Supplementary File 2
R code of Sports Medicine Open study: Temporal Robustness of Subjective and Behavioural Responses to Mental Fatigue: a Follow-Up Study
Jelle Habay
2025-09-29
- Set CRAN mirror
- Installing necessary packages
- Loading necessary packages
- Importing data sets
- Preparing complete data file
- Separation into specific files and imputation of missing values
- Preliminary data check
- Analyses
#Introduction
This is an R Markdown document containing the code used during analysis of the following paper:
Habay et al. (2025): Temporal Robustness of Subjective and Behavioural Responses to Mental Fatigue: a Follow-Up Study
This study details a follow up investigation of a previously published randomized crossover trial (https://journals.lww.com/acsm-msse/abstract/9900/mental_fatigue_negatively_impacts_cognitive_and.902.aspx). This trial examins the effects of mental fatigue on cognitive (represented by a GoNoGo task) and physical (represented by a cycling time trial) performance. Mental fatigue was induced by a 45 min individualized Stroop task, while the control condition featured a documentary of the same duration. Within this follow-up investigation, we invited 25 participants to come back after one year to perform the entire trial again. The aim of this follow-up was to examine how the subjective and behavioural effects of mental fatigue vary over time. The figure below details the overall design of the study:
Overview of the design of the study
Questions on the content of the present document are to be send to the corresponding author: bart.roelands@vub.be
#Data preparation
Set CRAN mirror
options(repos = c(CRAN = "https://cloud.r-project.org"))Installing necessary packages
install.packages("readxl")
install.packages("tidyverse")
install.packages("skimr")
install.packages("zoo")
install.packages("stats")
install.packages("MASS")
install.packages("rcompanion")
install.packages("effectsize")
install.packages("lsr")
install.packages("lme4")
install.packages("emmeans")
install.packages("pbkrtest")
install.packages("lmerTest")
install.packages("ggforce")
install.packages("irr")
install.packages("ggpmisc")
install.packages("ggpubr")
install.packages("gghalves")
install.packages("clubSandwich")Loading necessary packages
library(readxl)
library(tidyverse)
library(skimr)
library(zoo)
library(stats)
library(MASS)
library(rcompanion)
library(effectsize)
library(lsr)
library(lme4)
library(emmeans)
library(pbkrtest)
library(lmerTest)
library(ggforce)
library(irr)
library(emmeans)
library(ggpmisc)
library(ggpubr)
library(ordinal)
library(gghalves)
library(clubSandwich)Importing data sets
Excel files were imported using readxl. The first sheet of the excel file (“Original”) details all data related to the baseline measurement. The second sheet (“Follow Up”) details all measurements related to the follow up measurement. The last sheet (“Date”) the dates of the last measurement of the baseline experiment, and the first measurement of the follow up experiment, specifically to calculate the mean time between sessions.
MFINDDIFF <- read_excel("Results_MFROB.xlsx", sheet = "Original", na = "9999")
MFROB <- read_excel("Results_MFROB.xlsx", sheet = "Follow up", na = "9999")
Dates <- read_excel("Results_MFROB.xlsx", sheet = "Date", col_types = c("text", "date", "date"))Preparing complete data file
One data file (i.e., MFIDRB) was made by combining the baseline and follow up data files.
MFROB <- MFROB %>%
rename_with(~ paste0(.x, "_ROB"), .cols = -c(PP_number_ROB, PP_number_MFINDDIFF))
MFIDRB <- left_join(MFINDDIFF, MFROB, by = c("PP_number_ROB", "PP_number_MFINDDIFF"))Separation into specific files and imputation of missing values
The GoNoGo and time trial difference values were already calculated here. For the RPE and MVAS differences, a separate file was constructed later.
MFIDRB_IF <- MFIDRB[c(1:45, 248:287)] %>%
mutate(BSS_ROB = na.approx(BSS_ROB, na.rm = FALSE))
MFIDRB_GoNoGoRT <- MFIDRB[c(1, 90, 93, 96, 99, 332, 335, 338, 341)] %>%
mutate(Diff_GoRT_Pre_OM = Go_RT_MF_Pre - Go_RT_CON_Pre,
Diff_GoRT_Pre_FU = Go_RT_MF_Pre_ROB - Go_RT_CON_Pre_ROB,
Diff_GoRT_Post_OM = Go_RT_MF_Post - Go_RT_CON_Post,
Diff_GoRT_Post_FU = Go_RT_MF_Post_ROB - Go_RT_CON_Post_ROB)
MFIDRB_TTDist <- MFIDRB[c(1, 102, 108, 344, 350)] %>%
mutate(Diff_TTOM = TT_MF_Distance - TT_CON_Distance,
Diff_TTFU = TT_MF_Distance_ROB - TT_CON_Distance_ROB)
MFIDRB_RPE <- MFIDRB[c(1, 200:213, 442:455)]
MFIDRB_MVAS <- MFIDRB[c(1, 46:71, 288:313)] %>%
mutate(MVAS_CON_1_ROB = na.approx(MVAS_CON_1_ROB, na.rm = FALSE)) Preliminary data check
Outliers
Outlier function based on the z method
detect_outliers_z <- function(column, proefpersonen) {
if(!is.numeric(column)) {
return(NULL)
}
valid_indices <- !is.na(column) & !is.na(proefpersonen)
column_clean <- column[valid_indices]
proefpersonen_clean <- proefpersonen[valid_indices]
if (length(unique(column_clean)) == 1) {
return(NULL)
}
z_scores <- scale(column_clean)
outlier_indices <- which(abs(z_scores) >3)
if (length(outlier_indices) > 0) {
outlier_proefpersonen <- proefpersonen_clean[outlier_indices]
return(outlier_proefpersonen)
} else {
return(NULL)
}
}Outlier detection in all data files
OutliersIF <- lapply(MFIDRB_IF[, -1], function(col) {
detect_outliers_z(col, MFIDRB_IF[, 1])
})
OutliersIF
OutliersGoNoGoRT <- lapply(MFIDRB_GoNoGoRT[, -1], function(col) {
detect_outliers_z(col, MFIDRB_GoNoGoRT[, 1])
})
OutliersGoNoGoRT
MFIDRB_GoNoGoRT_R010 <- MFIDRB_GoNoGoRT %>%
dplyr::slice(-c(7))
OutliersGoNoGoRT010 <- lapply(MFIDRB_GoNoGoRT_R010[, -1], function(col) {
detect_outliers_z(col, MFIDRB_GoNoGoRT_R010[, 1])
})
OutliersGoNoGoRT010
OutliersTTDist <- lapply(MFIDRB_TTDist[, -1], function(col) {
detect_outliers_z(col, MFIDRB_TTDist[, 1])
})
OutliersTTDist
OutliersMVAS <- lapply(MFIDRB_MVAS[, -1], function(col) {
detect_outliers_z(col, MFIDRB_MVAS[, 1])
})
OutliersMVAS
OutliersRPE <- lapply(MFIDRB_RPE[, -1], function(col) {
detect_outliers_z(col, MFIDRB_RPE[, 1])
})
OutliersRPENormality
Function
shapiro_test <- function(column) {
if (is.numeric(column) && length(column) > 3 && length(unique(column)) > 1) {
test_result <- shapiro.test(column)
return(test_result$p.value)
} else {
return(NA)
}
}QQplots
qqnorm(MFIDRB_IF$Lengte) #individual features
qqline(MFIDRB_IF$Lengte, col = "red", lwd = 2)qqnorm(MFIDRB_IF$Lengte_ROB)
qqline(MFIDRB_IF$Lengte_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$Gewicht)
qqline(MFIDRB_IF$Gewicht, col = "red", lwd = 2)qqnorm(MFIDRB_IF$Gewicht_ROB)
qqline(MFIDRB_IF$Gewicht_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$BMI)
qqline(MFIDRB_IF$BMI, col = "red", lwd = 2)qqnorm(MFIDRB_IF$BMI_ROB)
qqline(MFIDRB_IF$BMI_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`Vet%(Dexa)`)
qqline(MFIDRB_IF$`Vet%(Dexa)`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`Vet%(Dexa)_ROB`)
qqline(MFIDRB_IF$`Vet%(Dexa)_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$VO2Max)
qqline(MFIDRB_IF$VO2Max, col = "red", lwd = 2)qqnorm(MFIDRB_IF$VO2Max_ROB)
qqline(MFIDRB_IF$VO2Max_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$rVO2Max)
qqline(MFIDRB_IF$rVO2Max, col = "red", lwd = 2)qqnorm(MFIDRB_IF$rVO2Max_ROB)
qqline(MFIDRB_IF$rVO2Max_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`IPAQ (MET-min/week)`)
qqline(MFIDRB_IF$`IPAQ (MET-min/week)`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`IPAQ (MET-min/week)_ROB`)
qqline(MFIDRB_IF$`IPAQ (MET-min/week)_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`SART(ACC)`)
qqline(MFIDRB_IF$`SART(ACC)`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$RI_SART_ACC_ROB)
qqline(MFIDRB_IF$RI_SART_ACC_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`NBACK(ACC)`)
qqline(MFIDRB_IF$`NBACK(ACC)`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$WM_NBACK_ACC_ROB)
qqline(MFIDRB_IF$WM_NBACK_ACC_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`NBACK(RT(ms))`)
qqline(MFIDRB_IF$`NBACK(RT(ms))`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`WM_NBACK_RT(ms)_ROB`)
qqline(MFIDRB_IF$`WM_NBACK_RT(ms)_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`PVT(RT(ms))`)
qqline(MFIDRB_IF$`PVT(RT(ms))`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$`ATT_PVT_RT(ms)_ROB`)
qqline(MFIDRB_IF$`ATT_PVT_RT(ms)_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_IF$SAI)
qqline(MFIDRB_IF$SAI, col = "red", lwd = 2)qqnorm(MFIDRB_IF$SAI_ROB)
qqline(MFIDRB_IF$SAI_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$BSS)
qqline(MFIDRB_IF$BSS, col = "red", lwd = 2)qqnorm(MFIDRB_IF$BSS_ROB)
qqline(MFIDRB_IF$BSS_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$MT)
qqline(MFIDRB_IF$MT, col = "red", lwd = 2)qqnorm(MFIDRB_IF$MT_ROB)
qqline(MFIDRB_IF$MT_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$PSQI)
qqline(MFIDRB_IF$PSQI, col = "red", lwd = 2)qqnorm(MFIDRB_IF$PSQI_ROB)
qqline(MFIDRB_IF$PSQI_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$CCQ)
qqline(MFIDRB_IF$CCQ, col = "red", lwd = 2)qqnorm(MFIDRB_IF$CCQ_ROB)
qqline(MFIDRB_IF$CCQ_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_IF$Stroop_MAX_level)
qqline(MFIDRB_IF$Stroop_MAX_level, col = "red", lwd = 2)qqnorm(MFIDRB_IF$Stroop_MAX_level_ROB)
qqline(MFIDRB_IF$Stroop_MAX_level_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_MF_Pre) #GoNoGo
qqline(MFIDRB_GoNoGoRT$Go_RT_MF_Pre, col = "red", lwd = 2) qqnorm(MFIDRB_GoNoGoRT$Go_RT_MF_Pre_ROB)
qqline(MFIDRB_GoNoGoRT$Go_RT_MF_Pre_ROB, col = "red", lwd = 2) qqnorm(MFIDRB_GoNoGoRT$Go_RT_MF_Post)
qqline(MFIDRB_GoNoGoRT$Go_RT_MF_Post, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_MF_Post_ROB)
qqline(MFIDRB_GoNoGoRT$Go_RT_MF_Post_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_CON_Pre)
qqline(MFIDRB_GoNoGoRT$Go_RT_CON_Pre, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_CON_Pre_ROB)
qqline(MFIDRB_GoNoGoRT$Go_RT_CON_Pre_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_CON_Post)
qqline(MFIDRB_GoNoGoRT$Go_RT_CON_Post, col = "red", lwd = 2)qqnorm(MFIDRB_GoNoGoRT$Go_RT_CON_Post_ROB)
qqline(MFIDRB_GoNoGoRT$Go_RT_CON_Post_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_TTDist$TT_MF_Distance) #time trial
qqline(MFIDRB_TTDist$TT_MF_Distance, col = "red", lwd = 2) qqnorm(MFIDRB_TTDist$TT_MF_Distance_ROB)
qqline(MFIDRB_TTDist$TT_MF_Distance_ROB, col = "red", lwd = 2) qqnorm(MFIDRB_TTDist$TT_CON_Distance)
qqline(MFIDRB_TTDist$TT_CON_Distance, col = "red", lwd = 2) qqnorm(MFIDRB_TTDist$TT_CON_Distance_ROB)
qqline(MFIDRB_TTDist$TT_CON_Distance_ROB, col = "red", lwd = 2) qqnorm(MFIDRB_RPE$`RPE_1_MF`) #rate of perceived exertion
qqline(MFIDRB_RPE$`RPE_1_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_1_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_1_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_2_MF`)
qqline(MFIDRB_RPE$`RPE_2_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_2_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_2_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_3_MF`)
qqline(MFIDRB_RPE$`RPE_3_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_3_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_3_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_4_MF`)
qqline(MFIDRB_RPE$`RPE_4_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_4_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_4_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_5_MF`)
qqline(MFIDRB_RPE$`RPE_5_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_5_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_5_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_6_MF`)
qqline(MFIDRB_RPE$`RPE_6_MF`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_6_MF_ROB`)
qqline(MFIDRB_RPE$`RPE_6_MF_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_1_CON`)
qqline(MFIDRB_RPE$`RPE_1_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_1_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_1_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_2_CON`)
qqline(MFIDRB_RPE$`RPE_2_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_2_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_2_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_3_CON`)
qqline(MFIDRB_RPE$`RPE_3_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_3_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_3_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_4_CON`)
qqline(MFIDRB_RPE$`RPE_4_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_4_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_4_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_5_CON`)
qqline(MFIDRB_RPE$`RPE_5_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_5_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_5_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_6_CON`)
qqline(MFIDRB_RPE$`RPE_6_CON`, col = "red", lwd = 2)qqnorm(MFIDRB_RPE$`RPE_6_CON_ROB`)
qqline(MFIDRB_RPE$`RPE_6_CON_ROB`, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_1) #subjective feeling of mental fatigue
qqline(MFIDRB_MVAS$MVAS_MF_1, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_1_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_1_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_2)
qqline(MFIDRB_MVAS$MVAS_MF_2, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_2_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_2_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_3)
qqline(MFIDRB_MVAS$MVAS_MF_3, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_3_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_3_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_4)
qqline(MFIDRB_MVAS$MVAS_MF_4, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_4_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_4_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_5)
qqline(MFIDRB_MVAS$MVAS_MF_5, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_5_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_5_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_6)
qqline(MFIDRB_MVAS$MVAS_MF_6, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_6_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_6_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_7)
qqline(MFIDRB_MVAS$MVAS_MF_7, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_7_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_7_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_8)
qqline(MFIDRB_MVAS$MVAS_MF_8, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_8_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_8_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_9)
qqline(MFIDRB_MVAS$MVAS_MF_9, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_9_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_9_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_10)
qqline(MFIDRB_MVAS$MVAS_MF_10, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_10_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_10_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_11)
qqline(MFIDRB_MVAS$MVAS_MF_11, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_11_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_11_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_12)
qqline(MFIDRB_MVAS$MVAS_MF_12, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_12_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_12_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_13)
qqline(MFIDRB_MVAS$MVAS_MF_13, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_MF_13_ROB)
qqline(MFIDRB_MVAS$MVAS_MF_13_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_1)
qqline(MFIDRB_MVAS$MVAS_CON_1, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_1_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_1_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_2)
qqline(MFIDRB_MVAS$MVAS_CON_2, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_2_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_2_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_3)
qqline(MFIDRB_MVAS$MVAS_CON_3, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_3_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_3_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_4)
qqline(MFIDRB_MVAS$MVAS_CON_4, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_4_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_4_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_5)
qqline(MFIDRB_MVAS$MVAS_CON_5, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_5_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_5_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_6)
qqline(MFIDRB_MVAS$MVAS_CON_6, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_6_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_6_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_7)
qqline(MFIDRB_MVAS$MVAS_CON_7, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_7_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_7_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_8)
qqline(MFIDRB_MVAS$MVAS_CON_8, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_8_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_8_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_9)
qqline(MFIDRB_MVAS$MVAS_CON_9, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_9_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_9_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_10)
qqline(MFIDRB_MVAS$MVAS_CON_10, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_10_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_10_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_11)
qqline(MFIDRB_MVAS$MVAS_CON_11, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_11_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_11_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_12)
qqline(MFIDRB_MVAS$MVAS_CON_12, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_12_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_12_ROB, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_13)
qqline(MFIDRB_MVAS$MVAS_CON_13, col = "red", lwd = 2)qqnorm(MFIDRB_MVAS$MVAS_CON_13_ROB)
qqline(MFIDRB_MVAS$MVAS_CON_13_ROB, col = "red", lwd = 2)Shapiro Wilk
shapiro_results_IFROB<- lapply(MFIDRB_IF, shapiro_test)
shapiro_results_IFROB
shapiro_results_GoNoGo <- lapply(MFIDRB_GoNoGoRT, shapiro_test)
shapiro_results_GoNoGo
shapiro_results_GoNoGoR010 <- lapply(MFIDRB_GoNoGoRT_R010, shapiro_test)
shapiro_results_GoNoGoR010
shapiro_results_TTDist <- lapply(MFIDRB_TTDist, shapiro_test)
shapiro_results_TTDist
shapiro_results_MVAS <- lapply(MFIDRB_MVAS, shapiro_test)
shapiro_results_MVAS
shapiro_results_RPE <- lapply(MFIDRB_RPE, shapiro_test)
shapiro_results_RPEAlmost all outcomes featured no significant outliers, or issues with normality. In the GoNoGo task, participants R010 showed very high reaction time scores in the follow up measurement, as opposed to the baseline measurement. These values were also so high that the overall GoNoGo task failed normality checks. In the end, it was decided to exclude participant R010 from the GoNoGo task analyses.
