c(1, 
    1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 
    0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 
    1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 
    1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 
    1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 
    0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1), oral = c(0, 0, 
    0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 
    0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 
    1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -110L))

quant <- structure(list(id = c("FS_1F4ndmfv4FRhMl7", "FS_2aQanMGUwpJZDYl", 
"R_06gon5h75y2OtZD", "R_10YNVdHuP1M7BLY", "R_1E585h59C3GfybR", 
"R_1FlfqH1Mt6W4OV4", "R_1GDeDAdiJnVB0s2", "R_1hG9g6mIxGn8ZWj", 
"R_1IKw2OhleiYRL4q", "R_1IMcAojZOzeJuvF", "R_1IMYbcaCx7KeA38", 
"R_1kXIx2HhCumDkmq", "R_1l04SIKet4JM5oK", "R_1LLqU4C3umOUznu", 
"R_1llydHpgYrmJIMS", "R_1mVMgqxtCtSwBXO", "R_1P8gxZvb4H0Shv9", 
"R_1qaiQQyr8bu2Dnh", "R_1Qz719yHd5BQBFv", "R_22RcWofEoI56dsf", 
"R_29azxBaN1eR6tn9", "R_29c9bWPlbniXPhg", "R_2ClhxDDaB38uJd2", 
"R_2coWnUVOwj7YBN4", "R_2dQ26mYjJVtDDmw", "R_2EHnrhzveVCptoc", 
"R_2eXNk9BXMzAp5g2", "R_2f7C3tT9Jk2UJEP", "R_2qsM6jMLPpUDkUF", 
"R_2rHO6Flyml9FP5p", "R_2yjWe8cVqJm89zO", "R_30ipYcmaXJLh732", 
"R_31mUdlu6JrQ0uw8", "R_3CMVIvsrKobFDCA", "R_3dRGOnsGmQcGnLh", 
"R_3g2czbQSlDvx8JP", "R_3illfbJFkdNqkGM", "R_3kKVLswnnIpQiFE", 
"R_3LhEYMu6J9UMzja", "R_3TWNY0AZLcA7S7v", "R_6PfXWjedMivcSZ3", 
"R_8oVjn5iCjrNPjlT", "R_9XZ9sme0CvR8wKt", "R_AGSkDnfSNkFxg7T", 
"R_bI3SnTlrBKH6Xx7", "R_bm7GkgUVCQpM9Mt", "R_bx6Yr5nMbIfTTtn", 
"R_C9duWufyN6qomIh", "R_DwQX8mRiBs84YGR", "R_pu7nnDIsRnwFhp7", 
"R_RrkyKiRuWP7rTIR", "R_SAUEuXWSTQWNFeh", "R_WwiQDYmMooTAA2l", 
"R_Z2BJEtTVJ4rVOqR", "R_ZwY8Rfs5r6csG9X"), age = c(25, 25, 29, 
38, 27, 25, 26, 24, 27, 25, 24, 29, 24, 29, 25, 27, 22, 25, NA, 
27, 23, 31, 25, 24, 23, 27, 24, 36, 28, 27, 26, NA, 26, 24, 32, 
31, 24, 22, 23, 28, 23, 28, 29, 24, 29, 23, 30, 28, 23, 23, 25, 
28, 41, 28, 25), sex = c("Female", "Male", "Female", "Male", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Male", "Female", "Male", 
"Male", "Female", "Female", "Female", "Male", "Female", "Female", 
"Female", "Male", "Female", "Female", "Female", "Male", "Female", 
"Female", "Male", "Male", "Female", "Male", "Male", "Female", 
"Female", "Female", "Female", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Female", "Female", "Female", "Female", 
"Female", "Male"), dvm_year = c("4th year", "4th year", "3rd year", 
"3rd year", "3rd year", "3rd year", "3rd year", "4th year", "4th year", 
"4th year", "4th year", "4th year", "4th year", "3rd year", "3rd year", 
"4th year", "3rd year", "3rd year", "4th year", "4th year", "4th year", 
"3rd year", "4th year", "4th year", "4th year", "4th year", "4th year", 
"3rd year", "4th year", "4th year", "4th year", "4th year", "3rd year", 
"4th year", "3rd year", "3rd year", "4th year", "4th year", "4th year", 
"3rd year", "4th year", "3rd year", "4th year", "4th year", "3rd year", 
"4th year", "3rd year", "3rd year", "4th year", "3rd year", "3rd year", 
"4th year", "4th year", "4th year", "4th year"), `using_apps?` = c("Yes", 
"Yes", "Yes", "No", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", 
"Yes", "No", "Yes", "No", "Yes", "Yes", "No", "No", "No", "Yes", 
"No", "Yes", "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "Yes", 
"No", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", 
"No", "No", "Yes", "Yes", "Yes", "No", "No", "Yes", "No", "No", 
"Yes", "Yes", "No", "Yes"), manual_confidence = c("Low", "Low", 
"Low", "Moderate", "Low", "Very low", "Moderate", NA, "Moderate", 
"Moderate", "Low", "Low", "Moderate", "High", "Very low", "Low", 
"Low", "Low", "Moderate", "Moderate", "Low", "Low", "Moderate", 
"Low", "Very low", "Low", "Low", "Low", "Very low", NA, "Moderate", 
"Very low", "Moderate", "Moderate", "Moderate", "Low", "Moderate", 
"Moderate", "Moderate", "Moderate", "Low", "Low", "Moderate", 
"Moderate", "Moderate", "Moderate", "Very low", "Moderate", "Moderate", 
"Low", "Moderate", "Low", "High", "Low", "Moderate"), `will_experience_help?` = c("Definitely yes", 
"Probably yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", NA, "Definitely yes", "Definitely yes", 
"Definitely yes", "Probably yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Probably yes", "Definitely yes", "Probably yes", 
"Definitely yes", "Probably yes", "Definitely yes", "Definitely yes", 
"Probably yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Definitely yes", NA, "Probably yes", 
"Probably yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Probably yes", "Probably yes", "Probably yes", 
"Definitely yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Definitely yes", "Definitely yes"
), preference_simplified = c("No Answer", "No Answer", "app", 
"app", "app", "combination", "app", "app", "app", "app", "manual", 
"app", "app", "combination", "app", "app", "combination", "app", 
"app", "app", "app", "app", "app", "app", "app", "app", "app", 
"app", "app", "app", "app", "combination", "app", "No Answer", 
"app", "app", "manual", "app", "app", "app", "app", "app", "app", 
"app", "app", "app", "app", "combination", "app", "combination", 
"app", "app", "app", "app", "app"), user_friendly = c("No Answer", 
"No Answer", "Easy to use", "Neutral", "Easy to use", "Easy to use", 
"Very easy to use", "Easy to use", "Easy to use", "Easy to use", 
"Neutral", "Very easy to use", "Easy to use", "Very easy to use", 
"Easy to use", "Very easy to use", "Neutral", "Very easy to use", 
"Easy to use", "Easy to use", "Very easy to use", "Easy to use", 
"Easy to use", "Easy to use", "Easy to use", "Very easy to use", 
"Easy to use", "Easy to use", "Very easy to use", "Very easy to use", 
"Very easy to use", "Easy to use", "Very easy to use", "Neutral", 
"Easy to use", "Neutral", "Hard to use", "Easy to use", "Easy to use", 
"Easy to use", "Very easy to use", "Very easy to use", "Easy to use", 
"Easy to use", "Easy to use", "Very easy to use", "Very easy to use", 
"Neutral", "Very easy to use", "Easy to use", "Very easy to use", 
"Very easy to use", "Easy to use", "Very easy to use", "Easy to use"
), demo_video = c("No Answer", "No Answer", "Definitely yes", 
"Might or might not", "Definitely yes", "Definitely yes", "Probably yes", 
"Probably yes", "Probably yes", "Probably yes", "Probably yes", 
"Might or might not", "Probably yes", "Might or might not", "Probably yes", 
"Probably yes", "Probably yes", "Might or might not", "Probably yes", 
"Probably yes", "Probably yes", "Probably yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Definitely yes", "Definitely yes", 
"Definitely yes", "Definitely yes", "Probably not", "Might or might not", 
"Probably yes", "Probably yes", "Probably yes", "Definitely yes", 
"Definitely yes", "Probably yes", "Probably yes", "Probably yes", 
"Definitely yes", "Probably yes", "Probably yes", "Probably yes", 
"Might or might not", "Definitely yes", "Probably yes", "Probably yes", 
"Probably yes", "Probably yes", "Definitely yes", "Might or might not", 
"Probably yes", "Might or might not", "Might or might not", "Might or might not"
), native_v_web = c("No Answer", "No Answer", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Makes no difference to me", 
"Native mobile app", "Native mobile app", "Makes no difference to me", 
"Makes no difference to me", "Native mobile app", "Makes no difference to me", 
"Makes no difference to me", "Native mobile app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Makes no difference to me", "Native mobile app", "Makes no difference to me", 
"Native mobile app", "Native mobile app", "Makes no difference to me", 
"Makes no difference to me", "Web-based app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Makes no difference to me", "Makes no difference to me", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Native mobile app", "Native mobile app", "Native mobile app", 
"Makes no difference to me", "Native mobile app", "Native mobile app", 
"Native mobile app", "Makes no difference to me", "Native mobile app", 
"Native mobile app")), row.names = c(NA, -55L), class = c("tbl_df", 
"tbl", "data.frame"))

