Nothing
process.AP <-
function(directory,name.of.log.subjects,name.of.log.bed=NULL,name.of.log.on.off=NULL)
{
counter <- 1
subs <- identifySubjects(directory, name.of.log.subjects)
visit <- identifyVisits(directory, name.of.log.subjects)
study <- identifyStudy(directory, name.of.log.subjects)
for (s in subs)
{
for (v in visit)
{
print(s)
print(v)
print(study)
temp <- Sys.glob(paste(directory,study,"_",s,"_",v,".csv",sep=""))
if (length(temp)>0)
{
file.name.and.path <- paste(directory,study,"_",s,"_",v,".csv",sep="")
temp <- activpal.file.reader(file.name.and.path)
# head(temp)
# dim(temp)
n <- dim(temp)[1]
temp <- second.by.second(temp)
# loop to label sleep time
bed.log.temp <- Sys.glob(paste(directory,name.of.log.bed,".csv",sep=""))
temp$in.bed <- 1
temp$day.for.wearer <- NA
if(length(bed.log.temp>0))
{
bed.log <- read.csv(bed.log.temp)
bed.log$id <- as.character(bed.log$id)
bed.log <- bed.log[bed.log$id==s&bed.log$visit==v,]
if(dim(bed.log)[1]>0)
{
bed.log$date.out <- paste(bed.log$date.out.month,bed.log$date.out.day,bed.log$date.out.year,sep="/")
bed.log$time.out <- paste(bed.log$time.out.hour,bed.log$time.out.minute,bed.log$time.out.seconds,sep=":")
bed.log$date.in <- paste(bed.log$date.in.month,bed.log$date.in.day,bed.log$date.in.year,sep="/")
bed.log$time.in <- paste(bed.log$time.in.hour,bed.log$time.in.minute,bed.log$time.in.seconds,sep=":")
bed.log$date.time.out <- paste(bed.log$date.out, bed.log$time.out, sep=" ")
bed.log$date.time.in <- paste(bed.log$date.in, bed.log$time.in, sep=" ")
bed.log$date.time.out <- strptime(bed.log$date.time.out,"%m/%d/%Y %H:%M:%S")
bed.log$date.time.in <- strptime(bed.log$date.time.in,"%m/%d/%Y %H:%M:%S")
bed.log$hours.up <- as.vector(difftime(strptime(bed.log$date.time.in,format="%Y-%m-%d %H:%M:%S"),strptime(bed.log$date.time.out,format="%Y-%m-%d %H:%M:%S"), units="hours"))
# if bed times recorded - loop through and label time in bed
bed.log$day.for.wearer <- 1:dim(bed.log)[1]
for (t in (1:dim(bed.log)[1]))
{
wake <- strptime(bed.log$date.time.out[t],"%Y-%m-%d %H:%M:%S")
bed <- strptime(bed.log$date.time.in[t],"%Y-%m-%d %H:%M:%S")
n <- dim(temp)[1]
inds <- (1:n)[((temp$time>=wake)&(temp$time<=bed))]
if (length(inds)>0)
temp$in.bed[inds] <- 0
temp$day.for.wearer[inds] <- bed.log$day.for.wearer[t]
}
inds.time.awake <- (1:(dim(temp)[1]))[temp$in.bed==0]
a <- length(inds.time.awake)
if(a==0)
temp$in.bed <- "AP and bed.log do not match"
}
if(dim(bed.log)[1]==0)
temp$in.bed <- "Subject/Visit not in bed.log"
} #end sleep time loop
if(length(bed.log.temp)==0)
temp$in.bed <- "No.Bed.Log"
# loop to remove label on/off time
on.off.log.temp <- Sys.glob(paste(directory,name.of.log.on.off,".csv",sep=""))
temp$off <- 1
if(length(on.off.log.temp>0))
{
on.off.log <- read.csv(on.off.log.temp)
on.off.log$id <- as.character(on.off.log$id)
on.off.log <- on.off.log[on.off.log$id==s& on.off.log$visit==v,]
if(dim(on.off.log)[1]>0)
{
on.off.log$date.on <- paste(on.off.log$date.on.month,on.off.log$date.on.day,on.off.log$date.on.year,sep="/")
on.off.log$time.on <- paste(on.off.log$time.on.hour,on.off.log$time.on.minute,on.off.log$time.on.seconds,sep=":")
on.off.log$date.off <- paste(on.off.log$date.off.month,on.off.log$date.off.day,on.off.log$date.off.year,sep="/")
on.off.log$time.off <- paste(on.off.log$time.off.hour,on.off.log$time.off.minute,on.off.log$time.off.seconds,sep=":")
