# Introduction

## What is GGIR?

GGIR is an R-package to process multi-day raw accelerometer data for physical activity and sleep research. GGIR will write all output files into two sub-directories of ./meta and ./results. GGIR is increasingly being used by a number of academic institutes across the world.

## What is postGGIR?

postGGIR is an R-package to data processing after running GGIR for accelerometer data. In detail, all necessary R/Rmd/shell files were generated for data processing after running GGIR for accelerometer data. Then in part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average ENMO over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few explortatory plots and multiple commonly used features extracted from minute level actigraphy data in part 5-7. This vignette provides a general introduction to postGGIR.

# Setting up your work environment

## Install R and RStudio

Download GGIR with its dependencies, you can do this with one command from the console command line:

install.packages("postGGIR", dependencies = TRUE)

## Prepare folder structure

1. folder of .bin files for GGIR or a file listing all .bin files
• R program will check the missing in the GGIR output by comparing with all raw .bin files
2. foder of the GGIR output with two sub-folders
• meta (./basic, ./csv, etc)
• results (partsummary.csv)

# Quick start

## Create a template shell script of postGGIR

library(postGGIR)
create.postGGIR()

The function will create a template shell script of postGGIR in the current directory, names as STUDYNAME_part0.maincall.R.

cat STUDYNAME_part0.maincall.R
options(width=2000)
argv = commandArgs(TRUE);
print(argv)
print(paste("length=",length(argv),sep=""))
mode<-as.numeric(argv[1])
print(c("mode =", mode))

#########################################################################
# (user-define 1) you got to redefine this according different study!!!!
#########################################################################
# colaus
filename2id.1<-function(x) {
y1<-unlist(strsplit(x,"\\_"))[1]
y2<-unlist(strsplit(y1,"\\."))[1]
return(y2)
}

# nimh (use csv file =c("filename","ggirID"))
filename2id.2<-function(x) {
y1<-which(d[,"filename"]==x)
if (length(y1)==0) stop(paste("Missing ",x," in filename2id.csv file",sep=""))
if (length(y1)>=1) y2<-d[y1[1],"newID"]
return(as.character(y2))
}

#########################################################################
#  main call
#########################################################################

call.afterggir<-function(mode,rmDup=FALSE,filename2id=filename2id.1){

library(postGGIR)
#################################################
# (user-define 2) Fill in parameters of your ggir output
#################################################
currentdir =
studyname =
bindir =
binfile.list = NULL
outputdir =

epochIn = 5
epochOut = 5
flag.epochOut = 60
use.cluster = FALSE
log.multiplier = 9250
QCdays.alpha = 7
QChours.alpha = 16
useIDs.FN<-NULL
setwd(currentdir)
#########################################################################
#   remove duplicate sample IDs for plotting and feature extraction
#########################################################################
if (mode==3 & rmDup){
# step 1: read ./summary/*remove_temp.csv file (output of mode=2)
keep.last<-TRUE #keep the latest visit for each sample
sumdir<-paste(currentdir,"/summary",sep="")
setwd(sumdir)
inFN<-paste(studyname,"_samples_remove_temp.csv",sep="")
useIDs.FN<-paste(sumdir,"/",studyname,"_samples_remove.csv",sep="")

#################################################
# (user-define 3 as rmDup=TRUE)  create useIDs.FN file
#################################################
# step 2: create the ./summary/*remove.csv file manually or by R commands
d<-d[order(d[,"Date"]),]
d<-d[order(d[,"newID"]),]
d[which(is.na(d[,"newID"])),]
S<-duplicated(d[,"newID"],fromLast=keep.last) #keep the last copy for nccr
d[S,"duplicate"]<-"remove"
write.csv(d,file=useIDs.FN,row.names=F)
}
#########################################################################
#  maincall
#########################################################################
setwd(currentdir)
afterggir(mode=mode,useIDs.FN,currentdir,studyname,bindir,
outputdir,epochIn,epochOut,flag.epochOut,log.multiplier,use.cluster,QCdays.alpha=QCdays.alpha,QChours.alpha=QChours.alpha,Rversion="R/3.6.3",filename2id=filename2id)
}
##############################################
call.afterggir(mode)
##############################################
#   Note:   call.afterggir(mode=0)
#        mode =0 : creat sw/Rmd file
#        mode =1 : data transform using cluster or not
#        mode =2 : summary
#        mode =3 : clean
#        mode =4 : impu

## Edit shell script

Three places were marked as “user-define” and need to be edited by user in the STUDYNAME_part0.maincall.R file. Please rename the file by replacing your real studyname after the edition.

