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---
title: "HIIT vs. MICT"
output:
html_document:
fig_caption: yes
theme: cerulean
toc: yes
toc_depth: 3
toc_float: yes
pdf_document:
toc: yes
toc_depth: '3'
word_document:
toc: yes
toc_depth: '3'
---
This is designed to track study progress! Below is a weekly update.
```{r setup, include=FALSE}
# Load packages
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(readxl)
library(ggplot2)
library(table1)
library(lubridate)
library(knitr)
library(kableExtra)
library(ggpubr)
library(stringr)
library(zoo)
```
```{r demog, message=FALSE, warning=FALSE, echo=FALSE}
# Read in the data generated by REDCapAPI.Rmd
demog<-read.csv("/Users/aubreykr/Google Drive/Shared drives/HIIT and Endurance Study/Data/data/Redcap/Processed_Data/64208_Demographics_Processed.csv")
#----Data Cleaning, features----------
# Pull in data to see if EP surveys are done
epsurveys<-read.csv("/Users/aubreykr/Google Drive/Shared drives/HIIT and Endurance Study/Data/data/Redcap/Raw_Data/64208_WorkoutDates_Raw.csv")
epsurveys<-epsurveys %>%
dplyr::select(record_id, redcap_event_name, perceived_stress_scale_pss10_complete)
epsurveys<-epsurveys %>%
filter(redcap_event_name == "week_12_ep_surveys_arm_1")
epsurveys<-epsurveys %>%
dplyr::rename(EP_surveys = perceived_stress_scale_pss10_complete) %>%
dplyr::select(record_id, EP_surveys)
epsurveys<-epsurveys %>%
mutate(EP_surveys = ifelse(EP_surveys == "Complete", "YES", ""))
# Create variable for crossover ppts
demog<-demog %>%
mutate(crossover = ifelse(record_id >=1000, "yes", "no"))
# Calculate expected num of workouts based on week they are on
demog$expected_num <- gsub("Week ", "", demog$current_week)
demog$expected_num <- (as.numeric(demog$expected_num) - 1)*3
# Flag people who need to be extended
demog$EXTEND <- ifelse(demog$expected_num - demog$total_workouts_in_REDCap >=4, "YES", " ")
demog$EXTEND[demog$Group == "Control"]<- " "
# EP vo2 complete?
demog$EP_VO2[is.na(demog$ep_vo2_max)==FALSE] <- "YES"
demog$EP_VO2[is.na(demog$ep_vo2_max)==TRUE] <- " "
# if missing, they have enrolled and update this
demog$enrollment_status[is.na(demog$enrollment_status) == TRUE] <- "Enrolled (not screened yet)"
# Specify order of weeks for plots
demog$current_week <- factor(demog$current_week, levels=c("Not Training Yet", "Week 1", "Week 2", "Week 3", "Week 4", "Week 5", "Week 6", "Week 7", "Week 8", "Week 9", "Week 10", "Week 11", "Week 12", "Week 13", "Week 14"))
# new column for target HR zone depending on group
demog<-demog %>%
mutate(HR_zone = case_when(Group == "MICT" ~ bl_heart_rate_mict,
Group == "HIIT" ~ bl_heart_rate_hiit))
# manually assign HR zone for crossover records
demog$HR_zone[demog$record_id == "1070"] <- "122-134"
demog$HR_zone[demog$record_id == "1076"] <- "114-126"
demog$HR_zone[demog$record_id == "1103"] <- "133-149"
demog$HR_zone[demog$record_id == "1112"] <- "141-162"
demog$HR_zone[demog$record_id == "1116"] <- "112-120"
demog<-full_join(demog, epsurveys, by=c("record_id"))
# Create sex + agebin variable
demog$strata[demog$sex == "Female" & demog$agegroup == "18-34 yrs"]<-"F, 18-34"
demog$strata[demog$sex == "Female" & demog$agegroup == "35-65 yrs"]<-"F, 35-65"
demog$strata[demog$sex == "Male" & demog$agegroup == "18-34 yrs"]<-"M, 18-34"
demog$strata[demog$sex == "Male" & demog$agegroup == "35-65 yrs"]<-"M, 35-65"
```
## Where are participants this week?
This was last updated on `r format(Sys.Date(), format="%B %d, %Y")`.
```{r firstcode, message=FALSE, warning=FALSE, echo=FALSE, fig.width=7, fig.height=7}
#---------Snapshot of participant progress------------
ongoing<-demog %>%
filter(!record_id %in% c(114, 121, 134)) %>%
filter(enrollment_status == "On-going Randomized" & is.na(ep_vo2_max)==TRUE | enrollment_status=="Enrolled (not screened yet)")
current_num<-length(unique(ongoing$record_id))
demog %>%
filter(!record_id %in% c(114, 121, 134)) %>%
filter(enrollment_status == "On-going Randomized" & is.na(ep_vo2_max)==TRUE | enrollment_status=="Enrolled (not screened yet)") %>%
ggplot(aes(x = current_week, fill=Group, group = factor(record_id))) + geom_bar() +
geom_text(stat = "count", aes(label = paste0(record_id, sep="\n(", total_workouts_in_REDCap, sep=")"), y = ..count..),
position = position_stack(vjust = 0.5), color = "black", size = 3, alpha=1) +
scale_x_discrete(drop = FALSE) + theme_bw() +
theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1)) +
xlab(paste0("Plot Last Updated:", sep = " ", Sys.Date())) +
ggtitle(paste0("Current Participants: n=", sep="", current_num)) + labs(fill = "Group") + theme(plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(breaks = seq(0, 30, 2))
demog %>%
filter(!record_id %in% c(114, 121, 134)) %>%
filter(enrollment_status == "On-going Randomized" & is.na(ep_vo2_max)==TRUE | enrollment_status=="Enrolled (not screened yet)") %>%
ggplot(aes(x = current_week, fill=strata, group = factor(record_id))) + geom_bar() +
geom_text(stat = "count", aes(label = paste0(record_id, sep="\n(", total_workouts_in_REDCap, sep=")"), y = ..count..),
position = position_stack(vjust = 0.5), color = "black", size = 3, alpha=1) +
scale_x_discrete(drop = FALSE) + theme_bw() +
theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1)) +
xlab(paste0("Plot Last Updated:", sep = " ", Sys.Date())) +
ggtitle(paste0("Current Participants: n=", sep="", current_num)) + labs(fill = "Sex") + theme(plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(breaks = seq(0, 30, 2))
```
- **Things to keep track of:**
- Are green/blue participants on track with workouts?