on.off.log$date.time.on <- paste(on.off.log$date.on, on.off.log$time.on, sep=" ")
on.off.log$date.time.off <- paste(on.off.log$date.off, on.off.log$time.off, sep=" ")
on.off.log$date.time.on <- strptime(on.off.log$date.time.on,"%m/%d/%Y %H:%M:%S")
on.off.log$date.time.off <- strptime(on.off.log$date.time.off,"%m/%d/%Y %H:%M:%S")
on.off.log$hours.on <- as.vector(difftime(strptime(on.off.log$date.time.off,format="%Y-%m-%d %H:%M:%S"),strptime(on.off.log$date.time.on,format="%Y-%m-%d %H:%M:%S"), units="hours"))
# if on/off times recorded - loop through and label time monitor is not worn
for (t in (1:dim(on.off.log)[1]))
{
on <- strptime(on.off.log$date.time.on[t],"%Y-%m-%d %H:%M:%S")
off <- strptime(on.off.log$date.time.off[t],"%Y-%m-%d %H:%M:%S")
n <- dim(temp)[1]
inds <- (1:n)[((temp$time>=on)&(temp$time<=off))]
if (length(inds)>0)
temp$off[inds] <- 0
}
inds.time.worn <- (1:(dim(temp)[1]))[temp$off==0]
i <- length(inds.time.worn)
if(i==0)
temp$off <- "AP and on.off.log do not match"
}
if(dim(on.off.log)[1]==0)
temp$off <- "Subject/Visit not in on.off.log"
} #end on/off loop
if(length(on.off.log.temp)==0)
temp$off <- "No.On.Off.Log"
temp$counter <- 1
## add intensity column to file
d <- dim(temp)[1]
temp$intensity <- "light"
inds.sed <- (1:d)[temp$ap.posture==0]
temp$intensity[inds.sed] <- "sedentary"
inds.mvpa <- (1:d)[temp$mets>=3]
temp$intensity[inds.mvpa] <- "mvpa"
# get step count - cumulative steps reported in file so need to figure out total steps/day
d <- dim(temp)[1]
steps.inds <- c((1:d)[temp$date[-1]!=temp$date[-d]],d)
steps.1 <- temp[steps.inds,]
steps.2 <- as.vector(steps.1$steps)
d.2 <- length(steps.2)
steps.3 <- data.frame(date=temp$date[steps.inds],steps=c(steps.2[1],steps.2[-1]-steps.2[-d.2]))
####
if(is.numeric(temp$in.bed)==T&is.numeric(temp$off)==T)
{
inds.time.worn <- (1:(dim(temp)[1]))[temp$off==0]
inds.first.time.worn <- inds.time.worn[1]
first.time.worn <- temp$time[inds.first.time.worn]
inds.last.time.worn <- inds.time.worn[i]
last.time.worn <- temp$time[inds.last.time.worn]
only.measurement.period <- temp[temp$time>=first.time.worn&temp$time<=last.time.worn,]
dim(only.measurement.period)
only.measurement.period.awake <- only.measurement.period[only.measurement.period$in.bed==0,]
dim(only.measurement.period.awake)
sleep.wake.wear.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(only.measurement.period$date),
awake.hours=tapply(only.measurement.period$in.bed==0, only.measurement.period$date,sum)/3600,
total.sleep.hours=tapply(only.measurement.period$in.bed==1, only.measurement.period$date,sum)/3600,
total.wear.hours=tapply(only.measurement.period$off==0, only.measurement.period$date,sum)/3600,
non.wear.hours=tapply(only.measurement.period$off==1, only.measurement.period$date,sum)/3600,
hours.awake.worn=(tapply(only.measurement.period$in.bed==0&only.measurement.period$off==0,only.measurement.period$date,sum))/3600,
hours.awake.not.worn=(tapply(only.measurement.period$in.bed==0&only.measurement.period$off==1,only.measurement.period$date,sum))/3600,
hours.sleep.worn=(tapply(only.measurement.period$in.bed==1&only.measurement.period$off==0,only.measurement.period$date,sum))/3600,
hours.sleep.not.worn=(tapply(only.measurement.period$in.bed==1&only.measurement.period$off==1,only.measurement.period$date,sum))/3600