### 1. Define the function filename2id( )

This user-defined function will change the filename of the raw accelerometer file to the short ID. For example, the first example change "0002__026907_2016-03-11 13-05-59.bin" to new ID of “0002”. If you prefer to define new ID by other way, you could create a .CSV file including “filename” and “newID” at least and then defined this function as the second example. The new variable of “newID”, included in the output files, could be used as the key ID in the summary report of postGGIR and be used to define the duplicate samples as well.

### 2. Parameters of shell script

User needs to define the following parameters as follows,

Variables Description
currentdir Directory where the output needs to be stored. Note that this directory must exist
studyname Specify the study name that used in the output file names. Give this variable an appropriate name as it will be used later to specify output file names.
bindir Directory where the accelerometer files are stored or list
binfile.list File list of bin files when running GGIR. Default is NULL, but it has to be a filename in the .csv format when bindir=NULL.
outputdir Directory where the GGIR output was stored.
epochIn Epoch size to which acceleration was averaged (seconds) in GGIR output. Defaut is 5 seconds.
epochOut Epoch size to which acceleration was averaged (seconds) in part1. Defaut is 5 seconds.
flag.epochOut Epoch size to which acceleration was averaged (seconds) in part 3. Defaut is 60 seconds.
log.multiplier The coefficient used in the log transformation of the ENMO data, i.e. log( log.multiplier * ENMO + 1), which have been used in part2a_postGGIR.report and part5. Defaut is 9250.
use.cluster Specify if part1 will be done by parallel computing. Default is TRUE, and the CSV file in GGIR output will be merged for every 20 files first, and then combined for all.
QCdays.alpha Minimum required number of valid days in subject specific analysis as a quality control step in part2. Default is 7 days.
QChours.alpha Minimum required number of valid hours in day specific analysis as a quality control step in part2. Default is 16 hours.

### 3. Subset of samples (optional)

The postGGIR package not only simply transform/merge the activity and sleep data, but it also can do some prelimary data analysis such as principle componet analysis and feature extraction. Therefore, the basic data clean will be processed first as follows,

• data clean by removing valid days/samples defined by minimum required number of valid hours/days in the activity data
• remove duplicate samples

If you prefer to use all samples, just skip this part and use rmDup=FALSE as the default. Otherwise, if you want to remove some samples such as duplicates, there are two ways as follows,

• Edit R codes of “step 2” in this part. For example, the template will keep the later copy for duplicate samples
• Remove R codes of “step 2” in this part, and create studyname_samples_remove.csv file by filling “remove” in the “duplicate” column in the template file of studyname_samples_remove_temp.csv. The data will be kept unless duplicate=“remove”.

## Run R script

call.afterggir(mode,rmDup=FALSE)   
Variables Description
rmDup Set rmDup = TRUE if user want to remove some samples such as duplicates. Set rmDup = FALSE if user want to keep all samples.
mode Specify which of the five parts need to be run, e.g. mode = 0 makes that all R/Rmd/sh files are generated for other parts. When mode = 1, all csv files in the GGIR output directory were read, transformed and then merged. When mode = 2, the GGIR output files were checked and summarized in one excel sheet. When mode = 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. When mode = 4, the cleaned data was imputed.

## Run script in a cluster

#!/bin/bash
#
#$-cwd #$ -j y
#\$ -S /bin/bash
source ~/.bash_profile

cd /postGGIR/inst/example/afterGGIR;
R --no-save --no-restore --args  < studyname_ggir9s_postGGIR.pipeline.maincall.R  0
R --no-save --no-restore --args  < studyname_ggir9s_postGGIR.pipeline.maincall.R  1
R --no-save --no-restore --args  < studyname_ggir9s_postGGIR.pipeline.maincall.R  2
R --no-save --no-restore --args  < studyname_ggir9s_postGGIR.pipeline.maincall.R  3
R --no-save --no-restore --args  < studyname_ggir9s_postGGIR.pipeline.maincall.R  4