- Have participants at the end...
- completed EP surveys and enough workouts?
- willing to crossover?
- Are new participants willing to do biopsy?
## Workout Adherence
### On-going participants
```{r workouts0, message=FALSE, warning=FALSE, echo=FALSE}
counts<-demog %>%
filter(!record_id %in% c(114, 121)) %>%
filter(enrollment_status == "On-going Randomized" & current_week !="Not Training Yet" & is.na(ep_vo2_max)==TRUE)
active_num <- length(unique(counts$record_id))
# Filter for participants who are on-going
demog %>%
filter(!record_id %in% c(114, 121)) %>%
filter(enrollment_status == "On-going Randomized" & current_week !="Not Training Yet" & is.na(ep_vo2_max)==TRUE) %>%
dplyr::select(record_id, current_week, Group, total_workouts_in_REDCap, expected_num, quality_fraction, name_of_participant_buddy, HR_zone, EXTEND, EP_surveys) %>%
arrange(current_week, record_id) %>%
kbl(col.names = c("Record ID","Current Week","Group","Total Wkts", "Expected Num", "Quality (%)", "Buddy", "HR Target", "EXTEND", "EP_surveys"), align="c", escape=FALSE, caption=paste0("Active Participants: n=", sep="", active_num)) %>%
kable_styling(full_width = F, position = "center", font_size = 14, latex_options = "striped", bootstrap_options = c("striped", "hover"))
demog$bl_VO2 <- ifelse(is.na(demog$bl_vo2_max), "No", "Yes")
# upcoming ppts
demog %>%
filter(enrollment_status %in% c( "On-going Randomized", "Enrolled (not screened yet)") & current_week =="Not Training Yet" ) %>%
dplyr::select(record_id, current_week, Group, sex, HR_zone, bl_VO2) %>%
kbl(col.names = c("Record ID","Current Week","Group","Sex", "HR zone", "BL VO2 done?"), align="c", escape=FALSE, caption="Upcoming ppts") %>%
kable_styling()
# EP survey check for those complete with EP CPX but not CTRU
demog %>%
filter(enrollment_status == "On-going Randomized" & (EP_VO2 != " " | current_week == "Week 12") & !record_id %in% c(76, 1076, 91, 92, 94, 98, 103, 105, 107, 110, 112, 1091, 117, 119, 123, 115, 116, 124, 126, 127, 128, 1103, 1112))%>%
dplyr::select(record_id, current_week, Group, total_workouts_in_REDCap, expected_num, quality_fraction, name_of_participant_buddy, HR_zone, EP_VO2, EP_surveys) %>%
arrange(current_week, record_id) %>%
kbl(col.names = c("Record ID","Current Week","Group","Total Wkts", "Expected Num", "Quality (%)", "Buddy", "HR Target", "EP_Vo2?", "EP_surveys"), align="c", escape=FALSE, caption=paste0("EP survey check (randomized and EP VO2 done / at Week 12)")) %>%
kable_styling(full_width = F, position = "center", font_size = 14, latex_options = "striped", bootstrap_options = c("striped", "hover"))
#Active controls
# demog %>%
# filter(enrollment_status == "On-going Randomized" & Group == "Control" & current_week !="Not Training Yet" & !record_id %in% c(103, 76, 98, 91) & record_id <= 1000) %>%
# dplyr::select(record_id, current_week, Group, name_of_participant_buddy, control_crossover, EP_VO2) %>%
# arrange(current_week, name_of_participant_buddy) %>%
# kbl(col.names = c("Record ID","Current Week","Group","Buddy", "Crossover?", "EP VO2?"), align="c", escape=FALSE, caption="Active Controls") %>%
# kable_styling()
# not marked as completed, but did VO2
# demog %>%
# filter(enrollment_status != "Completed" & is.na(ep_vo2_max)==FALSE) %>%
# dplyr::select(record_id, Group, name_of_participant_buddy, EP_surveys) %>%
# arrange(desc(record_id)) %>%
# kbl(col.names = c("Record ID","Group","Buddy", "EP surveys?"), align="c", escape=FALSE, caption="Check on completed ppt EP surveys:") %>%
# kable_styling()
#Mitra processing
# mitra<-demog %>%
# select(record_id, enrollment_status, Group)
#
# write.csv(mitra,"/Users/aubreykr/Desktop/mitradates.csv")
#
# View(mitra %>%
# filter(!record_id %in% c(3,4,5,6,7,8,10,12,14,18,19,20,21,26,29,30,36,37,38,40,45,46,50,51,56,60,64,71,72,73,74,75,76,77,80,81,87,88,89,90,95)))
#
# mitras<-mitra %>%
# filter(enrollment_status %in% c("Screen failed", "Withdrawn (before training)", "Withdrawn (after training)", "Lost to Follow-Up (before training)", "Lost to Follow-Up (after training)") | record_id >=74 & record_id <1000)
# #df<-read.csv("/Users/aubreykr/Desktop/Mitras/Mitra_ScreenFail_Withdraw.csv")
#mitras
#df<-df %>%
#rename(record_id = Record.ID)
#mitras<-left_join(mitras, df)
#mitras %>%
#filter(Date.of.Stress.Test.Visit != "" | Date.of.Endpoint.Stress.Test.Visit !="")
```
### Crossover Participant Tracking
```{r crossovers, message=FALSE, warning=FALSE, echo=FALSE}
demog$crossover_rand_group[demog$crossover_rand_group == "Endurance"]<- "MICT"
demog %>%
filter(crossover == "yes") %>%
dplyr::select(record_id, Group, EP_VO2) %>%
kbl(col.names = c("Record ID","Rand Group", "EP VO2 done?"), align="c", escape=FALSE, caption="Crossover Participants") %>%
kable_styling()
# Create sex + agebin variable
demog$strata[demog$sex == "Female" & demog$agegroup == "18-34 yrs"]<-"F, 18-34"
demog$strata[demog$sex == "Female" & demog$agegroup == "35-65 yrs"]<-"F, 35-65"
demog$strata[demog$sex == "Male" & demog$agegroup == "18-34 yrs"]<-"M, 18-34"
demog$strata[demog$sex == "Male" & demog$agegroup == "35-65 yrs"]<-"M, 35-65"
demog %>%
filter(crossover == "yes") %>%
ggplot(aes( x=Group, fill=Group, group = factor(record_id))) + geom_bar() +