)
# make file with bed and off time cleaned out
data <- temp[temp$in.bed==0&temp$off==0,]
if(dim(data)[1]>1)
{
# head(data)
# get step count - cumulative steps reported in file so need to figure out total steps/day
date <- unique(data$date)
dd <- dim(steps.3)[1]
inds <- vector(length=0)
for(i in (1:length(date)))
{
inds <- c(inds,(1:dd)[date[i]==steps.3$date])
}
steps <- steps.3$steps[inds]
# make .csv file with PA and SB variables per day
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(data$date),
hours.awake.worn = tapply(data$off==0,data$date,sum)/3600,
met.hours = tapply(data$met.hours,data$date,sum), step.count = steps,
sed.mins = tapply(data$ap.posture,data$date,sed.min.AP),
stand.mins = tapply(data$ap.posture,data$date,stand.min.AP),
step.mins = tapply(data$ap.posture,data$date,step.min.AP),
lit.mins = tapply(data$intensity=="light",data$date,sum)/60,
mvpa.mins = tapply(data$mets,data$date,mvpa.min.AP),
breaks = tapply(data$ap.posture,data$date,breaks.AP),
break.rate = tapply(data$ap.posture,data$date,breaks.AP)/((tapply(data$ap.posture,data$date,sed.min.AP))/60),
guideline.minutes = tapply(data$one.min.mets,data$date,guideline.bouts.min),
num.guideline.bouts = tapply(data$one.min.mets,data$date,guideline.bouts.num),
min.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=30),
min.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=60),
num.bouts.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=30),
num.bouts.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=60))
}
if(dim(data)[1]==0)
{
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date="No Valid Wear Hours. Check that AP file matches with on.off.log and bed.log",
hours.awake.worn = NA,
met.hours = NA,
step.count = NA,
sed.mins = NA,
stand.mins = NA,
step.mins = NA,
lit.mins = NA,
mvpa.mins = NA,
breaks = NA,
break.rate = NA,
guideline.minutes = NA,
num.guideline.bouts = NA,
min.in.sed.30 = NA,
min.in.sed.60 = NA,
num.bouts.in.sed.30 = NA,
num.bouts.in.sed.60 = NA)
}
}
####
if(is.numeric(temp$in.bed)==F&is.numeric(temp$off)==T)
{
inds.time.worn <- (1:(dim(temp)[1]))[temp$off==0]
i <- length(inds.time.worn)
inds.first.time.worn <- inds.time.worn[1]
first.time.worn <- temp$time[inds.first.time.worn]
inds.last.time.worn <- inds.time.worn[i]
last.time.worn <- temp$time[inds.last.time.worn]
only.measurement.period <- temp[temp$time>=first.time.worn&temp$time<=last.time.worn,]
dim(only.measurement.period)
sleep.wake.wear.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(only.measurement.period$date),
awake.hours="No valid bed.log data",
sleep.hours="No valid bed.log data",
total.wear.hours=tapply(only.measurement.period$off==0, only.measurement.period$date,sum)/3600,
total.non.wear.hours=tapply(only.measurement.period$off==1, only.measurement.period$date,sum)/3600,
hours.awake.worn="No valid bed.log data",
hours.awake.not.worn="No valid bed.log data",
hours.sleep.worn="No valid bed.log data",
hours.sleep.not.worn="No valid bed.log data"
)
# make file with bed and off time cleaned out
data <- temp[temp$off==0,]
if(dim(data)[1]>1)
{
# head(data)
# get step count - cumulative steps reported in file so need to figure out total steps/day
date <- unique(data$date)
dd <- dim(steps.3)[1]
inds <- vector(length=0)
for(i in (1:length(date)))