Descriptive statistics

print(summarytools::dfSummary(quant,valid.col=FALSE,varnumbers=F,na.col=F, graph.magnif=0.8, style="grid"))

## Data Frame Summary  
## quant  
## Dimensions: 55 x 11  
## Duplicates: 0  
## 
## +-----------------------+------------------------------+--------------------+---------------------+
## | Variable              | Stats / Values               | Freqs (% of Valid) | Graph               |
## +=======================+==============================+====================+=====================+
## | id                    | 1. FS_1F4ndmfv4FRhMl7        |  1 ( 1.8%)         |                     |
## | [character]           | 2. FS_2aQanMGUwpJZDYl        |  1 ( 1.8%)         |                     |
## |                       | 3. R_06gon5h75y2OtZD         |  1 ( 1.8%)         |                     |
## |                       | 4. R_10YNVdHuP1M7BLY         |  1 ( 1.8%)         |                     |
## |                       | 5. R_1E585h59C3GfybR         |  1 ( 1.8%)         |                     |
## |                       | 6. R_1FlfqH1Mt6W4OV4         |  1 ( 1.8%)         |                     |
## |                       | 7. R_1GDeDAdiJnVB0s2         |  1 ( 1.8%)         |                     |
## |                       | 8. R_1hG9g6mIxGn8ZWj         |  1 ( 1.8%)         |                     |
## |                       | 9. R_1IKw2OhleiYRL4q         |  1 ( 1.8%)         |                     |
## |                       | 10. R_1IMcAojZOzeJuvF        |  1 ( 1.8%)         |                     |
## |                       | [ 45 others ]                | 45 (81.8%)         | IIIIIIIIIIIIIIII    |
## +-----------------------+------------------------------+--------------------+---------------------+
## | age                   | Mean (sd) : 26.7 (3.8)       | 14 distinct values |   :                 |
## | [numeric]             | min < med < max:             |                    |   :                 |
## |                       | 22 < 26 < 41                 |                    | . : . :             |
## |                       | IQR (CV) : 4 (0.1)           |                    | : : : :             |
## |                       |                              |                    | : : : : : .   . . . |
## +-----------------------+------------------------------+--------------------+---------------------+
## | sex                   | 1. Female                    | 37 (67.3%)         | IIIIIIIIIIIII       |
## | [character]           | 2. Male                      | 18 (32.7%)         | IIIIII              |
## +-----------------------+------------------------------+--------------------+---------------------+
## | dvm_year              | 1. 3rd year                  | 21 (38.2%)         | IIIIIII             |
## | [character]           | 2. 4th year                  | 34 (61.8%)         | IIIIIIIIIIII        |
## +-----------------------+------------------------------+--------------------+---------------------+
## | using_apps?           | 1. No                        | 22 (40.0%)         | IIIIIIII            |
## | [character]           | 2. Yes                       | 33 (60.0%)         | IIIIIIIIIIII        |
## +-----------------------+------------------------------+--------------------+---------------------+
## | manual_confidence     | 1. High                      |  2 ( 3.8%)         |                     |
## | [character]           | 2. Low                       | 21 (39.6%)         | IIIIIII             |
## |                       | 3. Moderate                  | 24 (45.3%)         | IIIIIIIII           |
## |                       | 4. Very low                  |  6 (11.3%)         | II                  |
## +-----------------------+------------------------------+--------------------+---------------------+
## | will_experience_help? | 1. Definitely yes            | 42 (79.2%)         | IIIIIIIIIIIIIII     |
## | [character]           | 2. Probably yes              | 11 (20.8%)         | IIII                |
## +-----------------------+------------------------------+--------------------+---------------------+
## | preference_simplified | 1. app                       | 44 (80.0%)         | IIIIIIIIIIIIIIII    |
## | [character]           | 2. combination               |  6 (10.9%)         | II                  |
## |                       | 3. manual                    |  2 ( 3.6%)         |                     |
## |                       | 4. No Answer                 |  3 ( 5.5%)         | I                   |
## +-----------------------+------------------------------+--------------------+---------------------+
## | user_friendly         | 1. Easy to use               | 27 (49.1%)         | IIIIIIIII           |
## | [character]           | 2. Hard to use               |  1 ( 1.8%)         |                     |
## |                       | 3. Neutral                   |  6 (10.9%)         | II                  |
## |                       | 4. No Answer                 |  2 ( 3.6%)         |                     |
## |                       | 5. Very easy to use          | 19 (34.5%)         | IIIIII              |
## +-----------------------+------------------------------+--------------------+---------------------+
## | demo_video            | 1. Definitely yes            | 15 (27.3%)         | IIIII               |
## | [character]           | 2. Might or might not        | 10 (18.2%)         | III                 |
## |                       | 3. No Answer                 |  2 ( 3.6%)         |                     |
## |                       | 4. Probably not              |  1 ( 1.8%)         |                     |
## |                       | 5. Probably yes              | 27 (49.1%)         | IIIIIIIII           |
## +-----------------------+------------------------------+--------------------+---------------------+
## | native_v_web          | 1. Makes no difference to me | 13 (23.6%)         | IIII                |
## | [character]           | 2. Native mobile app         | 39 (70.9%)         | IIIIIIIIIIIIII      |
## |                       | 3. No Answer                 |  2 ( 3.6%)         |                     |
## |                       | 4. Web-based app             |  1 ( 1.8%)         |                     |
## +-----------------------+------------------------------+--------------------+---------------------+

library(table1)
table1::table1(~ age + sex + dvm_year, data=quant)