R -e "rmarkdown::render('part5_studyname_postGGIR.report.Rmd'   )"
R -e "rmarkdown::render('part6_studyname_postGGIR.nonwear.report.Rmd'   )"
R -e "rmarkdown::render('part7a_studyname_postGGIR_JIVE_1_somefeatures.Rmd'   )"
R -e "rmarkdown::render('part7b_studyname_postGGIR_JIVE_2_allfeatures.Rmd'   )"
R -e "rmarkdown::render('part7c_studyname_postGGIR_JIVE_3_excelReport.Rmd'   )" 

# Inspecting the results

## Output of part 0

• Command = call.afterggir(mode=0)
• Output folder = ./
Output Description
part1_data.transform.R (use.cluster=TRUE, optional) R code for data transformation and merge for every 20 files in each partition. When the number of .bin files is large ( > 1000), the data merge could take long time, user could split the job and submit the job to a cluster for parallel computing.
part1_data.transform.sw (use.cluster=TRUE, optional) Submit the job to a cluster for parallel computing
part1_data.transform.merge.sw (use.cluster=TRUE, optional) Merge all partitions for the ENMO and ANGLEZ data
part5_studyname_postGGIR.report.Rmd R markdown file for generate a comprehensive report of data processing and explortatory plots.
part6_studyname_postGGIR.nonwear.report.Rmd R markdown file for generate a report of nonwear score.
part7a_studyname_postGGIR_JIVE_1_somefeatures.Rmd Extract some features from the actigraphy data using R
part7b_studyname_postGGIR_JIVE_2_allfeatures.Rmd Extract other features from the GGIR output and merge all features together
part7c_studyname_postGGIR_JIVE_3_excelReport.Rmd Combine all features into one single excel file. This is optional since it might be killed due to the issue of out of memory in a cluster.
part7d_studyname_postGGIR_JIVE_4_outputReport.Rmd Perform JIVE Decomposition for All Features using r.jive
part9_swarm.sh shell script to submit all jobs to the cluster

## Output of part 1

• Command = call.afterggir(mode=1)
• Output folder = ./data
Output Description
studyname_filesummary_csvlist.csv File list in the ./csv folder of GGIR
studyname_filesummary_Rdatalist.csv File list in the ./basic folder of GGIR
All_studyname_ANGLEZ.data.csv Raw data of ANGLEZ after merge
All_studyname_ENMO.data.csv Raw data of ENMO after merge
nonwearscore_studyname_f0_f1_Xs.csv Data matrix of nonwearscore
nonwearscore_studyname_f0_f1_Xs.pdf Plots for nonwearscore
plot.nonwearVSnvalidhours.csv Nonwear data for plot
plot.nonwearVSnvalidhours.pdf Nonwear plots
lightmean_studyname_f0_f1_Xs.csv Data matrix of lightmean
lightpeak_studyname_f0_f1_Xs.csv Data matrix of lightpeak
temperaturemean_studyname_f0_f1_Xs.csv Data matrix of temperaturemean
clippingscore_studyname_f0_f1_Xs.csv Raw data of clippingscore
EN_studyname_f0_f1_Xs.csv Data matrix of EN

f0 and f1 are the file index to start and finish with
Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output

## Output of part 2

• Command = call.afterggir(mode=1)
• Output folder = ./summary
Output Description
studyname_ggir_output_summary.xlsx Description of all accelerometer files in the GGIR output. This excel file includs 9 pages as follows, (1) List of files in the GGIR output (2) Summary of files (3) List of duplicate IDs (4) ID errors (5) Number of valid days (6) Table of number of valid/missing days (7) Missing patten (8) Frequency of the missing pattern (9) Description of all accelerometer files.
part2daysummary.info.csv Intermediate results for description of each accelerometer file.
studyname_ggir_output_summary_plot.pdf Some plots such as the number of valid days, which were included in the part2a_studyname_postGGIR.report.html file as well.
studyname_samples_remove_temp.csv Create studyname_samples_remove.csv file by filling “remove” in the “duplicate” column in this template. If duplicate=“remove”, the accelerometer files will not be used in the data analysis of part5.

## Output of part 3

• Command = call.afterggir(mode=1)
• Output folder = ./data
Output Description
flag_All_studyname_ANGLEZ.data.Xs.csv Adding flags for data cleaning of the raw ANGLEZ data
flag_All_studyname_ENMO.data.Xs.csv Adding flags for data cleaning of the raw ENMO data
IDMatrix.flag_All_studyname_ENMO.data.60s.csv ID matrix

*Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output

## Output of part 4

• Command = call.afterggir(mode=1)
• Output folder = ./data
Output Description
impu.flag_All_studyname_ENMO.data.60s.csv Imputation data for the merged ENMO data, and the missing values were imputated by the average ENMO over all the valid days for each subject.