{
inds <- c(inds,(1:dd)[date[i]==steps.3$date])
}
steps <- steps.3$steps[inds]
# make .csv file with PA and SB variables per day
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(data$date),
hours.awake.worn = tapply(data$off==0,data$date,sum)/3600,
met.hours = tapply(data$met.hours,data$date,sum), step.count = steps,
sed.mins = tapply(data$ap.posture,data$date,sed.min.AP),
stand.mins = tapply(data$ap.posture,data$date,stand.min.AP),
step.mins = tapply(data$ap.posture,data$date,step.min.AP),
lit.mins = tapply(data$intensity=="light",data$date,sum)/60,
mvpa.mins = tapply(data$mets,data$date,mvpa.min.AP),
breaks = tapply(data$ap.posture,data$date,breaks.AP),
break.rate = tapply(data$ap.posture,data$date,breaks.AP)/((tapply(data$ap.posture,data$date,sed.min.AP))/60),
guideline.minutes = tapply(data$one.min.mets,data$date,guideline.bouts.min),
num.guideline.bouts = tapply(data$one.min.mets,data$date,guideline.bouts.num),
min.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=30),
min.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=60),
num.bouts.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=30),
num.bouts.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=60))
}
if(dim(data)[1]==0)
{
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date="No Valid Wear Hours. Check that AP file matches with on.off.log and bed.log",
hours.awake.worn = NA,
met.hours = NA,
step.count = NA,
sed.mins = NA,
stand.mins = NA,
step.mins = NA,
lit.mins = NA,
mvpa.mins = NA,
breaks = NA,
break.rate = NA,
guideline.minutes = NA,
num.guideline.bouts = NA,
min.in.sed.30 = NA,
min.in.sed.60 = NA,
num.bouts.in.sed.30 = NA,
num.bouts.in.sed.60 = NA)
}
}
####
if(is.numeric(temp$in.bed)==T&is.numeric(temp$off)==F)
{
only.measurement.period.awake <- temp[temp$in.bed==0,]
dim(only.measurement.period.awake)
sleep.wake.wear.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(only.measurement.period.awake$date),
awake.hours=tapply(only.measurement.period.awake$in.bed==0, only.measurement.period.awake$date,sum)/3600,
sleep.hours=tapply(only.measurement.period.awake$in.bed==1, only.measurement.period.awake$date,sum)/3600,
total.wear.hours="No valid on.off.log data",
total.non.wear.hours="No valid on.off.log data",
hours.awake.worn="No valid on.off.log data",
hours.awake.not.worn="No valid on.off.log data",
hours.sleep.worn="No valid on.off.log data",
hours.sleep.not.worn="No valid on.off.log data"
)
# make file with bed and off time cleaned out
data <- temp[temp$in.bed==0,]
if(dim(data)[1]>1)
{
# head(data)
# get step count - cumulative steps reported in file so need to figure out total steps/day
date <- unique(data$date)
dd <- dim(steps.3)[1]
inds <- vector(length=0)
for(i in (1:length(date)))
{
inds <- c(inds,(1:dd)[date[i]==steps.3$date])
}
steps <- steps.3$steps[inds]
# make .csv file with PA and SB variables per day
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(data$date),
hours.awake.worn = tapply(data$off==0,data$date,sum)/3600,
met.hours = tapply(data$met.hours,data$date,sum), step.count = steps,
sed.mins = tapply(data$ap.posture,data$date,sed.min.AP),
stand.mins = tapply(data$ap.posture,data$date,stand.min.AP),
step.mins = tapply(data$ap.posture,data$date,step.min.AP),
lit.mins = tapply(data$intensity=="light",data$date,sum)/60,