	Overall (N=55)
age
Mean (SD)	26.7 (3.82)
Median [Min, Max]	26.0 [22.0, 41.0]
Missing	2 (3.6%)
sex
Female	37 (67.3%)
Male	18 (32.7%)
dvm_year
3rd year	21 (38.2%)
4th year	34 (61.8%)

Analysis 1: Evaluating basic fluid therapy calculations

Show dataset 1

data$group <- fct_relevel(data$group,"control")
data$total <- (data$deficit + data$losses + data$mainten + data$base) / 8 * 100
data %>% kable

id	group	case	deficit	losses	mainten	base	total
FS_1F4ndmfv4FRhMl7	control	3w	0	1	0	0	12.5
FS_1F4ndmfv4FRhMl7	app	5d	1	1	2	2	75.0
FS_2aQanMGUwpJZDYl	control	5d	1	0	1	2	50.0
FS_2aQanMGUwpJZDYl	app	3w	2	1	0	0	37.5
R_06gon5h75y2OtZD	control	3w	1	2	0	2	62.5
R_06gon5h75y2OtZD	app	5d	2	0	2	2	75.0
R_10YNVdHuP1M7BLY	control	3w	2	2	2	1	87.5
R_10YNVdHuP1M7BLY	app	5d	2	2	2	2	100.0
R_1E585h59C3GfybR	control	5d	0	0	2	2	50.0
R_1E585h59C3GfybR	app	3w	2	2	2	2	100.0
R_1FlfqH1Mt6W4OV4	control	3w	1	2	2	2	87.5
R_1FlfqH1Mt6W4OV4	app	5d	2	2	2	2	100.0
R_1GDeDAdiJnVB0s2	control	5d	0	1	1	0	25.0
R_1GDeDAdiJnVB0s2	app	3w	2	2	2	2	100.0
R_1hG9g6mIxGn8ZWj	control	3w	2	1	2	1	75.0
R_1hG9g6mIxGn8ZWj	app	5d	2	2	2	2	100.0
R_1IKw2OhleiYRL4q	control	5d	2	1	2	1	75.0
R_1IKw2OhleiYRL4q	app	3w	2	1	2	2	87.5
R_1IMcAojZOzeJuvF	control	3w	0	2	2	1	62.5
R_1IMcAojZOzeJuvF	app	5d	2	2	2	2	100.0
R_1IMYbcaCx7KeA38	control	3w	0	0	2	0	25.0
R_1IMYbcaCx7KeA38	app	5d	1	2	2	0	62.5
R_1kXIx2HhCumDkmq	control	5d	1	1	0	2	50.0
R_1kXIx2HhCumDkmq	app	3w	2	1	2	2	87.5
R_1l04SIKet4JM5oK	control	3w	1	2	2	2	87.5
R_1l04SIKet4JM5oK	app	5d	2	2	2	2	100.0
R_1LLqU4C3umOUznu	control	3w	2	2	2	1	87.5
R_1LLqU4C3umOUznu	app	5d	2	2	0	2	75.0
R_1llydHpgYrmJIMS	control	3w	1	2	0	1	50.0
R_1llydHpgYrmJIMS	app	5d	2	2	2	2	100.0
R_1mVMgqxtCtSwBXO	control	5d	2	2	2	2	100.0
R_1mVMgqxtCtSwBXO	app	3w	2	2	2	2	100.0
R_1P8gxZvb4H0Shv9	control	5d	2	0	0	2	50.0
R_1P8gxZvb4H0Shv9	app	3w	2	1	2	2	87.5
R_1qaiQQyr8bu2Dnh	control	3w	1	2	2	2	87.5
R_1qaiQQyr8bu2Dnh	app	5d	2	2	2	2	100.0
R_1Qz719yHd5BQBFv	control	3w	0	2	2	2	75.0
R_1Qz719yHd5BQBFv	app	5d	2	2	2	2	100.0
R_22RcWofEoI56dsf	control	5d	1	1	2	1	62.5
R_22RcWofEoI56dsf	app	3w	2	2	2	2	100.0
R_29azxBaN1eR6tn9	control	5d	2	1	2	1	75.0
R_29azxBaN1eR6tn9	app	3w	2	2	2	2	100.0
R_29c9bWPlbniXPhg	control	3w	2	1	2	2	87.5
R_29c9bWPlbniXPhg	app	5d	2	2	2	2	100.0
R_2ClhxDDaB38uJd2	control	3w	1	0	2	2	62.5
R_2ClhxDDaB38uJd2	app	5d	2	1	2	2	87.5
R_2coWnUVOwj7YBN4	control	5d	2	1	2	2	87.5
R_2coWnUVOwj7YBN4	app	3w	2	2	2	2	100.0
R_2dQ26mYjJVtDDmw	control	5d	2	1	2	0	62.5
R_2dQ26mYjJVtDDmw	app	3w	0	2	2	2	75.0
R_2EHnrhzveVCptoc	control	5d	2	1	0	2	62.5
R_2EHnrhzveVCptoc	app	3w	2	2	2	2	100.0
R_2eXNk9BXMzAp5g2	control	3w	1	0	2	2	62.5
R_2eXNk9BXMzAp5g2	app	5d	2	2	2	2	100.0
R_2f7C3tT9Jk2UJEP	control	3w	2	2	2	2	100.0
R_2f7C3tT9Jk2UJEP	app	5d	2	2	1	1	75.0
R_2qsM6jMLPpUDkUF	control	3w	2	1	2	1	75.0
R_2qsM6jMLPpUDkUF	app	5d	2	2	2	2	100.0
R_2rHO6Flyml9FP5p	control	3w	2	0	2	1	62.5
R_2rHO6Flyml9FP5p	app	5d	2	1	2	2	87.5
R_2yjWe8cVqJm89zO	control	3w	2	2	2	2	100.0
R_2yjWe8cVqJm89zO	app	5d	2	2	2	2	100.0
R_30ipYcmaXJLh732	control	3w	0	2	1	2	62.5
R_30ipYcmaXJLh732	app	5d	2	2	2	2	100.0
R_31mUdlu6JrQ0uw8	control	3w	0	0	2	1	37.5
R_31mUdlu6JrQ0uw8	app	5d	2	2	2	2	100.0
R_3CMVIvsrKobFDCA	control	5d	2	2	2	2	100.0
R_3CMVIvsrKobFDCA	app	3w	2	2	2	2	100.0
R_3dRGOnsGmQcGnLh	control	3w	0	1	2	2	62.5
R_3dRGOnsGmQcGnLh	app	5d	2	0	2	1	62.5
R_3g2czbQSlDvx8JP	control	5d	2	0	2	0	50.0
R_3g2czbQSlDvx8JP	app	3w	1	0	2	2	62.5
R_3illfbJFkdNqkGM	control	5d	2	0	2	2	75.0
R_3illfbJFkdNqkGM	app	3w	1	0	2	1	50.0
R_3kKVLswnnIpQiFE	control	3w	1	2	2	2	87.5
R_3kKVLswnnIpQiFE	app	5d	1	2	2	1	75.0
R_3LhEYMu6J9UMzja	control	5d	2	0	0	2	50.0
R_3LhEYMu6J9UMzja	app	3w	2	0	2	2	75.0
R_3TWNY0AZLcA7S7v	control	5d	2	2	2	1	87.5
R_3TWNY0AZLcA7S7v	app	3w	1	2	2	2	87.5
R_6PfXWjedMivcSZ3	control	5d	2	2	2	2	100.0
R_6PfXWjedMivcSZ3	app	3w	2	2	2	2	100.0
R_8oVjn5iCjrNPjlT	control	5d	1	2	2	0	62.5
R_8oVjn5iCjrNPjlT	app	3w	2	2	2	2	100.0
R_9XZ9sme0CvR8wKt	control	5d	1	1	2	2	75.0
R_9XZ9sme0CvR8wKt	app	3w	2	2	2	2	100.0
R_AGSkDnfSNkFxg7T	control	5d	1	2	2	2	87.5
R_AGSkDnfSNkFxg7T	app	3w	0	2	2	2	75.0
R_bI3SnTlrBKH6Xx7	control	5d	2	2	2	1	87.5
R_bI3SnTlrBKH6Xx7	app	3w	2	2	2	1	87.5
R_bm7GkgUVCQpM9Mt	control	3w	0	0	2	2	50.0
R_bm7GkgUVCQpM9Mt	app	5d	0	1	2	2	62.5
R_bx6Yr5nMbIfTTtn	control	5d	2	0	2	1	62.5
R_bx6Yr5nMbIfTTtn	app	3w	1	0	2	2	62.5
R_C9duWufyN6qomIh	control	3w	0	2	0	2	50.0
R_C9duWufyN6qomIh	app	5d	1	2	1	1	62.5
R_DwQX8mRiBs84YGR	control	3w	2	1	2	2	87.5
R_DwQX8mRiBs84YGR	app	5d	2	2	2	2	100.0
R_pu7nnDIsRnwFhp7	control	3w	0	1	0	0	12.5
R_pu7nnDIsRnwFhp7	app	5d	2	2	2	2	100.0
R_RrkyKiRuWP7rTIR	control	5d	1	1	0	0	25.0
R_RrkyKiRuWP7rTIR	app	3w	2	2	2	2	100.0
R_SAUEuXWSTQWNFeh	control	3w	2	1	2	2	87.5
R_SAUEuXWSTQWNFeh	app	5d	2	1	2	2	87.5
R_WwiQDYmMooTAA2l	control	3w	1	2	2	2	87.5
R_WwiQDYmMooTAA2l	app	5d	2	2	2	2	100.0
R_Z2BJEtTVJ4rVOqR	control	5d	1	2	0	1	50.0
R_Z2BJEtTVJ4rVOqR	app	3w	2	2	2	1	87.5
R_ZwY8Rfs5r6csG9X	control	3w	1	2	2	1	75.0
R_ZwY8Rfs5r6csG9X	app	5d	2	2	2	2	100.0