## Output of part 5

• Command = part5_studyname_postGGIR.report.Rmd
• Output folder = ./
Output Description
part5_studyname_postGGIR.report.html A comprehensive report of data processing and explortatory plots.

## Output of part 6

• Command = part6_studyname_postGGIR.nonwear.report.Rmd
• Output folder = ./
Folder Output Description
./ part6_studyname_postGGIR.nonwear.report.html A report of nonwear score.
./data JIVEraw_nonwearscore_studyname_f0_f1_Xs.csv Imputation data matrix of nonwearscore (1/0)
./data JIVEimpu_nonwearscore_studyname_f0_f1_Xs.csv Data matrix of nonwearscore (1/0/NA)

f0 and f1 are the file index to start and finish with
Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output

## Output of part 7a

• Command = part7a_studyname_postGGIR_JIVE_1_somefeatures.Rmd
• Output folder = ./
Output Description
part7_studyname_all_features_dictionary.xlsx Description of features
part7a_studyname_postGGIR_JIVE_1_somefeatures.html Extract some features from the actigraphy data using R
part7a_studyname_some_features_page1_features.csv List of some features
part7a_studyname_some_features_page2_face_day_PCs.csv Function PCA at the day level using fpca.face( )
part7a_studyname_some_features_page3_face_subject_PCs.csv Function PCA at the subject level using fpca.face( )
part7a_studyname_some_features_page4_denseFLMM_day_PCs.csv Function PCA at the day level using denseFLMM( )
part7a_studyname_some_features_page5_denseFLMM_subject_PCs.csv Function PCA at the subject level using denseFLMM( )

## Output of part 7b

• Command = part7b_studyname_postGGIR_JIVE_2_allfeatures.Rmd
• Output folder = ./
Output Description
part7b_studyname_postGGIR_JIVE_2_allfeatures.html Extract other features from the GGIR output and merge all features together
part7b_studyname_all_features_1.csv Raw data of all features
part7b_studyname_all_features_2.csv Keep sample with valid ENMO inputs
part7b_studyname_all_features_2.csv.log Log file of each variable of part5b_studyname_all_features_2.csv
plot_part7b_studyname_all_features_2.csv.pdf Plot of each variable of part5b_studyname_all_features_2.csv
part7b_studyname_all_features_3.csv Average variable at the subject level
part7b_studyname_all_features_3.csv.log Log file of each variable of part5b_studyname_all_features_3.csv
plot_part7b_studyname_all_features_3.csv.pdf Plot of each variable of part5b_studyname_all_features_3.csv
part7b_studyname_all_features_4.csv subject level SD of each feature

## Output of part 7c

• Command = part7c_studyname_postGGIR_JIVE_3_excelReport.Rmd
• Output folder = ./
Output Description
part7c_studyname_postGGIR_JIVE_3_excelReport.html Combine all features into one single excel file. This is optional since it might be killed due to the issue of out of memory in a cluster.

## Output of part 7d

• Command = part7d_studyname_postGGIR_JIVE_4_outputReport.Rmd
• Output folder = ./
Output Description
part7d_studyname_postGGIR_JIVE_4_outputReport.html Perform JIVE Decomposition for All Features using r.jive
part7d_studyname_jive_Decomposition.csv Joint and individual structure estimates
part7d_studyname_jive_predScore.csv PCA scores of JIVE ( missing when jive.predict failes)
part7d_studyname_jive_predScore.csv PCA scores of JIVE ( missing when jive.predict failes)

## Output of part 7e

• Command = part7e_studyname_postGGIR_JIVE_5_somefeatures_weekday.Rmd
• Output folder = ./
Output Description
part7e_studyname_some_features_page1.csv Perform JIVE Decomposition for All Features using r.jive
part7e_weekday_studyname_all_features_3.csv subject level mean of each feature on weekday
part7e_weekday_studyname_some_features_page4_denseFLMM_day_PCs.csv Function PCA at the day level using denseFLMM( ) on weekday
part7e_weekday_studyname_some_features_page5_denseFLMM_subject_PCs.csv Function PCA at the subject level using denseFLMM( ) on weekday
part7e_weekend_studyname_all_features_3.csv subject level mean of each feature on weekend
part7e_weekend_studyname_some_features_page4_denseFLMM_day_PCs.csv Function PCA at the day level using denseFLMM( ) on weekend
part7e_weekend_studyname_some_features_page5_denseFLMM_subject_PCs.csv Function PCA at the subject level using denseFLMM( ) on weekend