geom_text(stat = "count", aes(label = paste0(record_id, sep="\n(", total_workouts_in_REDCap, sep=")"), y = ..count..),
position = position_stack(vjust = 0.5), color = "black", size = 3, alpha=1) +
scale_x_discrete(drop = FALSE) + theme_bw() +
theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1)) +
xlab(paste0("Plot Last Updated:", sep = " ", Sys.Date())) +
ggtitle(paste0("Crossover Participants")) + labs(fill = "Record ID") + theme(plot.title = element_text(hjust = 0.5)) +
ylim(c(0,3)) +facet_wrap(~strata)
```
```{r workouts,message=FALSE, warning=FALSE, echo=FALSE}
### Completed MICT participants
#Filter for participants who are completed and MICT
# completed_mict<-demog %>%
# filter(enrollment_status == "Completed" & Group == "MICT") %>%
# dplyr::select(record_id, quality_workouts_in_REDCap, percent_complete_of_36_wkts, number_polar_used, number_fitbit_used, total_workouts_in_REDCap, sex, agegroup, HR_zone)
#
# completed_mict %>%
# arrange(as.numeric(record_id)) %>%
# kable(col.names = c("Record ID", "Quality Wkts", "Percent Complete of 36 Wkts", "Polar Worn", "Fitbit Worn", "Total Wkts", "Sex", "Age", "HR Target"), align="c") %>%
# kable_styling(full_width = F, position = "center", font_size = 14, latex_options = "striped", bootstrap_options = c("striped", "hover"))
```
```{r workouts2,message=FALSE, warning=FALSE, echo=FALSE}
### Completed HIIT participants
# Filter for participants who are completed and HIIT
# completed_hiit<-demog %>%
# filter(enrollment_status == "Completed" & Group == "HIIT") %>%
# dplyr::select(record_id, quality_workouts_in_REDCap, percent_complete_of_36_wkts, number_polar_used,number_fitbit_used, total_workouts_in_REDCap, sex, agegroup, HR_zone)
#
# completed_hiit %>%
# arrange(as.numeric(record_id)) %>%
# kable(col.names = c("Record ID", "Quality Wkts", "Percent Complete of 36 Wkts", "Polar Worn", "Fitbit Worn", "Total Wkts", "Sex", "Age", "HR Target"), align="c") %>%
# kable_styling(full_width = F, position = "center", font_size = 14, latex_options = "striped", bootstrap_options = c("striped", "hover"))
```
```{r workouts3,message=FALSE, warning=FALSE, echo=FALSE}
### HIIT vs. MICT
#demog %>%
# filter(enrollment_status == "Completed" & Group %in% c("HIIT", "MICT")) %>%
# ggplot(aes(y=percent_complete_of_36_wkts, x=Group)) + geom_boxplot() + geom_point() + ylab("Percent Complete of 36 Workouts") +
# ggtitle("Percent Complete of 36 Workouts by Randomization Group") + theme_bw()+ theme(plot.title = element_text(hjust = 0.5)) + ylim(c(0,100))
#
# demog %>%
# filter(enrollment_status == "Completed" & Group %in% c("HIIT", "MICT")) %>%
# ggplot(aes(y=total_workouts_in_REDCap, x=Group)) + geom_boxplot() +geom_point() + geom_hline(yintercept = 32, linetype="dashed") + ylab("Total Workouts in REDCap") +
# ggtitle("Completed Participants (n=30): Total Workouts") + theme_bw()+ theme(plot.title = element_text(hjust = 0.5)) + ylim(c(0,40))
#
# demog %>%
# filter(enrollment_status == "Completed" & Group %in% c("HIIT", "MICT")) %>%
# ggplot(aes(y=quality_workouts_in_REDCap, x=Group)) + geom_boxplot() +geom_jitter(aes(color=factor(record_id))) + geom_hline(yintercept = 32, linetype="dashed") + ylab("Total Workouts in REDCap") +
# ggtitle("Completed Participants (n=30): Good Workouts") + theme_bw()+ theme(plot.title = element_text(hjust = 0.5)) + ylim(c(0,40)) + labs(color="Participant Num")
```
### NUMBER COMPLETED
```{r progressstrata3, message=FALSE, warning=FALSE, echo=FALSE}
cohort<-demog %>%
filter(is.na(ep_vo2_max)==FALSE)
completed_num<-cohort %>%
filter(is.na(ep_vo2_max)==FALSE & record_id <= 1000)
completed_num<-length(completed_num$record_id)
cohort %>%
filter(is.na(ep_vo2_max)==FALSE & record_id < 1000) %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+ theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste0(completed_num, sep=" ", "Completed with VO2 max pre/post")) + guides(alpha = "none")
# cohort %>%
# filter(is.na(ep_vo2_max)==FALSE & record_id > 1000) %>%
# ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+
# theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
# geom_text(aes(label=..count..),stat='count',vjust=1.5) +
# ggtitle(paste0("Num Crossovers Completed")) + guides(alpha = "none")
```
### Progress toward filling 4 strata
```{r progressstrata, message=FALSE, warning=FALSE, echo=FALSE}
# Create sex + agebin variable
demog$strata[demog$sex == "Female" & demog$agegroup == "18-34 yrs"]<-"F, 18-35"
demog$strata[demog$sex == "Female" & demog$agegroup == "35-65 yrs"]<-"F, 35-65"
demog$strata[demog$sex == "Male" & demog$agegroup == "18-34 yrs"]<-"M, 18-34"
demog$strata[demog$sex == "Male" & demog$agegroup == "35-65 yrs"]<-"M, 35-64"
cohort<-demog %>%
filter(enrollment_status == "On-going Randomized" & record_id <1000 | enrollment_status == "Completed" & record_id <1000)
# cohort %>%
# filter(is.na(ep_vo2_max)==FALSE & record_id < 1000) %>%
# ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+theme_bw()+
# theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
# geom_text(aes(label=..count..),stat='count',vjust=1.5) +
# ggtitle(paste0(completed_num, sep=" ", "Completed with CPX pre/post")) + guides(alpha = "none") + facet_wrap(~strata)
cohort %>%
filter(is.na(ep_vo2_max)==FALSE & record_id < 1000) %>%
ggplot(aes(x=sex, fill=sex, alpha=0.8))+geom_bar(stat="count")+theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste0(completed_num, sep=" ", "Completed with CPX pre/post")) + guides(alpha = "none") + facet_wrap(~Group)
cohort %>%
filter(is.na(ep_vo2_max)==FALSE & record_id < 1000) %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste0(completed_num, sep=" ", "Completed with CPX pre/post")) + guides(alpha = "none")
# Progress Toward Filling 4 Strata (M, F, 18-35 yrs, 35-55 yrs)
cohort %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+facet_wrap(~strata)+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste("On-going Randomized or Completed Study: n=", sep="", length(unique(cohort$record_id)))) + guides(alpha = "none")
cohort<-cohort %>%
filter(record_id != "81" & record_id !="69" & record_id !="1070" & record_id !="1076" & record_id !="1091")
cohort %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+ theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste("Sum of on-going + completed: n=", sep="", length(unique(cohort$record_id)))) + guides(alpha = "none")
```
### ALL PARTICIPANTS RANDOMIZED
```{r progressstrata2, message=FALSE, warning=FALSE, echo=FALSE}
randomized<-demog %>%
filter(Group != "New Participant")
randomized_exclude_crossover<-demog %>%
filter(Group != "New Participant" & record_id < 1000)
num_rand<-length(randomized$record_id)
num_rand_no_cross<-length(randomized_exclude_crossover$record_id)
randomized %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+ theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste0(num_rand, sep=" ", "were randomized (including crossovers)")) + guides(alpha = "none")
randomized_exclude_crossover %>%
ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+ theme_bw()+
theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
geom_text(aes(label=..count..),stat='count',vjust=1.5) +
ggtitle(paste0(num_rand_no_cross, sep=" ", "were randomized (excluding crossovers)")) + guides(alpha = "none")
withdraw<-demog %>%
filter(Group != "New Participant" & enrollment_status != "On-going Randomized" & enrollment_status != "Completed")
# withdraw %>%
# ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+ theme_bw()+
# theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
# geom_text(aes(label=..count..),stat='count',vjust=1.5) +
# ggtitle(paste0("Withdrew or LTFU")) + guides(alpha = "none") + facet_wrap(~strata)
# cohort %>%
# filter(is.na(ep_vo2_max)==FALSE & record_id > 1000) %>%
# ggplot(aes(x=Group, fill=Group, alpha=0.8))+geom_bar(stat="count")+
# theme(plot.title = element_text(hjust = 0.5))+xlab("Group")+ylab("Number")+scale_fill_discrete(name = "Group")+
# geom_text(aes(label=..count..),stat='count',vjust=1.5) +
# ggtitle(paste0("Num Crossovers Completed")) + guides(alpha = "none")
```
## Study Progress
```{r newppl, message=FALSE, warning=FALSE, echo=FALSE}
# Order levels of enrollment status
demog<-demog %>%
mutate(enrollment_status = ifelse(is.na(ep_vo2_max)==FALSE & record_id<1000, "Completed", ifelse(
is.na(ep_vo2_max)==FALSE & record_id>1000, "Completed (crossover)", enrollment_status
)))
demog$enrollment_status <- factor(demog$enrollment_status, levels = c("Enrolled (not screened yet)", "Lost to Follow-Up (before training)", "Screen failed", "Withdrawn (before training)", "On-going Randomized", "Withdrawn (after training)", "Lost to Follow-Up (after training)", "Completed", "Completed (crossover)"))
ggplot(aes(x=enrollment_status, fill = enrollment_status, alpha=0.8), data=demog)+geom_bar(stat="count")+theme_bw()+
ggtitle(paste("Total Participants: n=", sep="", length(unique(demog$record_id))))+ theme(plot.title = element_text(hjust = 0.5))+xlab("Participant Status")+ylab("Number")+scale_fill_discrete(name = "Group")+ theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+theme(legend.position = "none")+ geom_text(aes(label=..count..),stat='count',vjust=1.5)
#demog %>%
#filter(enrollment_status == "Withdrawn (before training)")
```
### NUMBER CONSENTED
```{r consented, echo=FALSE, message=FALSE, warning=FALSE}
# PLOT CONSENTING
demog$date_consent<-as.yearmon(demog$date_consent)
demog$date_consent <- as.factor(demog$date_consent)
demog$date_consent<-factor(demog$date_consent, levels=c("Feb 2023", "Mar 2023", "Apr 2023", "May 2023", "Jun 2023", "Jul 2023", "Aug 2023", "Sep 2023", "Oct 2023", "Nov 2023", "Dec 2023", "Jan 2024", "Feb 2024", "Mar 2024", "Apr 2024", "May 2024", "Jun 2024", "Jul 2024", "Aug 2024", "Sep 2024", "Oct 2024", "Nov 2024", "Dec 2024", "Jan 2025", "Feb 2025", "Mar 2025", "April 2025", "May 2025"))
consent<-demog %>%
filter(is.na(date_consent)==FALSE)
consented_num<-length(consent$record_id)
demog %>%
filter(is.na(date_consent)==FALSE) %>%
ggplot(aes(x=date_consent)) + geom_bar(color="purple")+stat_count(geom = "text", color="white", aes(label = ..count..), vjust = 2)+theme_bw()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + ggtitle(paste0("Number consented:", sep=" ", consented_num)) + theme(plot.title = element_text(hjust=0.5))+
scale_y_continuous(breaks = seq(0,15,by=2)) + xlab("Month Consented")
```
## Month-by-Month Projections
Note: average counts exclude the first month and Dec 2023 for winter closure which had lower rates.
```{r formatdates, echo=FALSE, message=FALSE, warning=FALSE}
# Filter for relevant variables: start and end month, rand group, record_id
projected <- demog %>%
filter(enrollment_status %in% c("Completed", "On-going Randomized")) %>%
dplyr::select(record_id, Group, start_month, end_month)
# Specify order of months
projected$start_month<-as.factor(projected$start_month)
projected$end_month<-as.factor(projected$end_month)
projected$start_month<-factor(projected$start_month, levels=c("Apr 2023", "May 2023", "Jun 2023", "Jul 2023", "Aug 2023", "Sep 2023", "Oct 2023", "Nov 2023", "Dec 2023", "Jan 2024", "Feb 2024", "Mar 2024", "Apr 2024", "May 2024", "Jun 2024", "Jul 2024", "Aug 2024", "Sep 2024", "Oct 2024", "Nov 2024", "Dec 2024", "Jan 2025", "Feb 2025", "Mar 2025", "April 2025", "May 2025"))
projected$end_month<-factor(projected$end_month, levels=c("Apr 2023", "May 2023", "Jun 2023", "Jul 2023", "Aug 2023", "Sep 2023", "Oct 2023", "Nov 2023", "Dec 2023", "Jan 2024", "Feb 2024", "Mar 2024", "Apr 2024", "May 2024", "Jun 2024", "Jul 2024", "Aug 2024", "Sep 2024", "Oct 2024", "Nov 2024", "Dec 2024", "Jan 2025", "Feb 2025", "Mar 2025", "April 2025", "May 2025"))
projected$start_month[is.na(projected$start_month)]<-"Jan 2025"
projected$end_month[is.na(projected$end_month)]<-"April 2025"
```
```{r plotprojected, echo=FALSE, message=FALSE, warning=FALSE}
# Plots
current_date <- Sys.Date()
current_date <- as.yearmon(current_date)
current_date <- as.factor(current_date)
counts_start<-projected %>%
group_by(start_month) %>%
count()
counts_start<-counts_start %>%
filter(!start_month %in% c("Apr 2023", "Jan 2025"))
avg_start <- round(mean(counts_start$n),2)
counts_end<-projected %>%
group_by(end_month) %>%
count()
counts_end<-counts_end %>%
filter(!end_month %in% c("Aug 2023", "Apr 2025"))
avg_end <- round(mean(counts_end$n),2)
projected %>%
ggplot(aes(x=start_month)) + geom_bar(fill="#F8766D") + stat_count(geom = "text", color="white", aes(label = ..count..), vjust = 2) +theme_bw()+
ggtitle(paste0("Start Months, average count/month:", sep=" ", avg_start)) + theme(plot.title = element_text(hjust = 0.5)) + geom_vline(xintercept = current_date, linetype="dotted", color="black")+ theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1))
projected %>%
ggplot(aes(x=end_month)) + geom_bar(fill="#00BFC4") + stat_count(geom = "text", color="white", aes(label = ..count..), vjust = 2) +theme_bw()+
ggtitle(paste0("End Months, average count/month:", sep=" ", avg_end)) + theme(plot.title = element_text(hjust = 0.5))+ geom_vline(xintercept = current_date, linetype="dotted", color="black") + theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1))
# Combined start + end plot
num_projected <- length(unique(projected$record_id))
# Gather and color by timepoint
projected <- gather(projected, key = "Timepoint", value = "Month", c("start_month", "end_month"))
projected$Timepoint <- factor(projected$Timepoint, levels=c("start_month", "end_month"))
projected$Month<-factor(projected$Month, levels=c("Apr 2023", "May 2023", "Jun 2023", "Jul 2023", "Aug 2023", "Sep 2023", "Oct 2023", "Nov 2023", "Dec 2023", "Jan 2024", "Feb 2024", "Mar 2024", "Apr 2024", "May 2024", "Jun 2024", "Jul 2024", "Aug 2024", "Sep 2024", "Oct 2024", "Nov 2024", "Dec 2024", "Jan 2025", "Feb 2025", "Mar 2025", "April 2025", "May 2025"))
ggplot(aes(x=Month, fill=Timepoint), data=projected) + geom_bar() + theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle(paste0(num_projected, sep=" ", "completing the study by the last date plotted")) + theme(plot.title = element_text(hjust = 0.5))+ geom_vline(xintercept = current_date, linetype="dotted", color="black") + stat_count(geom = "text", color="white", aes(label = ..count..), vjust = 2) + scale_y_continuous(breaks = seq(0,20,by=2)) +theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1))
```
```{r, include=FALSE}
make_table <- function(data){
# Simplify race categories
data$race[data$race == "Other Asian - please specify below (Hmong, Laotian, Thai, Pakistani, Cambodian, etc)"] <- "Other"
data$race <- as.factor(fct_infreq(data$race))
# Simplify ethnicity categories
data$ethnicity[data$ethnicity == "Yes, another Hispanic, Latino, or Spanish origin (please specify origin - Argentinean, Colombian, Dominical, Nicaraguan, Salvadoran, Spaniard, etc.)"] <- "Yes"
data$ethnicity[data$ethnicity == "Yes, Mexican, Mexican American, Chicano"] <- "Yes"
data$ethnicity[data$ethnicity == "No, not of Hispanic, Latino, or Spanish origin"] <- "No"
data$ethnicity <- as.factor(fct_infreq(data$ethnicity))
# Specify labels
label(data$Group) <- "Randomization Group"
label(data$sex) <- "Sex"
label(data$agegroup) <- "Age Group"
label(data$race) <- "Race"
label(data$bl_vo2_max) <- "VO2 max"
label(data$bl_bmi) <- "BMI"
label(data$age) <- "Age"
label(data$bl_percent_body_fat) <- "Body Fat"
label(data$quality_workouts_in_REDCap) <- "Workouts Completed"
label(data$ethnicity) <- "Hispanic"
# Specify units
units(data$age) <- "years"
units(data$bl_vo2_max) <- "mL/kg*min"
units(data$bl_bmi) <- "kg/m2"
units(data$bl_percent_body_fat) <- "%"
units(data$quality_workouts_in_REDCap) <- "%"
# Define custom renderer for continuous variables
my.render.cont <- function(x) {
with(stats.default(x), sprintf("%0.1f (%0.1f)", MEAN, SD))
}
# Count the number of records
count <- length(data$record_id)
# Generate Table 1
return(table1(
~ sex + age + bl_bmi + bl_vo2_max + ep_vo2_max | Group,
render.continuous = my.render.cont,
render.missing = NULL,
caption = paste0(
"Table 1. Baseline Demographics of Participants (n=",
count,
")"
),
data = data
))
}
demog %>%
filter(enrollment_status %in% c("Completed")) %>%
make_table()
demog %>%
filter(enrollment_status %in% c("Completed", "On-going Randomized", "Enrolled (not screened yet)")) %>%
make_table()
# Randomized Participants
randomized_exclude_crossover %>%
make_table()
randomized %>%
make_table()
withdraw %>%
make_table()
# Completed participants
cohort %>%
filter(is.na(ep_vo2_max)==FALSE & record_id < 1000) %>%
make_table()
# Projected Participants
cohort %>%
make_table()
cohort %>%
ggplot(aes(y=age, x=sex))+geom_violin()+geom_jitter(width = 0.2)+facet_wrap(~agegroup)
cohort %>%
ggplot(aes(y=age, x=sex))+geom_violin()+geom_jitter(width = 0.2, aes(color=agegroup))
cohort %>%
ggplot(aes(y=age, x=sex))+geom_violin()+geom_jitter(width = 0.2)+ggtitle("Age for Females vs. Males")
cohort %>%
ggplot(aes(x=age, color=sex))+theme_bw()+geom_density()+ggtitle("Age for Females vs. Males")+theme(plot.title=element_text(hjust=0.5))
cohort %>%
ggplot(aes(y=bl_vo2_max, x=Group, color=Group))+theme_bw()+geom_boxplot()+ggtitle("Baseline VO2 Max across Groups")+theme(plot.title=element_text(hjust=0.5))+geom_jitter(width=0.2)
cohort %>%
ggplot(aes(y=bl_bmi, x=Group, color=Group))+theme_bw()+geom_boxplot()+ggtitle("Baseline BMI across Groups")+theme(plot.title=element_text(hjust=0.5))+geom_jitter(width=0.2)
cohort %>%
ggplot(aes(y=age, x=Group, color=Group))+theme_bw()+geom_boxplot()+ggtitle("Age across Groups")+theme(plot.title=element_text(hjust=0.5))+geom_jitter(width=0.2)
cohort %>%
ggplot(aes(y=age, x=sex, color=sex))+theme_bw()+geom_boxplot()+ggtitle("Age by sex")+theme(plot.title=element_text(hjust=0.5))+geom_jitter(width=0.2)
cohort %>%
ggplot(aes(x=Group, fill=race))+theme_bw()+geom_bar(position="dodge")+ggtitle("Race across groups")+theme(plot.title=element_text(hjust=0.5))+ylab("Proportion")
cohort %>%
group_by(sex, agegroup) %>%
count()
cohort %>%
group_by(sex, agegroup) %>%
summarize(mean(age),
sd(age))
cohort %>%
group_by(sex) %>%
summarize(mean(age),
sd(age))
demog %>%
group_by(enrollment_status) %>%
count()
demog %>%
filter(Group !="New Participant" & record_id < 1000) %>%
group_by(Group) %>%
count()
demog %>%
filter(Group != "New Participant") %>%
group_by(Group, enrollment_status) %>%
count()
demog %>%
group_by(enrollment_status,Group) %>%
count()
demog %>%
filter(record_id < 1000) %>%
group_by(Group, enrollment_status) %>%
count()
demog %>%
filter(Group =="New Participant" & record_id < 1000) %>%
group_by(enrollment_status) %>%
count()
```
## Age of Cohort
```{r codehere,message=FALSE, warning=FALSE, echo=FALSE}
demog %>%
filter(enrollment_status %in% c("Enrolled (not screened yet)")) %>%
ggplot(aes(x=sex, y=age))+geom_boxplot()+geom_jitter()+theme_bw()+ggtitle("New Participants")
demog %>%
filter(enrollment_status %in% c("Completed", "On-going Randomized", "Enrolled (not screened yet)")) %>%
ggplot(aes(x=sex, y=age))+geom_boxplot()+geom_jitter()+theme_bw()+ggtitle("Age for All Participants (n=94)")
```
## Biopsy Tracker
```{r biopsy, message=FALSE, warning=FALSE, echo=FALSE}
# demog$baseline_visit_biopsy_complete <- ifelse(demog$baseline_visit_biopsy_complete=="Complete", 1, 0)
# demog$endpoint_visit_biopsy_complete <- ifelse(demog$endpoint_visit_biopsy_complete=="Complete", 1, 0)
#
# biopsy<-demog %>%
# filter(baseline_visit_biopsy_complete == 1 | endpoint_visit_biopsy_complete == 1) %>%
# gather(key = "Timepoint", value="biopsy_completed", c("baseline_visit_biopsy_complete", "endpoint_visit_biopsy_complete"))
#
# biopsy<-biopsy %>%
# dplyr::select(record_id, enrollment_status, sex, agegroup, Group, Timepoint, biopsy_completed) %>%
# mutate(Timepoint = ifelse(Timepoint=="baseline_visit_biopsy_complete", "Baseline", "Endpoint"))
#
# biopsy$Group[biopsy$record_id == 1076] <- "MICT"
#
# biopsy %>%
# filter(biopsy_completed==1) %>%
# group_by(Group, Timepoint) %>%
# count() %>%
# kable() %>%
# kable_styling(full_width = FALSE)
#
# biopsy %>%
# filter(biopsy_completed==1) %>%
# ggplot(aes(x=Group, fill=Timepoint))+geom_bar() + theme_bw() + ggtitle("Biopsies completed") + theme(plot.title=element_text(hjust=0.5)) + scale_y_continuous(n.breaks=10)
#
# biopsy %>%
# filter(biopsy_completed==1) %>%
# arrange(Group, record_id) %>%
# dplyr::select(record_id, Group, Timepoint) %>%
# kable() %>%
# kable_styling(full_width = FALSE)
# biopsy %>%
# filter(biopsy_completed==1) %>%
# ggplot(aes(x=Group, fill=Timepoint))+geom_bar(position="dodge") + theme_bw() + ylim(c(0,5)) + ggtitle("Number of biopsies complete") + theme(plot.title = element_text(hjust = 0.5))
# Recode for adipose, muscle biopsy
demog$bl_muscle <- ifelse(demog$bl_muscle=="Yes", 1, 0)
demog$ep_muscle <- ifelse(demog$ep_muscle=="Yes", 1, 0)
demog$bl_adipose <- ifelse(demog$bl_adipose=="Yes", 1, 0)
demog$ep_adipose <- ifelse(demog$ep_adipose=="Yes", 1, 0)
# Format muscle df
muscle_df<-demog %>%
select(record_id, sex, Group, bl_muscle, ep_muscle, sex) %>%
filter(is.na(bl_muscle)==FALSE)
muscle_df<-muscle_df %>%
gather(key="Timepoint", value="Completed", c(bl_muscle, ep_muscle)) %>%
mutate(Timepoint = ifelse(Timepoint=="bl_muscle", "Baseline", "Endpoint"))
# Format adipose df
adipose_df<-demog %>%
select(record_id, sex, Group, bl_adipose, ep_adipose) %>%
filter(is.na(bl_adipose)==FALSE)
adipose_df<-adipose_df %>%
gather(key="Timepoint", value="Completed", c(bl_adipose, ep_adipose)) %>%
mutate(Timepoint = ifelse(Timepoint=="bl_adipose", "Baseline", "Endpoint"))
# Plots
muscle_df %>%
filter(Completed == 1) %>%
ggplot(aes(x=Timepoint, fill=Group)) + geom_bar() +theme_bw() + theme(plot.title=element_text(hjust=0.5)) + ggtitle("Muscle biopsies completed") + geom_text(stat = "count", aes(label = ..count..), position = position_stack(vjust = 0.5)) + ylim(c(0,15))
muscle_df %>%
filter(Completed == 1) %>%
ggplot(aes(x=Timepoint, fill=Group)) + geom_bar() +theme_bw() + theme(plot.title=element_text(hjust=0.5)) + ggtitle("Muscle biopsies completed") + geom_text(stat = "count", aes(label = ..count..), position = position_stack(vjust = 0.5)) + ylim(c(0,15))+facet_wrap(~sex)
adipose_df %>%
filter(Completed == 1) %>%
ggplot(aes(x=Timepoint, fill=Group, group=Group)) + geom_bar()+theme_bw()+ theme(plot.title=element_text(hjust=0.5)) + ggtitle("Adipose biopsies completed")+ geom_text(stat = "count", aes(label = ..count..), position = position_stack(vjust = 0.5)) + ylim(c(0,15))
adipose_df %>%
filter(Completed == 1) %>%
ggplot(aes(x=Timepoint, fill=Group, group=Group)) + geom_bar()+theme_bw()+ theme(plot.title=element_text(hjust=0.5)) + ggtitle("Adipose biopsies completed")+ geom_text(stat = "count", aes(label = ..count..), position = position_stack(vjust = 0.5)) + ylim(c(0,15))+facet_wrap(~sex)
```
```{r demographics, inlude=FALSE, echo=FALSE, message=FALSE}
# withdraw %>%
# group_by(reason_for_withdraw_aft) %>%
# count()
#
# withdraw %>%
# group_by(reason_for_withdrawing_aft) %>%
# count()
# withdraw %>%
# group_by(enrollment_status) %>%
# count()
#
# withdraw %>%
# group_by
```
```{r wearables, message=FALSE, warning=FALSE, echo=FALSE}
## Workout Tracking: Wearables vs. REDCap
## PROCESSING DATA FROM GOOGLE DRIVE ##
# Read in wearable data from Google Drive
#data<-read.csv("/Users/aubreykr/Library/CloudStorage/GoogleDrive-aubreykr@stanford.edu/Shared drives/HIIT and Endurance 3Study/Data/data/workout/workout_allevents.csv")
# Rename to record_id and place at front of dataset
# data<-data %>%
# dplyr::rename(record_id = ppt_id) %>%
# dplyr::select(record_id, everything())
#
# # Create date
# data<-data %>%
# mutate(date = as.Date(data$X_realtime))
# Unique participants in the dataset
#length(unique(data$date))
#length(unique(data$record_id))
#(unique(data$sourcetype))
#paste0("Participants in this dataset:", sep=" ", (list(unique(data$record_id))))
# COUNT POLAR WORKOUTS
# Count number of polar workouts
# data_polar<-data %>%
# filter(sourcetype == "WearablePolar") %>%
# group_by(record_id, date) %>%
# count()
#
# # Create num minutes per workout column
# data_polar$polar_minutes = data_polar$n/60
#
# # COUNT FITBIT WORKOUTS
# # Count number of fitbit workouts
# data_fitbit<-data %>%
# filter(sourcetype == "WearableFitbit") %>%
# group_by(record_id, date) %>%
# count()
#
# # Create num minutes per workout column
# data_fitbit$fitbit_minutes = data_fitbit$n/60
#
# # remove n column
# data_polar<-data_polar %>%
# dplyr::select(-c(n))
# data_fitbit<-data_fitbit %>%
# dplyr::select(-c(n))
#
# # MERGE WEARABLE DATA
#
# # Merge polar and fitbit dataframes
# summary<-full_join(data_polar, data_fitbit, by=c("record_id", "date"))
#
# # Total number workouts for Polar, Fitbit
# num_polar_workouts <- summary %>%
# filter(is.na(polar_minutes)!=TRUE) %>%
# group_by(record_id) %>%
# summarize(wearable_polar_workouts = length(unique(date)))
#
# num_fitbit_workouts <- summary %>%
# filter(is.na(fitbit_minutes)!=TRUE) %>%
# group_by(record_id) %>%
# summarize(wearable_fitbit_workouts = length(unique(date)))
#
# # SUMMARIZE NUMBER WORKOUTS
# #num_polar_workouts
# #num_fitbit_workouts
#
# wearables<-full_join(num_polar_workouts, num_fitbit_workouts, by="record_id")
```
```{r wearable merging, messages=FALSE, warning=FALSE, echo=FALSE}
## JOIN MYPHD/POLAR DATA WITH DEMOG FRAME ##
# demog<-full_join(demog, wearables, by="record_id")
#
# # calculate num missing
# demog$num_missing_polar <- demog$wearable_polar_workouts - demog$number_polar_used
# demog$num_missing_fitbit <- demog$wearable_fitbit_workouts - demog$number_fitbit_used
#
# # set up data for table
# completed<-demog %>%
# filter(enrollment_status == "Completed" & Group != "Control") %>%
# dplyr::select(record_id, number_polar_used, wearable_polar_workouts, num_missing_polar, number_fitbit_used, wearable_fitbit_workouts, num_missing_fitbit)
#
# # create table
# completed %>%
# arrange(as.numeric(record_id)) %>%
# kable(col.names = c("Record ID", "REDCap polar", "Wearable polar", "Num missing polar", "REDCap fitbit", "Wearable fitbit", "Num missing fitbit"), align="c") %>%
# kable_styling(full_width = F, position = "center", font_size = 14, latex_options = "striped", bootstrap_options = c("striped", "hover"))
```
*Thanks everybody for your hard work!*
```{r vo2 analysis, echo=FALSE, include=FALSE, message=FALSE}
# vo2 <- gather(demog, key="Timepoint", value="VO2_Max", c(bl_vo2_max, ep_vo2_max))
# vo2$Timepoint[vo2$Timepoint == "bl_vo2_max"] <- "Week 0"
# vo2$Timepoint[vo2$Timepoint == "ep_vo2_max"] <- "Week 12"
#
# vo2 %>%
# filter(Group %in% c("HIIT", "MICT") & enrollment_status == "Completed") %>%
# ggplot(aes(x=Timepoint, y=VO2_Max)) + geom_boxplot() + geom_point()+
# labs(title = "VO2: Baseline vs. Endpoint", x = "Timepoint", y = "VO2 Peak (mL/kg)") + facet_wrap(~Group)+
# theme(plot.title=element_text(hjust=0.5)) + stat_compare_means(method = "t.test",
# label = "p.format",
# paired = TRUE)
# # percent change
# demog$vo2change <- (demog$ep_vo2_max - demog$bl_vo2_max) / demog$bl_vo2_max * 100
# demog$bmi_change <- demog$ep_bmi - demog$bl_bmi
# demog$bodyfatchange <- demog$ep_percent_body_fat - demog$bl_percent_body_fat
#
# demog %>%
# filter(enrollment_status == "Completed" & record_id != 4) %>%
# group_by(Group) %>%
# summarize(mean = mean(vo2change),
# sd = sd(vo2change))
#
#
# demog %>%
# filter(enrollment_status == "Completed" & record_id !="4") %>%
# ggplot(aes(y=vo2change, x=Group, color=sex, group=Group)) + geom_boxplot() + geom_point()+ theme_bw()+
# labs(title = "VO2 Max: Baseline vs. Endpoint", x = "Group", y = "% change in VO2 Max") +
# theme(plot.title=element_text(hjust=0.5)) + stat_compare_means(method = "t.test",
# label = "p.format",
# paired = TRUE) +
# geom_hline(yintercept = 0, linetype="dashed") + labs(color="Sex")
# colnames(demog)
```
```{r testdata, echo=FALSE, include=FALSE, message=FALSE, warning=FALSE}
# save<-demog %>%
# filter(enrollment_status == "Completed")
#
# label(save$Group) <- "Randomization Group"
# label(save$sex) <- "Sex"
# label(save$agegroup) <- "Age Group"
# label(save$race) <- "Race"
# label(save$vo2change) <- "VO2 max"
# label(save$bmi_change) <- "BMI "
# label(save$bodyfatchange) <- "Body Fat"
# label(save$quality_workouts_in_REDCap) <- "Workouts Completed"
# units(save$vo2change) <- "%"
# units(save$bmi_change) <- "kg/m2"
# units(save$bodyfatchange) <- "%"
# units(save$quality_workouts_in_REDCap) <- "%"
#
# my.render.cont <- function(x) {
# with(stats.default(x),
# sprintf("%0.2f (%0.1f)", MEAN, SD))
# }
#
# #table1(~sex + agegroup + vo2change + bmi_change + bodyfatchange + I(quality_workouts_in_REDCap/32 * 100) | Group, overall=F,
# # render.continuous=my.render.cont, render.missing=NULL, caption="Table 1. Participant Demographics and Preliminary Findings", data=save)
#
#
# # another table 1 version
# save<-demog %>%
# filter(enrollment_status == "Completed")
#
# label(save$Group) <- "Randomization Group"
# label(save$sex) <- "Sex"
# label(save$agegroup) <- "Age Group"
# label(save$race) <- "Race"
# label(save$vo2change) <- "VO2 max"
# label(save$bmi_change) <- "BMI "
# label(save$bodyfatchange) <- "Body Fat"
# label(save$quality_workouts_in_REDCap) <- "Workouts Completed"
# units(save$vo2change) <- "%"
# units(save$bmi_change) <- "kg/m2"
# units(save$bodyfatchange) <- "%"
# units(save$quality_workouts_in_REDCap) <- "%"
#
# save$race[save$race == "Other Asian - please specify below (Hmong, Laotian, Thai, Pakistani, Cambodian, etc)"] <- "Other"