print(summarytools::dfSummary(data,valid.col=FALSE, graph.magnif=0.8, style="grid"))

## Data Frame Summary  
## data  
## Dimensions: 110 x 8  
## Duplicates: 0  
## 
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | No | Variable    | Stats / Values          | Freqs (% of Valid)  | Graph            | Missing |
## +====+=============+=========================+=====================+==================+=========+
## | 1  | id          | 1. FS_1F4ndmfv4FRhMl7   |  2 ( 1.8%)          |                  | 0       |
## |    | [character] | 2. FS_2aQanMGUwpJZDYl   |  2 ( 1.8%)          |                  | (0.0%)  |
## |    |             | 3. R_06gon5h75y2OtZD    |  2 ( 1.8%)          |                  |         |
## |    |             | 4. R_10YNVdHuP1M7BLY    |  2 ( 1.8%)          |                  |         |
## |    |             | 5. R_1E585h59C3GfybR    |  2 ( 1.8%)          |                  |         |
## |    |             | 6. R_1FlfqH1Mt6W4OV4    |  2 ( 1.8%)          |                  |         |
## |    |             | 7. R_1GDeDAdiJnVB0s2    |  2 ( 1.8%)          |                  |         |
## |    |             | 8. R_1hG9g6mIxGn8ZWj    |  2 ( 1.8%)          |                  |         |
## |    |             | 9. R_1IKw2OhleiYRL4q    |  2 ( 1.8%)          |                  |         |
## |    |             | 10. R_1IMcAojZOzeJuvF   |  2 ( 1.8%)          |                  |         |
## |    |             | [ 45 others ]           | 90 (81.8%)          | IIIIIIIIIIIIIIII |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 2  | group       | 1. control              | 55 (50.0%)          | IIIIIIIIII       | 0       |
## |    | [factor]    | 2. app                  | 55 (50.0%)          | IIIIIIIIII       | (0.0%)  |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 3  | case        | 1. 3w                   | 55 (50.0%)          | IIIIIIIIII       | 0       |
## |    | [character] | 2. 5d                   | 55 (50.0%)          | IIIIIIIIII       | (0.0%)  |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 4  | deficit     | Mean (sd) : 1.5 (0.7)   | 0 : 15 (13.6%)      | II               | 0       |
## |    | [numeric]   | min < med < max:        | 1 : 26 (23.6%)      | IIII             | (0.0%)  |
## |    |             | 0 < 2 < 2               | 2 : 69 (62.7%)      | IIIIIIIIIIII     |         |
## |    |             | IQR (CV) : 1 (0.5)      |                     |                  |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 5  | losses      | Mean (sd) : 1.4 (0.8)   | 0 : 19 (17.3%)      | III              | 0       |
## |    | [numeric]   | min < med < max:        | 1 : 27 (24.5%)      | IIII             | (0.0%)  |
## |    |             | 0 < 2 < 2               | 2 : 64 (58.2%)      | IIIIIIIIIII      |         |
## |    |             | IQR (CV) : 1 (0.5)      |                     |                  |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 6  | mainten     | Mean (sd) : 1.7 (0.7)   | 0 : 13 (11.8%)      | II               | 0       |
## |    | [numeric]   | min < med < max:        | 1 :  5 ( 4.5%)      |                  | (0.0%)  |
## |    |             | 0 < 2 < 2               | 2 : 92 (83.6%)      | IIIIIIIIIIIIIIII |         |
## |    |             | IQR (CV) : 0 (0.4)      |                     |                  |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 7  | base        | Mean (sd) : 1.6 (0.7)   | 0 : 10 ( 9.1%)      | I                | 0       |
## |    | [numeric]   | min < med < max:        | 1 : 23 (20.9%)      | IIII             | (0.0%)  |
## |    |             | 0 < 2 < 2               | 2 : 77 (70.0%)      | IIIIIIIIIIIIII   |         |
## |    |             | IQR (CV) : 1 (0.4)      |                     |                  |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+
## | 8  | total       | Mean (sd) : 77.8 (21.9) | 12.50 :  2 ( 1.8%)  |                  | 0       |
## |    | [numeric]   | min < med < max:        | 25.00 :  3 ( 2.7%)  |                  | (0.0%)  |
## |    |             | 12.5 < 87.5 < 100       | 37.50 :  2 ( 1.8%)  |                  |         |
## |    |             | IQR (CV) : 37.5 (0.3)   | 50.00 : 11 (10.0%)  | II               |         |
## |    |             |                         | 62.50 : 18 (16.4%)  | III              |         |
## |    |             |                         | 75.00 : 16 (14.5%)  | II               |         |
## |    |             |                         | 87.50 : 23 (20.9%)  | IIII             |         |
## |    |             |                         | 100.00 : 35 (31.8%) | IIIIII           |         |
## +----+-------------+-------------------------+---------------------+------------------+---------+

data %>% tabyl(case,group)

##  case control app
##    3w      30  25
##    5d      25  30

Comparison of scores

print <- data %>% group_by(group) %>%
  summarise(
    fluid.deficit.score = (mean(deficit)/2) * 100,
    ongoing.losses.score = (mean(losses)/2) * 100,
    maintenance.score = (mean(mainten)/2) * 100,
    base.deficit.score = (mean(base)/2) * 100,
    total.score100 = (fluid.deficit.score + ongoing.losses.score + maintenance.score + base.deficit.score) / 4,
  )

tab <- as_tibble(t(print), rownames = "row_names")
tab <- tab %>%
  row_to_names(row_number = 1)
tab$difference <- as.numeric(tab$app) - as.numeric(tab$control)

tab <- tab %>% mutate(across(2:4,as.numeric))
tab <- tab %>% mutate(across(2:4,round,1))
tab

## # A tibble: 5 × 4
##   group                control   app difference
##   <chr>                  <dbl> <dbl>      <dbl>
## 1 fluid.deficit.score     61.8  87.3       25.5
## 2 ongoing.losses.score    60    80.9       20.9
## 3 maintenance.score       77.3  94.5       17.3
## 4 base.deficit.score      70.9  90         19.1
## 5 total.score100          67.5  88.2       20.7

data <- merge(data,quant %>% select(id,manual_confidence),by="id")
table1::table1(~ total | manual_confidence*group, data=data)

	High		Low		Moderate		Very low		Overall
	control (N=2)	app (N=2)	control (N=21)	app (N=21)	control (N=24)	app (N=24)	control (N=6)	app (N=6)	control (N=55)	app (N=55)
total
Mean (SD)	87.5 (0)	87.5 (17.7)	63.1 (26.7)	87.5 (17.2)	69.8 (21.5)	88.0 (15.8)	66.7 (12.9)	89.6 (16.6)	67.5 (22.4)	88.2 (15.9)
Median [Min, Max]	87.5 [87.5, 87.5]	87.5 [75.0, 100]	62.5 [12.5, 100]	100 [37.5, 100]	75.0 [25.0, 100]	100 [50.0, 100]	62.5 [50.0, 87.5]	100 [62.5, 100]	62.5 [12.5, 100]	100 [37.5, 100]

confidence.table <- data %>% group_by(manual_confidence,group) %>% summarise(
  n = n(),
  total = median(total),
  deficit = 100*mean(deficit)/2,
  losses = 100*mean(losses)/2,
  mainten = 100*mean(mainten)/2,
  base = 100*mean(base)/2
) %>% filter(group=='control')

confidence.table[c(4,2,3,1),]

## # A tibble: 4 × 8
## # Groups:   manual_confidence [4]
##   manual_confidence group       n total deficit losses mainten  base
##   <chr>             <fct>   <int> <dbl>   <dbl>  <dbl>   <dbl> <dbl>
## 1 Very low          control     6  62.5    66.7   66.7    75    58.3
## 2 Low               control    21  62.5    64.3   52.4    64.3  71.4
## 3 Moderate          control    24  75      54.2   64.6    85.4  75  
## 4 High              control     2  87.5    75    100     100    75

Boxplot comparing total scores (out of 100)

data.long <- data %>%
  pivot_longer(cols = deficit:total, names_to = "type", values_to = "score")

data.long <- data.long %>% filter(type == "total")

data.long$type[data.long$type == "total"] <- "Total"

plot1 <- ggplot(data = data.long, aes(x = type, y = score, fill=group)) +
  geom_boxplot() +
  labs(y = "Score out of 100", x="Group") +
  ylim(0, 100) +
  theme(axis.text.x = element_text(size=12, family="Times"),axis.title.x = element_text(size=15, family="Times",face="bold")) +
  theme(axis.text.y = element_text(size=15, family="Times"),axis.title.y = element_text(size=15, family="Times",face="bold")) +
  theme(legend.position = "bottom",
        plot.title = element_text(size=15, family="Times",hjust = 0.5),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.x=element_blank(),
        panel.border = element_rect(colour = "black", fill=NA, size=1),
        panel.background = element_rect(fill = "white", colour = "white",size = 0.5, linetype = "solid"),
        panel.grid.major = element_line(size =0, linetype = 'solid', colour = "grey"),
        panel.grid.minor = element_line(size = 0, linetype = 'solid',colour = "grey")) + 
  scale_fill_discrete(name = "Method", labels = c("Conventional", "App-based"))

tiff("plot.tiff", units="in", width=6, height=6, res=300)
plot1
dev.off()

## quartz_off_screen 
##                 2

Modelling

Will use a linear mixed model that accounts for the clustering of answers within students

lmm.total <- lmer(total ~ group + case + (1|id), data= data)
summary(lmm.total)

## Linear mixed model fit by REML ['lmerMod']
## Formula: total ~ group + case + (1 | id)
##    Data: data
## 
## REML criterion at convergence: 948.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7293 -0.5667  0.2264  0.6338  1.6996 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept)  77.75    8.818  
##  Residual             301.15   17.354  
## Number of obs: 110, groups:  id, 55
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)   67.216      3.028  22.196
## groupapp      20.625      3.323   6.207
## case5d         0.625      3.323   0.188
## 
## Correlation of Fixed Effects:
##          (Intr) groppp
## groupapp -0.499       
## case5d   -0.499 -0.091

output <- tidy(lmm.total,conf.int = T) %>% select(term,estimate,conf.low,conf.high) %>% filter(term == "groupapp")

icc(lmm.total, by_group = FALSE, tolerance = 1e-05)

## # Intraclass Correlation Coefficient
## 
##      Adjusted ICC: 0.205
##   Conditional ICC: 0.160

Effect estimate

diff <- paste0(round(output$estimate,2),
       " (",
       round(output$conf.low,2),
       ", ",
       round(output$conf.high,2),
       ")")

tab$difference[tab$group == "total.score100"] <- diff
write.csv(tab,"components.csv")

Diagnostics

icc(lmm.total, by_group = FALSE, tolerance = 1e-05)

## # Intraclass Correlation Coefficient
## 
##      Adjusted ICC: 0.205
##   Conditional ICC: 0.160

ggplot(data.frame(eta=predict(lmm.total,type="link"),pearson=residuals(lmm.total,type="pearson")),
       aes(x=eta,y=pearson)) + geom_point() + theme_bw()

qqnorm(residuals(lmm.total))

Analysis 2: Evaluating final fluid plans

Show dataset 2

data2$total <- data2$total / 6 * 100
data2 %>% kable

id	group	case	total	deficit	volume	alk	bicarb	rate.bicarb	oral
FS_1F4ndmfv4FRhMl7	control	3w	66.66667	1	0	1	1	1	0
FS_1F4ndmfv4FRhMl7	app	5d	50.00000	1	0	1	0	1	0
FS_2aQanMGUwpJZDYl	control	5d	33.33333	1	0	1	0	0	0
FS_2aQanMGUwpJZDYl	app	3w	33.33333	1	0	1	0	0	0
R_06gon5h75y2OtZD	control	3w	33.33333	1	0	1	0	0	0
R_06gon5h75y2OtZD	app	5d	83.33333	1	0	1	1	1	1
R_10YNVdHuP1M7BLY	control	3w	66.66667	1	0	1	1	1	0
R_10YNVdHuP1M7BLY	app	5d	66.66667	1	0	1	0	1	1
R_1E585h59C3GfybR	control	5d	33.33333	1	0	1	0	0	0
R_1E585h59C3GfybR	app	3w	50.00000	1	0	1	1	0	0
R_1FlfqH1Mt6W4OV4	control	3w	50.00000	1	1	1	0	0	0
R_1FlfqH1Mt6W4OV4	app	5d	33.33333	0	0	1	0	1	0
R_1GDeDAdiJnVB0s2	control	5d	33.33333	1	0	1	0	0	0
R_1GDeDAdiJnVB0s2	app	3w	66.66667	1	0	1	1	1	0
R_1hG9g6mIxGn8ZWj	control	3w	66.66667	1	1	1	0	1	0
R_1hG9g6mIxGn8ZWj	app	5d	100.00000	1	1	1	1	1	1
R_1IKw2OhleiYRL4q	control	5d	66.66667	1	1	1	0	1	0
R_1IKw2OhleiYRL4q	app	3w	100.00000	1	1	1	1	1	1
R_1IMcAojZOzeJuvF	control	3w	50.00000	1	0	1	1	0	0
R_1IMcAojZOzeJuvF	app	5d	100.00000	1	1	1	1	1	1
R_1IMYbcaCx7KeA38	control	3w	50.00000	1	1	1	0	0	0
R_1IMYbcaCx7KeA38	app	5d	33.33333	1	1	0	0	0	0
R_1kXIx2HhCumDkmq	control	5d	50.00000	1	0	1	0	1	0
R_1kXIx2HhCumDkmq	app	3w	66.66667	1	0	1	1	1	0
R_1l04SIKet4JM5oK	control	3w	33.33333	1	0	1	0	0	0
R_1l04SIKet4JM5oK	app	5d	50.00000	0	0	1	1	1	0
R_1LLqU4C3umOUznu	control	3w	66.66667	1	1	1	0	1	0
R_1LLqU4C3umOUznu	app	5d	66.66667	1	0	1	1	1	0
R_1llydHpgYrmJIMS	control	3w	66.66667	1	0	1	1	1	0
R_1llydHpgYrmJIMS	app	5d	66.66667	1	0	1	1	1	0
R_1mVMgqxtCtSwBXO	control	5d	66.66667	1	0	1	1	1	0
R_1mVMgqxtCtSwBXO	app	3w	66.66667	1	0	1	1	1	0
R_1P8gxZvb4H0Shv9	control	5d	50.00000	1	0	1	0	1	0
R_1P8gxZvb4H0Shv9	app	3w	16.66667	1	0	0	0	0	0
R_1qaiQQyr8bu2Dnh	control	3w	66.66667	1	0	1	1	1	0
R_1qaiQQyr8bu2Dnh	app	5d	83.33333	1	0	1	1	1	1
R_1Qz719yHd5BQBFv	control	3w	33.33333	1	0	1	0	0	0
R_1Qz719yHd5BQBFv	app	5d	66.66667	1	0	1	1	1	0
R_22RcWofEoI56dsf	control	5d	66.66667	1	1	1	0	1	0
R_22RcWofEoI56dsf	app	3w	100.00000	1	1	1	1	1	1
R_29azxBaN1eR6tn9	control	3w	33.33333	1	1	0	0	0	0
R_29azxBaN1eR6tn9	app	5d	100.00000	1	1	1	1	1	1
R_29c9bWPlbniXPhg	control	3w	33.33333	1	1	0	0	0	0
R_29c9bWPlbniXPhg	app	5d	83.33333	1	1	1	0	1	1
R_2ClhxDDaB38uJd2	control	3w	16.66667	0	0	1	0	0	0
R_2ClhxDDaB38uJd2	app	5d	16.66667	0	0	1	0	0	0
R_2coWnUVOwj7YBN4	control	5d	50.00000	1	1	1	0	0	0
R_2coWnUVOwj7YBN4	app	3w	100.00000	1	1	1	1	1	1
R_2dQ26mYjJVtDDmw	control	5d	66.66667	1	0	1	0	1	1
R_2dQ26mYjJVtDDmw	app	3w	100.00000	1	1	1	1	1	1
R_2EHnrhzveVCptoc	control	5d	16.66667	1	0	0	0	0	0
R_2EHnrhzveVCptoc	app	3w	66.66667	1	0	1	1	1	0
R_2eXNk9BXMzAp5g2	control	3w	100.00000	1	1	1	1	1	1
R_2eXNk9BXMzAp5g2	app	5d	83.33333	1	0	1	1	1	1
R_2f7C3tT9Jk2UJEP	control	3w	83.33333	1	0	1	1	1	1
R_2f7C3tT9Jk2UJEP	app	5d	50.00000	1	0	1	0	1	0
R_2qsM6jMLPpUDkUF	control	3w	33.33333	1	0	1	0	0	0
R_2qsM6jMLPpUDkUF	app	5d	50.00000	1	0	1	0	0	1
R_2rHO6Flyml9FP5p	control	3w	33.33333	0	0	1	0	1	0
R_2rHO6Flyml9FP5p	app	5d	100.00000	1	1	1	1	1	1
R_2yjWe8cVqJm89zO	control	3w	66.66667	1	0	1	1	1	0
R_2yjWe8cVqJm89zO	app	5d	66.66667	0	0	1	1	1	1
R_30ipYcmaXJLh732	control	3w	66.66667	1	1	1	1	0	0
R_30ipYcmaXJLh732	app	5d	66.66667	1	0	1	1	1	0
R_31mUdlu6JrQ0uw8	control	3w	33.33333	1	0	1	0	0	0
R_31mUdlu6JrQ0uw8	app	5d	100.00000	1	1	1	1	1	1
R_3CMVIvsrKobFDCA	control	5d	83.33333	1	1	1	1	1	0
R_3CMVIvsrKobFDCA	app	3w	83.33333	1	1	1	1	1	0
R_3dRGOnsGmQcGnLh	control	3w	50.00000	1	0	1	0	1	0
R_3dRGOnsGmQcGnLh	app	5d	66.66667	1	1	1	0	0	1
R_3g2czbQSlDvx8JP	control	5d	33.33333	0	0	1	0	1	0
R_3g2czbQSlDvx8JP	app	3w	66.66667	1	0	1	1	1	0
R_3illfbJFkdNqkGM	control	5d	16.66667	0	1	0	0	0	0
R_3illfbJFkdNqkGM	app	3w	50.00000	1	1	1	0	0	0
R_3kKVLswnnIpQiFE	control	3w	66.66667	1	0	1	1	1	0
R_3kKVLswnnIpQiFE	app	5d	50.00000	1	0	1	0	1	0
R_3LhEYMu6J9UMzja	control	5d	16.66667	0	0	1	0	0	0
R_3LhEYMu6J9UMzja	app	3w	50.00000	0	0	1	1	1	0
R_3TWNY0AZLcA7S7v	control	5d	66.66667	1	0	1	1	1	0
R_3TWNY0AZLcA7S7v	app	3w	50.00000	1	1	1	0	0	0
R_6PfXWjedMivcSZ3	control	5d	83.33333	1	1	1	1	1	0
R_6PfXWjedMivcSZ3	app	3w	83.33333	1	0	1	1	1	1
R_8oVjn5iCjrNPjlT	control	5d	16.66667	1	0	0	0	0	0
R_8oVjn5iCjrNPjlT	app	3w	66.66667	1	0	1	1	1	0
R_9XZ9sme0CvR8wKt	control	5d	50.00000	1	1	1	0	0	0
R_9XZ9sme0CvR8wKt	app	3w	66.66667	1	0	1	0	1	1
R_AGSkDnfSNkFxg7T	control	5d	66.66667	1	0	1	1	1	0
R_AGSkDnfSNkFxg7T	app	3w	33.33333	1	0	0	0	0	1
R_bI3SnTlrBKH6Xx7	control	5d	66.66667	1	1	1	0	0	1
R_bI3SnTlrBKH6Xx7	app	3w	83.33333	1	0	1	1	1	1
R_bm7GkgUVCQpM9Mt	control	3w	66.66667	1	0	1	1	1	0
R_bm7GkgUVCQpM9Mt	app	5d	66.66667	1	0	1	1	1	0
R_bx6Yr5nMbIfTTtn	control	5d	16.66667	1	0	0	0	0	0
R_bx6Yr5nMbIfTTtn	app	3w	16.66667	0	0	0	0	0	1
R_C9duWufyN6qomIh	control	3w	83.33333	1	1	1	1	1	0
R_C9duWufyN6qomIh	app	5d	0.00000	0	0	0	0	0	0
R_DwQX8mRiBs84YGR	control	3w	66.66667	1	1	1	1	0	0
R_DwQX8mRiBs84YGR	app	5d	83.33333	1	1	1	1	1	0
R_pu7nnDIsRnwFhp7	control	3w	16.66667	1	0	0	0	0	0
R_pu7nnDIsRnwFhp7	app	5d	100.00000	1	1	1	1	1	1
R_RrkyKiRuWP7rTIR	control	5d	33.33333	1	0	1	0	0	0
R_RrkyKiRuWP7rTIR	app	3w	50.00000	1	0	1	0	1	0
R_SAUEuXWSTQWNFeh	control	3w	66.66667	1	0	1	1	1	0
R_SAUEuXWSTQWNFeh	app	5d	66.66667	1	0	1	1	1	0
R_WwiQDYmMooTAA2l	control	3w	50.00000	1	0	1	1	0	0
R_WwiQDYmMooTAA2l	app	5d	66.66667	1	0	1	1	1	0
R_Z2BJEtTVJ4rVOqR	control	5d	66.66667	1	1	1	0	1	0
R_Z2BJEtTVJ4rVOqR	app	3w	50.00000	1	0	1	0	0	1
R_ZwY8Rfs5r6csG9X	control	3w	50.00000	1	0	1	0	1	0
R_ZwY8Rfs5r6csG9X	app	5d	83.33333	1	0	1	1	1	1

print(summarytools::dfSummary(data2,valid.col=FALSE, graph.magnif=0.8, style="grid"))

## Data Frame Summary  
## data2  
## Dimensions: 110 x 10  
## Duplicates: 0  
## 
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | No | Variable    | Stats / Values        | Freqs (% of Valid)  | Graph             | Missing |
## +====+=============+=======================+=====================+===================+=========+
## | 1  | id          | 1. FS_1F4ndmfv4FRhMl7 |  2 ( 1.8%)          |                   | 0       |
## |    | [character] | 2. FS_2aQanMGUwpJZDYl |  2 ( 1.8%)          |                   | (0.0%)  |
## |    |             | 3. R_06gon5h75y2OtZD  |  2 ( 1.8%)          |                   |         |
## |    |             | 4. R_10YNVdHuP1M7BLY  |  2 ( 1.8%)          |                   |         |
## |    |             | 5. R_1E585h59C3GfybR  |  2 ( 1.8%)          |                   |         |
## |    |             | 6. R_1FlfqH1Mt6W4OV4  |  2 ( 1.8%)          |                   |         |
## |    |             | 7. R_1GDeDAdiJnVB0s2  |  2 ( 1.8%)          |                   |         |
## |    |             | 8. R_1hG9g6mIxGn8ZWj  |  2 ( 1.8%)          |                   |         |
## |    |             | 9. R_1IKw2OhleiYRL4q  |  2 ( 1.8%)          |                   |         |
## |    |             | 10. R_1IMcAojZOzeJuvF |  2 ( 1.8%)          |                   |         |
## |    |             | [ 45 others ]         | 90 (81.8%)          | IIIIIIIIIIIIIIII  |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 2  | group       | 1. app                | 55 (50.0%)          | IIIIIIIIII        | 0       |
## |    | [character] | 2. control            | 55 (50.0%)          | IIIIIIIIII        | (0.0%)  |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 3  | case        | 1. 3w                 | 55 (50.0%)          | IIIIIIIIII        | 0       |
## |    | [character] | 2. 5d                 | 55 (50.0%)          | IIIIIIIIII        | (0.0%)  |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 4  | total       | Mean (sd) : 58.5 (24) | 0.00 :  1 ( 0.9%)   |                   | 0       |
## |    | [numeric]   | min < med < max:      | 16.67!: 10 ( 9.1%)  | I                 | (0.0%)  |
## |    |             | 0 < 66.7 < 100        | 33.33!: 17 (15.5%)  | III               |         |
## |    |             | IQR (CV) : 29.2 (0.4) | 50.00 : 21 (19.1%)  | III               |         |
## |    |             |                       | 66.67!: 37 (33.6%)  | IIIIII            |         |
## |    |             |                       | 83.33!: 13 (11.8%)  | II                |         |
## |    |             |                       | 100.00 : 11 (10.0%) | II                |         |
## |    |             |                       | ! rounded           |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 5  | deficit     | Min  : 0              | 0 : 12 (10.9%)      | II                | 0       |
## |    | [numeric]   | Mean : 0.9            | 1 : 98 (89.1%)      | IIIIIIIIIIIIIIIII | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 6  | volume      | Min  : 0              | 0 : 74 (67.3%)      | IIIIIIIIIIIII     | 0       |
## |    | [numeric]   | Mean : 0.3            | 1 : 36 (32.7%)      | IIIIII            | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 7  | alk         | Min  : 0              | 0 : 12 (10.9%)      | II                | 0       |
## |    | [numeric]   | Mean : 0.9            | 1 : 98 (89.1%)      | IIIIIIIIIIIIIIIII | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 8  | bicarb      | Min  : 0              | 0 : 55 (50.0%)      | IIIIIIIIII        | 0       |
## |    | [numeric]   | Mean : 0.5            | 1 : 55 (50.0%)      | IIIIIIIIII        | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 9  | rate.bicarb | Min  : 0              | 0 : 40 (36.4%)      | IIIIIII           | 0       |
## |    | [numeric]   | Mean : 0.6            | 1 : 70 (63.6%)      | IIIIIIIIIIII      | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+
## | 10 | oral        | Min  : 0              | 0 : 81 (73.6%)      | IIIIIIIIIIIIII    | 0       |
## |    | [numeric]   | Mean : 0.3            | 1 : 29 (26.4%)      | IIIII             | (0.0%)  |
## |    |             | Max  : 1              |                     |                   |         |
## +----+-------------+-----------------------+---------------------+-------------------+---------+

data2$group <- fct_relevel(data2$group,"control")


#data2 %>% glimpse
tab2 <- data2 %>% group_by(group) %>%
  summarise(
    total.score.mean = (mean(total)),
    deficit.score = mean(deficit)*100,
    volume.score = mean(volume)*100,
    alk.score = mean(alk)*100,
    bicarb.score = mean(bicarb)*100,
    rate.bicarb.score = mean(rate.bicarb)*100,
    oral.score = mean(oral)*100
  )

tab2 <- as_tibble(t(tab2), rownames = "row_names")
tab2 <- tab2 %>%
  row_to_names(row_number = 1)
tab2$difference <- as.numeric(tab2$app) - as.numeric(tab2$control)

tab2 <- tab2 %>% mutate(across(2:4,as.numeric))
tab2 <- tab2 %>% mutate(across(2:4,round,1))
tab2

## # A tibble: 7 × 4
##   group             control   app difference
##   <chr>               <dbl> <dbl>      <dbl>
## 1 total.score.mean     51.2  65.8       14.5
## 2 deficit.score        90.9  87.3       -3.6
## 3 volume.score         34.5  30.9       -3.6
## 4 alk.score            87.3  90.9        3.6
## 5 bicarb.score         36.4  63.6       27.3
## 6 rate.bicarb.score    50.9  76.4       25.5
## 7 oral.score            7.3  45.5       38.2

data2 <- merge(data2,quant %>% select(id,manual_confidence),by="id")
table1::table1(~ total | manual_confidence*group, data=data2)

	High		Low		Moderate		Very low		Overall
	control (N=2)	app (N=2)	control (N=21)	app (N=21)	control (N=24)	app (N=24)	control (N=6)	app (N=6)	control (N=55)	app (N=55)
total
Mean (SD)	58.3 (11.8)	66.7 (0)	50.0 (23.6)	66.7 (23.0)	52.1 (20.4)	64.6 (25.7)	50.0 (21.1)	55.6 (29.2)	51.2 (21.0)	65.8 (24.7)
Median [Min, Max]	58.3 [50.0, 66.7]	66.7 [66.7, 66.7]	50.0 [16.7, 100]	66.7 [16.7, 100]	58.3 [16.7, 83.3]	66.7 [0, 100]	58.3 [16.7, 66.7]	58.3 [16.7, 100]	50.0 [16.7, 100]	66.7 [0, 100]

data.long <- data2 %>%
  pivot_longer(cols = total:oral, names_to = "type", values_to = "score")

data.long$type[data.long$type == "total"] <- "Total"
data.long$type[data.long$type == "isotonic"] <- "Appropriate isotonic fluid"
data.long$type[data.long$type == "deficit"] <- "Volume sufficient to correct deficit"
data.long$type[data.long$type == "volume"] <- "Volume sufficient overall"
data.long$type[data.long$type == "alk"] <- "Use of alkalinising solution"
data.long$type[data.long$type == "bicarb"] <- "Appropriate volume of alkalinising solution"
data.long$type[data.long$type == "rate.bicarb"] <- "Appropriate rate for alkalinising solution"
data.long$type[data.long$type == "rate.other"] <- "Appropriate rate for other fluids"
data.long$type[data.long$type == "oral"] <- "Fluid plan incorperates oral fluids"


plot1 <- ggplot(data = data.long %>% filter(type != "Total"), aes(x = type, y = score)) +
  geom_boxplot(aes(fill=group)) +
  labs(y = "Score", x="Calculation") +
  theme(axis.text.x = element_text(size=12, family="Times"),axis.title.x = element_text(size=15, family="Times",face="bold")) +
  theme(axis.text.y = element_text(size=15, family="Times"),axis.title.y = element_text(size=15, family="Times",face="bold")) +
  theme(plot.title = element_text(size=15, family="Times",hjust = 0.5)) + 
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1),
        panel.background = element_rect(fill = "white", colour = "white",size = 0.5, linetype = "solid"),
        panel.grid.major = element_line(size =0, linetype = 'solid', colour = "grey"),
        panel.grid.minor = element_line(size = 0, linetype = 'solid',colour = "grey")) # +scale_fill_brewer(palette="Set1")

#plot1

Boxplot comparing total score (out of 100) for App and Control scenarios

data.long <- data.long %>% filter(type == "Total")

plot2 <- ggplot(data = data.long, aes(x = type, y = score, fill=group)) +
  geom_boxplot() +
  labs(y = "Score out of 100", x="Group") +
  theme(axis.text.x = element_text(size=12, family="Times"),axis.title.x = element_text(size=15, family="Times",face="bold")) +
  theme(axis.text.y = element_text(size=15, family="Times"),axis.title.y = element_text(size=15, family="Times",face="bold")) +
  theme(legend.position = "bottom",
        plot.title = element_text(size=15, family="Times",hjust = 0.5),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.x=element_blank(),
        panel.border = element_rect(colour = "black", fill=NA, size=1),
        panel.background = element_rect(fill = "white", colour = "white",size = 0.5, linetype = "solid"),
        panel.grid.major = element_line(size =0, linetype = 'solid', colour = "grey"),
        panel.grid.minor = element_line(size = 0, linetype = 'solid',colour = "grey")) + 
  scale_fill_discrete(name = "Method", labels = c("Conventional", "App-based"))

tiff("plot2.tiff", units="in", width=6, height=6, res=300)
plot2
dev.off()

## quartz_off_screen 
##                 2

Modelling

Will use a linear mixed model that accounts for the clustering of answers within students

lmm.total <- lmer(total ~ group + case + (1|id), data = data2)
summary(lmm.total)

## Linear mixed model fit by REML ['lmerMod']
## Formula: total ~ group + case + (1 | id)
##    Data: data2
## 
## REML criterion at convergence: 985.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.86037 -0.70381  0.02638  0.62353  1.92919 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  id       (Intercept)  58.59    7.654  
##  Residual             471.77   21.720  
## Number of obs: 110, groups:  id, 55
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept)  51.4956     3.6004  14.303
## groupapp     14.6281     4.1758   3.503
## case5d       -0.6496     4.1758  -0.156
## 
## Correlation of Fixed Effects:
##          (Intr) groppp
## groupapp -0.506       
## case5d   -0.506 -0.127

output <- tidy(lmm.total,conf.int = T) %>% select(term,estimate,conf.low,conf.high) %>% filter(term == "groupapp")

Outcome from linear mixed model

diff2 <- paste0(round(output$estimate,2),
       " (",
       round(output$conf.low,2),
       ", ",
       round(output$conf.high,2),
       ")")

tab2$difference[tab2$group == "total.score.mean"] <- diff2

write.csv(tab2,"plan.csv")

Model diagnostics

#check_model(lmm.total)
icc(lmm.total, by_group = FALSE, tolerance = 1e-05)

## # Intraclass Correlation Coefficient
## 
##      Adjusted ICC: 0.110
##   Conditional ICC: 0.100

ggplot(data.frame(eta=predict(lmm.total,type="link"),pearson=residuals(lmm.total,type="pearson")),
       aes(x=eta,y=pearson)) + geom_point() + theme_bw()

qqnorm(residuals(lmm.total))

library(stats)
wilcox.test(data2$total ~ data2$group)

con <- data2 %>% filter(group == "control")
app <- data2 %>% filter(group == "app")

data2.wide <- merge(con,app, by="id")
data2.wide$total <- data2.wide$total.y - data2.wide$total.x
hist(data2.wide$total)

Analysis log for: Can Apps be Used to Formulate Fluid Therapy Plans in Veterinary Medicine?

Simona Aukse Zduoba, John House, Sam Rowe

11/04/2022

Descriptive statistics

Analysis 1: Evaluating basic fluid therapy calculations

Show dataset 1

Comparison of scores

Modelling

Effect estimate

Analysis 2: Evaluating final fluid plans

Modelling

Outcome from linear mixed model