## Description of variables in the output data

Variable Description
filename accelerometer file name
Date date recored from the GGIR part2.summary file
id IDs recored from the GGIR part2.summary file
calender_date date in the format of yyyy-mm-dd
N.valid.hours number of hours with valid data recored from the part2_daysummary.csv file in the GGIR output
N.hours number of hours of measurement recored from the part2_daysummary.csv file in the GGIR output
weekday day of the week-Day of the week
measurementday day of measurement-Day number relative to start of the measurement
newID new IDs defined as the user-defined function of filename2id(), e.g. substrings of the filename
Nmiss_c9_c31 number of NAs from the 9th to 31th column in the part2_daysummary.csv file in the GGIR output
missing “M” indicates missing for an invalid day, and “C” indicates completeness for a valid day
Ndays number of days of measurement
ith_day rank of the measurementday, for example, the value is 1,2,3,4,-3,-2,-1 for measurementday = 1,…,7
Nmiss number of missing (invalid) days
Nnonmiss number of non-missing (valid) days
misspattern indicators of missing/nonmissing for all measurement days at the subject level
RowNonWear number of columnns in the non-wearing matrix
NonWearMin number of minutes of non-wearing
remove16h7day indicator of a key qulity control output. If remove16h7day=1, the day need to be removed. If remove16h7day=0, the day need to be kept.
duplicate If duplicate=“remove”, the accelerometer files will not be used in the data analysis of part5.
ImpuMiss.b number of missing values on the ENMO data before imputation
ImpuMiss.a number of missing values on the ENMO data after imputation
KEEP The value is “keep”/“remove”, e.g. KEEP=“remove” if remove16h7day=1 or duplicate=“remove” or ImpuMiss.a>0

## Description of features of domains of physical activity, sleep and circadian rhythmicity

Sleep Domain

Variable Description
sleeponset Detected onset of sleep expressed as hours since the midnight of the previous night.
wakeup Detected waking time (after sleep period) expressed as hours since the midnight of the previous night.
number_sib_wakinghours Number of sustained inactivity bouts during the day, with day referring to the time outside the Sleep Period Time window.
SleepDurationInSpt Total sleep duration, which equals the accumulated nocturnal sustained inactivity bouts within the Sleep Period Time
sleep_Midpoint Sleep Midpoint
sleep_efficiency Sleep Efficiency
N_atleast5minwakenight Number of times awake during the night for at least 5 minutes
ACC_spt_sleep_mg Average acceleration during sleep (mg)
Nblocks_spt_sleep Number of blocks of night sleep within the Sleep period time window.