Descriptives and data wrangling
###Individual features
skim(MFIDRB_IF) #overview of all individual features collected during the familiarization trial| Name | MFIDRB_IF |
| Number of rows | 25 |
| Number of columns | 85 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| numeric | 69 |
| POSIXct | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PP_number_ROB | 0 | 1.00 | 10 | 10 | 0 | 25 | 0 |
| PP_number_MFINDDIFF | 0 | 1.00 | 16 | 16 | 0 | 25 | 0 |
| Chosen_Docu | 0 | 1.00 | 7 | 25 | 0 | 9 | 0 |
| Geslacht | 0 | 1.00 | 1 | 1 | 0 | 2 | 0 |
| Practised Sport | 0 | 1.00 | 4 | 16 | 0 | 15 | 0 |
| Open_Closed | 1 | 0.96 | 4 | 6 | 0 | 2 | 0 |
| Chosen_Docu_ROB | 0 | 1.00 | 7 | 23 | 0 | 6 | 0 |
| Geslacht_ROB | 0 | 1.00 | 1 | 1 | 0 | 2 | 0 |
| Practised Sport_ROB | 0 | 1.00 | 4 | 21 | 0 | 15 | 0 |
| Open_Closed_ROB | 0 | 1.00 | 4 | 6 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| SessionnumberINT | 0 | 1.00 | 1.44 | 0.51 | 1.00 | 1.00 | 1.00 | 2.00 | 2.00 | ▇▁▁▁▆ |
| SessionnumberCON | 0 | 1.00 | 1.56 | 0.51 | 1.00 | 1.00 | 2.00 | 2.00 | 2.00 | ▆▁▁▁▇ |
| Headsize | 0 | 1.00 | 56.08 | 1.68 | 54.00 | 54.00 | 56.00 | 58.00 | 58.00 | ▇▁▇▁▇ |
| Stroop_MAX_level | 0 | 1.00 | 972.00 | 162.07 | 700.00 | 900.00 | 1000.00 | 1100.00 | 1200.00 | ▇▇▇▇▅ |
| Seat_Height | 0 | 1.00 | 14.04 | 5.64 | 4.00 | 10.00 | 15.00 | 16.00 | 27.50 | ▃▅▇▁▂ |
| File_number | 0 | 1.00 | 9464.52 | 1680.14 | 2674.00 | 9858.00 | 9923.00 | 9973.00 | 10135.00 | ▁▁▁▁▇ |
| Max_HR | 0 | 1.00 | 185.76 | 8.82 | 170.00 | 181.00 | 187.00 | 194.00 | 197.00 | ▃▂▆▅▇ |
| Max_Lac | 0 | 1.00 | 10.32 | 1.60 | 7.68 | 9.32 | 10.26 | 11.39 | 13.30 | ▃▅▇▂▃ |
| Leeftijd | 0 | 1.00 | 30.88 | 8.03 | 21.00 | 25.00 | 27.00 | 38.00 | 47.00 | ▇▆▁▅▂ |
| Lengte | 0 | 1.00 | 177.00 | 9.52 | 158.00 | 169.00 | 178.00 | 180.00 | 200.00 | ▂▅▇▂▂ |
| Gewicht | 0 | 1.00 | 72.28 | 11.26 | 53.10 | 63.70 | 71.10 | 83.10 | 91.70 | ▇▇▇▅▇ |
| BMI | 0 | 1.00 | 22.97 | 2.25 | 19.49 | 21.27 | 22.80 | 24.16 | 28.30 | ▇▇▇▅▁ |
| Vet%(Calc) | 0 | 1.00 | 18.08 | 5.23 | 10.09 | 13.86 | 17.40 | 21.31 | 29.04 | ▇▇▇▅▂ |
| Vet%(Dexa) | 0 | 1.00 | 19.98 | 5.87 | 7.90 | 15.10 | 19.20 | 24.10 | 30.90 | ▁▇▆▅▅ |
| VO2Max | 0 | 1.00 | 3.54 | 0.93 | 2.30 | 2.64 | 3.49 | 4.33 | 5.40 | ▇▅▃▆▂ |
| rVO2Max | 0 | 1.00 | 48.52 | 8.69 | 37.00 | 41.00 | 49.00 | 54.00 | 66.00 | ▇▂▆▂▂ |
| PPO | 0 | 1.00 | 274.40 | 61.31 | 200.00 | 200.00 | 290.00 | 320.00 | 380.00 | ▇▂▂▃▃ |
| rPPO | 0 | 1.00 | 3.79 | 0.55 | 2.89 | 3.39 | 3.84 | 4.23 | 4.92 | ▇▇▇▇▂ |
| PerformanceLevel | 0 | 1.00 | 2.28 | 0.84 | 1.00 | 2.00 | 2.00 | 3.00 | 4.00 | ▂▇▁▂▂ |
| IPAQ (MET-min/week) | 0 | 1.00 | 4559.88 | 2950.73 | 1596.00 | 2490.00 | 3742.00 | 5652.00 | 14022.00 | ▇▅▁▁▁ |
| IPAQ (kcal/week) | 0 | 1.00 | 5597.45 | 3781.16 | 1777.60 | 2612.25 | 5188.91 | 7253.40 | 16365.84 | ▇▅▂▁▁ |
| YearsOfExperience | 0 | 1.00 | 6.68 | 6.43 | 0.00 | 3.00 | 4.00 | 10.00 | 24.00 | ▇▂▁▂▁ |
| PSF | 1 | 0.96 | 2.08 | 0.83 | 1.00 | 1.00 | 2.00 | 3.00 | 3.00 | ▆▁▇▁▇ |
| SPQ | 0 | 1.00 | 1.60 | 0.91 | 0.00 | 1.00 | 2.00 | 2.00 | 3.00 | ▂▆▁▇▃ |
| SART(ACC) | 0 | 1.00 | 0.54 | 0.20 | 0.04 | 0.42 | 0.58 | 0.71 | 0.83 | ▁▃▇▇▇ |
| NBACK(ACC) | 0 | 1.00 | 0.85 | 0.07 | 0.74 | 0.80 | 0.84 | 0.90 | 0.95 | ▂▇▂▅▅ |
| NBACK(RT(ms)) | 0 | 1.00 | 855.56 | 265.11 | 462.04 | 630.11 | 859.64 | 1088.34 | 1351.54 | ▇▆▇▅▃ |
| PVT(FA) | 0 | 1.00 | 0.56 | 1.39 | 0.00 | 0.00 | 0.00 | 0.00 | 6.00 | ▇▁▁▁▁ |
| PVT(Lap) | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| PVT(RT(ms)) | 0 | 1.00 | 280.83 | 24.94 | 227.29 | 263.86 | 281.38 | 289.39 | 339.79 | ▁▇▇▃▂ |
| SAI | 0 | 1.00 | 30.24 | 5.82 | 22.00 | 26.00 | 29.00 | 33.00 | 46.00 | ▆▇▃▂▁ |
| BSS | 0 | 1.00 | 3.60 | 0.44 | 2.77 | 3.31 | 3.62 | 3.92 | 4.62 | ▃▆▇▆▁ |
| MT | 0 | 1.00 | 5.20 | 0.58 | 3.88 | 4.88 | 5.12 | 5.62 | 6.50 | ▂▆▇▅▂ |
| PSQI | 0 | 1.00 | 3.68 | 2.01 | 1.00 | 2.00 | 4.00 | 4.00 | 8.00 | ▆▅▇▁▂ |
| Chronotype | 1 | 0.96 | 3.29 | 1.43 | 1.00 | 2.75 | 3.00 | 5.00 | 5.00 | ▃▂▇▃▇ |
| CCQ | 0 | 1.00 | 1461.03 | 1360.65 | 46.36 | 411.84 | 1322.22 | 2144.11 | 6309.93 | ▇▇▁▁▁ |
| SessionnumberINT_ROB | 0 | 1.00 | 1.44 | 0.51 | 1.00 | 1.00 | 1.00 | 2.00 | 2.00 | ▇▁▁▁▆ |
| SessionnumberCON_ROB | 0 | 1.00 | 1.56 | 0.51 | 1.00 | 1.00 | 2.00 | 2.00 | 2.00 | ▆▁▁▁▇ |
| Stroop_MAX_level_ROB | 0 | 1.00 | 848.00 | 122.88 | 600.00 | 800.00 | 800.00 | 900.00 | 1100.00 | ▃▇▆▃▁ |
| Max_HR_ROB | 0 | 1.00 | 184.56 | 10.59 | 165.00 | 175.00 | 187.00 | 191.00 | 202.00 | ▅▇▅▇▇ |
| Max_Lac_ROB | 0 | 1.00 | 10.40 | 2.66 | 5.67 | 8.64 | 10.00 | 12.27 | 17.26 | ▂▇▅▂▁ |
| Leeftijd_ROB | 0 | 1.00 | 31.96 | 7.98 | 22.00 | 26.00 | 29.00 | 39.00 | 49.00 | ▇▆▂▃▂ |
| Lengte_ROB | 0 | 1.00 | 177.28 | 9.57 | 158.00 | 170.00 | 178.00 | 180.00 | 200.00 | ▂▅▇▃▂ |
| Gewicht_ROB | 0 | 1.00 | 72.78 | 11.37 | 55.80 | 64.10 | 70.10 | 82.00 | 95.90 | ▆▇▃▅▂ |
| BMI_ROB | 0 | 1.00 | 23.06 | 2.26 | 18.19 | 21.84 | 22.89 | 24.13 | 29.60 | ▂▇▇▂▁ |
| Vet%(Calc)_ROB | 1 | 0.96 | 20.72 | 6.53 | 11.48 | 14.66 | 20.74 | 26.06 | 31.31 | ▇▅▅▃▇ |
| Vet%(Dexa)_ROB | 0 | 1.00 | 20.55 | 6.43 | 8.40 | 15.70 | 19.20 | 25.80 | 32.20 | ▂▇▃▂▃ |
| VO2Max_ROB | 0 | 1.00 | 3.65 | 0.85 | 2.36 | 2.87 | 3.75 | 4.43 | 5.06 | ▇▃▂▇▃ |
| rVO2Max_ROB | 0 | 1.00 | 49.92 | 7.31 | 39.00 | 44.00 | 48.00 | 57.00 | 65.00 | ▇▆▃▆▂ |
| PPO_ROB | 0 | 1.00 | 273.20 | 60.05 | 200.00 | 230.00 | 260.00 | 320.00 | 380.00 | ▇▂▁▅▃ |
| rPPO_ROB | 0 | 1.00 | 3.75 | 0.59 | 2.66 | 3.34 | 3.67 | 4.15 | 4.99 | ▂▇▅▃▃ |
| PerformanceLevel_ROB | 0 | 1.00 | 2.36 | 0.64 | 1.00 | 2.00 | 2.00 | 3.00 | 4.00 | ▁▇▁▅▁ |
| IPAQ (MET-min/week)_ROB | 0 | 1.00 | 3929.12 | 2174.50 | 1356.00 | 2518.00 | 2933.00 | 4902.00 | 10053.00 | ▇▃▂▂▁ |
| IPAQ (kcal/week)_ROB | 0 | 1.00 | 4881.42 | 3091.47 | 1744.72 | 2608.65 | 3726.72 | 6191.94 | 13889.90 | ▇▃▂▁▁ |
| YearsOfExperience_ROB | 0 | 1.00 | 8.28 | 8.08 | 0.50 | 3.00 | 5.00 | 12.00 | 30.00 | ▇▂▂▁▁ |
| P_S_F_ROB | 0 | 1.00 | 2.00 | 0.71 | 1.00 | 2.00 | 2.00 | 2.00 | 3.00 | ▃▁▇▁▃ |
| SPQ_ROB | 0 | 1.00 | 1.44 | 0.82 | 0.00 | 1.00 | 1.00 | 2.00 | 3.00 | ▁▇▁▅▂ |
| RI_SART_ACC_ROB | 0 | 1.00 | 0.54 | 0.23 | 0.00 | 0.38 | 0.58 | 0.71 | 0.88 | ▁▅▇▇▇ |
| WM_NBACK_ACC_ROB | 0 | 1.00 | 0.87 | 0.07 | 0.70 | 0.84 | 0.89 | 0.91 | 0.96 | ▂▂▅▇▆ |
| WM_NBACK_RT(ms)_ROB | 0 | 1.00 | 743.88 | 234.05 | 395.07 | 586.20 | 728.46 | 893.75 | 1249.46 | ▇▇▇▃▃ |
| ATT_PVT_FA_ROB | 0 | 1.00 | 0.04 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| ATT_PVT_Lap_ROB | 0 | 1.00 | 0.24 | 0.52 | 0.00 | 0.00 | 0.00 | 0.00 | 2.00 | ▇▁▂▁▁ |
| ATT_PVT_RT(ms)_ROB | 0 | 1.00 | 303.04 | 41.99 | 246.93 | 272.56 | 288.05 | 330.78 | 386.65 | ▇▆▃▃▃ |
| SAI_ROB | 0 | 1.00 | 29.16 | 6.99 | 21.00 | 24.00 | 27.00 | 31.00 | 50.00 | ▇▆▃▁▁ |
| BSS_ROB | 0 | 1.00 | 3.69 | 0.53 | 2.69 | 3.31 | 3.77 | 4.00 | 4.54 | ▃▃▅▇▃ |
| MT_ROB | 0 | 1.00 | 5.42 | 0.72 | 4.00 | 5.00 | 5.38 | 6.12 | 6.75 | ▃▆▇▃▅ |
| PSQI_ROB | 0 | 1.00 | 4.04 | 2.11 | 0.00 | 2.00 | 4.00 | 6.00 | 7.00 | ▃▅▇▆▇ |
| Chronotype_ROB | 0 | 1.00 | 2.92 | 1.35 | 1.00 | 2.00 | 3.00 | 4.00 | 5.00 | ▅▃▇▃▃ |
| CCQ_ROB | 0 | 1.00 | 1669.06 | 1348.32 | 44.78 | 555.77 | 1281.08 | 2489.60 | 4507.82 | ▇▃▃▂▂ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| DateFAM | 0 | 1 | 2022-07-13 | 2023-06-28 | 2022-11-23 | 23 |
| DateINT | 0 | 1 | 2022-08-03 | 2023-09-04 | 2022-12-19 | 25 |
| DateCON | 0 | 1 | 2022-07-30 | 2023-08-08 | 2022-12-23 | 24 |
| DateFAM_ROB | 0 | 1 | 2023-08-31 | 2024-07-17 | 2024-01-17 | 22 |
| DateINT_ROB | 0 | 1 | 2023-09-15 | 2024-08-23 | 2024-02-14 | 24 |
| DateCON_ROB | 0 | 1 | 2023-09-11 | 2024-08-12 | 2024-02-21 | 24 |
table(MFIDRB_IF$Geslacht) #frequency overview of all categorical variables##
## M V
## 15 10table(MFIDRB_IF$PSF)##
## 1 2 3
## 7 8 9table(MFIDRB_IF$P_S_F_ROB)##
## 1 2 3
## 6 13 6table(MFIDRB_IF$SPQ)##
## 0 1 2 3
## 3 8 10 4table(MFIDRB_IF$SPQ_ROB)##
## 0 1 2 3
## 2 13 7 3table(MFIDRB_IF$PerformanceLevel)##
## 1 2 3 4
## 3 15 4 3table(MFIDRB_IF$PerformanceLevel_ROB)##
## 1 2 3 4
## 1 15 8 1table(MFIDRB_IF$Chronotype, useNA = "ifany")##
## 1 2 3 4 5 <NA>
## 4 2 8 3 7 1table(MFIDRB_IF$Chronotype_ROB)##
## 1 2 3 4 5
## 5 4 8 4 4Dates$Difference <- as.numeric(difftime(Dates$Date_first_visit, Dates$Date_last_visit, units = "days")) #calculating mean time between sessions
MeanDifferenceDate <- mean(Dates$Difference)
print(MeanDifferenceDate)## [1] 375.72SDDifferenceDate <- sd(Dates$Difference)
print(SDDifferenceDate)## [1] 65.7999Cognitive performance
skim(MFIDRB_GoNoGoRT) #overview of data related to the cognitive performance task| Name | MFIDRB_GoNoGoRT |
| Number of rows | 25 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PP_number_ROB | 0 | 1 | 10 | 10 | 0 | 25 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Go_RT_MF_Pre | 0 | 1 | 368.64 | 29.97 | 316.48 | 345.69 | 370.78 | 397.06 | 441.03 | ▆▇▇▇▁ |
| Go_RT_MF_Post | 0 | 1 | 375.02 | 32.14 | 313.66 | 348.53 | 376.06 | 400.44 | 432.51 | ▅▇▇▇▇ |
| Go_RT_CON_Pre | 0 | 1 | 364.69 | 34.50 | 298.03 | 346.22 | 363.75 | 378.95 | 455.47 | ▃▆▇▁▂ |
| Go_RT_CON_Post | 0 | 1 | 367.69 | 35.10 | 298.48 | 339.01 | 378.38 | 389.78 | 444.34 | ▅▂▇▅▂ |
| Go_RT_MF_Pre_ROB | 0 | 1 | 375.76 | 43.77 | 319.11 | 354.10 | 366.52 | 387.53 | 542.99 | ▇▇▁▁▁ |
| Go_RT_MF_Post_ROB | 0 | 1 | 387.32 | 46.26 | 328.99 | 355.02 | 376.51 | 398.85 | 512.74 | ▆▇▂▂▂ |
| Go_RT_CON_Pre_ROB | 0 | 1 | 365.80 | 33.11 | 309.26 | 347.58 | 359.05 | 375.03 | 479.87 | ▃▇▃▁▁ |
| Go_RT_CON_Post_ROB | 0 | 1 | 373.82 | 45.29 | 308.87 | 348.09 | 364.99 | 399.31 | 548.31 | ▇▇▁▁▁ |
| Diff_GoRT_Pre_OM | 0 | 1 | 3.95 | 17.16 | -26.69 | -7.85 | 4.91 | 18.45 | 33.22 | ▅▅▇▇▅ |
| Diff_GoRT_Pre_FU | 0 | 1 | 9.97 | 19.75 | -17.80 | -4.87 | 9.13 | 18.94 | 63.12 | ▇▅▅▂▁ |
| Diff_GoRT_Post_OM | 0 | 1 | 7.32 | 20.34 | -39.19 | -8.66 | 9.23 | 18.59 | 59.79 | ▁▃▇▂▁ |
| Diff_GoRT_Post_FU | 0 | 1 | 13.50 | 27.88 | -35.57 | -4.49 | 16.09 | 28.42 | 95.83 | ▃▇▇▂▁ |
summary(MFIDRB_GoNoGoRT)## PP_number_ROB Go_RT_MF_Pre Go_RT_MF_Post Go_RT_CON_Pre
## Length:25 Min. :316.5 Min. :313.7 Min. :298.0
## Class :character 1st Qu.:345.7 1st Qu.:348.5 1st Qu.:346.2
## Mode :character Median :370.8 Median :376.1 Median :363.8
## Mean :368.6 Mean :375.0 Mean :364.7
## 3rd Qu.:397.1 3rd Qu.:400.4 3rd Qu.:378.9
## Max. :441.0 Max. :432.5 Max. :455.5
## Go_RT_CON_Post Go_RT_MF_Pre_ROB Go_RT_MF_Post_ROB Go_RT_CON_Pre_ROB
## Min. :298.5 Min. :319.1 Min. :329.0 Min. :309.3
## 1st Qu.:339.0 1st Qu.:354.1 1st Qu.:355.0 1st Qu.:347.6
## Median :378.4 Median :366.5 Median :376.5 Median :359.1
## Mean :367.7 Mean :375.8 Mean :387.3 Mean :365.8
## 3rd Qu.:389.8 3rd Qu.:387.5 3rd Qu.:398.9 3rd Qu.:375.0
## Max. :444.3 Max. :543.0 Max. :512.7 Max. :479.9
## Go_RT_CON_Post_ROB Diff_GoRT_Pre_OM Diff_GoRT_Pre_FU Diff_GoRT_Post_OM
## Min. :308.9 Min. :-26.690 Min. :-17.800 Min. :-39.190
## 1st Qu.:348.1 1st Qu.: -7.850 1st Qu.: -4.870 1st Qu.: -8.660
## Median :365.0 Median : 4.910 Median : 9.130 Median : 9.230
## Mean :373.8 Mean : 3.946 Mean : 9.969 Mean : 7.324
## 3rd Qu.:399.3 3rd Qu.: 18.450 3rd Qu.: 18.940 3rd Qu.: 18.590
## Max. :548.3 Max. : 33.220 Max. : 63.120 Max. : 59.790
## Diff_GoRT_Post_FU
## Min. :-35.57
## 1st Qu.: -4.49
## Median : 16.09
## Mean : 13.50
## 3rd Qu.: 28.42
## Max. : 95.83MFIDRB_GoNoGoRT_OM <- MFIDRB_GoNoGoRT[c(1:5)] %>% #separate file on the baseline measurement
dplyr::slice(-c(7)) # outlier
MFIDRB_GoNoGoRT_FU <- MFIDRB_GoNoGoRT[c(1, 6:9)] %>% #separate file on the follow-up measurement
dplyr::slice(-c(7)) #outlier
Pivot_GoRT_OM <- MFIDRB_GoNoGoRT_OM %>% #pivot for linear model
pivot_longer(
cols = starts_with("Go_RT"),
names_to = c("condition", "time"),
names_pattern = "Go_RT_([^_]+)_([^_]+)",
values_to = "value"
) %>%
mutate(type = "original")
Pivot_GoRT_FU <- MFIDRB_GoNoGoRT_FU %>% #pivot for linear model
pivot_longer(
cols = starts_with("Go_RT"),
names_to = c("condition", "time"),
names_pattern = "Go_RT_([^_]+)_([^_]+)",
values_to = "value"
) %>%
mutate(type = "follow_up")Physical performance
skim(MFIDRB_TTDist) #overview of data related to the physical performance task| Name | MFIDRB_TTDist |
| Number of rows | 25 |
| Number of columns | 7 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PP_number_ROB | 0 | 1 | 10 | 10 | 0 | 25 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| TT_MF_Distance | 0 | 1 | 12.61 | 1.62 | 8.87 | 11.79 | 12.56 | 13.36 | 15.59 | ▂▃▇▃▃ |
| TT_CON_Distance | 0 | 1 | 12.82 | 1.54 | 9.81 | 11.79 | 12.68 | 13.74 | 15.97 | ▃▇▇▃▃ |
| TT_MF_Distance_ROB | 0 | 1 | 13.57 | 1.78 | 10.13 | 12.15 | 13.21 | 14.72 | 16.22 | ▁▇▅▅▆ |
| TT_CON_Distance_ROB | 0 | 1 | 13.62 | 1.77 | 10.92 | 12.07 | 13.25 | 14.58 | 16.72 | ▇▆▇▂▆ |
| Diff_TTOM | 0 | 1 | -0.20 | 0.92 | -3.06 | -0.61 | -0.28 | 0.30 | 1.51 | ▁▁▆▇▂ |
| Diff_TTFU | 0 | 1 | -0.05 | 0.89 | -1.77 | -0.68 | -0.06 | 0.40 | 1.61 | ▃▇▇▆▅ |
summary(MFIDRB_TTDist)## PP_number_ROB TT_MF_Distance TT_CON_Distance TT_MF_Distance_ROB
## Length:25 Min. : 8.87 Min. : 9.81 Min. :10.13
## Class :character 1st Qu.:11.79 1st Qu.:11.79 1st Qu.:12.15
## Mode :character Median :12.56 Median :12.68 Median :13.21
## Mean :12.61 Mean :12.82 Mean :13.57
## 3rd Qu.:13.36 3rd Qu.:13.74 3rd Qu.:14.72
## Max. :15.59 Max. :15.97 Max. :16.22
## TT_CON_Distance_ROB Diff_TTOM Diff_TTFU
## Min. :10.92 Min. :-3.0600 Min. :-1.7700
## 1st Qu.:12.07 1st Qu.:-0.6100 1st Qu.:-0.6800
## Median :13.25 Median :-0.2800 Median :-0.0600
## Mean :13.62 Mean :-0.2032 Mean :-0.0508
## 3rd Qu.:14.58 3rd Qu.: 0.3000 3rd Qu.: 0.4000
## Max. :16.72 Max. : 1.5100 Max. : 1.6100Rate of perceived exertion
skim(MFIDRB_RPE) #overview of data related to the rate of perceived exertion| Name | MFIDRB_RPE |
| Number of rows | 25 |
| Number of columns | 29 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 28 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PP_number_ROB | 0 | 1 | 10 | 10 | 0 | 25 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| RPE_1_MF | 0 | 1 | 13.12 | 7.21 | 0 | 10 | 13 | 15 | 25 | ▆▇▇▂▅ |
| RPE_2_MF | 0 | 1 | 45.00 | 16.71 | 10 | 35 | 50 | 55 | 85 | ▂▇▇▂▁ |
| RPE_3_MF | 0 | 1 | 54.20 | 14.27 | 30 | 45 | 50 | 60 | 85 | ▃▇▇▁▃ |
| RPE_4_MF | 0 | 1 | 63.40 | 14.20 | 40 | 50 | 60 | 75 | 90 | ▇▅▃▆▂ |
| RPE_5_MF | 0 | 1 | 69.88 | 16.32 | 40 | 65 | 70 | 80 | 100 | ▅▁▇▅▂ |
| RPE_6_MF | 0 | 1 | 83.40 | 16.82 | 45 | 75 | 90 | 95 | 120 | ▃▇▇▇▁ |
| RPE_7_MF | 0 | 1 | 25.32 | 17.55 | 0 | 13 | 25 | 35 | 80 | ▇▅▃▂▁ |
| RPE_1_CON | 0 | 1 | 10.52 | 8.44 | 0 | 5 | 10 | 15 | 25 | ▇▆▃▁▃ |
| RPE_2_CON | 0 | 1 | 38.60 | 17.53 | 10 | 30 | 35 | 50 | 80 | ▃▇▆▂▂ |
| RPE_3_CON | 0 | 1 | 51.40 | 17.47 | 20 | 40 | 50 | 65 | 85 | ▃▇▃▇▃ |
| RPE_4_CON | 0 | 1 | 59.52 | 16.44 | 25 | 50 | 60 | 70 | 90 | ▂▃▅▇▂ |
| RPE_5_CON | 0 | 1 | 71.20 | 16.54 | 30 | 60 | 75 | 85 | 95 | ▁▅▃▇▇ |
| RPE_6_CON | 0 | 1 | 80.68 | 17.90 | 35 | 70 | 87 | 95 | 100 | ▁▃▁▃▇ |
| RPE_7_CON | 0 | 1 | 23.88 | 13.98 | 0 | 20 | 25 | 30 | 60 | ▅▃▇▂▁ |
| RPE_1_MF_ROB | 0 | 1 | 13.64 | 7.00 | 2 | 10 | 10 | 15 | 30 | ▂▇▂▂▂ |
| RPE_2_MF_ROB | 0 | 1 | 41.60 | 16.12 | 10 | 30 | 40 | 50 | 75 | ▂▇▅▅▂ |
| RPE_3_MF_ROB | 0 | 1 | 52.20 | 15.42 | 30 | 40 | 50 | 60 | 85 | ▇▅▅▂▂ |
| RPE_4_MF_ROB | 0 | 1 | 63.80 | 16.47 | 35 | 55 | 60 | 75 | 90 | ▅▇▅▇▇ |
| RPE_5_MF_ROB | 0 | 1 | 72.68 | 15.85 | 35 | 65 | 75 | 80 | 100 | ▂▃▃▇▃ |
| RPE_6_MF_ROB | 0 | 1 | 81.00 | 17.44 | 40 | 75 | 85 | 90 | 110 | ▂▂▆▇▃ |
| RPE_7_MF_ROB | 0 | 1 | 19.20 | 12.88 | 5 | 10 | 15 | 25 | 70 | ▇▆▁▁▁ |
| RPE_1_CON_ROB | 0 | 1 | 9.12 | 6.04 | 0 | 7 | 10 | 10 | 25 | ▃▇▃▁▁ |
| RPE_2_CON_ROB | 0 | 1 | 39.00 | 15.28 | 10 | 25 | 40 | 50 | 70 | ▃▅▇▅▃ |
| RPE_3_CON_ROB | 0 | 1 | 52.28 | 18.76 | 15 | 40 | 50 | 60 | 100 | ▂▆▇▃▁ |
| RPE_4_CON_ROB | 0 | 1 | 59.80 | 18.62 | 20 | 45 | 55 | 75 | 100 | ▁▇▃▆▂ |
| RPE_5_CON_ROB | 0 | 1 | 70.40 | 16.83 | 30 | 60 | 75 | 80 | 100 | ▂▂▅▇▃ |
| RPE_6_CON_ROB | 0 | 1 | 81.00 | 13.92 | 50 | 75 | 85 | 90 | 100 | ▂▁▇▇▃ |
| RPE_7_CON_ROB | 0 | 1 | 19.28 | 10.23 | 2 | 10 | 20 | 25 | 40 | ▂▇▂▇▂ |
summary(MFIDRB_RPE)## PP_number_ROB RPE_1_MF RPE_2_MF RPE_3_MF RPE_4_MF
## Length:25 Min. : 0.00 Min. :10 Min. :30.0 Min. :40.0
## Class :character 1st Qu.:10.00 1st Qu.:35 1st Qu.:45.0 1st Qu.:50.0
## Mode :character Median :13.00 Median :50 Median :50.0 Median :60.0
## Mean :13.12 Mean :45 Mean :54.2 Mean :63.4
## 3rd Qu.:15.00 3rd Qu.:55 3rd Qu.:60.0 3rd Qu.:75.0
## Max. :25.00 Max. :85 Max. :85.0 Max. :90.0
## RPE_5_MF RPE_6_MF RPE_7_MF RPE_1_CON
## Min. : 40.00 Min. : 45.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 65.00 1st Qu.: 75.0 1st Qu.:13.00 1st Qu.: 5.00
## Median : 70.00 Median : 90.0 Median :25.00 Median :10.00
## Mean : 69.88 Mean : 83.4 Mean :25.32 Mean :10.52
## 3rd Qu.: 80.00 3rd Qu.: 95.0 3rd Qu.:35.00 3rd Qu.:15.00
## Max. :100.00 Max. :120.0 Max. :80.00 Max. :25.00
## RPE_2_CON RPE_3_CON RPE_4_CON RPE_5_CON RPE_6_CON
## Min. :10.0 Min. :20.0 Min. :25.00 Min. :30.0 Min. : 35.00
## 1st Qu.:30.0 1st Qu.:40.0 1st Qu.:50.00 1st Qu.:60.0 1st Qu.: 70.00
## Median :35.0 Median :50.0 Median :60.00 Median :75.0 Median : 87.00
## Mean :38.6 Mean :51.4 Mean :59.52 Mean :71.2 Mean : 80.68
## 3rd Qu.:50.0 3rd Qu.:65.0 3rd Qu.:70.00 3rd Qu.:85.0 3rd Qu.: 95.00
## Max. :80.0 Max. :85.0 Max. :90.00 Max. :95.0 Max. :100.00
## RPE_7_CON RPE_1_MF_ROB RPE_2_MF_ROB RPE_3_MF_ROB RPE_4_MF_ROB
## Min. : 0.00 Min. : 2.00 Min. :10.0 Min. :30.0 Min. :35.0
## 1st Qu.:20.00 1st Qu.:10.00 1st Qu.:30.0 1st Qu.:40.0 1st Qu.:55.0
## Median :25.00 Median :10.00 Median :40.0 Median :50.0 Median :60.0
## Mean :23.88 Mean :13.64 Mean :41.6 Mean :52.2 Mean :63.8
## 3rd Qu.:30.00 3rd Qu.:15.00 3rd Qu.:50.0 3rd Qu.:60.0 3rd Qu.:75.0
## Max. :60.00 Max. :30.00 Max. :75.0 Max. :85.0 Max. :90.0
## RPE_5_MF_ROB RPE_6_MF_ROB RPE_7_MF_ROB RPE_1_CON_ROB RPE_2_CON_ROB
## Min. : 35.00 Min. : 40 Min. : 5.0 Min. : 0.00 Min. :10
## 1st Qu.: 65.00 1st Qu.: 75 1st Qu.:10.0 1st Qu.: 7.00 1st Qu.:25
## Median : 75.00 Median : 85 Median :15.0 Median :10.00 Median :40
## Mean : 72.68 Mean : 81 Mean :19.2 Mean : 9.12 Mean :39
## 3rd Qu.: 80.00 3rd Qu.: 90 3rd Qu.:25.0 3rd Qu.:10.00 3rd Qu.:50
## Max. :100.00 Max. :110 Max. :70.0 Max. :25.00 Max. :70
## RPE_3_CON_ROB RPE_4_CON_ROB RPE_5_CON_ROB RPE_6_CON_ROB RPE_7_CON_ROB
## Min. : 15.00 Min. : 20.0 Min. : 30.0 Min. : 50 Min. : 2.00
## 1st Qu.: 40.00 1st Qu.: 45.0 1st Qu.: 60.0 1st Qu.: 75 1st Qu.:10.00
## Median : 50.00 Median : 55.0 Median : 75.0 Median : 85 Median :20.00
## Mean : 52.28 Mean : 59.8 Mean : 70.4 Mean : 81 Mean :19.28
## 3rd Qu.: 60.00 3rd Qu.: 75.0 3rd Qu.: 80.0 3rd Qu.: 90 3rd Qu.:25.00
## Max. :100.00 Max. :100.0 Max. :100.0 Max. :100 Max. :40.00MFIDRB_RPE_OM <- MFIDRB_RPE[c(1:15)] #separate file on the baseline measurement
MFIDRB_RPE_FU <- MFIDRB_RPE[c(1, 16:29)] #separate file on the follow-up measurement
Pivot_RPE_OM <- MFIDRB_RPE_OM[c(1:7, 9:14)] %>% #pivot for linear model
pivot_longer(
cols = starts_with("RPE"),
names_to = c("time", "condition"),
names_pattern = "RPE_([^_]+)_([^_]+)",
values_to = "value"
) %>%
mutate(type = "original")
Pivot_RPE_FU <- MFIDRB_RPE_FU[c(1:7, 9:14)] %>% #pivot for linear model
pivot_longer(
cols = starts_with("RPE"),
names_to = c("time", "condition"),
names_pattern = "RPE_([^_]+)_([^_]+)",
values_to = "value"
) %>%
mutate(type = "follow_up")
MFIDRB_RPE_Diff <- MFIDRB_RPE %>% #calculation of condition delta values
transmute(
PP_number_ROB,
!!!setNames(
lapply(1:7, function(i) {
mf_col <- paste0("RPE_", i, "_MF")
con_col <- paste0("RPE_", i, "_CON")
mf_rob_col <- paste0("RPE_", i, "_MF_ROB")
con_rob_col <- paste0("RPE_", i, "_CON_ROB")
diff_norm <- if (all(c(mf_col, con_col) %in% names(MFIDRB_RPE))) {
MFIDRB_RPE[[mf_col]] - MFIDRB_RPE[[con_col]]
} else {
rep(NA, nrow(MFIDRB_RPE))
}
diff_rob <- if (all(c(mf_rob_col, con_rob_col) %in% names(MFIDRB_RPE))) {
MFIDRB_RPE[[mf_rob_col]] - MFIDRB_RPE[[con_rob_col]]
} else {
rep(NA, nrow(MFIDRB_RPE))
}
list(diff_norm, diff_rob)
}) %>%
unlist(recursive = FALSE),
nm = unlist(lapply(1:7, function(i) {
c(paste0("RPE_", i, "_Diff"),
paste0("RPE_", i, "_Diff_ROB"))
}))
)
)Feeling of Mental fatigue
skim(MFIDRB_MVAS) #overview of data related to the subjective feeling of mental fatigue| Name | MFIDRB_MVAS |
| Number of rows | 25 |
| Number of columns | 53 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 52 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PP_number_ROB | 0 | 1 | 10 | 10 | 0 | 25 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MVAS_MF_1 | 0 | 1 | 14.20 | 14.60 | 0 | 6 | 10 | 18 | 64 | ▇▃▁▁▁ |
| MVAS_MF_2 | 0 | 1 | 19.40 | 17.83 | 0 | 6 | 15 | 28 | 77 | ▇▃▂▁▁ |
| MVAS_MF_3 | 0 | 1 | 35.60 | 16.73 | 5 | 20 | 30 | 50 | 70 | ▂▇▃▃▂ |
| MVAS_MF_4 | 0 | 1 | 54.48 | 17.50 | 20 | 45 | 50 | 70 | 85 | ▃▇▃▇▃ |
| MVAS_MF_5 | 0 | 1 | 66.64 | 17.69 | 30 | 55 | 70 | 80 | 100 | ▂▃▇▃▂ |
| MVAS_MF_6 | 0 | 1 | 57.20 | 15.97 | 19 | 51 | 62 | 68 | 81 | ▂▃▆▇▆ |
| MVAS_MF_7 | 0 | 1 | 32.00 | 17.14 | 0 | 20 | 30 | 40 | 65 | ▅▃▆▇▂ |
| MVAS_MF_8 | 0 | 1 | 30.60 | 16.91 | 0 | 20 | 30 | 45 | 60 | ▅▃▇▂▆ |
| MVAS_MF_9 | 0 | 1 | 29.80 | 16.61 | 0 | 20 | 25 | 40 | 60 | ▅▇▇▅▇ |
| MVAS_MF_10 | 0 | 1 | 29.96 | 18.86 | 0 | 15 | 30 | 45 | 65 | ▇▇▇▅▅ |
| MVAS_MF_11 | 0 | 1 | 30.60 | 22.19 | 0 | 15 | 30 | 40 | 80 | ▇▆▅▃▂ |
| MVAS_MF_12 | 0 | 1 | 31.68 | 21.75 | 0 | 15 | 35 | 45 | 80 | ▇▃▇▃▂ |
| MVAS_MF_13 | 0 | 1 | 25.00 | 17.80 | 0 | 10 | 20 | 35 | 60 | ▇▇▇▂▅ |
| MVAS_CON_1 | 0 | 1 | 17.00 | 17.64 | 0 | 7 | 11 | 18 | 71 | ▇▂▁▁▁ |
| MVAS_CON_2 | 0 | 1 | 19.60 | 17.83 | 0 | 10 | 15 | 18 | 67 | ▆▇▂▁▂ |
| MVAS_CON_3 | 0 | 1 | 17.28 | 15.22 | 0 | 10 | 10 | 20 | 60 | ▇▃▂▁▁ |
| MVAS_CON_4 | 0 | 1 | 20.72 | 16.93 | 0 | 8 | 20 | 30 | 80 | ▇▇▂▁▁ |
| MVAS_CON_5 | 0 | 1 | 24.20 | 19.15 | 0 | 7 | 20 | 35 | 80 | ▇▅▆▁▁ |
| MVAS_CON_6 | 0 | 1 | 33.08 | 20.75 | 0 | 20 | 29 | 50 | 71 | ▆▇▆▃▆ |
| MVAS_CON_7 | 0 | 1 | 16.48 | 10.71 | 0 | 10 | 20 | 20 | 40 | ▆▇▇▆▁ |
| MVAS_CON_8 | 0 | 1 | 17.32 | 10.23 | 0 | 10 | 20 | 25 | 30 | ▅▃▂▃▇ |
| MVAS_CON_9 | 0 | 1 | 21.16 | 12.82 | 0 | 15 | 20 | 30 | 40 | ▇▇▃▇▇ |
| MVAS_CON_10 | 0 | 1 | 22.32 | 14.54 | 0 | 10 | 20 | 30 | 50 | ▇▇▇▅▂ |
| MVAS_CON_11 | 0 | 1 | 25.32 | 15.53 | 0 | 15 | 20 | 35 | 50 | ▅▇▁▇▃ |
| MVAS_CON_12 | 0 | 1 | 27.52 | 19.31 | 0 | 15 | 25 | 40 | 70 | ▇▇▇▆▁ |
| MVAS_CON_13 | 0 | 1 | 22.44 | 14.71 | 0 | 15 | 20 | 35 | 50 | ▇▇▅▇▂ |
| MVAS_MF_1_ROB | 0 | 1 | 17.60 | 15.51 | 0 | 8 | 12 | 26 | 65 | ▇▃▂▁▁ |
| MVAS_MF_2_ROB | 0 | 1 | 22.12 | 13.66 | 0 | 14 | 20 | 26 | 54 | ▂▇▃▁▂ |
| MVAS_MF_3_ROB | 0 | 1 | 43.40 | 18.43 | 10 | 30 | 40 | 60 | 80 | ▃▇▇▃▃ |
| MVAS_MF_4_ROB | 0 | 1 | 59.36 | 19.87 | 20 | 40 | 65 | 75 | 90 | ▂▆▃▇▆ |
| MVAS_MF_5_ROB | 0 | 1 | 67.68 | 19.16 | 25 | 50 | 70 | 81 | 99 | ▁▇▆▇▇ |
| MVAS_MF_6_ROB | 0 | 1 | 59.52 | 15.94 | 15 | 51 | 62 | 71 | 80 | ▁▂▅▇▇ |
| MVAS_MF_7_ROB | 0 | 1 | 40.00 | 20.05 | 0 | 30 | 40 | 50 | 80 | ▃▇▆▇▃ |
| MVAS_MF_8_ROB | 0 | 1 | 36.40 | 16.49 | 0 | 25 | 35 | 50 | 65 | ▁▇▅▇▃ |
| MVAS_MF_9_ROB | 0 | 1 | 35.00 | 17.38 | 0 | 25 | 30 | 50 | 75 | ▂▇▃▅▁ |
| MVAS_MF_10_ROB | 0 | 1 | 35.40 | 16.39 | 0 | 30 | 40 | 40 | 75 | ▃▆▇▃▁ |
| MVAS_MF_11_ROB | 0 | 1 | 35.60 | 19.86 | 0 | 30 | 30 | 45 | 95 | ▃▇▅▂▁ |
| MVAS_MF_12_ROB | 0 | 1 | 39.40 | 22.65 | 0 | 30 | 40 | 50 | 100 | ▅▇▅▂▁ |
| MVAS_MF_13_ROB | 0 | 1 | 30.20 | 17.17 | 0 | 20 | 30 | 40 | 70 | ▃▅▇▂▂ |
| MVAS_CON_1_ROB | 0 | 1 | 15.00 | 12.37 | 0 | 7 | 11 | 20 | 51 | ▇▆▂▂▁ |
| MVAS_CON_2_ROB | 0 | 1 | 21.32 | 13.09 | 0 | 13 | 20 | 33 | 48 | ▅▇▇▆▂ |
| MVAS_CON_3_ROB | 0 | 1 | 16.80 | 9.12 | 0 | 10 | 20 | 20 | 40 | ▂▇▇▂▁ |
| MVAS_CON_4_ROB | 0 | 1 | 18.92 | 11.73 | 0 | 10 | 20 | 30 | 50 | ▆▇▆▁▁ |
| MVAS_CON_5_ROB | 0 | 1 | 22.92 | 14.09 | 0 | 15 | 20 | 35 | 50 | ▅▇▅▅▂ |
| MVAS_CON_6_ROB | 0 | 1 | 34.48 | 17.20 | 8 | 24 | 33 | 40 | 82 | ▅▇▅▁▂ |
| MVAS_CON_7_ROB | 0 | 1 | 18.12 | 12.14 | 0 | 10 | 20 | 25 | 45 | ▅▅▇▃▂ |
| MVAS_CON_8_ROB | 0 | 1 | 21.80 | 12.74 | 0 | 10 | 20 | 30 | 45 | ▂▇▇▃▃ |
| MVAS_CON_9_ROB | 0 | 1 | 25.40 | 17.44 | 0 | 15 | 25 | 30 | 85 | ▆▇▃▁▁ |
| MVAS_CON_10_ROB | 0 | 1 | 28.00 | 19.63 | 0 | 15 | 25 | 35 | 100 | ▅▇▁▁▁ |
| MVAS_CON_11_ROB | 0 | 1 | 30.60 | 20.83 | 0 | 20 | 30 | 40 | 100 | ▅▇▂▁▁ |
| MVAS_CON_12_ROB | 0 | 1 | 33.40 | 23.44 | 0 | 20 | 30 | 40 | 100 | ▇▇▂▂▁ |
| MVAS_CON_13_ROB | 0 | 1 | 25.20 | 13.58 | 0 | 20 | 25 | 35 | 50 | ▆▇▇▆▂ |
summary(MFIDRB_MVAS)## PP_number_ROB MVAS_MF_1 MVAS_MF_2 MVAS_MF_3
## Length:25 Min. : 0.0 Min. : 0.0 Min. : 5.0
## Class :character 1st Qu.: 6.0 1st Qu.: 6.0 1st Qu.:20.0
## Mode :character Median :10.0 Median :15.0 Median :30.0
## Mean :14.2 Mean :19.4 Mean :35.6
## 3rd Qu.:18.0 3rd Qu.:28.0 3rd Qu.:50.0
## Max. :64.0 Max. :77.0 Max. :70.0
## MVAS_MF_4 MVAS_MF_5 MVAS_MF_6 MVAS_MF_7 MVAS_MF_8
## Min. :20.00 Min. : 30.00 Min. :19.0 Min. : 0 Min. : 0.0
## 1st Qu.:45.00 1st Qu.: 55.00 1st Qu.:51.0 1st Qu.:20 1st Qu.:20.0
## Median :50.00 Median : 70.00 Median :62.0 Median :30 Median :30.0
## Mean :54.48 Mean : 66.64 Mean :57.2 Mean :32 Mean :30.6
## 3rd Qu.:70.00 3rd Qu.: 80.00 3rd Qu.:68.0 3rd Qu.:40 3rd Qu.:45.0
## Max. :85.00 Max. :100.00 Max. :81.0 Max. :65 Max. :60.0
## MVAS_MF_9 MVAS_MF_10 MVAS_MF_11 MVAS_MF_12 MVAS_MF_13
## Min. : 0.0 Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0
## 1st Qu.:20.0 1st Qu.:15.00 1st Qu.:15.0 1st Qu.:15.00 1st Qu.:10
## Median :25.0 Median :30.00 Median :30.0 Median :35.00 Median :20
## Mean :29.8 Mean :29.96 Mean :30.6 Mean :31.68 Mean :25
## 3rd Qu.:40.0 3rd Qu.:45.00 3rd Qu.:40.0 3rd Qu.:45.00 3rd Qu.:35
## Max. :60.0 Max. :65.00 Max. :80.0 Max. :80.00 Max. :60
## MVAS_CON_1 MVAS_CON_2 MVAS_CON_3 MVAS_CON_4 MVAS_CON_5
## Min. : 0 Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.: 7 1st Qu.:10.0 1st Qu.:10.00 1st Qu.: 8.00 1st Qu.: 7.0
## Median :11 Median :15.0 Median :10.00 Median :20.00 Median :20.0
## Mean :17 Mean :19.6 Mean :17.28 Mean :20.72 Mean :24.2
## 3rd Qu.:18 3rd Qu.:18.0 3rd Qu.:20.00 3rd Qu.:30.00 3rd Qu.:35.0
## Max. :71 Max. :67.0 Max. :60.00 Max. :80.00 Max. :80.0
## MVAS_CON_6 MVAS_CON_7 MVAS_CON_8 MVAS_CON_9
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:20.00 1st Qu.:10.00 1st Qu.:10.00 1st Qu.:15.00
## Median :29.00 Median :20.00 Median :20.00 Median :20.00
## Mean :33.08 Mean :16.48 Mean :17.32 Mean :21.16
## 3rd Qu.:50.00 3rd Qu.:20.00 3rd Qu.:25.00 3rd Qu.:30.00
## Max. :71.00 Max. :40.00 Max. :30.00 Max. :40.00
## MVAS_CON_10 MVAS_CON_11 MVAS_CON_12 MVAS_CON_13 MVAS_MF_1_ROB
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.:10.00 1st Qu.:15.00 1st Qu.:15.00 1st Qu.:15.00 1st Qu.: 8.0
## Median :20.00 Median :20.00 Median :25.00 Median :20.00 Median :12.0
## Mean :22.32 Mean :25.32 Mean :27.52 Mean :22.44 Mean :17.6
## 3rd Qu.:30.00 3rd Qu.:35.00 3rd Qu.:40.00 3rd Qu.:35.00 3rd Qu.:26.0
## Max. :50.00 Max. :50.00 Max. :70.00 Max. :50.00 Max. :65.0
## MVAS_MF_2_ROB MVAS_MF_3_ROB MVAS_MF_4_ROB MVAS_MF_5_ROB MVAS_MF_6_ROB
## Min. : 0.00 Min. :10.0 Min. :20.00 Min. :25.00 Min. :15.00
## 1st Qu.:14.00 1st Qu.:30.0 1st Qu.:40.00 1st Qu.:50.00 1st Qu.:51.00
## Median :20.00 Median :40.0 Median :65.00 Median :70.00 Median :62.00
## Mean :22.12 Mean :43.4 Mean :59.36 Mean :67.68 Mean :59.52
## 3rd Qu.:26.00 3rd Qu.:60.0 3rd Qu.:75.00 3rd Qu.:81.00 3rd Qu.:71.00
## Max. :54.00 Max. :80.0 Max. :90.00 Max. :99.00 Max. :80.00
## MVAS_MF_7_ROB MVAS_MF_8_ROB MVAS_MF_9_ROB MVAS_MF_10_ROB MVAS_MF_11_ROB
## Min. : 0 Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0
## 1st Qu.:30 1st Qu.:25.0 1st Qu.:25 1st Qu.:30.0 1st Qu.:30.0
## Median :40 Median :35.0 Median :30 Median :40.0 Median :30.0
## Mean :40 Mean :36.4 Mean :35 Mean :35.4 Mean :35.6
## 3rd Qu.:50 3rd Qu.:50.0 3rd Qu.:50 3rd Qu.:40.0 3rd Qu.:45.0
## Max. :80 Max. :65.0 Max. :75 Max. :75.0 Max. :95.0
## MVAS_MF_12_ROB MVAS_MF_13_ROB MVAS_CON_1_ROB MVAS_CON_2_ROB MVAS_CON_3_ROB
## Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0.00 Min. : 0.0
## 1st Qu.: 30.0 1st Qu.:20.0 1st Qu.: 7 1st Qu.:13.00 1st Qu.:10.0
## Median : 40.0 Median :30.0 Median :11 Median :20.00 Median :20.0
## Mean : 39.4 Mean :30.2 Mean :15 Mean :21.32 Mean :16.8
## 3rd Qu.: 50.0 3rd Qu.:40.0 3rd Qu.:20 3rd Qu.:33.00 3rd Qu.:20.0
## Max. :100.0 Max. :70.0 Max. :51 Max. :48.00 Max. :40.0
## MVAS_CON_4_ROB MVAS_CON_5_ROB MVAS_CON_6_ROB MVAS_CON_7_ROB MVAS_CON_8_ROB
## Min. : 0.00 Min. : 0.00 Min. : 8.00 Min. : 0.00 Min. : 0.0
## 1st Qu.:10.00 1st Qu.:15.00 1st Qu.:24.00 1st Qu.:10.00 1st Qu.:10.0
## Median :20.00 Median :20.00 Median :33.00 Median :20.00 Median :20.0
## Mean :18.92 Mean :22.92 Mean :34.48 Mean :18.12 Mean :21.8
## 3rd Qu.:30.00 3rd Qu.:35.00 3rd Qu.:40.00 3rd Qu.:25.00 3rd Qu.:30.0
## Max. :50.00 Max. :50.00 Max. :82.00 Max. :45.00 Max. :45.0
## MVAS_CON_9_ROB MVAS_CON_10_ROB MVAS_CON_11_ROB MVAS_CON_12_ROB MVAS_CON_13_ROB
## Min. : 0.0 Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.0
## 1st Qu.:15.0 1st Qu.: 15 1st Qu.: 20.0 1st Qu.: 20.0 1st Qu.:20.0
## Median :25.0 Median : 25 Median : 30.0 Median : 30.0 Median :25.0
## Mean :25.4 Mean : 28 Mean : 30.6 Mean : 33.4 Mean :25.2
## 3rd Qu.:30.0 3rd Qu.: 35 3rd Qu.: 40.0 3rd Qu.: 40.0 3rd Qu.:35.0
## Max. :85.0 Max. :100 Max. :100.0 Max. :100.0 Max. :50.0MFIDRB_MVAS_OM <- MFIDRB_MVAS[c(1:27)] #separate file on the baseline measurement
MFIDRB_MVAS_FU <- MFIDRB_MVAS[c(1, 28:53)] #separate file on the follow-up measurement
Pivot_MVAS_OM <- MFIDRB_MVAS_OM %>% #pivot for linear model
pivot_longer(
cols = starts_with("MVAS"),
names_to = c("condition", "time"),
names_pattern = "MVAS_([^_]+)_(\\d+)",
values_to = "value"
) %>%
mutate(type = "original")
Pivot_MVAS_FU <- MFIDRB_MVAS_FU %>% #pivot for linear model
pivot_longer(
cols = starts_with("MVAS"),
names_to = c("condition", "time"),
names_pattern = "MVAS_([^_]+)_(\\d+)",
values_to = "value"
) %>%
mutate(type = "follow_up")
MFIDRB_MVAS_Diff <- MFIDRB_MVAS %>% #calculation of condition delta values
transmute(
PP_number_ROB,
!!!setNames(
lapply(1:13, function(i) {
mf_col <- paste0("MVAS_MF_", i)
con_col <- paste0("MVAS_CON_", i)
mf_rob_col <- paste0("MVAS_MF_", i, "_ROB")
con_rob_col <- paste0("MVAS_CON_", i, "_ROB")
diff_norm <- if (all(c(mf_col, con_col) %in% names(MFIDRB_MVAS))) {
MFIDRB_MVAS[[mf_col]] - MFIDRB_MVAS[[con_col]]
} else {
rep(NA, nrow(MFIDRB_MVAS))
}
diff_rob <- if (all(c(mf_rob_col, con_rob_col) %in% names(MFIDRB_MVAS))) {
MFIDRB_MVAS[[mf_rob_col]] - MFIDRB_MVAS[[con_rob_col]]
} else {
rep(NA, nrow(MFIDRB_MVAS))
}
list(diff_norm, diff_rob)
}) %>%
unlist(recursive = FALSE),
nm = unlist(lapply(1:13, function(i) {
c(paste0("MVAS_", i, "_Diff"),
paste0("MVAS_", i, "_Diff_ROB"))
}))
)
)Analyses
Individual feature comparison between baseline and follow up
Numerical variables
Differences in numerical individual feature variables were analyzed using either paired sample t tests or Wilcoxon signed rank tests. Comparision were analysed using interclass correlation coefficients.
Differences
t_test_height <- t.test(MFIDRB_IF$Lengte, MFIDRB_IF$Lengte_ROB, method = "paired") #height
print(t_test_height)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$Lengte and MFIDRB_IF$Lengte_ROB
## t = -0.10374, df = 47.999, p-value = 0.9178
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.706881 5.146881
## sample estimates:
## mean of x mean of y
## 177.00 177.28t_test_weight <- t.test(MFIDRB_IF$Gewicht, MFIDRB_IF$Gewicht_ROB, method = "paired") #weight
t_test_weight##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$Gewicht and MFIDRB_IF$Gewicht_ROB
## t = -0.15747, df = 47.996, p-value = 0.8755
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.93942 5.93142
## sample estimates:
## mean of x mean of y
## 72.276 72.780t_test_BMI <- t.test(MFIDRB_IF$BMI, MFIDRB_IF$BMI_ROB, method = "paired") #BMI
print(t_test_BMI)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$BMI and MFIDRB_IF$BMI_ROB
## t = -0.14421, df = 47.999, p-value = 0.8859
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.374724 1.190724
## sample estimates:
## mean of x mean of y
## 22.9696 23.0616t_test_fatperc <- t.test(MFIDRB_IF$`Vet%(Dexa)`, MFIDRB_IF$`Vet%(Dexa)_ROB`, method = "paired") #fat percentage based on dexa measurement
t_test_fatperc##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$`Vet%(Dexa)` and MFIDRB_IF$`Vet%(Dexa)_ROB`
## t = -0.32615, df = 47.599, p-value = 0.7457
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.070387 2.934387
## sample estimates:
## mean of x mean of y
## 19.980 20.548Wilcoxon_aVO2Max <- wilcox.test(MFIDRB_IF$VO2Max, MFIDRB_IF$VO2Max_ROB, paired = TRUE, conf.int = TRUE) #absolute VO2Max
print(Wilcoxon_aVO2Max)##
## Wilcoxon signed rank exact test
##
## data: MFIDRB_IF$VO2Max and MFIDRB_IF$VO2Max_ROB
## V = 115, p-value = 0.2099
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.266 0.040
## sample estimates:
## (pseudo)median
## -0.117t_test_rVO2 <- t.test(MFIDRB_IF$rVO2Max, MFIDRB_IF$rVO2Max_ROB, method = "paired") #relative VO2Max
print(t_test_rVO2)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$rVO2Max and MFIDRB_IF$rVO2Max_ROB
## t = -0.61651, df = 46.63, p-value = 0.5406
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.969337 3.169337
## sample estimates:
## mean of x mean of y
## 48.52 49.92Wilcoxon_IPAQ <- wilcox.test(MFIDRB_IF$`IPAQ (MET-min/week)`, MFIDRB_IF$`IPAQ (MET-min/week)_ROB`, paired = TRUE, conf.int = TRUE) #weekly amount of physical activity
print(Wilcoxon_IPAQ)##
## Wilcoxon signed rank exact test
##
## data: MFIDRB_IF$`IPAQ (MET-min/week)` and MFIDRB_IF$`IPAQ (MET-min/week)_ROB`
## V = 180, p-value = 0.6528
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -694.5 1272.5
## sample estimates:
## (pseudo)median
## 214t_test_SART <- t.test(MFIDRB_IF$`SART(ACC)`, MFIDRB_IF$RI_SART_ACC_ROB, method = "paired") #base level of response inhibition
print(t_test_SART)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$`SART(ACC)` and MFIDRB_IF$RI_SART_ACC_ROB
## t = -0.15297, df = 47.037, p-value = 0.8791
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1301877 0.1117877
## sample estimates:
## mean of x mean of y
## 0.5352 0.5444Wilcoxon_NBackacc <- wilcox.test(MFIDRB_IF$`NBACK(ACC)`, MFIDRB_IF$WM_NBACK_ACC_ROB, paired = TRUE, conf.int = TRUE) #base level of working memory (acc)## Warning in wilcox.test.default(MFIDRB_IF$`NBACK(ACC)`,
## MFIDRB_IF$WM_NBACK_ACC_ROB, : cannot compute exact p-value with ties## Warning in wilcox.test.default(MFIDRB_IF$`NBACK(ACC)`,
## MFIDRB_IF$WM_NBACK_ACC_ROB, : cannot compute exact confidence interval with
## ties## Warning in wilcox.test.default(MFIDRB_IF$`NBACK(ACC)`,
## MFIDRB_IF$WM_NBACK_ACC_ROB, : cannot compute exact p-value with zeroes## Warning in wilcox.test.default(MFIDRB_IF$`NBACK(ACC)`,
## MFIDRB_IF$WM_NBACK_ACC_ROB, : cannot compute exact confidence interval with
## zeroesprint(Wilcoxon_NBackacc)##
## Wilcoxon signed rank test with continuity correction
##
## data: MFIDRB_IF$`NBACK(ACC)` and MFIDRB_IF$WM_NBACK_ACC_ROB
## V = 96.5, p-value = 0.1294
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.049998765 0.009995132
## sample estimates:
## (pseudo)median
## -0.02495217t_test_NBackRT <- t.test(MFIDRB_IF$`NBACK(RT(ms))`, MFIDRB_IF$`WM_NBACK_RT(ms)_ROB`, method = "paired") #base level of working memory (reaction time)
print(t_test_NBackRT)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$`NBACK(RT(ms))` and MFIDRB_IF$`WM_NBACK_RT(ms)_ROB`
## t = 1.579, df = 47.273, p-value = 0.121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -30.58169 253.94489
## sample estimates:
## mean of x mean of y
## 855.5608 743.8792Wilcoxon_PVT <- wilcox.test(MFIDRB_IF$`PVT(RT(ms))`, MFIDRB_IF$`ATT_PVT_RT(ms)_ROB`, paired = TRUE, conf.int = TRUE) #base level of attention
print(Wilcoxon_PVT)##
## Wilcoxon signed rank exact test
##
## data: MFIDRB_IF$`PVT(RT(ms))` and MFIDRB_IF$`ATT_PVT_RT(ms)_ROB`
## V = 59, p-value = 0.004175
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -35.590 -5.215
## sample estimates:
## (pseudo)median
## -18.17rPVT <- abs((qnorm(Wilcoxon_PVT$p.value/2))/sqrt(length(MFIDRB_IF$`PVT(RT(ms))`) + length(MFIDRB_IF$`ATT_PVT_RT(ms)_ROB`)))
print(rPVT)## [1] 0.4051175Wilcoxon_TAI <- wilcox.test(MFIDRB_IF$SAI, MFIDRB_IF$SAI_ROB, paired = TRUE, conf.int = TRUE) #base level of trait anxiety ## Warning in wilcox.test.default(MFIDRB_IF$SAI, MFIDRB_IF$SAI_ROB, paired = TRUE,
## : cannot compute exact p-value with ties## Warning in wilcox.test.default(MFIDRB_IF$SAI, MFIDRB_IF$SAI_ROB, paired = TRUE,
## : cannot compute exact confidence interval with ties## Warning in wilcox.test.default(MFIDRB_IF$SAI, MFIDRB_IF$SAI_ROB, paired = TRUE,
## : cannot compute exact p-value with zeroes## Warning in wilcox.test.default(MFIDRB_IF$SAI, MFIDRB_IF$SAI_ROB, paired = TRUE,
## : cannot compute exact confidence interval with zeroesprint(Wilcoxon_TAI)##
## Wilcoxon signed rank test with continuity correction
##
## data: MFIDRB_IF$SAI and MFIDRB_IF$SAI_ROB
## V = 150, p-value = 0.2332
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.5000437 3.0000404
## sample estimates:
## (pseudo)median
## 1.00003t_test_BSS <- t.test(MFIDRB_IF$BSS, MFIDRB_IF$BSS_ROB, method = "paired") #base level of self control
print(t_test_BSS)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$BSS and MFIDRB_IF$BSS_ROB
## t = -0.66098, df = 46.493, p-value = 0.5119
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3671127 0.1855742
## sample estimates:
## mean of x mean of y
## 3.600000 3.690769t_test_MT <- t.test(MFIDRB_IF$MT, MFIDRB_IF$MT_ROB, method = "paired") #base level of mental toughness
print(t_test_MT)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$MT and MFIDRB_IF$MT_ROB
## t = -1.2405, df = 46.048, p-value = 0.2211
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6032131 0.1432131
## sample estimates:
## mean of x mean of y
## 5.195 5.425Wilcoxon_PSQI <- wilcox.test(MFIDRB_IF$PSQI, MFIDRB_IF$PSQI_ROB, paired = TRUE, conf.int = TRUE) #sleep quality ## Warning in wilcox.test.default(MFIDRB_IF$PSQI, MFIDRB_IF$PSQI_ROB, paired =
## TRUE, : cannot compute exact p-value with ties## Warning in wilcox.test.default(MFIDRB_IF$PSQI, MFIDRB_IF$PSQI_ROB, paired =
## TRUE, : cannot compute exact confidence interval with ties## Warning in wilcox.test.default(MFIDRB_IF$PSQI, MFIDRB_IF$PSQI_ROB, paired =
## TRUE, : cannot compute exact p-value with zeroes## Warning in wilcox.test.default(MFIDRB_IF$PSQI, MFIDRB_IF$PSQI_ROB, paired =
## TRUE, : cannot compute exact confidence interval with zeroesprint(Wilcoxon_PSQI)##
## Wilcoxon signed rank test with continuity correction
##
## data: MFIDRB_IF$PSQI and MFIDRB_IF$PSQI_ROB
## V = 81, p-value = 0.3726
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -1.5000176 0.9999616
## sample estimates:
## (pseudo)median
## -0.5Wilcoxon_CCQ <- wilcox.test(MFIDRB_IF$CCQ, MFIDRB_IF$CCQ_ROB, paired = TRUE, conf.int = TRUE) #weekly caffeine consumption
print(Wilcoxon_CCQ)##
## Wilcoxon signed rank exact test
##
## data: MFIDRB_IF$CCQ and MFIDRB_IF$CCQ_ROB
## V = 128, p-value = 0.3666
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -568.8274 115.3752
## sample estimates:
## (pseudo)median
## -80.88003t_test_StroopMax <- t.test(MFIDRB_IF$Stroop_MAX_level, MFIDRB_IF$Stroop_MAX_level_ROB, method = "paired") #Stroop max level
print(t_test_StroopMax)##
## Welch Two Sample t-test
##
## data: MFIDRB_IF$Stroop_MAX_level and MFIDRB_IF$Stroop_MAX_level_ROB
## t = 3.0484, df = 44.74, p-value = 0.003855
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 42.05797 205.94203
## sample estimates:
## mean of x mean of y
## 972 848cohensD(MFIDRB_IF$Stroop_MAX_level, MFIDRB_IF$Stroop_MAX_level_ROB, method = "paired")## [1] 1.033333ICC
ICC_Height <- icc(MFIDRB_IF %>% dplyr::select(Lengte, Lengte_ROB), model = "twoway", type = "consistency") #height
print(ICC_Height)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.995
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 414 , p = 4.98e-26
##
## 95%-Confidence Interval for ICC Population Values:
## 0.989 < ICC < 0.998ICC_Weight <- icc(MFIDRB_IF %>% dplyr::select(Gewicht, Gewicht_ROB), model = "twoway", type = "consistency") #weight
print(ICC_Weight)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.978
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 88.4 , p = 4.63e-18
##
## 95%-Confidence Interval for ICC Population Values:
## 0.95 < ICC < 0.99ICC_BMI <- icc(MFIDRB_IF %>% dplyr::select(BMI, BMI_ROB), model = "twoway", type = "consistency") #BMI
print(ICC_BMI)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.931
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 28.1 , p = 2.57e-12
##
## 95%-Confidence Interval for ICC Population Values:
## 0.851 < ICC < 0.969ICC_fatperc <- icc(MFIDRB_IF %>% dplyr::select(`Vet%(Dexa)`, `Vet%(Dexa)_ROB`), model = "twoway", type = "consistency") #fat percentage base on dexa measurement
print(ICC_fatperc)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.943
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 34.2 , p = 2.76e-13
##
## 95%-Confidence Interval for ICC Population Values:
## 0.876 < ICC < 0.975ICC_aVO2max <- icc(MFIDRB_IF %>% dplyr::select(VO2Max, VO2Max_ROB), model = "twoway", type = "consistency") #absolute VO2Max
print(ICC_aVO2max)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.926
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 26.1 , p = 6e-12
##
## 95%-Confidence Interval for ICC Population Values:
## 0.84 < ICC < 0.967ICC_rVO2Max <- icc(MFIDRB_IF %>% dplyr::select(rVO2Max, rVO2Max_ROB), model = "twoway", type = "consistency") #relative VO2Max
print(ICC_rVO2Max)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.852
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 12.5 , p = 1.68e-08
##
## 95%-Confidence Interval for ICC Population Values:
## 0.693 < ICC < 0.932ICC_IPAQ <- icc(MFIDRB_IF %>% dplyr::select(`IPAQ (MET-min/week)`, `IPAQ (MET-min/week)_ROB`), model = "twoway", type = "consistency") #self reported weekly amount of physical activity
print(ICC_IPAQ)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.0877
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.19 , p = 0.335
##
## 95%-Confidence Interval for ICC Population Values:
## -0.311 < ICC < 0.46ICC_SART <- icc(MFIDRB_IF %>% dplyr::select(`SART(ACC)`, RI_SART_ACC_ROB), model = "twoway", type = "consistency") #base level of response inhibition
print(ICC_SART)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.598
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 3.98 , p = 0.000621
##
## 95%-Confidence Interval for ICC Population Values:
## 0.274 < ICC < 0.801ICC_NbackACC <- icc(MFIDRB_IF %>% dplyr::select(`NBACK(ACC)`, WM_NBACK_ACC_ROB), model = "twoway", type = "consistency") #base leve lof working memory (acc)
print(ICC_NbackACC)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.331
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.99 , p = 0.0493
##
## 95%-Confidence Interval for ICC Population Values:
## -0.066 < ICC < 0.637ICC_NbackRT <- icc(MFIDRB_IF %>% dplyr::select(`NBACK(RT(ms))`, `WM_NBACK_RT(ms)_ROB`), model = "twoway", type = "consistency") #base level of working memory (rt)
print(ICC_NbackRT)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.856
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 12.9 , p = 1.21e-08
##
## 95%-Confidence Interval for ICC Population Values:
## 0.701 < ICC < 0.934ICC_PVT <- icc(MFIDRB_IF %>% dplyr::select(`PVT(RT(ms))`, `ATT_PVT_RT(ms)_ROB`), model = "twoway", type = "consistency") #base level of attention
print(ICC_PVT)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.507
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 3.06 , p = 0.00412
##
## 95%-Confidence Interval for ICC Population Values:
## 0.148 < ICC < 0.748ICC_TAI <- icc(MFIDRB_IF %>% dplyr::select(SAI, SAI_ROB), model = "twoway", type = "consistency") #base level of trait anxiety
print(ICC_TAI)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.835
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 11.1 , p = 5.67e-08
##
## 95%-Confidence Interval for ICC Population Values:
## 0.661 < ICC < 0.924ICC_BSS <- icc(MFIDRB_IF %>% dplyr::select(BSS, BSS_ROB), model = "twoway", type = "consistency") #base level of self control
print(ICC_BSS)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.604
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 4.06 , p = 0.000537
##
## 95%-Confidence Interval for ICC Population Values:
## 0.282 < ICC < 0.804ICC_MT <- icc(MFIDRB_IF %>% dplyr::select(MT, MT_ROB), model = "twoway", type = "consistency") #base level of mental toughness
print(ICC_MT)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.572
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 3.67 , p = 0.00114
##
## 95%-Confidence Interval for ICC Population Values:
## 0.236 < ICC < 0.786ICC_PSQI <- icc(MFIDRB_IF %>% dplyr::select(PSQI, PSQI_ROB), model = "twoway", type = "consistency") #sleep quality
print(ICC_PSQI)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.522
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 3.18 , p = 0.00313
##
## 95%-Confidence Interval for ICC Population Values:
## 0.167 < ICC < 0.757ICC_CCQ <- icc(MFIDRB_IF %>% dplyr::select(CCQ, CCQ_ROB), model = "twoway", type = "consistency") #weekly caffeine consumption
print(ICC_CCQ)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.738
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 6.64 , p = 8.35e-06
##
## 95%-Confidence Interval for ICC Population Values:
## 0.491 < ICC < 0.876ICC_StroopMax <- icc(MFIDRB_IF %>% dplyr::select(Stroop_MAX_level, Stroop_MAX_level_ROB), model = "twoway", type = "consistency") #Stroop max level
print(ICC_StroopMax)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.652
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 4.75 , p = 0.000154
##
## 95%-Confidence Interval for ICC Population Values:
## 0.353 < ICC < 0.83###Categorical variables
Differences in distribution between baseline and follow up in categorical individual features were analysed using McNemar and symmetry tests.
TabelPL <- table(MFIDRB_IF$PerformanceLevel, MFIDRB_IF$PerformanceLevel_ROB) #performance level based on VO2Max
dimnames(TabelPL) <- list(
T1 = c("1", "2", "3", "4"),
T2 = c("1", "2", "3", "4")
)
print(TabelPL)## T2
## T1 1 2 3 4
## 1 1 2 0 0
## 2 0 12 3 0
## 3 0 0 4 0
## 4 0 1 1 1class(TabelPL)## [1] "table"McNemarPL <- mcnemar.test(TabelPL)
print(McNemarPL)##
## McNemar's Chi-squared test
##
## data: TabelPL
## McNemar's chi-squared = NaN, df = 6, p-value = NAPostHocPL <- nominalSymmetryTest(TabelPL, exact = TRUE)print(PostHocPL)## $Global.test.for.symmetry
## Dimensions p.value
## 1 4 x 4 NA
##
## $Pairwise.symmetry.tests
## Comparison p.value p.adjust
## 1 1/1 : 2/2 0.5 1
## 2 1/1 : 3/3 <NA> NA
## 3 1/1 : 4/4 <NA> NA
## 4 2/2 : 3/3 0.25 1
## 5 2/2 : 4/4 1 1
## 6 3/3 : 4/4 1 1
##
## $p.adjustment
## Method
## 1 fdr
##
## $statistical.method
## Method
## 1 binomial testTabelSPQ <- table(MFIDRB_IF$SPQ, MFIDRB_IF$SPQ_ROB) #performance level based on classification of McKay
dimnames(TabelSPQ) <- list(
T1 = c("0", "1", "2", "3"),
T2 = c("0", "1", "2", "3")
)
print(TabelSPQ)## T2
## T1 0 1 2 3
## 0 0 3 0 0
## 1 1 6 1 0
## 2 1 3 5 1
## 3 0 1 1 2class(TabelSPQ)## [1] "table"McNemarSPQ <- mcnemar.test(TabelSPQ)
print(McNemarSPQ)##
## McNemar's Chi-squared test
##
## data: TabelSPQ
## McNemar's chi-squared = NaN, df = 6, p-value = NAPostHocSPQ <- nominalSymmetryTest(TabelSPQ, exact = TRUE)print(PostHocSPQ)## $Global.test.for.symmetry
## Dimensions p.value
## 1 4 x 4 NA
##
## $Pairwise.symmetry.tests
## Comparison p.value p.adjust
## 1 0/0 : 1/1 0.625 1
## 2 0/0 : 2/2 1 1
## 3 0/0 : 3/3 <NA> NA
## 4 1/1 : 2/2 0.625 1
## 5 1/1 : 3/3 1 1
## 6 2/2 : 3/3 1 1
##
## $p.adjustment
## Method
## 1 fdr
##
## $statistical.method
## Method
## 1 binomial testTabelChrono <- table(MFIDRB_IF$Chronotype, MFIDRB_IF$Chronotype_ROB, useNA = "ifany") #chronotype
TabelChrono <- cbind(TabelChrono, `NA` = rep(0, nrow(TabelChrono)))
dimnames(TabelChrono) <- list(
T1 = c("1", "2", "3", "4", "5", "NA"),
T2 = c("1", "2", "3", "4", "5", "NA")
)
print(TabelChrono)## T2
## T1 1 2 3 4 5 NA
## 1 2 0 2 0 0 0
## 2 0 1 0 1 0 0
## 3 0 1 5 1 1 0
## 4 1 0 0 2 0 0
## 5 2 1 1 0 3 0
## NA 0 1 0 0 0 0class(TabelChrono)## [1] "matrix" "array"McNemarChrono <- mcnemar.test(TabelChrono)
print(McNemarChrono)##
## McNemar's Chi-squared test
##
## data: TabelChrono
## McNemar's chi-squared = NaN, df = 15, p-value = NAPostHocChrono <- nominalSymmetryTest(TabelChrono, exact = TRUE)print(PostHocChrono)## $Global.test.for.symmetry
## Dimensions p.value
## 1 6 x 6 NA
##
## $Pairwise.symmetry.tests
## Comparison p.value p.adjust
## 1 1/1 : 2/2 <NA> NA
## 2 1/1 : 3/3 0.5 1
## 3 1/1 : 4/4 1 1
## 4 1/1 : 5/5 0.5 1
## 5 1/1 : NA/NA <NA> NA
## 6 2/2 : 3/3 1 1
## 7 2/2 : 4/4 1 1
## 8 2/2 : 5/5 1 1
## 9 2/2 : NA/NA 1 1
## 10 3/3 : 4/4 1 1
## 11 3/3 : 5/5 1 1
## 12 3/3 : NA/NA <NA> NA
## 13 4/4 : 5/5 <NA> NA
## 14 4/4 : NA/NA <NA> NA
## 15 5/5 : NA/NA <NA> NA
##
## $p.adjustment
## Method
## 1 fdr
##
## $statistical.method
## Method
## 1 binomial testInfluence of mental fatigue on separate linear models of selected subjective and behavioural variables
Physical performance
t_test_Distance <- t.test(MFIDRB_TTDist$TT_MF_Distance, MFIDRB_TTDist$TT_CON_Distance, method = "paired") #baseline
print(t_test_Distance)##
## Welch Two Sample t-test
##
## data: MFIDRB_TTDist$TT_MF_Distance and MFIDRB_TTDist$TT_CON_Distance
## t = -0.45388, df = 47.861, p-value = 0.652
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1034119 0.6970119
## sample estimates:
## mean of x mean of y
## 12.6140 12.8172cohensD(MFIDRB_TTDist$TT_MF_Distance, MFIDRB_TTDist$TT_CON_Distance, method = "paired")## [1] 0.2205942diff(t_test_Distance$estimate)## mean of y
## 0.2032t_test_DistanceROB <- t.test(MFIDRB_TTDist$TT_MF_Distance_ROB, MFIDRB_TTDist$TT_CON_Distance_ROB, method = "paired") #follow-up
print(t_test_DistanceROB)##
## Welch Two Sample t-test
##
## data: MFIDRB_TTDist$TT_MF_Distance_ROB and MFIDRB_TTDist$TT_CON_Distance_ROB
## t = -0.10142, df = 47.998, p-value = 0.9196
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0578668 0.9562668
## sample estimates:
## mean of x mean of y
## 13.5656 13.6164cohensD(MFIDRB_TTDist$TT_MF_Distance_ROB, MFIDRB_TTDist$TT_CON_Distance_ROB, method = "paired")## [1] 0.057134diff(t_test_DistanceROB$estimate)## mean of y
## 0.0508Cognitive performance
Baseline measurement
Pivot_GoRT_OM$condition <- factor(Pivot_GoRT_OM$condition, levels = c("MF", "CON"))
Pivot_GoRT_OM$time <- factor(Pivot_GoRT_OM$time, levels = c("Pre", "Post"))
model_GoRT_OM <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_GoRT_OM)
anova_GoRT_OM <- anova(model_GoRT_OM)
effectsize_GoRT_OM <- effectsize::eta_squared(model_GoRT_OM)
CI_GoRT_OM_Condition <- contrast(emmeans(model_GoRT_OM, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_GoRT_OM_Time <- contrast(emmeans(model_GoRT_OM, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_GoRT_OM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_GoRT_OM
##
## REML criterion at convergence: 799.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0590 -0.5656 -0.1453 0.6350 2.6296
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 740.3 27.21
## Residual 138.8 11.78
## Number of obs: 96, groups: PP_number_ROB, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 365.623 6.052 29.415 60.413 <2e-16 ***
## conditionCON -4.712 3.400 69.000 -1.386 0.1703
## timePost 6.999 3.400 69.000 2.058 0.0434 *
## conditionCON:timePost -3.409 4.809 69.000 -0.709 0.4808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndCON timPst
## conditinCON -0.281
## timePost -0.281 0.500
## cndtnCON:tP 0.199 -0.707 -0.707print(anova_GoRT_OM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 988.30 988.30 1 69 7.1224 0.009482 **
## time 672.68 672.68 1 69 4.8479 0.031027 *
## condition:time 69.73 69.73 1 69 0.5026 0.480765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_GoRT_OM)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.09 | [0.01, 1.00]
## time | 0.07 | [0.00, 1.00]
## condition:time | 7.23e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_GoRT_OM_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 6.42 2.4 69 1.62 11.2 2.669 0.0095
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_GoRT_OM_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Pre - Post -5.29 2.4 69 -10.1 -0.497 -2.202 0.0310
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95plot(fitted(model_GoRT_OM), resid(model_GoRT_OM), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
Follow up measurement
Pivot_GoRT_FU$condition <- factor(Pivot_GoRT_FU$condition, levels = c("MF", "CON"))
Pivot_GoRT_FU$time <- factor(Pivot_GoRT_FU$time, levels = c("Pre", "Post"))
model_GoRT_FU <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_GoRT_FU)
anova_GoRT_FU <- anova(model_GoRT_FU)
effectsize_GoRT_FU <- effectsize::eta_squared(model_GoRT_FU)
CI_GoRT_FU_Condition <- contrast(emmeans(model_GoRT_FU, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_GoRT_FU_Time <- contrast(emmeans(model_GoRT_FU, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_GoRT_FU)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_GoRT_FU
##
## REML criterion at convergence: 849.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0791 -0.5129 -0.1564 0.4081 4.0584
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 588.3 24.25
## Residual 304.3 17.44
## Number of obs: 96, groups: PP_number_ROB, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 368.796 6.098 39.944 60.475 <2e-16 ***
## conditionCON -7.754 5.036 69.000 -1.540 0.1282
## timePost 13.297 5.036 69.000 2.641 0.0102 *
## conditionCON:timePost -7.786 7.121 69.000 -1.093 0.2781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndCON timPst
## conditinCON -0.413
## timePost -0.413 0.500
## cndtnCON:tP 0.292 -0.707 -0.707print(anova_GoRT_FU)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 3255.7 3255.7 1 69 10.6997 0.001674 **
## time 2122.3 2122.3 1 69 6.9749 0.010216 *
## condition:time 363.7 363.7 1 69 1.1953 0.278060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_GoRT_FU)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.13 | [0.03, 1.00]
## time | 0.09 | [0.01, 1.00]
## condition:time | 0.02 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_GoRT_FU_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 11.6 3.56 69 4.54 18.8 3.271 0.0017
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_GoRT_FU_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Pre - Post -9.4 3.56 69 -16.5 -2.3 -2.641 0.0102
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95plot(fitted(model_GoRT_FU), resid(model_GoRT_FU), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
Rating of perceived exertion
Baseline measurement
Pivot_RPE_OM$condition <- factor(Pivot_RPE_OM$condition, levels = c("MF", "CON"))
Pivot_RPE_OM$time <- factor(Pivot_RPE_OM$time, levels = c("1", "2", "3", "4", "5", "6"))
model_RPE_OM <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_RPE_OM)
anova_RPE_OM <- anova(model_RPE_OM)
effectsize_RPE_OM <- effectsize::eta_squared(model_RPE_OM)
CI_RPE_OM_Condition <- contrast(emmeans(model_RPE_OM, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_RPE_OM_Time <- contrast(emmeans(model_RPE_OM, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_RPE_OM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_RPE_OM
##
## REML criterion at convergence: 2284.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86369 -0.51429 0.09578 0.62341 2.20454
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 121.3 11.01
## Residual 114.8 10.72
## Number of obs: 300, groups: PP_number_ROB, 25
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 13.120 3.073 73.801 4.269 5.75e-05 ***
## conditionCON -2.600 3.031 264.000 -0.858 0.392
## time2 31.880 3.031 264.000 10.519 < 2e-16 ***
## time3 41.080 3.031 264.000 13.554 < 2e-16 ***
## time4 50.280 3.031 264.000 16.590 < 2e-16 ***
## time5 56.760 3.031 264.000 18.728 < 2e-16 ***
## time6 70.280 3.031 264.000 23.189 < 2e-16 ***
## conditionCON:time2 -3.800 4.286 264.000 -0.887 0.376
## conditionCON:time3 -0.200 4.286 264.000 -0.047 0.963
## conditionCON:time4 -1.280 4.286 264.000 -0.299 0.765
## conditionCON:time5 3.920 4.286 264.000 0.915 0.361
## conditionCON:time6 -0.120 4.286 264.000 -0.028 0.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndCON time2 time3 time4 time5 time6 cCON:2 cCON:3
## conditinCON -0.493
## time2 -0.493 0.500
## time3 -0.493 0.500 0.500
## time4 -0.493 0.500 0.500 0.500
## time5 -0.493 0.500 0.500 0.500 0.500
## time6 -0.493 0.500 0.500 0.500 0.500 0.500
## cndtnCON:t2 0.349 -0.707 -0.707 -0.354 -0.354 -0.354 -0.354
## cndtnCON:t3 0.349 -0.707 -0.354 -0.707 -0.354 -0.354 -0.354 0.500
## cndtnCON:t4 0.349 -0.707 -0.354 -0.354 -0.707 -0.354 -0.354 0.500 0.500
## cndtnCON:t5 0.349 -0.707 -0.354 -0.354 -0.354 -0.707 -0.354 0.500 0.500
## cndtnCON:t6 0.349 -0.707 -0.354 -0.354 -0.354 -0.354 -0.707 0.500 0.500
## cCON:4 cCON:5
## conditinCON
## time2
## time3
## time4
## time5
## time6
## cndtnCON:t2
## cndtnCON:t3
## cndtnCON:t4
## cndtnCON:t5 0.500
## cndtnCON:t6 0.500 0.500print(anova_RPE_OM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 608 607.8 1 264 5.2932 0.02219 *
## time 152140 30428.1 5 264 265.0063 < 2e-16 ***
## condition:time 389 77.8 5 264 0.6779 0.64056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_RPE_OM)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.02 | [0.00, 1.00]
## time | 0.83 | [0.81, 1.00]
## condition:time | 0.01 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_RPE_OM_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 2.85 1.24 264 0.41 5.28 2.301 0.0222
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_RPE_OM_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## time1 - time2 -29.98 2.14 264 -36.1 -23.83 -13.989 <.0001
## time1 - time3 -40.98 2.14 264 -47.1 -34.83 -19.122 <.0001
## time1 - time4 -49.64 2.14 264 -55.8 -43.49 -23.163 <.0001
## time1 - time5 -58.72 2.14 264 -64.9 -52.57 -27.400 <.0001
## time1 - time6 -70.22 2.14 264 -76.4 -64.07 -32.766 <.0001
## time2 - time3 -11.00 2.14 264 -17.2 -4.85 -5.133 <.0001
## time2 - time4 -19.66 2.14 264 -25.8 -13.51 -9.174 <.0001
## time2 - time5 -28.74 2.14 264 -34.9 -22.59 -13.411 <.0001
## time2 - time6 -40.24 2.14 264 -46.4 -34.09 -18.777 <.0001
## time3 - time4 -8.66 2.14 264 -14.8 -2.51 -4.041 0.0010
## time3 - time5 -17.74 2.14 264 -23.9 -11.59 -8.278 <.0001
## time3 - time6 -29.24 2.14 264 -35.4 -23.09 -13.644 <.0001
## time4 - time5 -9.08 2.14 264 -15.2 -2.93 -4.237 0.0004
## time4 - time6 -20.58 2.14 264 -26.7 -14.43 -9.603 <.0001
## time5 - time6 -11.50 2.14 264 -17.7 -5.35 -5.366 <.0001
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 6 estimates
## P value adjustment: tukey method for comparing a family of 6 estimatesplot(fitted(model_RPE_OM), resid(model_RPE_OM), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
Follow up measurement
Pivot_RPE_FU$condition <- factor(Pivot_RPE_FU$condition, levels = c("MF", "CON"))
Pivot_RPE_FU$time <- factor(Pivot_RPE_FU$time, levels = c("1", "2", "3", "4", "5", "6"))
model_RPE_FU <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_RPE_FU)
anova_RPE_FU <- anova(model_RPE_FU)
effectsize_RPE_FU <- effectsize::eta_squared(model_RPE_FU)
CI_RPE_FU_Condition <- contrast(emmeans(model_RPE_FU, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_RPE_FU_Time <- contrast(emmeans(model_RPE_FU, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_RPE_FU)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_RPE_FU
##
## REML criterion at convergence: 2255.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8056 -0.5165 0.0382 0.6250 2.8548
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 133.0 11.53
## Residual 101.9 10.09
## Number of obs: 300, groups: PP_number_ROB, 25
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 13.640 3.065 63.630 4.450 3.53e-05 ***
## conditionCON -4.520 2.855 264.000 -1.583 0.115
## time2 27.960 2.855 264.000 9.792 < 2e-16 ***
## time3 38.560 2.855 264.000 13.505 < 2e-16 ***
## time4 50.160 2.855 264.000 17.568 < 2e-16 ***
## time5 59.040 2.855 264.000 20.678 < 2e-16 ***
## time6 67.360 2.855 264.000 23.592 < 2e-16 ***
## conditionCON:time2 1.920 4.038 264.000 0.475 0.635
## conditionCON:time3 4.600 4.038 264.000 1.139 0.256
## conditionCON:time4 0.520 4.038 264.000 0.129 0.898
## conditionCON:time5 2.240 4.038 264.000 0.555 0.580
## conditionCON:time6 4.520 4.038 264.000 1.119 0.264
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndCON time2 time3 time4 time5 time6 cCON:2 cCON:3
## conditinCON -0.466
## time2 -0.466 0.500
## time3 -0.466 0.500 0.500
## time4 -0.466 0.500 0.500 0.500
## time5 -0.466 0.500 0.500 0.500 0.500
## time6 -0.466 0.500 0.500 0.500 0.500 0.500
## cndtnCON:t2 0.329 -0.707 -0.707 -0.354 -0.354 -0.354 -0.354
## cndtnCON:t3 0.329 -0.707 -0.354 -0.707 -0.354 -0.354 -0.354 0.500
## cndtnCON:t4 0.329 -0.707 -0.354 -0.354 -0.707 -0.354 -0.354 0.500 0.500
## cndtnCON:t5 0.329 -0.707 -0.354 -0.354 -0.354 -0.707 -0.354 0.500 0.500
## cndtnCON:t6 0.329 -0.707 -0.354 -0.354 -0.354 -0.354 -0.707 0.500 0.500
## cCON:4 cCON:5
## conditinCON
## time2
## time3
## time4
## time5
## time6
## cndtnCON:t2
## cndtnCON:t3
## cndtnCON:t4
## cndtnCON:t5 0.500
## cndtnCON:t6 0.500 0.500print(anova_RPE_FU)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 370 369.6 1 264 3.6272 0.05793 .
## time 154963 30992.5 5 264 304.1284 < 2e-16 ***
## condition:time 235 47.1 5 264 0.4618 0.80449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_RPE_FU)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.01 | [0.00, 1.00]
## time | 0.85 | [0.83, 1.00]
## condition:time | 8.67e-03 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_RPE_FU_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 2.22 1.17 264 -0.0752 4.52 1.905 0.0579
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_RPE_FU_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## time1 - time2 -28.92 2.02 264 -34.7 -23.12 -14.324 <.0001
## time1 - time3 -40.86 2.02 264 -46.7 -35.06 -20.238 <.0001
## time1 - time4 -50.42 2.02 264 -56.2 -44.62 -24.973 <.0001
## time1 - time5 -60.16 2.02 264 -66.0 -54.36 -29.797 <.0001
## time1 - time6 -69.62 2.02 264 -75.4 -63.82 -34.483 <.0001
## time2 - time3 -11.94 2.02 264 -17.7 -6.14 -5.914 <.0001
## time2 - time4 -21.50 2.02 264 -27.3 -15.70 -10.649 <.0001
## time2 - time5 -31.24 2.02 264 -37.0 -25.44 -15.473 <.0001
## time2 - time6 -40.70 2.02 264 -46.5 -34.90 -20.159 <.0001
## time3 - time4 -9.56 2.02 264 -15.4 -3.76 -4.735 0.0001
## time3 - time5 -19.30 2.02 264 -25.1 -13.50 -9.559 <.0001
## time3 - time6 -28.76 2.02 264 -34.6 -22.96 -14.245 <.0001
## time4 - time5 -9.74 2.02 264 -15.5 -3.94 -4.824 <.0001
## time4 - time6 -19.20 2.02 264 -25.0 -13.40 -9.510 <.0001
## time5 - time6 -9.46 2.02 264 -15.3 -3.66 -4.686 0.0001
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 6 estimates
## P value adjustment: tukey method for comparing a family of 6 estimatesplot(fitted(model_RPE_FU), resid(model_RPE_FU), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
Feeling of mental fatigue
Baseline measurement
Pivot_MVAS_OM$condition <- factor(Pivot_MVAS_OM$condition, levels = c("MF", "CON"))
Pivot_MVAS_OM$time <- factor(Pivot_MVAS_OM$time, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"))
model_MVAS_OM <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_MVAS_OM)
anova_MVAS_OM <- anova(model_MVAS_OM)
effectsize_MVAS_OM <- effectsize::eta_squared(model_MVAS_OM)
CI_MVAS_OM_Condition <- contrast(emmeans(model_MVAS_OM, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_MVAS_OM_Time <- contrast(emmeans(model_MVAS_OM, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_MVAS_OM)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_MVAS_OM
##
## REML criterion at convergence: 5188.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9022 -0.6569 -0.1120 0.5642 4.0070
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 102.0 10.10
## Residual 188.3 13.72
## Number of obs: 650, groups: PP_number_ROB, 25
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.200 3.408 152.775 4.167 5.15e-05 ***
## conditionCON 2.800 3.882 600.000 0.721 0.470991
## time2 5.200 3.882 600.000 1.340 0.180882
## time3 21.400 3.882 600.000 5.513 5.25e-08 ***
## time4 40.280 3.882 600.000 10.377 < 2e-16 ***
## time5 52.440 3.882 600.000 13.509 < 2e-16 ***
## time6 43.000 3.882 600.000 11.077 < 2e-16 ***
## time7 17.800 3.882 600.000 4.586 5.51e-06 ***
## time8 16.400 3.882 600.000 4.225 2.76e-05 ***
## time9 15.600 3.882 600.000 4.019 6.59e-05 ***
## time10 15.760 3.882 600.000 4.060 5.56e-05 ***
## time11 16.400 3.882 600.000 4.225 2.76e-05 ***
## time12 17.480 3.882 600.000 4.503 8.05e-06 ***
## time13 10.800 3.882 600.000 2.782 0.005568 **
## conditionCON:time2 -2.600 5.490 600.000 -0.474 0.635942
## conditionCON:time3 -21.120 5.490 600.000 -3.847 0.000132 ***
## conditionCON:time4 -36.560 5.490 600.000 -6.660 6.20e-11 ***
## conditionCON:time5 -45.240 5.490 600.000 -8.241 1.08e-15 ***
## conditionCON:time6 -26.920 5.490 600.000 -4.904 1.21e-06 ***
## conditionCON:time7 -18.320 5.490 600.000 -3.337 0.000899 ***
## conditionCON:time8 -16.080 5.490 600.000 -2.929 0.003528 **
## conditionCON:time9 -11.440 5.490 600.000 -2.084 0.037589 *
## conditionCON:time10 -10.440 5.490 600.000 -1.902 0.057680 .
## conditionCON:time11 -8.080 5.490 600.000 -1.472 0.141581
## conditionCON:time12 -6.960 5.490 600.000 -1.268 0.205344
## conditionCON:time13 -5.360 5.490 600.000 -0.976 0.329266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 26 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itprint(anova_MVAS_OM)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 28685 28684.8 1 600 152.296 < 2.2e-16 ***
## time 48772 4064.3 12 600 21.578 < 2.2e-16 ***
## condition:time 27167 2263.9 12 600 12.020 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_MVAS_OM)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.20 | [0.16, 1.00]
## time | 0.30 | [0.24, 1.00]
## condition:time | 0.19 | [0.14, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_MVAS_OM_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 13.3 1.08 600 11.2 15.4 12.341 <.0001
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_MVAS_OM_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## time1 - time2 -3.90 2.74 600 -13.03 5.23 -1.421 0.9709
## time1 - time3 -10.84 2.74 600 -19.97 -1.71 -3.949 0.0058
## time1 - time4 -22.00 2.74 600 -31.13 -12.87 -8.015 <.0001
## time1 - time5 -29.82 2.74 600 -38.95 -20.69 -10.864 <.0001
## time1 - time6 -29.54 2.74 600 -38.67 -20.41 -10.762 <.0001
## time1 - time7 -8.64 2.74 600 -17.77 0.49 -3.148 0.0845
## time1 - time8 -8.36 2.74 600 -17.49 0.77 -3.046 0.1116
## time1 - time9 -9.88 2.74 600 -19.01 -0.75 -3.600 0.0206
## time1 - time10 -10.54 2.74 600 -19.67 -1.41 -3.840 0.0087
## time1 - time11 -12.36 2.74 600 -21.49 -3.23 -4.503 0.0006
## time1 - time12 -14.00 2.74 600 -23.13 -4.87 -5.101 <.0001
## time1 - time13 -8.12 2.74 600 -17.25 1.01 -2.958 0.1401
## time2 - time3 -6.94 2.74 600 -16.07 2.19 -2.528 0.3576
## time2 - time4 -18.10 2.74 600 -27.23 -8.97 -6.594 <.0001
## time2 - time5 -25.92 2.74 600 -35.05 -16.79 -9.443 <.0001
## time2 - time6 -25.64 2.74 600 -34.77 -16.51 -9.341 <.0001
## time2 - time7 -4.74 2.74 600 -13.87 4.39 -1.727 0.8831
## time2 - time8 -4.46 2.74 600 -13.59 4.67 -1.625 0.9217
## time2 - time9 -5.98 2.74 600 -15.11 3.15 -2.179 0.6073
## time2 - time10 -6.64 2.74 600 -15.77 2.49 -2.419 0.4318
## time2 - time11 -8.46 2.74 600 -17.59 0.67 -3.082 0.1012
## time2 - time12 -10.10 2.74 600 -19.23 -0.97 -3.680 0.0156
## time2 - time13 -4.22 2.74 600 -13.35 4.91 -1.537 0.9471
## time3 - time4 -11.16 2.74 600 -20.29 -2.03 -4.066 0.0037
## time3 - time5 -18.98 2.74 600 -28.11 -9.85 -6.915 <.0001
## time3 - time6 -18.70 2.74 600 -27.83 -9.57 -6.813 <.0001
## time3 - time7 2.20 2.74 600 -6.93 11.33 0.802 0.9999
## time3 - time8 2.48 2.74 600 -6.65 11.61 0.904 0.9995
## time3 - time9 0.96 2.74 600 -8.17 10.09 0.350 1.0000
## time3 - time10 0.30 2.74 600 -8.83 9.43 0.109 1.0000
## time3 - time11 -1.52 2.74 600 -10.65 7.61 -0.554 1.0000
## time3 - time12 -3.16 2.74 600 -12.29 5.97 -1.151 0.9952
## time3 - time13 2.72 2.74 600 -6.41 11.85 0.991 0.9988
## time4 - time5 -7.82 2.74 600 -16.95 1.31 -2.849 0.1829
## time4 - time6 -7.54 2.74 600 -16.67 1.59 -2.747 0.2306
## time4 - time7 13.36 2.74 600 4.23 22.49 4.867 0.0001
## time4 - time8 13.64 2.74 600 4.51 22.77 4.969 0.0001
## time4 - time9 12.12 2.74 600 2.99 21.25 4.416 0.0009
## time4 - time10 11.46 2.74 600 2.33 20.59 4.175 0.0024
## time4 - time11 9.64 2.74 600 0.51 18.77 3.512 0.0277
## time4 - time12 8.00 2.74 600 -1.13 17.13 2.915 0.1562
## time4 - time13 13.88 2.74 600 4.75 23.01 5.057 <.0001
## time5 - time6 0.28 2.74 600 -8.85 9.41 0.102 1.0000
## time5 - time7 21.18 2.74 600 12.05 30.31 7.716 <.0001
## time5 - time8 21.46 2.74 600 12.33 30.59 7.818 <.0001
## time5 - time9 19.94 2.74 600 10.81 29.07 7.265 <.0001
## time5 - time10 19.28 2.74 600 10.15 28.41 7.024 <.0001
## time5 - time11 17.46 2.74 600 8.33 26.59 6.361 <.0001
## time5 - time12 15.82 2.74 600 6.69 24.95 5.764 <.0001
## time5 - time13 21.70 2.74 600 12.57 30.83 7.906 <.0001
## time6 - time7 20.90 2.74 600 11.77 30.03 7.614 <.0001
## time6 - time8 21.18 2.74 600 12.05 30.31 7.716 <.0001
## time6 - time9 19.66 2.74 600 10.53 28.79 7.163 <.0001
## time6 - time10 19.00 2.74 600 9.87 28.13 6.922 <.0001
## time6 - time11 17.18 2.74 600 8.05 26.31 6.259 <.0001
## time6 - time12 15.54 2.74 600 6.41 24.67 5.662 <.0001
## time6 - time13 21.42 2.74 600 12.29 30.55 7.804 <.0001
## time7 - time8 0.28 2.74 600 -8.85 9.41 0.102 1.0000
## time7 - time9 -1.24 2.74 600 -10.37 7.89 -0.452 1.0000
## time7 - time10 -1.90 2.74 600 -11.03 7.23 -0.692 1.0000
## time7 - time11 -3.72 2.74 600 -12.85 5.41 -1.355 0.9801
## time7 - time12 -5.36 2.74 600 -14.49 3.77 -1.953 0.7631
## time7 - time13 0.52 2.74 600 -8.61 9.65 0.189 1.0000
## time8 - time9 -1.52 2.74 600 -10.65 7.61 -0.554 1.0000
## time8 - time10 -2.18 2.74 600 -11.31 6.95 -0.794 0.9999
## time8 - time11 -4.00 2.74 600 -13.13 5.13 -1.457 0.9646
## time8 - time12 -5.64 2.74 600 -14.77 3.49 -2.055 0.6957
## time8 - time13 0.24 2.74 600 -8.89 9.37 0.087 1.0000
## time9 - time10 -0.66 2.74 600 -9.79 8.47 -0.240 1.0000
## time9 - time11 -2.48 2.74 600 -11.61 6.65 -0.904 0.9995
## time9 - time12 -4.12 2.74 600 -13.25 5.01 -1.501 0.9557
## time9 - time13 1.76 2.74 600 -7.37 10.89 0.641 1.0000
## time10 - time11 -1.82 2.74 600 -10.95 7.31 -0.663 1.0000
## time10 - time12 -3.46 2.74 600 -12.59 5.67 -1.261 0.9892
## time10 - time13 2.42 2.74 600 -6.71 11.55 0.882 0.9996
## time11 - time12 -1.64 2.74 600 -10.77 7.49 -0.597 1.0000
## time11 - time13 4.24 2.74 600 -4.89 13.37 1.545 0.9452
## time12 - time13 5.88 2.74 600 -3.25 15.01 2.142 0.6338
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 13 estimates
## P value adjustment: tukey method for comparing a family of 13 estimatesemmeans(model_MVAS_OM, pairwise ~ time | condition)## $emmeans
## condition = MF:
## time emmean SE df lower.CL upper.CL
## 1 14.2 3.41 153 7.47 20.9
## 2 19.4 3.41 153 12.67 26.1
## 3 35.6 3.41 153 28.87 42.3
## 4 54.5 3.41 153 47.75 61.2
## 5 66.6 3.41 153 59.91 73.4
## 6 57.2 3.41 153 50.47 63.9
## 7 32.0 3.41 153 25.27 38.7
## 8 30.6 3.41 153 23.87 37.3
## 9 29.8 3.41 153 23.07 36.5
## 10 30.0 3.41 153 23.23 36.7
## 11 30.6 3.41 153 23.87 37.3
## 12 31.7 3.41 153 24.95 38.4
## 13 25.0 3.41 153 18.27 31.7
##
## condition = CON:
## time emmean SE df lower.CL upper.CL
## 1 17.0 3.41 153 10.27 23.7
## 2 19.6 3.41 153 12.87 26.3
## 3 17.3 3.41 153 10.55 24.0
## 4 20.7 3.41 153 13.99 27.5
## 5 24.2 3.41 153 17.47 30.9
## 6 33.1 3.41 153 26.35 39.8
## 7 16.5 3.41 153 9.75 23.2
## 8 17.3 3.41 153 10.59 24.1
## 9 21.2 3.41 153 14.43 27.9
## 10 22.3 3.41 153 15.59 29.1
## 11 25.3 3.41 153 18.59 32.1
## 12 27.5 3.41 153 20.79 34.3
## 13 22.4 3.41 153 15.71 29.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## condition = MF:
## contrast estimate SE df t.ratio p.value
## time1 - time2 -5.20 3.88 600 -1.340 0.9819
## time1 - time3 -21.40 3.88 600 -5.513 <.0001
## time1 - time4 -40.28 3.88 600 -10.377 <.0001
## time1 - time5 -52.44 3.88 600 -13.509 <.0001
## time1 - time6 -43.00 3.88 600 -11.077 <.0001
## time1 - time7 -17.80 3.88 600 -4.586 0.0004
## time1 - time8 -16.40 3.88 600 -4.225 0.0019
## time1 - time9 -15.60 3.88 600 -4.019 0.0044
## time1 - time10 -15.76 3.88 600 -4.060 0.0037
## time1 - time11 -16.40 3.88 600 -4.225 0.0019
## time1 - time12 -17.48 3.88 600 -4.503 0.0006
## time1 - time13 -10.80 3.88 600 -2.782 0.2133
## time2 - time3 -16.20 3.88 600 -4.173 0.0024
## time2 - time4 -35.08 3.88 600 -9.037 <.0001
## time2 - time5 -47.24 3.88 600 -12.170 <.0001
## time2 - time6 -37.80 3.88 600 -9.738 <.0001
## time2 - time7 -12.60 3.88 600 -3.246 0.0637
## time2 - time8 -11.20 3.88 600 -2.885 0.1677
## time2 - time9 -10.40 3.88 600 -2.679 0.2665
## time2 - time10 -10.56 3.88 600 -2.720 0.2443
## time2 - time11 -11.20 3.88 600 -2.885 0.1677
## time2 - time12 -12.28 3.88 600 -3.164 0.0808
## time2 - time13 -5.60 3.88 600 -1.443 0.9672
## time3 - time4 -18.88 3.88 600 -4.864 0.0001
## time3 - time5 -31.04 3.88 600 -7.996 <.0001
## time3 - time6 -21.60 3.88 600 -5.565 <.0001
## time3 - time7 3.60 3.88 600 0.927 0.9994
## time3 - time8 5.00 3.88 600 1.288 0.9870
## time3 - time9 5.80 3.88 600 1.494 0.9571
## time3 - time10 5.64 3.88 600 1.453 0.9654
## time3 - time11 5.00 3.88 600 1.288 0.9870
## time3 - time12 3.92 3.88 600 1.010 0.9986
## time3 - time13 10.60 3.88 600 2.731 0.2389
## time4 - time5 -12.16 3.88 600 -3.133 0.0881
## time4 - time6 -2.72 3.88 600 -0.701 1.0000
## time4 - time7 22.48 3.88 600 5.791 <.0001
## time4 - time8 23.88 3.88 600 6.152 <.0001
## time4 - time9 24.68 3.88 600 6.358 <.0001
## time4 - time10 24.52 3.88 600 6.317 <.0001
## time4 - time11 23.88 3.88 600 6.152 <.0001
## time4 - time12 22.80 3.88 600 5.874 <.0001
## time4 - time13 29.48 3.88 600 7.595 <.0001
## time5 - time6 9.44 3.88 600 2.432 0.4229
## time5 - time7 34.64 3.88 600 8.924 <.0001
## time5 - time8 36.04 3.88 600 9.284 <.0001
## time5 - time9 36.84 3.88 600 9.491 <.0001
## time5 - time10 36.68 3.88 600 9.449 <.0001
## time5 - time11 36.04 3.88 600 9.284 <.0001
## time5 - time12 34.96 3.88 600 9.006 <.0001
## time5 - time13 41.64 3.88 600 10.727 <.0001
## time6 - time7 25.20 3.88 600 6.492 <.0001
## time6 - time8 26.60 3.88 600 6.853 <.0001
## time6 - time9 27.40 3.88 600 7.059 <.0001
## time6 - time10 27.24 3.88 600 7.017 <.0001
## time6 - time11 26.60 3.88 600 6.853 <.0001
## time6 - time12 25.52 3.88 600 6.574 <.0001
## time6 - time13 32.20 3.88 600 8.295 <.0001
## time7 - time8 1.40 3.88 600 0.361 1.0000
## time7 - time9 2.20 3.88 600 0.567 1.0000
## time7 - time10 2.04 3.88 600 0.526 1.0000
## time7 - time11 1.40 3.88 600 0.361 1.0000
## time7 - time12 0.32 3.88 600 0.082 1.0000
## time7 - time13 7.00 3.88 600 1.803 0.8477
## time8 - time9 0.80 3.88 600 0.206 1.0000
## time8 - time10 0.64 3.88 600 0.165 1.0000
## time8 - time11 0.00 3.88 600 0.000 1.0000
## time8 - time12 -1.08 3.88 600 -0.278 1.0000
## time8 - time13 5.60 3.88 600 1.443 0.9672
## time9 - time10 -0.16 3.88 600 -0.041 1.0000
## time9 - time11 -0.80 3.88 600 -0.206 1.0000
## time9 - time12 -1.88 3.88 600 -0.484 1.0000
## time9 - time13 4.80 3.88 600 1.237 0.9909
## time10 - time11 -0.64 3.88 600 -0.165 1.0000
## time10 - time12 -1.72 3.88 600 -0.443 1.0000
## time10 - time13 4.96 3.88 600 1.278 0.9879
## time11 - time12 -1.08 3.88 600 -0.278 1.0000
## time11 - time13 5.60 3.88 600 1.443 0.9672
## time12 - time13 6.68 3.88 600 1.721 0.8857
##
## condition = CON:
## contrast estimate SE df t.ratio p.value
## time1 - time2 -2.60 3.88 600 -0.670 1.0000
## time1 - time3 -0.28 3.88 600 -0.072 1.0000
## time1 - time4 -3.72 3.88 600 -0.958 0.9992
## time1 - time5 -7.20 3.88 600 -1.855 0.8207
## time1 - time6 -16.08 3.88 600 -4.142 0.0027
## time1 - time7 0.52 3.88 600 0.134 1.0000
## time1 - time8 -0.32 3.88 600 -0.082 1.0000
## time1 - time9 -4.16 3.88 600 -1.072 0.9975
## time1 - time10 -5.32 3.88 600 -1.371 0.9782
## time1 - time11 -8.32 3.88 600 -2.143 0.6330
## time1 - time12 -10.52 3.88 600 -2.710 0.2497
## time1 - time13 -5.44 3.88 600 -1.401 0.9739
## time2 - time3 2.32 3.88 600 0.598 1.0000
## time2 - time4 -1.12 3.88 600 -0.289 1.0000
## time2 - time5 -4.60 3.88 600 -1.185 0.9937
## time2 - time6 -13.48 3.88 600 -3.473 0.0315
## time2 - time7 3.12 3.88 600 0.804 0.9999
## time2 - time8 2.28 3.88 600 0.587 1.0000
## time2 - time9 -1.56 3.88 600 -0.402 1.0000
## time2 - time10 -2.72 3.88 600 -0.701 1.0000
## time2 - time11 -5.72 3.88 600 -1.474 0.9614
## time2 - time12 -7.92 3.88 600 -2.040 0.7057
## time2 - time13 -2.84 3.88 600 -0.732 1.0000
## time3 - time4 -3.44 3.88 600 -0.886 0.9996
## time3 - time5 -6.92 3.88 600 -1.783 0.8578
## time3 - time6 -15.80 3.88 600 -4.070 0.0036
## time3 - time7 0.80 3.88 600 0.206 1.0000
## time3 - time8 -0.04 3.88 600 -0.010 1.0000
## time3 - time9 -3.88 3.88 600 -1.000 0.9987
## time3 - time10 -5.04 3.88 600 -1.298 0.9861
## time3 - time11 -8.04 3.88 600 -2.071 0.6843
## time3 - time12 -10.24 3.88 600 -2.638 0.2899
## time3 - time13 -5.16 3.88 600 -1.329 0.9830
## time4 - time5 -3.48 3.88 600 -0.897 0.9996
## time4 - time6 -12.36 3.88 600 -3.184 0.0762
## time4 - time7 4.24 3.88 600 1.092 0.9970
## time4 - time8 3.40 3.88 600 0.876 0.9997
## time4 - time9 -0.44 3.88 600 -0.113 1.0000
## time4 - time10 -1.60 3.88 600 -0.412 1.0000
## time4 - time11 -4.60 3.88 600 -1.185 0.9937
## time4 - time12 -6.80 3.88 600 -1.752 0.8722
## time4 - time13 -1.72 3.88 600 -0.443 1.0000
## time5 - time6 -8.88 3.88 600 -2.288 0.5270
## time5 - time7 7.72 3.88 600 1.989 0.7400
## time5 - time8 6.88 3.88 600 1.772 0.8627
## time5 - time9 3.04 3.88 600 0.783 0.9999
## time5 - time10 1.88 3.88 600 0.484 1.0000
## time5 - time11 -1.12 3.88 600 -0.289 1.0000
## time5 - time12 -3.32 3.88 600 -0.855 0.9997
## time5 - time13 1.76 3.88 600 0.453 1.0000
## time6 - time7 16.60 3.88 600 4.276 0.0015
## time6 - time8 15.76 3.88 600 4.060 0.0037
## time6 - time9 11.92 3.88 600 3.071 0.1044
## time6 - time10 10.76 3.88 600 2.772 0.2182
## time6 - time11 7.76 3.88 600 1.999 0.7333
## time6 - time12 5.56 3.88 600 1.432 0.9690
## time6 - time13 10.64 3.88 600 2.741 0.2336
## time7 - time8 -0.84 3.88 600 -0.216 1.0000
## time7 - time9 -4.68 3.88 600 -1.206 0.9927
## time7 - time10 -5.84 3.88 600 -1.504 0.9549
## time7 - time11 -8.84 3.88 600 -2.277 0.5346
## time7 - time12 -11.04 3.88 600 -2.844 0.1850
## time7 - time13 -5.96 3.88 600 -1.535 0.9476
## time8 - time9 -3.84 3.88 600 -0.989 0.9989
## time8 - time10 -5.00 3.88 600 -1.288 0.9870
## time8 - time11 -8.00 3.88 600 -2.061 0.6915
## time8 - time12 -10.20 3.88 600 -2.628 0.2960
## time8 - time13 -5.12 3.88 600 -1.319 0.9841
## time9 - time10 -1.16 3.88 600 -0.299 1.0000
## time9 - time11 -4.16 3.88 600 -1.072 0.9975
## time9 - time12 -6.36 3.88 600 -1.638 0.9172
## time9 - time13 -1.28 3.88 600 -0.330 1.0000
## time10 - time11 -3.00 3.88 600 -0.773 0.9999
## time10 - time12 -5.20 3.88 600 -1.340 0.9819
## time10 - time13 -0.12 3.88 600 -0.031 1.0000
## time11 - time12 -2.20 3.88 600 -0.567 1.0000
## time11 - time13 2.88 3.88 600 0.742 0.9999
## time12 - time13 5.08 3.88 600 1.309 0.9851
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 13 estimatesemmeans(model_MVAS_OM, pairwise ~ condition | time)## $emmeans
## time = 1:
## condition emmean SE df lower.CL upper.CL
## MF 14.2 3.41 153 7.47 20.9
## CON 17.0 3.41 153 10.27 23.7
##
## time = 2:
## condition emmean SE df lower.CL upper.CL
## MF 19.4 3.41 153 12.67 26.1
## CON 19.6 3.41 153 12.87 26.3
##
## time = 3:
## condition emmean SE df lower.CL upper.CL
## MF 35.6 3.41 153 28.87 42.3
## CON 17.3 3.41 153 10.55 24.0
##
## time = 4:
## condition emmean SE df lower.CL upper.CL
## MF 54.5 3.41 153 47.75 61.2
## CON 20.7 3.41 153 13.99 27.5
##
## time = 5:
## condition emmean SE df lower.CL upper.CL
## MF 66.6 3.41 153 59.91 73.4
## CON 24.2 3.41 153 17.47 30.9
##
## time = 6:
## condition emmean SE df lower.CL upper.CL
## MF 57.2 3.41 153 50.47 63.9
## CON 33.1 3.41 153 26.35 39.8
##
## time = 7:
## condition emmean SE df lower.CL upper.CL
## MF 32.0 3.41 153 25.27 38.7
## CON 16.5 3.41 153 9.75 23.2
##
## time = 8:
## condition emmean SE df lower.CL upper.CL
## MF 30.6 3.41 153 23.87 37.3
## CON 17.3 3.41 153 10.59 24.1
##
## time = 9:
## condition emmean SE df lower.CL upper.CL
## MF 29.8 3.41 153 23.07 36.5
## CON 21.2 3.41 153 14.43 27.9
##
## time = 10:
## condition emmean SE df lower.CL upper.CL
## MF 30.0 3.41 153 23.23 36.7
## CON 22.3 3.41 153 15.59 29.1
##
## time = 11:
## condition emmean SE df lower.CL upper.CL
## MF 30.6 3.41 153 23.87 37.3
## CON 25.3 3.41 153 18.59 32.1
##
## time = 12:
## condition emmean SE df lower.CL upper.CL
## MF 31.7 3.41 153 24.95 38.4
## CON 27.5 3.41 153 20.79 34.3
##
## time = 13:
## condition emmean SE df lower.CL upper.CL
## MF 25.0 3.41 153 18.27 31.7
## CON 22.4 3.41 153 15.71 29.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## time = 1:
## contrast estimate SE df t.ratio p.value
## MF - CON -2.80 3.88 600 -0.721 0.4710
##
## time = 2:
## contrast estimate SE df t.ratio p.value
## MF - CON -0.20 3.88 600 -0.052 0.9589
##
## time = 3:
## contrast estimate SE df t.ratio p.value
## MF - CON 18.32 3.88 600 4.720 <.0001
##
## time = 4:
## contrast estimate SE df t.ratio p.value
## MF - CON 33.76 3.88 600 8.697 <.0001
##
## time = 5:
## contrast estimate SE df t.ratio p.value
## MF - CON 42.44 3.88 600 10.933 <.0001
##
## time = 6:
## contrast estimate SE df t.ratio p.value
## MF - CON 24.12 3.88 600 6.214 <.0001
##
## time = 7:
## contrast estimate SE df t.ratio p.value
## MF - CON 15.52 3.88 600 3.998 0.0001
##
## time = 8:
## contrast estimate SE df t.ratio p.value
## MF - CON 13.28 3.88 600 3.421 0.0007
##
## time = 9:
## contrast estimate SE df t.ratio p.value
## MF - CON 8.64 3.88 600 2.226 0.0264
##
## time = 10:
## contrast estimate SE df t.ratio p.value
## MF - CON 7.64 3.88 600 1.968 0.0495
##
## time = 11:
## contrast estimate SE df t.ratio p.value
## MF - CON 5.28 3.88 600 1.360 0.1743
##
## time = 12:
## contrast estimate SE df t.ratio p.value
## MF - CON 4.16 3.88 600 1.072 0.2843
##
## time = 13:
## contrast estimate SE df t.ratio p.value
## MF - CON 2.56 3.88 600 0.659 0.5098
##
## Degrees-of-freedom method: kenward-rogerplot(fitted(model_MVAS_OM), resid(model_MVAS_OM), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
Follow up measurement
Pivot_MVAS_FU$condition <- factor(Pivot_MVAS_FU$condition, levels = c("MF", "CON"))
Pivot_MVAS_FU$time <- factor(Pivot_MVAS_FU$time, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"))
model_MVAS_FU <- lmer(value ~ condition*time + (1 | PP_number_ROB), data = Pivot_MVAS_FU)
anova_MVAS_FU <- anova(model_MVAS_FU)
effectsize_MVAS_FU <- effectsize::eta_squared(model_MVAS_FU)
CI_MVAS_FU_Condition <- contrast(emmeans(model_MVAS_FU, ~ condition), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionsCI_MVAS_FU_Time <- contrast(emmeans(model_MVAS_FU, ~ time), method = "pairwise", infer = c(TRUE, TRUE))## NOTE: Results may be misleading due to involvement in interactionssummary(model_MVAS_FU)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ condition * time + (1 | PP_number_ROB)
## Data: Pivot_MVAS_FU
##
## REML criterion at convergence: 5148.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4931 -0.5959 -0.0436 0.5465 3.6669
##
## Random effects:
## Groups Name Variance Std.Dev.
## PP_number_ROB (Intercept) 110.3 10.50
## Residual 175.7 13.26
## Number of obs: 650, groups: PP_number_ROB, 25
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.600 3.382 132.338 5.204 7.27e-07 ***
## conditionCON -2.600 3.750 600.000 -0.693 0.488328
## time2 4.520 3.750 600.000 1.205 0.228506
## time3 25.800 3.750 600.000 6.881 1.50e-11 ***
## time4 41.760 3.750 600.000 11.137 < 2e-16 ***
## time5 50.080 3.750 600.000 13.356 < 2e-16 ***
## time6 41.920 3.750 600.000 11.180 < 2e-16 ***
## time7 22.400 3.750 600.000 5.974 3.97e-09 ***
## time8 18.800 3.750 600.000 5.014 7.04e-07 ***
## time9 17.400 3.750 600.000 4.640 4.27e-06 ***
## time10 17.800 3.750 600.000 4.747 2.58e-06 ***
## time11 18.000 3.750 600.000 4.800 2.00e-06 ***
## time12 21.800 3.750 600.000 5.814 9.92e-09 ***
## time13 12.600 3.750 600.000 3.360 0.000828 ***
## conditionCON:time2 1.800 5.303 600.000 0.339 0.734395
## conditionCON:time3 -24.000 5.303 600.000 -4.526 7.25e-06 ***
## conditionCON:time4 -37.840 5.303 600.000 -7.136 2.79e-12 ***
## conditionCON:time5 -42.160 5.303 600.000 -7.951 9.26e-15 ***
## conditionCON:time6 -22.440 5.303 600.000 -4.232 2.68e-05 ***
## conditionCON:time7 -19.280 5.303 600.000 -3.636 0.000301 ***
## conditionCON:time8 -12.000 5.303 600.000 -2.263 0.023995 *
## conditionCON:time9 -7.000 5.303 600.000 -1.320 0.187319
## conditionCON:time10 -4.800 5.303 600.000 -0.905 0.365732
## conditionCON:time11 -2.400 5.303 600.000 -0.453 0.651007
## conditionCON:time12 -3.400 5.303 600.000 -0.641 0.521657
## conditionCON:time13 -2.400 5.303 600.000 -0.453 0.651007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 26 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itprint(anova_MVAS_FU)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## condition 42291 42291 1 600 240.63 < 2.2e-16 ***
## time 43402 3617 12 600 20.58 < 2.2e-16 ***
## condition:time 31529 2627 12 600 14.95 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(effectsize_MVAS_FU)## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## condition | 0.29 | [0.24, 1.00]
## time | 0.29 | [0.23, 1.00]
## condition:time | 0.23 | [0.17, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].print(CI_MVAS_FU_Condition)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## MF - CON 16.1 1.04 600 14.1 18.2 15.512 <.0001
##
## Results are averaged over the levels of: time
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95print(CI_MVAS_FU_Time)## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## time1 - time2 -5.42 2.65 600 -14.2392 3.399 -2.044 0.7030
## time1 - time3 -13.80 2.65 600 -22.6192 -4.981 -5.205 <.0001
## time1 - time4 -22.84 2.65 600 -31.6592 -14.021 -8.614 <.0001
## time1 - time5 -29.00 2.65 600 -37.8192 -20.181 -10.938 <.0001
## time1 - time6 -30.70 2.65 600 -39.5192 -21.881 -11.579 <.0001
## time1 - time7 -12.76 2.65 600 -21.5792 -3.941 -4.813 0.0001
## time1 - time8 -12.80 2.65 600 -21.6192 -3.981 -4.828 0.0001
## time1 - time9 -13.90 2.65 600 -22.7192 -5.081 -5.243 <.0001
## time1 - time10 -15.40 2.65 600 -24.2192 -6.581 -5.808 <.0001
## time1 - time11 -16.80 2.65 600 -25.6192 -7.981 -6.336 <.0001
## time1 - time12 -20.10 2.65 600 -28.9192 -11.281 -7.581 <.0001
## time1 - time13 -11.40 2.65 600 -20.2192 -2.581 -4.300 0.0014
## time2 - time3 -8.38 2.65 600 -17.1992 0.439 -3.161 0.0815
## time2 - time4 -17.42 2.65 600 -26.2392 -8.601 -6.570 <.0001
## time2 - time5 -23.58 2.65 600 -32.3992 -14.761 -8.893 <.0001
## time2 - time6 -25.28 2.65 600 -34.0992 -16.461 -9.535 <.0001
## time2 - time7 -7.34 2.65 600 -16.1592 1.479 -2.768 0.2200
## time2 - time8 -7.38 2.65 600 -16.1992 1.439 -2.783 0.2127
## time2 - time9 -8.48 2.65 600 -17.2992 0.339 -3.198 0.0731
## time2 - time10 -9.98 2.65 600 -18.7992 -1.161 -3.764 0.0115
## time2 - time11 -11.38 2.65 600 -20.1992 -2.561 -4.292 0.0014
## time2 - time12 -14.68 2.65 600 -23.4992 -5.861 -5.537 <.0001
## time2 - time13 -5.98 2.65 600 -14.7992 2.839 -2.255 0.5507
## time3 - time4 -9.04 2.65 600 -17.8592 -0.221 -3.410 0.0386
## time3 - time5 -15.20 2.65 600 -24.0192 -6.381 -5.733 <.0001
## time3 - time6 -16.90 2.65 600 -25.7192 -8.081 -6.374 <.0001
## time3 - time7 1.04 2.65 600 -7.7792 9.859 0.392 1.0000
## time3 - time8 1.00 2.65 600 -7.8192 9.819 0.377 1.0000
## time3 - time9 -0.10 2.65 600 -8.9192 8.719 -0.038 1.0000
## time3 - time10 -1.60 2.65 600 -10.4192 7.219 -0.603 1.0000
## time3 - time11 -3.00 2.65 600 -11.8192 5.819 -1.131 0.9959
## time3 - time12 -6.30 2.65 600 -15.1192 2.519 -2.376 0.4624
## time3 - time13 2.40 2.65 600 -6.4192 11.219 0.905 0.9995
## time4 - time5 -6.16 2.65 600 -14.9792 2.659 -2.323 0.5008
## time4 - time6 -7.86 2.65 600 -16.6792 0.959 -2.964 0.1379
## time4 - time7 10.08 2.65 600 1.2608 18.899 3.802 0.0101
## time4 - time8 10.04 2.65 600 1.2208 18.859 3.787 0.0106
## time4 - time9 8.94 2.65 600 0.1208 17.759 3.372 0.0434
## time4 - time10 7.44 2.65 600 -1.3792 16.259 2.806 0.2021
## time4 - time11 6.04 2.65 600 -2.7792 14.859 2.278 0.5340
## time4 - time12 2.74 2.65 600 -6.0792 11.559 1.033 0.9983
## time4 - time13 11.44 2.65 600 2.6208 20.259 4.315 0.0013
## time5 - time6 -1.70 2.65 600 -10.5192 7.119 -0.641 1.0000
## time5 - time7 16.24 2.65 600 7.4208 25.059 6.125 <.0001
## time5 - time8 16.20 2.65 600 7.3808 25.019 6.110 <.0001
## time5 - time9 15.10 2.65 600 6.2808 23.919 5.695 <.0001
## time5 - time10 13.60 2.65 600 4.7808 22.419 5.129 <.0001
## time5 - time11 12.20 2.65 600 3.3808 21.019 4.601 0.0004
## time5 - time12 8.90 2.65 600 0.0808 17.719 3.357 0.0455
## time5 - time13 17.60 2.65 600 8.7808 26.419 6.638 <.0001
## time6 - time7 17.94 2.65 600 9.1208 26.759 6.766 <.0001
## time6 - time8 17.90 2.65 600 9.0808 26.719 6.751 <.0001
## time6 - time9 16.80 2.65 600 7.9808 25.619 6.336 <.0001
## time6 - time10 15.30 2.65 600 6.4808 24.119 5.771 <.0001
## time6 - time11 13.90 2.65 600 5.0808 22.719 5.243 <.0001
## time6 - time12 10.60 2.65 600 1.7808 19.419 3.998 0.0048
## time6 - time13 19.30 2.65 600 10.4808 28.119 7.279 <.0001
## time7 - time8 -0.04 2.65 600 -8.8592 8.779 -0.015 1.0000
## time7 - time9 -1.14 2.65 600 -9.9592 7.679 -0.430 1.0000
## time7 - time10 -2.64 2.65 600 -11.4592 6.179 -0.996 0.9988
## time7 - time11 -4.04 2.65 600 -12.8592 4.779 -1.524 0.9504
## time7 - time12 -7.34 2.65 600 -16.1592 1.479 -2.768 0.2200
## time7 - time13 1.36 2.65 600 -7.4592 10.179 0.513 1.0000
## time8 - time9 -1.10 2.65 600 -9.9192 7.719 -0.415 1.0000
## time8 - time10 -2.60 2.65 600 -11.4192 6.219 -0.981 0.9990
## time8 - time11 -4.00 2.65 600 -12.8192 4.819 -1.509 0.9539
## time8 - time12 -7.30 2.65 600 -16.1192 1.519 -2.753 0.2275
## time8 - time13 1.40 2.65 600 -7.4192 10.219 0.528 1.0000
## time9 - time10 -1.50 2.65 600 -10.3192 7.319 -0.566 1.0000
## time9 - time11 -2.90 2.65 600 -11.7192 5.919 -1.094 0.9970
## time9 - time12 -6.20 2.65 600 -15.0192 2.619 -2.338 0.4897
## time9 - time13 2.50 2.65 600 -6.3192 11.319 0.943 0.9993
## time10 - time11 -1.40 2.65 600 -10.2192 7.419 -0.528 1.0000
## time10 - time12 -4.70 2.65 600 -13.5192 4.119 -1.773 0.8626
## time10 - time13 4.00 2.65 600 -4.8192 12.819 1.509 0.9539
## time11 - time12 -3.30 2.65 600 -12.1192 5.519 -1.245 0.9903
## time11 - time13 5.40 2.65 600 -3.4192 14.219 2.037 0.7082
## time12 - time13 8.70 2.65 600 -0.1192 17.519 3.281 0.0573
##
## Results are averaged over the levels of: condition
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 13 estimates
## P value adjustment: tukey method for comparing a family of 13 estimatesemmeans(model_MVAS_FU, pairwise ~ time | condition)## $emmeans
## condition = MF:
## time emmean SE df lower.CL upper.CL
## 1 17.6 3.38 132 10.91 24.3
## 2 22.1 3.38 132 15.43 28.8
## 3 43.4 3.38 132 36.71 50.1
## 4 59.4 3.38 132 52.67 66.1
## 5 67.7 3.38 132 60.99 74.4
## 6 59.5 3.38 132 52.83 66.2
## 7 40.0 3.38 132 33.31 46.7
## 8 36.4 3.38 132 29.71 43.1
## 9 35.0 3.38 132 28.31 41.7
## 10 35.4 3.38 132 28.71 42.1
## 11 35.6 3.38 132 28.91 42.3
## 12 39.4 3.38 132 32.71 46.1
## 13 30.2 3.38 132 23.51 36.9
##
## condition = CON:
## time emmean SE df lower.CL upper.CL
## 1 15.0 3.38 132 8.31 21.7
## 2 21.3 3.38 132 14.63 28.0
## 3 16.8 3.38 132 10.11 23.5
## 4 18.9 3.38 132 12.23 25.6
## 5 22.9 3.38 132 16.23 29.6
## 6 34.5 3.38 132 27.79 41.2
## 7 18.1 3.38 132 11.43 24.8
## 8 21.8 3.38 132 15.11 28.5
## 9 25.4 3.38 132 18.71 32.1
## 10 28.0 3.38 132 21.31 34.7
## 11 30.6 3.38 132 23.91 37.3
## 12 33.4 3.38 132 26.71 40.1
## 13 25.2 3.38 132 18.51 31.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## condition = MF:
## contrast estimate SE df t.ratio p.value
## time1 - time2 -4.52 3.75 600 -1.205 0.9927
## time1 - time3 -25.80 3.75 600 -6.881 <.0001
## time1 - time4 -41.76 3.75 600 -11.137 <.0001
## time1 - time5 -50.08 3.75 600 -13.356 <.0001
## time1 - time6 -41.92 3.75 600 -11.180 <.0001
## time1 - time7 -22.40 3.75 600 -5.974 <.0001
## time1 - time8 -18.80 3.75 600 -5.014 0.0001
## time1 - time9 -17.40 3.75 600 -4.640 0.0003
## time1 - time10 -17.80 3.75 600 -4.747 0.0002
## time1 - time11 -18.00 3.75 600 -4.800 0.0001
## time1 - time12 -21.80 3.75 600 -5.814 <.0001
## time1 - time13 -12.60 3.75 600 -3.360 0.0450
## time2 - time3 -21.28 3.75 600 -5.675 <.0001
## time2 - time4 -37.24 3.75 600 -9.932 <.0001
## time2 - time5 -45.56 3.75 600 -12.150 <.0001
## time2 - time6 -37.40 3.75 600 -9.974 <.0001
## time2 - time7 -17.88 3.75 600 -4.768 0.0002
## time2 - time8 -14.28 3.75 600 -3.808 0.0098
## time2 - time9 -12.88 3.75 600 -3.435 0.0356
## time2 - time10 -13.28 3.75 600 -3.542 0.0251
## time2 - time11 -13.48 3.75 600 -3.595 0.0209
## time2 - time12 -17.28 3.75 600 -4.608 0.0004
## time2 - time13 -8.08 3.75 600 -2.155 0.6246
## time3 - time4 -15.96 3.75 600 -4.256 0.0017
## time3 - time5 -24.28 3.75 600 -6.475 <.0001
## time3 - time6 -16.12 3.75 600 -4.299 0.0014
## time3 - time7 3.40 3.75 600 0.907 0.9995
## time3 - time8 7.00 3.75 600 1.867 0.8140
## time3 - time9 8.40 3.75 600 2.240 0.5620
## time3 - time10 8.00 3.75 600 2.134 0.6401
## time3 - time11 7.80 3.75 600 2.080 0.6781
## time3 - time12 4.00 3.75 600 1.067 0.9976
## time3 - time13 13.20 3.75 600 3.520 0.0269
## time4 - time5 -8.32 3.75 600 -2.219 0.5777
## time4 - time6 -0.16 3.75 600 -0.043 1.0000
## time4 - time7 19.36 3.75 600 5.163 <.0001
## time4 - time8 22.96 3.75 600 6.123 <.0001
## time4 - time9 24.36 3.75 600 6.497 <.0001
## time4 - time10 23.96 3.75 600 6.390 <.0001
## time4 - time11 23.76 3.75 600 6.337 <.0001
## time4 - time12 19.96 3.75 600 5.323 <.0001
## time4 - time13 29.16 3.75 600 7.777 <.0001
## time5 - time6 8.16 3.75 600 2.176 0.6091
## time5 - time7 27.68 3.75 600 7.382 <.0001
## time5 - time8 31.28 3.75 600 8.342 <.0001
## time5 - time9 32.68 3.75 600 8.715 <.0001
## time5 - time10 32.28 3.75 600 8.609 <.0001
## time5 - time11 32.08 3.75 600 8.555 <.0001
## time5 - time12 28.28 3.75 600 7.542 <.0001
## time5 - time13 37.48 3.75 600 9.996 <.0001
## time6 - time7 19.52 3.75 600 5.206 <.0001
## time6 - time8 23.12 3.75 600 6.166 <.0001
## time6 - time9 24.52 3.75 600 6.539 <.0001
## time6 - time10 24.12 3.75 600 6.433 <.0001
## time6 - time11 23.92 3.75 600 6.379 <.0001
## time6 - time12 20.12 3.75 600 5.366 <.0001
## time6 - time13 29.32 3.75 600 7.819 <.0001
## time7 - time8 3.60 3.75 600 0.960 0.9992
## time7 - time9 5.00 3.75 600 1.333 0.9826
## time7 - time10 4.60 3.75 600 1.227 0.9915
## time7 - time11 4.40 3.75 600 1.173 0.9943
## time7 - time12 0.60 3.75 600 0.160 1.0000
## time7 - time13 9.80 3.75 600 2.614 0.3043
## time8 - time9 1.40 3.75 600 0.373 1.0000
## time8 - time10 1.00 3.75 600 0.267 1.0000
## time8 - time11 0.80 3.75 600 0.213 1.0000
## time8 - time12 -3.00 3.75 600 -0.800 0.9999
## time8 - time13 6.20 3.75 600 1.653 0.9119
## time9 - time10 -0.40 3.75 600 -0.107 1.0000
## time9 - time11 -0.60 3.75 600 -0.160 1.0000
## time9 - time12 -4.40 3.75 600 -1.173 0.9943
## time9 - time13 4.80 3.75 600 1.280 0.9877
## time10 - time11 -0.20 3.75 600 -0.053 1.0000
## time10 - time12 -4.00 3.75 600 -1.067 0.9976
## time10 - time13 5.20 3.75 600 1.387 0.9760
## time11 - time12 -3.80 3.75 600 -1.013 0.9986
## time11 - time13 5.40 3.75 600 1.440 0.9677
## time12 - time13 9.20 3.75 600 2.454 0.4078
##
## condition = CON:
## contrast estimate SE df t.ratio p.value
## time1 - time2 -6.32 3.75 600 -1.685 0.9000
## time1 - time3 -1.80 3.75 600 -0.480 1.0000
## time1 - time4 -3.92 3.75 600 -1.045 0.9981
## time1 - time5 -7.92 3.75 600 -2.112 0.6554
## time1 - time6 -19.48 3.75 600 -5.195 <.0001
## time1 - time7 -3.12 3.75 600 -0.832 0.9998
## time1 - time8 -6.80 3.75 600 -1.814 0.8425
## time1 - time9 -10.40 3.75 600 -2.774 0.2174
## time1 - time10 -13.00 3.75 600 -3.467 0.0321
## time1 - time11 -15.60 3.75 600 -4.160 0.0025
## time1 - time12 -18.40 3.75 600 -4.907 0.0001
## time1 - time13 -10.20 3.75 600 -2.720 0.2444
## time2 - time3 4.52 3.75 600 1.205 0.9927
## time2 - time4 2.40 3.75 600 0.640 1.0000
## time2 - time5 -1.60 3.75 600 -0.427 1.0000
## time2 - time6 -13.16 3.75 600 -3.510 0.0279
## time2 - time7 3.20 3.75 600 0.853 0.9997
## time2 - time8 -0.48 3.75 600 -0.128 1.0000
## time2 - time9 -4.08 3.75 600 -1.088 0.9972
## time2 - time10 -6.68 3.75 600 -1.782 0.8584
## time2 - time11 -9.28 3.75 600 -2.475 0.3932
## time2 - time12 -12.08 3.75 600 -3.222 0.0684
## time2 - time13 -3.88 3.75 600 -1.035 0.9982
## time3 - time4 -2.12 3.75 600 -0.565 1.0000
## time3 - time5 -6.12 3.75 600 -1.632 0.9193
## time3 - time6 -17.68 3.75 600 -4.715 0.0002
## time3 - time7 -1.32 3.75 600 -0.352 1.0000
## time3 - time8 -5.00 3.75 600 -1.333 0.9826
## time3 - time9 -8.60 3.75 600 -2.294 0.5226
## time3 - time10 -11.20 3.75 600 -2.987 0.1302
## time3 - time11 -13.80 3.75 600 -3.680 0.0156
## time3 - time12 -16.60 3.75 600 -4.427 0.0008
## time3 - time13 -8.40 3.75 600 -2.240 0.5620
## time4 - time5 -4.00 3.75 600 -1.067 0.9976
## time4 - time6 -15.56 3.75 600 -4.150 0.0026
## time4 - time7 0.80 3.75 600 0.213 1.0000
## time4 - time8 -2.88 3.75 600 -0.768 0.9999
## time4 - time9 -6.48 3.75 600 -1.728 0.8826
## time4 - time10 -9.08 3.75 600 -2.422 0.4301
## time4 - time11 -11.68 3.75 600 -3.115 0.0925
## time4 - time12 -14.48 3.75 600 -3.862 0.0081
## time4 - time13 -6.28 3.75 600 -1.675 0.9041
## time5 - time6 -11.56 3.75 600 -3.083 0.1010
## time5 - time7 4.80 3.75 600 1.280 0.9877
## time5 - time8 1.12 3.75 600 0.299 1.0000
## time5 - time9 -2.48 3.75 600 -0.661 1.0000
## time5 - time10 -5.08 3.75 600 -1.355 0.9802
## time5 - time11 -7.68 3.75 600 -2.048 0.7003
## time5 - time12 -10.48 3.75 600 -2.795 0.2072
## time5 - time13 -2.28 3.75 600 -0.608 1.0000
## time6 - time7 16.36 3.75 600 4.363 0.0011
## time6 - time8 12.68 3.75 600 3.382 0.0421
## time6 - time9 9.08 3.75 600 2.422 0.4301
## time6 - time10 6.48 3.75 600 1.728 0.8826
## time6 - time11 3.88 3.75 600 1.035 0.9982
## time6 - time12 1.08 3.75 600 0.288 1.0000
## time6 - time13 9.28 3.75 600 2.475 0.3932
## time7 - time8 -3.68 3.75 600 -0.981 0.9989
## time7 - time9 -7.28 3.75 600 -1.942 0.7701
## time7 - time10 -9.88 3.75 600 -2.635 0.2917
## time7 - time11 -12.48 3.75 600 -3.328 0.0497
## time7 - time12 -15.28 3.75 600 -4.075 0.0035
## time7 - time13 -7.08 3.75 600 -1.888 0.8019
## time8 - time9 -3.60 3.75 600 -0.960 0.9992
## time8 - time10 -6.20 3.75 600 -1.653 0.9119
## time8 - time11 -8.80 3.75 600 -2.347 0.4836
## time8 - time12 -11.60 3.75 600 -3.094 0.0981
## time8 - time13 -3.40 3.75 600 -0.907 0.9995
## time9 - time10 -2.60 3.75 600 -0.693 1.0000
## time9 - time11 -5.20 3.75 600 -1.387 0.9760
## time9 - time12 -8.00 3.75 600 -2.134 0.6401
## time9 - time13 0.20 3.75 600 0.053 1.0000
## time10 - time11 -2.60 3.75 600 -0.693 1.0000
## time10 - time12 -5.40 3.75 600 -1.440 0.9677
## time10 - time13 2.80 3.75 600 0.747 0.9999
## time11 - time12 -2.80 3.75 600 -0.747 0.9999
## time11 - time13 5.40 3.75 600 1.440 0.9677
## time12 - time13 8.20 3.75 600 2.187 0.6012
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 13 estimatesemmeans(model_MVAS_FU, pairwise ~ condition | time)## $emmeans
## time = 1:
## condition emmean SE df lower.CL upper.CL
## MF 17.6 3.38 132 10.91 24.3
## CON 15.0 3.38 132 8.31 21.7
##
## time = 2:
## condition emmean SE df lower.CL upper.CL
## MF 22.1 3.38 132 15.43 28.8
## CON 21.3 3.38 132 14.63 28.0
##
## time = 3:
## condition emmean SE df lower.CL upper.CL
## MF 43.4 3.38 132 36.71 50.1
## CON 16.8 3.38 132 10.11 23.5
##
## time = 4:
## condition emmean SE df lower.CL upper.CL
## MF 59.4 3.38 132 52.67 66.1
## CON 18.9 3.38 132 12.23 25.6
##
## time = 5:
## condition emmean SE df lower.CL upper.CL
## MF 67.7 3.38 132 60.99 74.4
## CON 22.9 3.38 132 16.23 29.6
##
## time = 6:
## condition emmean SE df lower.CL upper.CL
## MF 59.5 3.38 132 52.83 66.2
## CON 34.5 3.38 132 27.79 41.2
##
## time = 7:
## condition emmean SE df lower.CL upper.CL
## MF 40.0 3.38 132 33.31 46.7
## CON 18.1 3.38 132 11.43 24.8
##
## time = 8:
## condition emmean SE df lower.CL upper.CL
## MF 36.4 3.38 132 29.71 43.1
## CON 21.8 3.38 132 15.11 28.5
##
## time = 9:
## condition emmean SE df lower.CL upper.CL
## MF 35.0 3.38 132 28.31 41.7
## CON 25.4 3.38 132 18.71 32.1
##
## time = 10:
## condition emmean SE df lower.CL upper.CL
## MF 35.4 3.38 132 28.71 42.1
## CON 28.0 3.38 132 21.31 34.7
##
## time = 11:
## condition emmean SE df lower.CL upper.CL
## MF 35.6 3.38 132 28.91 42.3
## CON 30.6 3.38 132 23.91 37.3
##
## time = 12:
## condition emmean SE df lower.CL upper.CL
## MF 39.4 3.38 132 32.71 46.1
## CON 33.4 3.38 132 26.71 40.1
##
## time = 13:
## condition emmean SE df lower.CL upper.CL
## MF 30.2 3.38 132 23.51 36.9
## CON 25.2 3.38 132 18.51 31.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## time = 1:
## contrast estimate SE df t.ratio p.value
## MF - CON 2.6 3.75 600 0.693 0.4883
##
## time = 2:
## contrast estimate SE df t.ratio p.value
## MF - CON 0.8 3.75 600 0.213 0.8311
##
## time = 3:
## contrast estimate SE df t.ratio p.value
## MF - CON 26.6 3.75 600 7.094 <.0001
##
## time = 4:
## contrast estimate SE df t.ratio p.value
## MF - CON 40.4 3.75 600 10.785 <.0001
##
## time = 5:
## contrast estimate SE df t.ratio p.value
## MF - CON 44.8 3.75 600 11.937 <.0001
##
## time = 6:
## contrast estimate SE df t.ratio p.value
## MF - CON 25.0 3.75 600 6.678 <.0001
##
## time = 7:
## contrast estimate SE df t.ratio p.value
## MF - CON 21.9 3.75 600 5.835 <.0001
##
## time = 8:
## contrast estimate SE df t.ratio p.value
## MF - CON 14.6 3.75 600 3.894 0.0001
##
## time = 9:
## contrast estimate SE df t.ratio p.value
## MF - CON 9.6 3.75 600 2.560 0.0107
##
## time = 10:
## contrast estimate SE df t.ratio p.value
## MF - CON 7.4 3.75 600 1.974 0.0489
##
## time = 11:
## contrast estimate SE df t.ratio p.value
## MF - CON 5.0 3.75 600 1.333 0.1829
##
## time = 12:
## contrast estimate SE df t.ratio p.value
## MF - CON 6.0 3.75 600 1.600 0.1101
##
## time = 13:
## contrast estimate SE df t.ratio p.value
## MF - CON 5.0 3.75 600 1.333 0.1829
##
## Degrees-of-freedom method: kenward-rogerplot(fitted(model_MVAS_FU), resid(model_MVAS_FU), xlab = "Predicted", ylab = "residuals", main = "homoscedacity check")
abline(h = 0, col = "red")
coef_test(model_MVAS_FU, vcov = "CR2")## Alternative hypothesis: two-sided
## Coef. Estimate SE Null value t-stat d.f. (Satt) p-val (Satt)
## (Intercept) 17.60 3.10 0 5.675 24 < 0.001
## conditionCON -2.60 3.17 0 -0.820 24 0.42019
## time2 4.52 1.64 0 2.752 24 0.01109
## time3 25.80 3.53 0 7.312 24 < 0.001
## time4 41.76 4.05 0 10.306 24 < 0.001
## time5 50.08 4.18 0 11.982 24 < 0.001
## time6 41.92 4.08 0 10.275 24 < 0.001
## time7 22.40 4.05 0 5.531 24 < 0.001
## time8 18.80 3.93 0 4.781 24 < 0.001
## time9 17.40 4.60 0 3.784 24 < 0.001
## time10 17.80 4.67 0 3.813 24 < 0.001
## time11 18.00 5.74 0 3.134 24 0.00451
## time12 21.80 6.26 0 3.485 24 0.00191
## time13 12.60 5.28 0 2.385 24 0.02534
## conditionCON:time2 1.80 2.66 0 0.678 24 0.50438
## conditionCON:time3 -24.00 4.10 0 -5.857 24 < 0.001
## conditionCON:time4 -37.84 4.86 0 -7.784 24 < 0.001
## conditionCON:time5 -42.16 5.09 0 -8.277 24 < 0.001
## conditionCON:time6 -22.44 4.39 0 -5.108 24 < 0.001
## conditionCON:time7 -19.28 4.29 0 -4.494 24 < 0.001
## conditionCON:time8 -12.00 3.72 0 -3.226 24 0.00360
## conditionCON:time9 -7.00 3.94 0 -1.776 24 0.08846
## conditionCON:time10 -4.80 3.95 0 -1.216 24 0.23594
## conditionCON:time11 -2.40 4.22 0 -0.569 24 0.57473
## conditionCON:time12 -3.40 4.48 0 -0.759 24 0.45536
## conditionCON:time13 -2.40 4.88 0 -0.492 24 0.62714
## Sig.
## ***
##
## *
## ***
## ***
## ***
## ***
## ***
## ***
## ***
## ***
## **
## **
## *
##
## ***
## ***
## ***
## ***
## ***
## **
## .
##
##
##
## Temporal robustness of mental fatigue response of selected subjective and behavioural variables
Analysed using ICCs comparing condition difference values.
Physical performance
ICC_TT <- icc(MFIDRB_TTDist %>% dplyr::select(Diff_TTOM, Diff_TTFU), model = "twoway", type = "consistency")
print(ICC_TT)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.448
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 2.63 , p = 0.0108
##
## 95%-Confidence Interval for ICC Population Values:
## 0.073 < ICC < 0.713LinearRegressionTT <- lm(Diff_TTOM ~ Diff_TTFU, data = MFIDRB_TTDist) #linear regression equation of baseline and follow up measurements
summary(LinearRegressionTT)##
## Call:
## lm(formula = Diff_TTOM ~ Diff_TTFU, data = MFIDRB_TTDist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0575 -0.4025 0.2089 0.5754 1.2386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1796 0.1685 -1.066 0.2975
## Diff_TTFU 0.4649 0.1930 2.408 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8409 on 23 degrees of freedom
## Multiple R-squared: 0.2014, Adjusted R-squared: 0.1666
## F-statistic: 5.799 on 1 and 23 DF, p-value: 0.02445Cognitive performance
ICC_GoRT_Pre <- icc(MFIDRB_GoNoGoRT %>% dplyr::select(Diff_GoRT_Pre_OM, Diff_GoRT_Pre_FU), model = "twoway", type = "consistency")
ICC_GoRT_Post <- icc(MFIDRB_GoNoGoRT %>% dplyr::select(Diff_GoRT_Post_OM, Diff_GoRT_Post_FU), model = "twoway", type = "consistency")
print(ICC_GoRT_Pre)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.174
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.704 , p = 0.802
##
## 95%-Confidence Interval for ICC Population Values:
## -0.526 < ICC < 0.23print(ICC_GoRT_Post)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.289
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.81 , p = 0.0762
##
## 95%-Confidence Interval for ICC Population Values:
## -0.112 < ICC < 0.609Rating of perceived exertion
ICC_RPE_1 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_1_Diff, RPE_1_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_2 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_2_Diff, RPE_2_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_3 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_3_Diff, RPE_3_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_4 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_4_Diff, RPE_4_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_5 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_5_Diff, RPE_5_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_6 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_6_Diff, RPE_6_Diff_ROB), model = "twoway", type = "consistency")
ICC_RPE_7 <- icc(MFIDRB_RPE_Diff %>% dplyr::select(RPE_7_Diff, RPE_7_Diff_ROB), model = "twoway", type = "consistency")
print(ICC_RPE_1)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.306
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.532 , p = 0.936
##
## 95%-Confidence Interval for ICC Population Values:
## -0.62 < ICC < 0.094print(ICC_RPE_2)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.00384
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.992 , p = 0.507
##
## 95%-Confidence Interval for ICC Population Values:
## -0.392 < ICC < 0.385print(ICC_RPE_3)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.177
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.699 , p = 0.807
##
## 95%-Confidence Interval for ICC Population Values:
## -0.529 < ICC < 0.226print(ICC_RPE_4)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.264
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.582 , p = 0.904
##
## 95%-Confidence Interval for ICC Population Values:
## -0.592 < ICC < 0.138print(ICC_RPE_5)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.279
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.564 , p = 0.916
##
## 95%-Confidence Interval for ICC Population Values:
## -0.602 < ICC < 0.122print(ICC_RPE_6)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.279
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.77 , p = 0.0839
##
## 95%-Confidence Interval for ICC Population Values:
## -0.123 < ICC < 0.602print(ICC_RPE_7)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.138
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.758 , p = 0.749
##
## 95%-Confidence Interval for ICC Population Values:
## -0.499 < ICC < 0.265Feeling of Mental Fatigue
ICC_MVAS_1 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_1_Diff, MVAS_1_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_2 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_2_Diff, MVAS_2_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_3 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_3_Diff, MVAS_3_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_4 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_4_Diff, MVAS_4_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_5 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_5_Diff, MVAS_5_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_6 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_6_Diff, MVAS_6_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_7 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_7_Diff, MVAS_7_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_8 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_8_Diff, MVAS_8_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_9 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_9_Diff, MVAS_9_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_10 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_10_Diff, MVAS_10_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_11 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_11_Diff, MVAS_11_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_12 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_12_Diff, MVAS_12_Diff_ROB), model = "twoway", type = "consistency")
ICC_MVAS_13 <- icc(MFIDRB_MVAS_Diff %>% dplyr::select(MVAS_13_Diff, MVAS_13_Diff_ROB), model = "twoway", type = "consistency")
print(ICC_MVAS_1)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.00254
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.995 , p = 0.505
##
## 95%-Confidence Interval for ICC Population Values:
## -0.39 < ICC < 0.386print(ICC_MVAS_2)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = -0.383
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 0.446 , p = 0.973
##
## 95%-Confidence Interval for ICC Population Values:
## -0.671 < ICC < 0.007print(ICC_MVAS_3)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.363
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 2.14 , p = 0.034
##
## 95%-Confidence Interval for ICC Population Values:
## -0.029 < ICC < 0.659print(ICC_MVAS_4)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.248
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.66 , p = 0.111
##
## 95%-Confidence Interval for ICC Population Values:
## -0.155 < ICC < 0.581print(ICC_MVAS_5)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.336
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 2.01 , p = 0.0469
##
## 95%-Confidence Interval for ICC Population Values:
## -0.061 < ICC < 0.64print(ICC_MVAS_6)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.245
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.65 , p = 0.114
##
## 95%-Confidence Interval for ICC Population Values:
## -0.159 < ICC < 0.578print(ICC_MVAS_7)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.0762
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.16 , p = 0.356
##
## 95%-Confidence Interval for ICC Population Values:
## -0.322 < ICC < 0.451print(ICC_MVAS_8)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.278
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.77 , p = 0.0846
##
## 95%-Confidence Interval for ICC Population Values:
## -0.124 < ICC < 0.601print(ICC_MVAS_9)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.244
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.65 , p = 0.115
##
## 95%-Confidence Interval for ICC Population Values:
## -0.159 < ICC < 0.578print(ICC_MVAS_10)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.171
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.41 , p = 0.202
##
## 95%-Confidence Interval for ICC Population Values:
## -0.233 < ICC < 0.524print(ICC_MVAS_11)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.0417
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.09 , p = 0.42
##
## 95%-Confidence Interval for ICC Population Values:
## -0.352 < ICC < 0.423print(ICC_MVAS_12)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.134
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.31 , p = 0.257
##
## 95%-Confidence Interval for ICC Population Values:
## -0.268 < ICC < 0.496print(ICC_MVAS_13)## Single Score Intraclass Correlation
##
## Model: twoway
## Type : consistency
##
## Subjects = 25
## Raters = 2
## ICC(C,1) = 0.282
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(24,24) = 1.78 , p = 0.0816
##
## 95%-Confidence Interval for ICC Population Values:
## -0.12 < ICC < 0.604