mvpa.mins = tapply(data$mets,data$date,mvpa.min.AP),
breaks = tapply(data$ap.posture,data$date,breaks.AP),
break.rate = tapply(data$ap.posture,data$date,breaks.AP)/((tapply(data$ap.posture,data$date,sed.min.AP))/60),
guideline.minutes = tapply(data$one.min.mets,data$date,guideline.bouts.min),
num.guideline.bouts = tapply(data$one.min.mets,data$date,guideline.bouts.num),
min.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=30),
min.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=60),
num.bouts.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=30),
num.bouts.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=60))
}
if(dim(data)[1]==0)
{
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date="No Valid Wear Hours. Check that AP file matches with on.off.log and bed.log",
hours.awake.worn = NA,
met.hours = NA,
step.count = NA,
sed.mins = NA,
stand.mins = NA,
step.mins = NA,
lit.mins = NA,
mvpa.mins = NA,
breaks = NA,
break.rate = NA,
guideline.minutes = NA,
num.guideline.bouts = NA,
min.in.sed.30 = NA,
min.in.sed.60 = NA,
num.bouts.in.sed.30 = NA,
num.bouts.in.sed.60 = NA)
}
}
####
if(is.numeric(temp$in.bed)==F&is.numeric(temp$off)==F)
{
sleep.wake.wear.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(temp$date),
awake.hours="No valid bed.log or on.off.log data",
sleep.hours="No valid bed.log or on.off.log data",
total.wear.hours="No valid bed.log or on.off.log data",
total.non.wear.hours="No valid bed.log or on.off.log data",
hours.awake.worn="No valid bed.log or on.off.log data",
hours.awake.not.worn="No valid bed.log or on.off.log data",
hours.sleep.worn="No valid bed.log or on.off.log data",
hours.sleep.not.worn="No valid bed.log or on.off.log data"
)
# make file with bed and off time cleaned out
data <- temp
if(dim(data)[1]>1)
{
# head(data)
# get step count - cumulative steps reported in file so need to figure out total steps/day
date <- unique(data$date)
dd <- dim(steps.3)[1]
inds <- vector(length=0)
for(i in (1:length(date)))
{
inds <- c(inds,(1:dd)[date[i]==steps.3$date])
}
steps <- steps.3$steps[inds]
# make .csv file with PA and SB variables per day
results.table <- data.frame(study=study,sub=as.numeric(s),visit=v,date=unique(data$date),
hours.awake.worn = tapply(data$off==0,data$date,sum)/3600,
met.hours = tapply(data$met.hours,data$date,sum), step.count = steps,
sed.mins = tapply(data$ap.posture,data$date,sed.min.AP),
stand.mins = tapply(data$ap.posture,data$date,stand.min.AP),
step.mins = tapply(data$ap.posture,data$date,step.min.AP),
lit.mins = tapply(data$intensity=="light",data$date,sum)/60,
mvpa.mins = tapply(data$mets,data$date,mvpa.min.AP),
breaks = tapply(data$ap.posture,data$date,breaks.AP),
break.rate = tapply(data$ap.posture,data$date,breaks.AP)/((tapply(data$ap.posture,data$date,sed.min.AP))/60),
guideline.minutes = tapply(data$one.min.mets,data$date,guideline.bouts.min),
num.guideline.bouts = tapply(data$one.min.mets,data$date,guideline.bouts.num),
min.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=30),
min.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.min,n=60),
num.bouts.in.sed.30 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=30),
num.bouts.in.sed.60 = tapply(data$ap.posture,data$date,prolonged.sed.bouts.num,n=60))
}
}
results.table$percent.of.hours.awake.worn.sed <- results.table$sed.mins/(results.table$hours.awake.worn*60)
results.table$percent.of.hours.awake.worn.lit <- results.table$lit.mins/(results.table$hours.awake.worn*60)
results.table$percent.of.hours.awake.worn.mvpa <- results.table$mvpa.mins/(results.table$hours.awake.worn*60)
rt <- dim(results.table)[1]
inds.sed <- results.table$percent.of.hours.awake.worn.sed
inds.inf.sed <- (1:rt)[inds.sed=="Inf"]
results.table$percent.of.hours.awake.worn.sed[inds.inf.sed] <- NA
inds.lit <- results.table$percent.of.hours.awake.worn.lit
inds.inf.lit <- (1:rt)[inds.lit=="Inf"]
results.table$percent.of.hours.awake.worn.lit[inds.inf.lit] <- NA
inds.mvpa <- results.table$percent.of.hours.awake.worn.mvpa
inds.inf.mvpa <- (1:rt)[inds.mvpa=="Inf"]
results.table$percent.of.hours.awake.worn.mvpa[inds.inf.mvpa] <- NA
means.table <- data.frame(study=study,sub=as.numeric(s),visit=v,
hours.awake.worn = mean(results.table$hours.awake.worn,na.rm=T),
sd.hours.awake.worn = sd(results.table$hours.awake.worn,na.rm=T),
low.hours.awake.worn = mean(results.table$hours.awake.worn,na.rm=T)-1.96*(sd(results.table$hours.awake.worn,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.hours.awake.worn = mean(results.table$hours.awake.worn,na.rm=T)+1.96*(sd(results.table$hours.awake.worn,na.rm=T)/(sqrt(dim(results.table)[1]))),
met.hours = mean(results.table$met.hours,na.rm=T),
sd.met.hours = sd(results.table$met.hours,na.rm=T),
low.met.hours = mean(results.table$met.hours,na.rm=T)-1.96*(sd(results.table$met.hours,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.met.hours = mean(results.table$met.hours,na.rm=T)+1.96*(sd(results.table$met.hours,na.rm=T)/(sqrt(dim(results.table)[1]))),
step.count = mean(results.table$step.count,na.rm=T),
sd.step.count = sd(results.table$step.count,na.rm=T),
low.step.count = mean(results.table$step.count,na.rm=T)-1.96*(sd(results.table$step.count,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.step.count = mean(results.table$step.count,na.rm=T)+1.96*(sd(results.table$step.count,na.rm=T)/(sqrt(dim(results.table)[1]))),
sed.mins = mean(results.table$sed.mins,na.rm=T),
sd.sed.mins = sd(results.table$sed.mins,na.rm=T),
low.sed.mins = mean(results.table$sed.mins,na.rm=T)-1.96*(sd(results.table$sed.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.sed.mins = mean(results.table$sed.mins,na.rm=T)+1.96*(sd(results.table$sed.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
stand.mins = mean(results.table$stand.mins,na.rm=T),
sd.stand.mins = sd(results.table$stand.mins,na.rm=T),
low.stand.mins = mean(results.table$stand.mins,na.rm=T)-1.96*(sd(results.table$stand.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.stand.mins = mean(results.table$stand.mins,na.rm=T)+1.96*(sd(results.table$stand.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
step.mins = mean(results.table$step.mins,na.rm=T),
sd.step.mins = sd(results.table$step.mins,na.rm=T),
low.step.mins = mean(results.table$step.mins,na.rm=T)-1.96*(sd(results.table$step.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.step.mins = mean(results.table$step.mins,na.rm=T)+1.96*(sd(results.table$step.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
lit.mins = mean(results.table$lit.mins,na.rm=T),
sd.lit.mins = sd(results.table$lit.mins,na.rm=T),
low.lit.mins = mean(results.table$lit.mins,na.rm=T)-1.96*(sd(results.table$lit.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.lit.mins = mean(results.table$lit.mins,na.rm=T)+1.96*(sd(results.table$lit.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
mvpa.mins = mean(results.table$mvpa.mins,na.rm=T),
sd.mvpa.mins = sd(results.table$mvpa.mins,na.rm=T),
low.mvpa.mins = mean(results.table$mvpa.mins,na.rm=T)-1.96*(sd(results.table$mvpa.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.mvpa.mins = mean(results.table$mvpa.mins,na.rm=T)+1.96*(sd(results.table$mvpa.mins,na.rm=T)/(sqrt(dim(results.table)[1]))),
breaks = mean(results.table$breaks,na.rm=T),
sd.breaks = sd(results.table$breaks,na.rm=T),
low.breaks = mean(results.table$breaks,na.rm=T)-1.96*(sd(results.table$breaks,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.breaks = mean(results.table$breaks,na.rm=T)+1.96*(sd(results.table$breaks,na.rm=T)/(sqrt(dim(results.table)[1]))),
break.rate = mean(results.table$break.rate,na.rm=T),
sd.break.rate = sd(results.table$break.rate,na.rm=T),
low.break.rate = mean(results.table$break.rate,na.rm=T)-1.96*(sd(results.table$break.rate,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.break.rate = mean(results.table$break.rate,na.rm=T)+1.96*(sd(results.table$break.rate,na.rm=T)/(sqrt(dim(results.table)[1]))),
guideline.minutes = mean(results.table$guideline.minutes,na.rm=T),
sd.guideline.minutes = sd(results.table$guideline.minutes,na.rm=T),
low.guideline.minutes = mean(results.table$guideline.minutes,na.rm=T)-1.96*(sd(results.table$guideline.minutes,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.guideline.minutes = mean(results.table$guideline.minutes,na.rm=T)+1.96*(sd(results.table$guideline.minutes,na.rm=T)/(sqrt(dim(results.table)[1]))),
num.guideline.bouts = mean(results.table$num.guideline.bouts,na.rm=T),
sd.num.guideline.bouts = sd(results.table$num.guideline.bouts,na.rm=T),
low.num.guideline.bouts = mean(results.table$num.guideline.bouts,na.rm=T)-1.96*(sd(results.table$num.guideline.bouts,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.num.guideline.bouts = mean(results.table$num.guideline.bouts,na.rm=T)+1.96*(sd(results.table$num.guideline.bouts,na.rm=T)/(sqrt(dim(results.table)[1]))),
min.in.sed.30 = mean(results.table$min.in.sed.30,na.rm=T),
sd.min.in.sed.30 = sd(results.table$min.in.sed.30,na.rm=T),
low.min.in.sed.30 = mean(results.table$min.in.sed.30,na.rm=T)-1.96*(sd(results.table$min.in.sed.30,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.min.in.sed.30 = mean(results.table$min.in.sed.30,na.rm=T)+1.96*(sd(results.table$min.in.sed.30,na.rm=T)/(sqrt(dim(results.table)[1]))),
min.in.sed.60 = mean(results.table$min.in.sed.60,na.rm=T),
sd.min.in.sed.60 = sd(results.table$min.in.sed.60,na.rm=T),
low.min.in.sed.60 = mean(results.table$min.in.sed.60,na.rm=T)-1.96*(sd(results.table$min.in.sed.60,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.min.in.sed.60 = mean(results.table$min.in.sed.60,na.rm=T)+1.96*(sd(results.table$min.in.sed.60,na.rm=T)/(sqrt(dim(results.table)[1]))),
num.bouts.in.sed.30 = mean(results.table$num.bouts.in.sed.30,na.rm=T),
sd.num.bouts.in.sed.30 = sd(results.table$num.bouts.in.sed.30,na.rm=T),
low.num.bouts.in.sed.30 = mean(results.table$num.bouts.in.sed.30,na.rm=T)-1.96*(sd(results.table$num.bouts.in.sed.30,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.num.bouts.in.sed.30 = mean(results.table$num.bouts.in.sed.30,na.rm=T)+1.96*(sd(results.table$num.bouts.in.sed.30,na.rm=T)/(sqrt(dim(results.table)[1]))),
num.bouts.in.sed.60 = mean(results.table$num.bouts.in.sed.60,na.rm=T),
sd.num.bouts.in.sed.60 = sd(results.table$num.bouts.in.sed.60,na.rm=T),
low.num.bouts.in.sed.60 = mean(results.table$num.bouts.in.sed.60,na.rm=T)-1.96*(sd(results.table$num.bouts.in.sed.60,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.num.bouts.in.sed.60 = mean(results.table$num.bouts.in.sed.60,na.rm=T)+1.96*(sd(results.table$num.bouts.in.sed.60,na.rm=T)/(sqrt(dim(results.table)[1]))),
percent.of.hours.awake.worn.sed = mean(results.table$percent.of.hours.awake.worn.sed,na.rm=T),
sd.percent.of.hours.awake.worn.sed = sd(results.table$percent.of.hours.awake.worn.sed,na.rm=T),
low.percent.of.hours.awake.worn.sed = mean(results.table$percent.of.hours.awake.worn.sed,na.rm=T)-1.96*(sd(results.table$percent.of.hours.awake.worn.sed,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.percent.of.hours.awake.worn.sed = mean(results.table$percent.of.hours.awake.worn.sed,na.rm=T)+1.96*(sd(results.table$percent.of.hours.awake.worn.sed,na.rm=T)/(sqrt(dim(results.table)[1]))),
percent.of.hours.awake.worn.lit = mean(results.table$percent.of.hours.awake.worn.lit,na.rm=T),
sd.percent.of.hours.awake.worn.lit = sd(results.table$percent.of.hours.awake.worn.lit,na.rm=T),
low.percent.of.hours.awake.worn.lit = mean(results.table$percent.of.hours.awake.worn.lit,na.rm=T)-1.96*(sd(results.table$percent.of.hours.awake.worn.lit,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.percent.of.hours.awake.worn.lit = mean(results.table$percent.of.hours.awake.worn.lit,na.rm=T)+1.96*(sd(results.table$percent.of.hours.awake.worn.lit,na.rm=T)/(sqrt(dim(results.table)[1]))),
percent.of.hours.awake.worn.mvpa = mean(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T),
sd.percent.of.hours.awake.worn.mvpa = sd(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T),
low.percent.of.hours.awake.worn.mvpa = mean(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T)-1.96*(sd(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T)/(sqrt(dim(results.table)[1]))),
up.percent.of.hours.awake.worn.mvpa = mean(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T)+1.96*(sd(results.table$percent.of.hours.awake.worn.mvpa,na.rm=T)/(sqrt(dim(results.table)[1])))
)
if (counter==1)
write.table(results.table,file=paste(directory,"results.table.csv",sep=""),sep=",",row.names=F,col.names=T,append=F)
if (counter==1)
write.table(sleep.wake.wear.table,file=paste(directory,"sleep.wake.wear.table.csv",sep=""),sep=",",row.names=F,col.names=T,append=F)
if (counter==1)
write.table(means.table,file=paste(directory,"means.table.csv",sep=""),sep=",",row.names=F,col.names=T,append=F)
if (counter>1)
write.table(results.table,file=paste(directory,"results.table.csv",sep=""),sep=",",row.names=F,col.names=F,append=T)
if (counter>1)
write.table(sleep.wake.wear.table,file=paste(directory,"sleep.wake.wear.table.csv",sep=""),sep=",",row.names=F,col.names=F,append=T)
if (counter>1)
write.table(means.table,file=paste(directory,"means.table.csv",sep=""),sep=",",row.names=F,col.names=F,append=T)
counter <- counter+1
}
}
}
}
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