Physical Activity Domain

Variable Description
TAC Total volume of physical activity
TLAC Total volume of log-transformed physical activity
sed_time Total daytime duration in minutes of sedentary activity
light_time Total daytime duration in minutes of light activity
mod_time Total daytime duration in minutes of moderate activity
vig_time Total daytime duration in minutes of vigorous activity
MVPA_time Total daytime duration in minutes of moderate to vigorous activity
activity_time Total daytime duration in minutes of light/moderate/vigorous activity
sed_time_M10 Total duration in minutes of sedentary activity within M10 window
light_time_M10 Total duration in minutes of light activity within M10 window
mod_time_M10 Total duration in minutes of moderate activity within M10 window
vig_time_M10 Total duration in minutes of vigorous activity within M10 window
MVPA_time_M10 Total duration in minutes of moderate to vigorous activity within M10 window
activity_time_M10 Total duration in minutes of light/moderate/vigorous activity within M10 window
sed_time_24h Total duration in minutes of sedentary activity within 24 hours
light_time_24h Total duration in minutes of light activity within 24 hours
mod_time_24h Total duration in minutes of moderate activity within 24 hours
vig_time_24h Total duration in minutes of vigorous activity within 24 hours
MVPA_time_24h Total duration in minutes of moderate to vigorous activity within 24 hours
activity_time_24h Total duration in minutes of light/moderate/vigorous activity within 24 hours
mean_r average bout duration (resting)
mean_a average bout duration (active)
SATP sedentary-to-active transition probabilities
ASTP active-to-sedentary transition probabilities
Gini_r Gini index, absolute variability normalized to the average bout duration (resting)
Gini_a Gini index, absolute variability normalized to the average bout duration (active)
alpha_r power-law distribution parameter (resting)
alpha_a power-law distribution parameter (active)
h_r average hazard (resting)
h_a average hazard (active)
dur_day_total_IN_min Total duration of day in minutes spent in total inactivity during the day
dur_day_total_LIG_min Total duration of day in minutes of light activity during the day
dur_day_total_MOD_min Total duration of day in minutes of moderate activity during the day
dur_day_total_VIG_min Total duration of day in minutes of vigorous activity during the day
dur_day_MVPA_bts_10_min Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 10 minutes or more
dur_day_MVPA_bts_5_10_min Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 5 to 10 minutes
dur_day_MVPA_bts_1_5_min Total duration in minutes of Moderate and Vigorous Physical Activity (MVPA) for bouts 1 to 5 minutes
Nbouts_day_IN_bts_30 Number of bouts of inactivity for bouts 30 minutes or more
Nbouts_day_IN_bts_20_30 Number of bouts of inactivity for bouts 20 to 30 minutes
Nbouts_day_IN_bts_10_20 Number of bouts of inactivity for bouts 10 to 20 minutes
Nbouts_day_LIG_bts_10 Number of bouts of light for bouts 10 minutes or more
Nbouts_day_LIG_bts_5_10 Number of bouts of light for bouts 5 to 10 minutes
Nbouts_day_LIG_bts_1_5 Number of bouts of light for bouts 1 to 5 minutes
Nbouts_day_MVPA_bts_10 Number of bouts of Moderate and Vigorous Physical Activity (MVPA) for bouts 10 minutes or more
Nbouts_day_MVPA_bts_5_10 Number of bouts of Moderate and Vigorous Physical Activity (MVPA) for bouts 5 to 10 minutes
Nbouts_day_MVPA_bts_1_5 Number of bouts of Moderate and Vigorous Physical Activity (MVPA) for bouts 1 to 5 minutes
Nblocks_day_total_IN Number of blocks of total inactivity during day
Nblocks_day_total_LIG Number of blocks of total light activity during day
Nblocks_day_total_MOD Number of blocks of total moderate activity during day
Nblocks_day_total_VIG Number of blocks of total vigorous activity during day

Variable Description
RA_ggir Relative amplitude: (M10VALUE-L5VALUE)/(M10VALUE+L5VALUE) where M10VALUE= most active 10 hrs, L5VALUE = least active 5 hour from GGIR output.
RA Relative amplitude: (M10-L5)/(M10+L5) where M10= most active 10 hrs, L5 = least active 5 hour.
L5TIME_num Time of the lowest acceleration value of the lowest acceleration 5 hours
M10TIME_num Time of the highest acceleration value of the highest acceleration 10 hours
L5VALUE Average acceleration value (mg) of the lowest Average acceleration 5 hours
M10VALUE Average acceleration value (mg) of the lowest Average acceleration value in highest Average acceleration 10 hours
M10 Average acceleration value (mg) of the lowest Average acceleration value in highest Average acceleration 10 hours
L5 Average acceleration value (mg) of the lowest Average acceleration 5 hours
L5TIME Time of the lowest acceleration value of the lowest acceleration 5 hours
M10TIME Time of the highest acceleration value of the highest acceleration 10 hours
IV_intradailyvariability Intra-daily variability measures fragmentation in the rest/activity rhythms
IS_interdailystability Inter-daily stability measures fragmentation in the rest/activity rhythms
IV Intra-daily variability measures fragmentation in the rest/activity rhythms
IS Inter-daily stability measures fragmentation in the rest/activity rhythms
amp amplitude (a measure of variability around the mean)
acro acrophase (the timing of the peak activity)
mesor_L mesor log-transformed
amp_L amplitude log-transformed
acro_L acrophase log-transformed
PC1 1st principal component score from fPCA
PC2 2nd principal component score from fPCA
PC3 3rd principal component score from fPCA
PC4 4th principal component score from fPCA
PC5 5th principal component score from fPCA
PC6 6th principal component score from fPCA
PC7 7th principal component score from fPCA
PC8 8th principal component score from fPCA
PC9 9th principal component score from fPCA
PC10 10th principal component score from fPCA

Reference: