1 Introduction

1.1 What is GGIR?

GGIR is an R-package to process multi-day raw accelerometer data for physical activity and sleep research. The term raw refers to data being expressed in m/s2 or gravitational acceleration as opposed to the previous generation accelerometers which stored data in accelerometer brand specific units. The signal processing includes automatic calibration, detection of sustained abnormally high values, detection of non-wear and calculation of average magnitude of dynamic acceleration based on a variety of metrics. Next, GGIR uses this information to describe the data per recording, per day of measurement, and (optionally) per segment of a day of measurement, including estimates of physical activity, inactivity and sleep. We published an overview paper of GGIR in 2019 link.

This vignette provides a general introduction on how to use GGIR and interpret the output, additionally you can find a introduction video and a mini-tutorial on YouTube. If you want to use your own algorithms for raw data then GGIR facilitates this with it’s external function embedding feature, documented in a separate vignette: Embedding external functions in GGIR. GGIR is increasingly being used by research groups across the world. A non-exhaustive overview of academic publications related to GGIR can be found here. R package GGIR would not have been possible without the support of the contributors listed in the author list at GGIR, with specific code contributions over time since April 2016 (when GGIR development moved to GitHub) shown here.

Cite GGIR:

When you use GGIR in publications do not forget to cite it properly as that makes your research more reproducible and it gives credit to it’s developers. See paragraph on Citing GGIR for details.

1.2 Contributing, Support, and Keeping up to date

How to contribute to the code?

The development version of GGIR can be found on github, which is also where you will find guidance on how to contribute.

How can I get service and support?

GGIR is open source software and does not come with service or support guarantees. However, as user-community you can help each other via the GGIR google group or the GitHub issue tracker. Please use these public platform rather than private e-mails such that other users can learn from the conversations.

If you need dedicated support with the use of GGIR or need someone to adapt GGIR to your needs then Vincent van Hees is available as independent consultant.

Change log

Our log of main changes to GGIR over time can be found here.

2 Setting up your work environment

2.1 Install R and RStudio

Download and install R

Download and install RStudio (optional, but recommended)

Install GGIR with its dependencies from CRAN. You can do this with one command from the console command line:

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

Alternatively, to install the latest development version with the latest bug fixes use instead:

install.packages("remotes")
remotes::install_github("wadpac/GGIR")

2.2 Prepare folder structure

  1. GGIR works with the following accelerometer brands and formats:
    • GENEActiv .bin and .csv
    • Axivity AX3 and AX6 .wav, .csv and .cwa
    • ActiGraph .csv and .gt3x (.gt3x only the newer format generated with firmware versions above 2.5.0). Note for Actigraph users: If you want to work with .csv exports via the ActiLife then note that you have the option to export data with timestamps. Please do not do this as this causes memory issues for GGIR. To cope with the absence of timestamps GGIR will re-caculate timestamps from the sample frequency and the start time and date as presented in the file header.
    • Movisens with data stored in folders.
    • Genea (an accelerometer that is not commercially available anymore, but which was used for some studies between 2007 and 2012) .bin and .csv
    • Any other accelerometer brand that generates csv output, see documentation for functions read.myacc.csv and argument rmc.noise in the GGIR function documentation (pdf).
  2. All accelerometer data that needs to be analysed should be stored in one folder, or subfolders of that folder.
  3. Give the folder an appropriate name, preferable with a reference to the study or project it is related to rather than just ‘data’, because the name of this folder will be used later on as an identifier of the dataset.

2.3 GGIR shell function

GGIR comes with a large number of functions and optional settings (arguments) per functions.

To ease interacting with GGIR there is one central function, named g.shell.GGIR, to talk to all the other functions.

In this paragraph we will guide you through the main arguments relevant for 99% of research. First of all, it is important to understand that GGIR is structured in two ways. It has a computational structure of five parts which are applied sequentially to the data, and that g.shell.GGIR controls each of these parts:

  • Part 1: Loads the data and stores derived features (aggregations) needed for the other parts. This is the time-consuming part. Once this is done, parts 2-5 can be run (or re-run with different parameters in parts 2-5) relatively quickly.
  • Part 2: Data quality analyses and low-level description of signal features per day and per file. At this point a day is defined from midnight to midnight
  • Part 3: Estimation of sustained inactivity and sleep periods, needed for input to Part 4 for sleep detection
  • Part 4: Labels the sustained inactive periods detected in Part 3 as sleep, or daytime sustained inactivity, per night and per file
  • Part 5: Derives sleep and physical activity characteristics by re-using information derived in part 2, 3 and 4. Total time in intensity categories, the number of bouts, time spent in bouts and average acceleration (overall activity) is calculated.

The reason why it split up in parts is that it avoids having the re-do all analysis if you only want to make a small change in the more downstream parts. The specific order and content of the parts has grown for historical and computational reasons.

Secondly, the function arguments which we will refer to as input parameters are structured thematically independently of the five parts they are used in:

  • params_rawdata: parameters related to handling the raw data such as resampling or calibrating
  • params_metrics: parameters related to aggregating the raw data to epoch level summary metrics
  • params_sleep: parameters related to sleep detection
  • params_physact: parameters related to physical activity
  • params_247: parameters related to 24/7 behaviours that do not fall into the typical sleep or physical activity research category.
  • params_output: parameters relating to how and whether output is stored.
  • params_general: general parameters not covered by any of the above categories

This structure was introduced in GGIR version 2.5-6 to make the GGIR code and documentation easier to navigate.

To see the parameters in each parameter category and their default values do:

library(GGIR)
print(load_params())

If you are only interested in one specific category like sleep:

library(GGIR)
print(load_params()$params_sleep)

If you are only interested in parameter “HASIB.algo” from the sleep_params object:

library(GGIR)
print(load_params()$params_sleep[["HASPT.algo"]])

Documentation for this parameter objects can be found in the (GGIR function documentation (pdf)). All of these are accepted as argument to function g.shell.GGIR, because g.shell.GGIR is a shell around all GGIR functionality. However, the params_ objects themselves can not be provided as input to g.shell.GGIR.

2.3.1 Key general arguments

You will probably never need to think about most of the arguments listed above, because a lot of arguments are only included to facilitate methodological studies where researchers want to have control over every little detail. See previous paragraph for links to the documentation and how to find the default value of each parameter.

The bare minimum input needed for g.shell.GGIR is:

library(GGIR)
g.shell.GGIR(datadir="C:/mystudy/mydata",
             outputdir="D:/myresults")

Argument datadir allows you to specify where you have stored your accelerometer data and outputdir allows you to specify where you would like the output of the analyses to be stored. This cannot be equal to datadir. If you copy paste the above code to a new R script (file ending with .R) and Source it in R(Studio) then the dataset will be processed and the output will be stored in the specified output directory.

Below we have highlighted the key arguments you may want to be aware of. We are not giving a detailed explanation, please see the package manual for that.

  • mode - which part of GGIR to run, GGIR is constructed in five parts.
  • overwrite - whether to overwrite previously produced milestone output. Between each GGIR part, GGIR stores milestone output to ease re-running parts of the pipeline.
  • idloc - tells GGIR where to find the participant ID (default: inside file header)
  • strategy - informs GGIR how to consider the design of the experiment.
    • If strategy is set to value 1, then check out arguments hrs.del.start and hrs.del.end.
    • If strategy is set to value 3, then check out arguments ndayswindow.
  • maxdur - maximum number of days you expect in a data file based on the study protocol.
  • desiredtz - time zone of the experiment.
  • chunksize - a way to tell GGIR to use less memory, which can be useful on machines with limited memory.
  • includedaycrit - tell GGIR how many hours of valid data per day (midnight-midnight) is acceptable.
  • includenightcrit - tell GGIR how many hours of a valid night (noon-noon) is acceptable.
  • qwindow - argument to tell GGIR whether and how to segment the day for day-segment specific analysis.
  • mvpathreshold and boutcriter - acceleration threshold and bout criteria used for calculating time spent in MVPA (only used in GGIR part2).
  • epochvalues2csv - to export epoch level magnitude of acceleration to a csv files (in addition to already being stored as RData file)
  • dayborder - to decide whether the edge of a day should be other than midnight.
  • iglevels - argument related to intensity gradient method proposed by A. Rowlands.
  • do.report - specify reports that need to be generated.
  • viewingwindow and visualreport - to create a visual report, this only works when all five parts of GGIR have successfully run.

2.3.2 Input arguments overview

The table below shows all GGIR input arguments, the GGIR part (1, 2, 3, 4 and/or 5) they are used in, and the parameter object they belong too. As you will see a few parameters are not part of any parameter object. Their default values can be found in the GGIR function documentation (pdf).

Argument (parameter) Used in GGIR part Parameter object
datadir 1, 2, 4, 5 not in parameter objects
f0 1, 2, 3, ,4 ,5 not in parameter objects
f1 1, 2, 3, 4, 5 not in parameter objects
windowsizes 1, 5 params_general
desiredtz 1, 2, 3, 4, 5 params_general
overwrite 1, 2, 3, 4, 5 params_general
do.parallel 1, 2, 3, 5 params_general
maxNcores 1, 2, 3, 5 params_general
myfun 1, 2, 3 not in parameter objects
outputdir 1 not in parameter objects
studyname 1 not in parameter objects
chunksize 1 params_rawdata
do.enmo 1 params_metrics
do.lfenmo 1 params_metrics
do.en 1 params_metrics
do.bfen 1 params_metrics
do.hfen 1 params_metrics
do.hfenplus 1 params_metrics
do.mad 1 params_metrics
do.anglex 1 params_metrics
do.angley 1 params_metrics
do.angle 1 params_metrics
do.enmoa 1 params_metrics
do.roll_med_acc_x 1 params_metrics
do.roll_med_acc_y 1 params_metrics
do.roll_med_acc_z 1 params_metrics
do.dev_roll_med_acc_x 1 params_metrics
do.dev_roll_med_acc_y 1 params_metrics
do.dev_roll_med_acc_z 1 params_metrics
do.lfen 1 params_metrics
do.lfx 1 params_metrics
do.lfy 1 params_metrics
do.lfz 1 params_metrics
do.hfx 1 params_metrics
do.hfy 1 params_metrics
do.hfz 1 params_metrics
do.bfx 1 params_metrics
do.bfy 1 params_metrics
do.bfz 1 params_metrics
do.zcx 1 params_metrics
do.zcy 1 params_metrics
do.zcz 1 params_metrics
lb 1 params_metrics
hb 1 params_metrics
n 1 params_metrics
do.cal 1 params_rawdata
spherecrit 1 params_rawdata
minloadcrit 1 params_rawdata
printsummary 1 params_rawdata
print.filename 1 params_general
backup.cal.coef 1 params_rawdata
rmc.noise 1 params_rawdata
rmc.dec 1 params_rawdata
rmc.firstrow.acc 1 params_rawdata
rmc.firstrow.header 1 params_rawdata
rmc.col.acc 1 params_rawdata
rmc.col.temp 1 params_rawdata
rmc.col.time 1 params_rawdata
rmc.unit.acc 1 params_rawdata
rmc.unit.temp 1 params_rawdata
rmc.origin 1 params_rawdata
rmc.header.length 1 params_rawdata
mc.format.time 1 params_rawdata
rmc.bitrate 1 params_rawdata
rmc.dynamic_range 1 params_rawdata
rmc.unsignedbit 1 params_rawdata
rmc.desiredtz 1 params_rawdata
rmc.sf 1 params_rawdata
rmc.headername.sf 1 params_rawdata
rmc.headername.sn 1 params_rawdata
rmc.headername.recordingid 1 params_rawdata
rmc.header.structure 1 params_rawdata
rmc.check4timegaps 1 params_rawdata
rmc.col.wear 1 params_rawdata
rmc.doresample 1 params_rawdata
imputeTimegaps 1 params_rawdata
selectdaysfile 1, 2 params_cleaning
dayborder 1, 2, 5 params_general
dynrange 1 params_rawdata
configtz 1 params_general
minimumFileSizeMB 1 params_rawdata
interpolationType 1 params_rawdata
metadatadir 2, 3, 4, 5 not in parameter objects
minimum_MM_length.part5 5 params_cleaning
strategy 2, 5 params_cleaning
hrs.del.start 2, 5 params_cleaning
hrs.del.end 2, 5 params_cleaning
maxdur 2, 5 params_cleaning
max_calendar_days 2 params_cleaning
includedaycrit 2 params_cleaning
L5M5window 2 params_247
M5L5res 2, 5 params_247
winhr 2, 5 params_247
qwindow 2 params_247
qlevels 2 params_247
ilevels 2 params_247
mvpathreshold 2 params_phyact
boutcriter 2 params_phyact
ndayswindow 2 params_cleaning
idloc 2, 4 params_general
do.imp 2 params_cleaning
storefolderstructure 2, 4, 5 params_output
epochvalues2csv 2 params_output
mvpadur 2 params_phyact
window.summary.size 2 params_247
bout.metric 2, 5 params_phyact
closedbout 2 params_phyact
IVIS_windowsize_minutes 2 params_247
IVIS_epochsize_seconds 2 params_247
IVIS.activity.metric 2 params_247
iglevels 2, 5 params_247
TimeSegments2ZeroFile 2 params_cleaning
qM5L5 2 params_247
MX.ig.min.dur 2 params_247
qwindow_dateformat 2 params_247
anglethreshold 3 params_sleep
timethreshold 3 params_sleep
acc.metric 3, 5 params_general
ignorenonwear 3 params_sleep
constrain2range 3 params_sleep
do.part3.pdf 3 params_output
sensor.location 3, 4 params_general
HASPT.algo 3 params_sleep
HASIB.algo 3 params_sleep
Sadeh_axis 3 params_sleep
longitudinal_axis 3 params_sleep
HASPT.ignore.invalid 3 params_sleep
loglocation 4, 5 params_sleep
colid 4 params_sleep
coln1 4 params_sleep
nnights 4 params_sleep
sleeplogidnum 4, 5 params_sleep
do.visual 4 params_output
outliers.only 4 params_output
excludefirstlast 4 params_cleaning
criterror 4 params_output
includenightcrit 4 params_cleaning
relyonguider 4 params_sleep
relyonsleeplog 4 not in parameter objects
def.noc.sleep 4 params_sleep
data_cleaning_file 4, 5 params_cleaning
excludefirst.part4 4 params_cleaning
excludelast.part4 4 params_cleaning
sleeplogsep 4 params_cleaning
sleepwindowType 4 params_cleaning
excludefirstlast.part5 5 params_cleaning
boutcriter.mvpa 5 params_phyact
boutcriter.in 5 params_phyact
boutcriter.lig 5 params_phyact
threshold.lig 5 params_phyact
threshold.mod 5 params_phyact
threshold.vig 5 params_phyact
timewindow 5 params_output
boutdur.mvpa 5 params_phyact
boutdur.in 5 params_phyact
boutdur.lig 5 params_phyact
save_ms5rawlevels 5 params_output
part5_agg2_60seconds 5 params_general
save_ms5raw_format 5 params_output
save_ms5raw_without_invalid 5 params_output
includedaycrit.part5 5 params_cleaning
frag.metrics 5 params_phyact
LUXthresholds 5 params_247
LUX_cal_constant 5 params_247
LUX_cal_exponent 5 params_247
LUX_day_segments 5 params_247
do.sibreport 5 params_output

2.3.5 Published cut-points and how to use them

Cut-points to estimate time spent in acceleration levels that are roughly liked to levels of energy metabolism have been proposed by:

Acceleration metric not available in GGIR? Some of the above publications make use of acceleration metrics that sum their values per epoch rather than average them per epoch like GGIR does. So, to use their cut-point value we need to multiply the proposed cut-point by the sample frequency used in the study that proposed it. For each of the studies this is detailed below. Note that GGIR intentionally does not sum values per epoch because that approach makes the cut-point sample frequency dependent, which complicates comparisons and harmonisation of literature. The explained variance and accuracy remains identical because we are only multiplying with a constant.

Esliger 2011, Phillips 2013, Fraysse 2020, Dibben2020:

  • In GGIR use metric ENMOa instead of ENMO with arguments do.enmoa = TRUE, do.enmo = FALSE, and acc.metric=”ENMOa”.
  • threshold.lig = (LightCutPointFromPaper_in_gmins/(sampleRateInStudy*60)) * 1000
  • threshold.mod = (ModerateCutPointFromPaper_in_gmins/(sampleRateInStudy*60)) * 1000
  • threshold.vig = (VigorousCutPointFromPaper_in_gmins/(sampleRateInStudy*60)) * 1000
  • mvpathreshold = (ModerateCutPointFromPaper_in_gmins/(sampleRateInStudy*60)) * 1000
  • The value for sampleRateInStudy was 80 for Esliger and Phillips and 100 for Fraysse.
  • Dibben et al. 2020 does not report sample frequency by which the proposed cut-points cannot be replicated.
  • In the part 2 results you will need the MVPA estimates that are related to ENMOa, not ENMO.
  • In the part 5 results everything will be based on the new cut-points.

Roscoe 2017:

  • In GGIR use metric ENMOa instead of ENMO with arguments do.enmoa = TRUE, do.enmo = FALSE, and acc.metric=”ENMOa”.
  • threshold.lig = (LightCutPointFromPaper_in_gsecs/85.7) * 1000
  • threshold.mod = (ModerateCutPointFromPaper_in_gsecs/85.7) * 1000
  • threshold.vig = (VigorousCutPointFromPaper_in_gsecs/85.7) * 1000
  • mvpathreshold = (ModerateCutPointFromPaper_in_gsecs/85.7) * 1000
  • In the part 2 results you will need the MVPA estimates that are related to ENMOa, not ENMO.
  • In the part 5 results everything will be based on the new cut-points.

Schaeffer 2014:

  • In GGIR use metric EN instead of ENMO with arguments do.en = TRUE, do.enmo = FALSE, and acc.metric=”EN”.
  • Specify Schaeffer cut-points as:
  • threshold.lig = (LightCutPointFromPaper/75) * 1000
  • threshold.mod = (ModerateCutPointFromPaper/75) * 1000
  • threshold.vig = (VigorousCutPointFromPaper/75) * 1000
  • mvpathreshold = (ModerateCutPointFromPaper/75) * 1000
  • In the part2 results you will need the MVPA estimates that are related to EN, not ENMO.
  • In the part 5 results everything will be based on the new cut-points.

Vaha-Ypya et al 2015:

  • Use default setting do.mad= TRUE, acc.metric=”MAD”
  • Use the cut-points as provided by Vaha-Ypya directly. No need for scaling.

Hildebrand 2014, Hildebrand 2016, Migueles 2021. Sanders 2018:

  • Use default setting do.enmo= TRUE, acc.metric=”ENMO”
  • Use the cut-points as provided by Hildebrand directly. No need for scaling.

2.3.5.1 Notes on cut-point validity and ‘validation’ studies

Sensor calibration

In all of the studies above, excluding Hildebrand et al. 2016, no effort was made to calibrate the acceleration sensors relative to gravitational acceleration prior to cut-point development. Theoretically this can be expected to cause a bias in the cut-point estimates proportional to the calibration error in each device, especially for cut-points based on acceleration metrics which rely on the assumption of accurate calibration such as metrics: ENMO, EN, ENMOa, and by that also metric SVMgs used by studies such as Esliger 2011, Phillips 2013, and Dibben 2020.

Idle sleep mode and ActiGraph

Studies done with ActiGraph devices when configured with ‘idle sleep mode’ on, will have zero-strings in all three axes during periods of no movement. Studies do not clarify how these zeros strings are accounted for. The insertion of zero strings is problematic as raw data accelerometers should always measure the gravitational component when not moving. This directly impacts metrics that rely on the presence of a gravitational component such as ENMO, EN, ENMOAa, and SVMgs. However, also other metrics may be affected as the sudden disappearance of gravitational acceleration will cause a spike at the start and end of the non-movement time segment. More generally speaking, we advise ActiGraph users to disable the ‘idle sleep mode’ as it harms the transparency and reproducibility since no mechanism exists to replicate it in other accelerometer brands, and it is likely to challenge accurate assessment of sleep and sedentary behaviour. We also advise that data collected with ‘idle sleep mode’ turned on is not be referred to as raw data accelerometry, because the data collection process has involved proprietary pre-processing steps which is violates the core principle of raw data collection.

Validity of validation studies

Several studies that aimed to independently evaluate cut-point methods failed at recognising these challenges. Further, validation studies are typically limited to laboratory conditions and a small population. Therefore, it is best to interpret cut-points with caution. Future methodological studies around cut-points are advised to account for accelerometer calibration error and the problematic time gaps in for example the ActiGraph when configured with ‘idle sleep mode’.

2.3.6 Example call

If you consider all the arguments above you me may end up with a call to g.shell.GGIR that could look as follows.

library(GGIR)
g.shell.GGIR(
             mode=c(1,2,3,4,5),
             datadir="C:/mystudy/mydata",
             outputdir="D:/myresults",
             do.report=c(2,4,5),
             #=====================
             # Part 2
             #=====================
             strategy = 1,
             hrs.del.start = 0,          hrs.del.end = 0,
             maxdur = 9,                 includedaycrit = 16,
             qwindow=c(0,24),
             mvpathreshold =c(100),
             bout.metric = 4,
             excludefirstlast = FALSE,
             includenightcrit = 16,
             #=====================
             # Part 3 + 4
             #=====================
             def.noc.sleep = 1,
             outliers.only = TRUE,
             criterror = 4,
             do.visual = TRUE,
             #=====================
             # Part 5
             #=====================
             threshold.lig = c(30), threshold.mod = c(100),  threshold.vig = c(400),
             boutcriter = 0.8,      boutcriter.in = 0.9,     boutcriter.lig = 0.8,
             boutcriter.mvpa = 0.8, boutdur.in = c(1,10,30), boutdur.lig = c(1,10),
             boutdur.mvpa = c(1),
             includedaycrit.part5 = 2/3,
             #=====================
             # Visual report
             #=====================
             timewindow = c("WW"),
             visualreport=TRUE)

Once you have used g.shell.GGIR and the output directory (outputdir) will be filled with milestone data and results.

2.3.7 Configuration file

Function g.shell.GGIR stores all the explicitly entered argument values and default values for the argument that are not explicitly provided in a csv-file named config.csv stored in the root of the output folder. The config.csv file is accepted as input to g.shell.GGIR with argument configfile to replace the specification of all the arguments, except datadir and outputdir, see example below.

library(GGIR)
g.shell.GGIR(datadir="C:/mystudy/mydata",
             outputdir="D:/myresults", configfile = "D:/myconfigfiles/config.csv")

The practical value of this is that it eases the replication of analysis, because instead of having to share you R script, sharing your config.csv file will be sufficient. Further, the config.csv file contribute to the reproducibility of your data analysis.

Note 1: When combining a configuration file with explicitly provided argument values, the explicitly provided argument values will overrule the argument values in the configuration file. Note 2: The config.csv file in the root of the output folder will be overwritten every time you use g.shell.GGIR. So, if you would like to add annotations in the file, e.g. in the fourth column, then you will need to store it somewhere outside the output folder and explicitly point to it with configfile argument.

3 Time for action: How to run your analysis?

3.1 From the console

You can use

source("pathtoscript/myshellscript.R")

or use the Source button in RStudio if you use RStudio.

3.2 In a cluster

GGIR by default support multi-thread processing, which can be turned off by seting argument do.parallel = FALSE. If this is still not fast enough then I advise using a GGIR on a computing cluster. The way I did it on a Sun Grid Engine cluster is shown below, please note that some of these commands are specific to the computing cluster you are working on. Also, you may actually want to use an R package like clustermq or snowfall, which avoids having to write bash script. Please consult your local cluster specialist to tailor this to your situation. In my case, I had three files for the SGE setting:

submit.sh

for i in {1..707}; do
    n=1
    s=$(($(($n * $[$i-1]))+1))
    e=$(($i * $n))
    qsub /home/nvhv/WORKING_DATA/bashscripts/run-mainscript.sh $s $e
done

run-mainscript.sh

#! /bin/bash
#$ -cwd -V
#$ -l h_vmem=12G
/usr/bin/R --vanilla --args f0=$1 f1=$2 < /home/nvhv/WORKING_DATA/test/myshellscript.R

myshellscript.R

options(echo=TRUE)
args = commandArgs(TRUE)
if(length(args) > 0) {
  for (i in 1:length(args)) {
    eval(parse(text = args[[i]]))
  }
}
g.shell.GGIR(f0=f0,f1=f1,...)

You will need to update the ... in the last line with the arguments you used for g.shell.GGIR. Note that f0=f0,f1=f1 is essential for this to work. The values of f0 and f1 are passed on from the bash script.

Once this is all setup you will need to call bash submit.sh from the command line.

Important Note:

Please make sure that you process one GGIR part at the same time on a cluster, because each part assumes that preceding parts have been ran. You can make sure of this by always specifying argument mode to a single part of GGIR. Once the analysis stops update argument mode to the next part until all parts are done. The speed of the parallel processing is obviously dependent on the capacity of your computing cluster and the size of your dataset.

4 Inspecting the results

GGIR generates the following types of output. - csv-spreadsheets with all the variables you need for physical activity, sleep and circadian rhythm research - Pdfs with on each page a low resolution plot of the data per file and quality indicators - R objects with milestone data - Pdfs with a visual summary of the physical activity and sleep patterns as identified (see example below)

4.1 Output part 2

Part 2 generates the following output:

  • part2_summary.csv: Person level summary (see below)
  • part2_daysummary.csv: Day level summary (see below)
  • QC/data_quality_report.csv: Overview of calibration results and whether or not a file was corrupt or too short to be processed,
  • QC/plots to check data quality 1.pdf: A pdf with visualisation of the acceleration time series in 15 minute resolution and with invalid data segments highlighted in colours (yellow: non-wear based on standard deviation threshold, brown: non-wear after extra filtering step (introduced in 2013), and purple: clipping)

4.1.1 Person level summary

(Part of) variable name Description
ID Participant id
device_sn Device serial number
bodylocation Body location extracted from file header
filename Name of the data file
start_time Timestamp when recording started
startday Day of the week on which recording started
samplefreq Sample frequency (Hz)
device Accelerometer brand, e.g. GENEACtiv
clipping_score The Clipping score: Fraction of 15 minute windows per file for which the acceleration in one of the three axis was close to the maximum for at least 80% of the time. This should be 0.
meas_dur_dys} Measurement duration (days)
complete_24hcycle Completeness score: Fraction of 15 minute windows per 24 hours for which no valid data is available at any day of the measurement.
meas_dur_def_proto_day measurement duration according to protocol (days): Measurement duration (days) minus the hours that are ignored at the beginning and end of the measurement motivated by protocol design
wear_dur_def_proto_day wear duration duration according to protocol (days): So, if the protocol was seven days of measurement, then wearing the accelerometer for 8 days and recording data for 8 days will still make that the wear duration is 7 days
calib_err Calibration error (static estimate) Estimated based on all ‘non-movement’ periods in the measurement after applying the autocalibration.
calib_status Calibration status: Summary statement about the status of the calibration error minimisation
ENMO_fullRecordingMean ENMO is the main summary measure of acceleration. The value presented is the average ENMO over all the available data normalised per 24-hour cycles (diurnal balanced), with invalid data imputed by the average at similar time points on different days of the week. In addition to ENMO it is possible to extract other acceleration metrics (i.e. BFEN, HFEN, HFENplus). We emphasize that it is calculated over the full recording because the alternative is that a variable is only calculated overmeasurement days with sufficient valid hours of data.
ENMO (only available if set to true in part1.R) ENMO is the main summary measure of acceleration. The value presented is the average ENMO over all the available data normalised per 24 hour cycles, with invalid data imputed by the average at similar timepoints on different days of the week. In addition to ENMO it is possible to extract other acceleration metrics in part1.R (i.e. BFEN, HFEN, HFENplus) See also van Hees PLoSONE April 2013 for a detailed description and comparison of these techniques.
pX_A_mg_0-24h_fullRecording This variable represents the Xth percentile in the distribution of short epoch metric value A of the average day. The average day may not be ideal for describing the distribution. Therefore, the code also extracts the following variable.
AD_pX_A_mg_0-24h This variable represents the Xth percentile in the distribution of short epoch metric value A per day averaged across all days.
L5_A_mg_0-24 Average of metric A during the least active five* hours in the day that is the lowest rolling average value of metric A. (* window size is modifiable by argument winhr)
M5_A_mg_0-24 Average of metric A during the most active five* hours in the day that is the lowest rolling average value of metric A. (* window size is modifiable by argument winhr)
L5hr_A_mg_0-24 Starting time in hours and fractions of hours of L5_A_mg_0-24
M5hr_A_mg_0-24 Starting time in hours and fractions of hours of M5_A_mg_0-24
ig_gradient_ENMO_0-24hr_fullRecording Intensity gradient calculated over the full recording.
1to6am_ENMO_mg Average metric value ENMO between 1am and 6am
N valid WEdays Number of valid weekend days
N valid WKdays Number of valid week days
IS_interdailystability inter daily stability. The movement count that is derived for this was an attempt to follow the original approach by Eus J. W. Van Someren (Chronobiology International. 1999. Volume 16, issue 4).
IV_intradailyvariability intra daily variability. In contrast to the original paper, we ignore the epoch transitions between the end of a day and the beginning of the next day for the numerator of the equation, this to make it a true measure of intradaily variability. Same note as for IS: The movement count that is derived for this was an attempt to follow the original approach.
IVIS_windowsize_minutes Sizes of the windows based on which IV and IS are calculated (note that this is modifiable)
IVIS_epochsize_seconds Argument has been deprecated
AD_ All days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over all the available days
WE_ Weekend days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over weekend days only
WD_ Week days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over week days only
WWE_ Weekend days (weighted average) The variable was calculated per day and then averaged over weekend days. Double weekend days are averaged. This is only relevant for experiments that last for more than seven days.
WWD_ Week days (weighted average) The variable was calculated per day and then averaged over week days. Double weekend days were averaged. This is only relevant for experiments that last for more than seven days)
WWD_MVPA_E5S_T100_ENMO Time spent in moderate-to-vigorous based on 5 second epoch size and an ENMO metric threshold of 100
WWE_MVPA_E5S_B1M80%_T100_ENMO Time spent in moderate-to-vigorous based on 5 second epoch size and an ENMO metric threshold of 100 based on a bout criteria of 100
WE_[100,150)_mg_0-24h_ENMO Time spent between (and including) 100 mg and 150 (excluding 150 itself) between 0 and 24 hours (the full day) using metric ENMO data exclusion strategy (value=1, ignore specific hours; value=2, ignore all data before the first midnight and after the last midnight)
_MVPA_E5S_B1M80_T100 MVPA calculated based on 5 second epoch setting bout duration 1 Minute and inclusion criterion of more than 80 percent. This is only done for metric ENMO at the moment, and only if mvpa threshold is not left blank
_ENMO_mg ENMO or other metric was first calculated per day and then average according to AD, WD, WWE, WWD
data exclusion strategy A log of the decision made when calling g.impute: value=1 mean ignore specific hours; value=2 mean ignore all data before the first midnight and after the last midnight
n hours ignored at start of meas (if strategy=1) number of hours ignored at the start of the measurement (if strategy = 1) A log of decision made in part2.R
n hours ignored at end of meas (if strategy=1) number of hours ignored at the end of the measurement (if strategy = 1). A log of decision made in part2.R
n hours ignored at end of meas (if strategy=1) number of days of measurement after which all data is ignored (if strategy = 1) A log of decision made in part2.R
epoch size to which acceleration was averaged (seconds) A log of decision made in part1.R
pdffilenumb Indicator of in which pdf-file the plot was stored
pdfpagecount Indicator of in which pdf-page the plot was stored

4.1.2 Day level summary

This is a non-exhaustive list, because most concepts have been explained in summary.csv

(Part of) variable name Description
ID Participant id
filename Name of the data file
calender_date Timestamp and date on which measurement started
bodylocation Location of the accelerometer as extracted from file header
N valid hours Number of hours with valid data in the day
N hours Number of hours of measurement in a day, which typically is 24, unless it is a day on which the clock changes (DST) resulting in 23 or 25 hours. The value can be less than 23 if the measurement started or ended this day
weekday Name of weekday
measurement Day of measurement Day number relative to start of the measurement
L5hr_ENMO_mg_0-24h Hour on which L5 starts for these 24 hours (defined with metric ENMO)
L5_ENMO_mg_0-24h Average acceleration for L5 (defined with metric ENMO)
[A,B)_mg_0-24h_ENMO Time spent in minutes between (and including) acceleration value A in mg and (excluding) acceleration value B in mg based on metric ENMO
ig_gradient_ENMO_0-24hr Gradient from intensity gradient analysis proposed by Rowlands et al. 2018 based on metric ENMO for the time segment 0 to 24 hours
ig_intercept_ENMO_0-24hr Intercept from intensity gradient analysis proposed by Rowlands et al. 2018 based on metric ENMO for the time segment 0 to 24 hours
ig_rsquared_ENMO_0-24hr r squared from intensity gradient analysis proposed by Rowlands et al. 2018 based on metric ENMO for the time segment 0 to 24 hours

4.2 Output part 4

Part 4 generates the following output:

4.2.1 Night level summaries

  • part4_nightsummary_sleep_cleaned.csv
  • QC/part4_nightsummary_sleep_full.csv

The csv. files contain the variables as shown below.

(Part of) variable name Description
ID Participant ID extracted from file
night Number of the night in the recording
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.
SptDuration Difference between onset and waking time.
sleepparam Definition of sustained inactivity by accelerometer.
guider guider used as discussed in paragraph Sleep analysis.
guider_onset Start of Sleep Period Time window derived from the guider.
guider_wake End of Sleep Period Time window derived guider.
guider_SptDuration Time SPT duration derived from guider_wake and guider_onset.
error_onset Difference between sleeponset and guider_onset
error_wake Difference between wakeup and guider_wake
fraction_night_invalid Fraction of the night (noon-noon or 6pm-6pm) for which the data was invalid, e.g. monitor not worn or no accelerometer measurement started/ended within the night.
SleepDurationInSpt Total sleep duration, which equals the accumulated nocturnal sustained inactivity bouts within the Sleep Period Time.
duration_sib_wakinghours Accumulated sustained inactivity bouts during the day. These are the periods we would label during the night as sleep, but during the day they form a subclass of inactivity, which may represent day time sleep or wakefulness while being motionless for a sustained period of time number_sib_sleepperiod} Number of nocturnal sleep periods, with nocturnal referring to the Sleep Period Time window.
duration_sib_wakinghours_atleast15min Same as duration_sib_wakinghours, but limited to SIBs that last at least 15 minutes.
number_sib_wakinghours Number of sustained inactivity bouts during the day, with day referring to the time outside the Sleep Period Time window.
sleeponset_ts sleeponset formatted as a timestamp
wakeup_ts wakeup formatted as a timestamp
guider_onset_ts guider_onset formatted as a timestamp
guider_wake_ts guider_wake formatted as a timestamp
page pdf page on which the visualisation can be found
daysleeper If 0 then the person is a nightsleeper (sleep period did not overlap with noon) if value=1 then the person is a daysleeper (sleep period did overlap with noon)
weekday Day of the week on which the night started
calendardate Calendar date on which the night started in day/month/year format.
filename Name of the accelerometer file
cleaningcode see paragraph Cleaningcode.
sleeplog_used Whether a sleep log was used (TRUE/FALSE)
acc_available Whether accelerometer data was available (TRUE/FALSE).
WASO Wake After Sleep Onset: SptDuration - SleepDurationInSpt
SptDuration Sleep Period Time window duration: wakeup - sleeponset
error_onset Difference between sleeponset and guider_onset (this variable is only available in the full report as stored in the QC folder)
error_wake Difference between wakeup and guider_wake (this variable is only available in the full report as stored in the QC folder)
SleepRegularityIndex The Sleep Regularity Index as proposed by Phillips et al. 2017, but calculated per day-pair to

enable user to study patterns across days. SriFractionValid | Fraction of the 24 hour period that was valid in both current as well as in matching timestamps for the next calendar day. See GGIR function manual for details.

4.2.1.1 Non-default variables in part 4 csv report

These additional are only stored if you used a sleeplog that captures time in bed, or when using guider HorAngle for hip-worn accelerometer data. If either of these applies set argument sleepwindowType to “TimeInBed”.

(Part of) variable name Description
guider_guider_inbedStart Time of getting in bed
guider_guider_inbedEnd Time of getting out of bed
guider_inbedDuration Time in Bed: guider_inbedEnd - guider_inbedStart
sleepefficiency Sleep efficiency, calculated as: SleepDurationInSpt / guider_inbedDuration
sleeplatency Sleep latency, calculated as: sleeponset - guider_inbedStart

4.2.2 Person level summaries

  • part4_summary_sleep_cleaned.csv
  • QC/part4_summary_sleep_full.csv

In the person level report the variables are derived from the variables in the night level summary. Minor extensions to the variable names explain how variables are aggregated across the days. Please find below extra clarification on a few of the variable names for which the meaning may not be obvious:

(Part of) variable name Description
_mn mean across days
_sd standard deviation across days
_AD All days
_WE Weekend days
_WD Week days
sleeplog_used Whether a sleeplog was available (TRUE) or not (FALSE)
sleep_efficiency Accelerometer detrive sleep efficiency within the sleep period time calculated as the ratio between acc_SleepDurationInSpt and acc_SptDuration (denominator). Only available at person level, because at night level the user can calculate this from existing variables.
n_nights_acc Number of nights of accelerometer data
n_nights_sleeplog Number of nights of sleeplog data.
n_WE_nights_complete Number of weekend nights complete which means both accelerometer and estimate from guider.
n_WD_nights_complete Number of weekday nights complete which means both accelerometer and estimate from guider.
n_WEnights_daysleeper Number of weekend nights on which the person slept until after noon.
n_WDnights_daysleeper Number of weekday nights on which the person slept until after noon.
duration_sib_wakinghour Total duration of sustained inactivity bouts during the waking hours.
number_sib_wakinghours Number of sustained inactivity bouts during the waking hours.
average_dur_sib_wakinghours Average duration of the sustained inactivity bouts during the day (outside the sleep period duration). Calculated as duration_sib_wakinghour divided by number_sib_wakinghours per day, after which the mean and standard deviation are calculated across days.

4.2.3 visualisation_sleep.pdf

Visualisation to support data quality checks: - visualisation_sleep.pdf (optional)

When input argument do.visual is set to TRUE GGIR can show the following visual comparison between the time window of being asleep (or in bed) according to the sleeplog and the detected sustained inactivity bouts according to the accelerometer data. This visualisation is stored in the results folder as visualisation_sleep.pdf.

Explanation of the image: Each line represents one night. Colours are used to distinguish definitions of sustained inactivity bouts (2 definitions in this case) and to indicate existence or absence of overlap with the sleeplog. When argument outliers.only is set to FALSE it will visualise all available nights in the dataset. If outliers.only is set to TRUE it will visualise only nights with a difference in onset or waking time between sleeplog and sustained inactivity bouts larger than the value of argument criterror.

This visualisation with outliers.only set to TRUE and critererror set to 4 was very powerful to identify entry errors in sleeplog data in van Hees et al PLoSONE 2015. We had over 25 thousand nights of data, and this visualisation allowed us to quickly zoom in on the most problematic nights to investigate possible mistakes in GGIR or mistakes in data entry.

4.3 Output part 5

The output of part 5 is dependent on the parameter configuration, it will generate as many output files as there are unique combination of the three thresholds provide. For example, the output could be:

For example, the following files will be generated if the threshold configuration was 30 for light activity, 100 for moderate and 400 for vigorous activity: - part5_daysummary_MM_L30M100V400_T5A5.csv - part5_daysummary_WW_L30M100V400_T5A5.csv - part5_personsummary_MM_L30M100V400_T5A5.csv - part5_personsummary_WW_L30M100V400_T5A5.csv - file summary reports/Report_nameofdatafile.pdf

4.3.1 Day level summary

(Term in) variable name Description
sleeponset onset of sleep expressed in hours since the midnight in the night preceding the night of interest, e.g. 26 is 2am.
wakeup waking up time express in the same way as sleeponset.
sleeponset_ts onset of sleep expressed as a timestamp hours:minutes:seconds
daysleeper if 0 then the person woke up before noon, if 1 then the person woke up after noon
cleaningcode See paragraph Cleaningcode.
dur_day_spt_min Total length of daytime waking hours and spt combined (typically 24 hours for MM report).
dur_ duration of a behavioral class that will be specified int he rest of the variable name
ACC_ (average) acceleration according to default metric specific by acc.metric
_spt_wake_ Wakefulness within the Sleep period time window.
_spt_sleep_ Sleep within the Sleep period time window.
_IN_ Inactivity
_LIG_ Light activity
_MOD_ Moderate activity
_VIG_ Vigorous activity
_MVPA_ Moderate or Vigorous activity
_unbt_ Unbouted
_bts_ Bouts (also known as sojourns), which are segments that for which the acceleration is within a specified range for a specified fraction of the time.
_bts_1_10_ Bouts lasting at least 1 minute and less than 10 minutes (1 and 9.99 minutes are included, but 10 minutes is not).
Nblock number of blocks of a certain behavioral class, not these are not bouts but a count of the number of times the behavioral class occurs without interruptions.
WW in filename refers to analyses based on the timewindow from waking to waking up
MM in filename refers to analyses done on windows between midnight and midnight
calendar_date calendar date on which the window started in day/month/year format. So, for WW window this could mean that you have two windows starting on the same date.
weekday weekday on which the window started. So, for WW window this could mean that you have two windows starting on the weekday.
_total_IN total time spent in inactivity (no distinction between bouted or unbouted behavior, this is a simple count of the number of epochs that meet the threshold criteria.
_total_LIG total time spent in light activity.
nonwear_perc_day Non-wear percentage during the waking hours of this day.
nonwear_perc_spt Non-wear percentage during the spt hours of this day.
nonwear_perc_day_spt Non-wear percentage during the whole day, including waking and spt.
dur_day_min Duration of waking hours within this day window
dur_spt_min Duration of Sleep Period Time within this day window.
dur_day_spt_min Duration this day window, including both waking hours and SPT.
sleep_efficiency sleep_efficiency in part 5 is not the same as in part 4, but calculated as the percentage of sleep within the sleep period time window. The conventional approach is the approach used in part 4.
L5TIME Timing of least active 5hrs, expressed as timestamp in the day
M5TIME Timing of most active 5hrs
L5TIME_num, M5TIME_num Timing of least/most active 5hrs, expressed as hours in the day. Note that L5/M5 timing variables are difficult to average across days because 23:00 and 1:00 would average to noon and not to midnight. So, caution is needed when interpreting person averages.
L5VALUE Acceleration value for least active 5hrs
M5VALUE Acceleration value for most active 5hrs
FRAG_ All variables related to behavioural fragmentation analysis
TP_ Transition probability
PA2IN Physical activity fragments followed by inactivity fragments
IN2PA Physical inactivity fragments followed by activity fragments
Nfrag Number of fragments
IN2LIPA Inactivity fragments followed by LIPA
IN2MVPA Inactivity fragments followed by MVPA
mean_dur mean duration of a fragment category
Gini_dur Gini index
CoV_dur Coefficient of Variation
alpha Power law exponent
x0.5 Derived from power law exponent alpha, see Chastin et al. 2010
W0.5 Derived from power law exponent alpha, see Chastin et al. 2010
nap_count Total number of naps, only calculated when argument do.sibreport = TRUE, currently optimised for 3.5-year olds. See function documentation for function g.part5.classifyNaps in the GGIR function documentation (pdf).
nap_totalduration Total nap duration, only calculated when argument do.sibreport = TRUE, currently optimised for 3.5-year old. See function documentation for function g.part5.classifyNaps in the GGIR function documentation (pdf).

Special note if you are working on compositional data analysis:

The duration of all dur_ variables that have _total_ in their name should add up to the total length of the waking hours in a day. Similarly, the duration of all other dur_ variables excluding the variables _total_ in their name and excluding the variable with dur_day_min, dur_spt_min, and dur_day_spt_min should also add up to the length of the full day.

Motivation for default boutcriter.in = 0.9:

The idea is that if you allow for bouts of 30 minutes it would not make sense to allow for breaks of 20 percent (6 minutes!) this is why I used a more stringent criteria for the highest category. Please note that you can change these criteria via arguments boutcriter.mvpa, boutcriter.in, and boutcriter.lig.

4.3.2 Person level summary

Most variables in the person level summary are derived from the day level summary, but extended with _pla to indicate that the variable was calculated as the plain average across all valid days. Variables extended with _wei represent the weighted average of across all days where weekend days always weighted 2/5 relative to the contribution of week days.

Variable name Description
Nvaliddays Total number of valid days.
Nvaliddays_WD Number of valid week days.
Nvaliddays_WE Number of valid weekend days, where the days that start on Saturday or Sunday are considered weekend.
NcleaningcodeX Number of days that had cleaning code X for the corresponding sleep analysis in part 4. In case of MM analysis this refers to the night at the end of the day.
Nvaliddays_AL10F_WD Number of valid week days with at least 10 fragments (5 inactivity or 5 inactive)
Nvaliddays_AL10F_WE Number of valid weekend days with at least 10 fragments (5 inactivity or 5 inactive)
_wei weighted average of weekend and week days, using a 2/5 ratio, see above.
_pla plain average of all days, see above

5 Motivation and clarification

In this chapter we will try to collect motivations and clarification behind GGIR which may not have been clear from the existing publications.

5.1 Reproducibilty of GGIR analyses

Some tips to increase reproducibility of your findings:

  1. When you publish your findings, please remember to add the GGIR package version number. All of GGIR are archived by CRAN and available from the archive section on the package website. GGIR has evolved over the years. To get a better understanding of how versions differ you should check the NEWS sections from the package website
  2. Report how you configured the accelerometer
  3. Report the study protocol and wear instructions given to the participants
  4. Report GGIR version
  5. Report how GGIR was used: Share the config.csv file or your R script
  6. Report how you post-processed / cleaned GGIR output
  7. Report how reported outcomes relate to the specific variable names in GGIR

5.2 Auto-calibration

An acceleration sensor works on the principle that acceleration is captured mechanically and converted into an electrical signal. The relationship between the electrical signal and the acceleration is usually assumed to be linear, involving an offset and a gain factor. We shall refer to the establishment of the offset and gain factor as the sensor calibration procedure. Accelerometers are usually calibrated as part of the manufacturing process under non-movement conditions using the local gravitational acceleration as a reference. The manufacturer calibration can later be evaluated by holding each sensor axis parallel (up and down) or perpendicular to the direction of gravity; readings for each axis should be ±1 and 0 g, respectively. However, this procedure can be cumbersome in studies with a high throughput. Furthermore, such a calibration check will not be possible for data that have been collected in the past and for which the corresponding accelerometer device does not exist anymore. Techniques have been proposed that can check and correct for calibration error based on the collected triaxial accelerometer data in the participant’s daily life without additional experiments, referred to as autocalibration. The general principle of these techniques is that a recording of acceleration is screened for nonmovement periods. Next, the moving average over the nonmovement periods is taken from each of the three orthogonal sensor axes and used to generate a three-dimensional ellipsoid representation that should ideally be a sphere with radius 1 g. Here, deviations between the radius of the three-dimensional ellipsoid and 1 g (ideal calibration) can then be used to derive correction factors for sensor axis-specific calibration error. This auto-calibration performed by GGIR uses this technique and a more detailed description and demonstration can be found in the published paper.

Reference:

  • van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. PMID: 25103964 link

Key decisions to be made:

  1. Whether to apply auto-calibration or not (default and recommended setting is YES). You can turn this off by setting argument do.call in g.shell.GGIR to do.call=FALSE.
  2. Other variables are probably best left in their default setting

Key output variables:

  1. Variable value cal.error.end as stored in data_quality_report.csv or variable value calib_err in summary.csv. These should be less than 0.01 g (10mg).

5.3 Non-wear detection

Accelerometer non-wear time is estimated on the basis of the standard deviation and the value range of the raw data from each accelerometer axis. Classification is done per 15 minute (or ws2) block and based on the characteristics of the 60 minute (or ws) window centred at these 15 minutes. A block is classified as non-wear time if the standard deviation of the 60 minute window is less than 13.0 mg (\(1 mg = 0.00981 m·s^−2\)) and the value range of the 60 minute window is less than 50 mg, for at least two out of the three accelerometer axes. The procedure for non-wear detection was modified in comparison to the procedure as applied in the 2011 PLoSONE publication link. Instead of 30-minute time windows 60-minute time windows were used to decrease the chance of accidently detecting short sedentary periods as non-wear time. The windows were overlapping (15 minute steps, window overlap of 45 minutes), which was done to improve the accuracy of detecting the boundaries of non-wear time as opposed to non-overlapping time windows. Inspection of unpublished data on non-wear classification by the algorithm as described in our published work indicated that the algorithm does not cope well with periods of monitor transportation per post. Here, long periods of non-wear are briefly interrupted by periods of movement, which are normally interpreted as monitor wear. Therefore, the algorithm was expanded with an additional stage in which the plausibility of “wear-periods” in-between non-wear periods is tested. Short periods of detected wear-time in-between longer periods of detected non-wear were classified as non-wear time based on the duration and the proportion of the duration relative to the bordering periods of detected non-wear-periods. The following criteria were derived from visual observation of various datasets using knowledge about study protocols. All detected wear-periods of less than six hours and less than 30% of the combined duration of their bordering non-wear periods were classified as non-wear. Additionally, all wear-periods of less than three hours and which formed less than 80% of their bordering non-wear periods were classified as non-wear. The motivation for selecting a relatively high criteria (< 30%) in combination with a long period (6hrs) and a low criteria (< 80%) in combination with a short period (3 hrs) was that long period are more likely to be actually related to monitor wear time. A visual model was created, see Figure 1. Here, units of time are presented in squares and marked grey if detected as non-wear time. Period C is detected as wear-time and borders to non-wear periods B and D, see Figure 1. If the length of C is less than six hours and C divided by the sum of B and D is less than 0.3 then the first criteria is met and block C is turned into a non-wear period.

By visual inspection of >100 traces from a large observational study it turned out that applying this stage in three iterative stages allowed for improved classification of periods characterised by intermittent periods of non-wear and apparent wear. Further, an additional rule was introduced for the final 24 hours of each measurement. The final 24 hours are often considered the period in which the accelerometer is potentially taken off but moved because of transportation, e.g. by the mail service. All wear-periods in the final 24 hrs of each measurement shorter than three hours and preceded by at least one hour of non-wear time were classified as non-wear. Finally, if the measurement starts or ends with a period of less than three hours of wear followed by non-wear (any length) then this period of wear is classified as non-wear. These additional criteria for screening the beginning and end of the accelerometer file reflect the likelihood of movements that are involved when starting the accelerometer or downloading the data from the accelerometer.

Reference:

  • van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S, Ekelund U, Renström F, Franks PW, Horsch A, Brage S. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013 Apr 23 link.

Key decisions to be made:

  1. Size of windows
  2. Whether to utilize the non-wear detection

Key output variables:

  1. Raw classification
  2. Non-wear duration
  3. Non-wear duration taking into account the protocol

5.4 Clipping score

The acceleration signal was screened for ‘clipping’. If more than 50% of the data points in a 15 minute time window are higher than 7.5g (close to the maximal dynamic range of this sensor) the corresponding time period is considered as potentially corrupt data, which may be explained by the sensor getting stuck at its extreme value.

Reference:

  • van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S, Ekelund U, Renström F, Franks PW, Horsch A, Brage S. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013 Apr 23 link

5.5 Why collapse information to epoch level?

Although many data points are collected we decide to only work with aggregated values (e.g. 1 or 5 second epochs) for the following reasons:

  1. Accelerometers are often used to describe patterns in metabolic energy expenditure. Metabolic energy expenditure is typically defined per breath or per minute (indirect calorimetry), per day (room calorimeter), or per multiple days (doubly labelled water method). In order to validate our methods against these reference standards we need to work with a similar time resolution.

  2. Collapsing the data to epoch summary measures helps to standardise for differences in sample frequency between studies.

  3. There is little evidence that the raw data is an accurate representation of body acceleration. All scientific evidence on the validity of accelerometer data has so far been based on epoch averages.

  4. Collapsing the data to epoch summary measures may help to average out different noise levels and make sensor brands more comparable.

5.6 Sleep analysis

In GGIR sleep analysis has been implemented in part 3 and 4. Sleep analysis comes at two levels: The identification of the main Sleep Period Time (SPT) window or the time in bed window (TIB), and the discrimination of sleep and wakefulness periods. The term sleep is somewhat controversial in the context of accelerometry, because accelerometer only capture lack of movement. To acknowledge this challenge GGIR refers to these classified sleep periods as sustained inactivity bouts (abbreviated as SIB).

Current, GGIR offers the user the choice to identify SIB period using any of the following algorithms:

  • vanHees2015: Heuristic algorithm proposed in 2015 link which looks for periods of time where the z-angle does not change by more than 5 degrees for at least 5 minutes. This in contrast to conventional sleep detection algorithms such as Sadeh and Galland which rely on acceleration to estimate sleep. The vanHees2015 algorithm is the default.
  • Sadeh1994: The algorithm proposed by Sadeh et al. link. To use the GGIR implementation of the zero-crossing counts and Sadeh algorithm, specify argument HASIB.algo = “Sadeh1994" and argument Sadeh_axis = "Y" to indicate that the algorithm should use the Y-axis of the sensor.
  • Galland2012: The count-scaled algorithm proposed by Galland et al. link. To use our implementation of the Galland2012 algorithm specify argument HASIB.algo = “Galland2012". Further, set Sadeh_axis = "Y" to specify that the algorithm should use the Y-axis.

Notes on the replication of the movement counts needed for the Sadeh and Galland algorithms:

The implementation of the zero-crossing count in GGIR is not an exact copy of the original approach as used in the AMA-32 Motionlogger Actigraph by Ambulatory-monitoring Inc. (“AMI”) that was used in the studies by Sadeh and colleagues in late 1980s and 1990s. No complete publicly accessible description of that approach exists. From personal correspondence with AMI, I learnt that the technique has been kept proprietary and has never been shared with or sold to other actigraphy manufacturers (time of correspondence October 2021). Therefore, if you would like to replicate the exact zero-crossing counts calculation used by Sadeh and colleague’s consider using AMI’s actigraph device. However, if you prioritise openness over methodological consistency with the original studies by Sadeh and colleagues then you may want to consider any of the open source techniques in this library.

Missing information about the calculation includes: (1) Sadeh specified that calculations were done on data from the Y-axis but the direction of the Y-axis was not clarified. Therefore, it is unclear whether the Y-axis at the time corresponds to the Y-axis of modern sensors, (2) Properties of the frequency filter are missing like the filter order and more generally it is unclear how to simulate original acceleration sensor behaviour with modern sensor data, and (3) Sensitivity of the sensor, we are now guessing that the Motionlogger had a sensitivity of 0.01 g but without direct proof.

The method proposed by Galland and colleagues in 2012 was designed for counts captured with the Actical device (Mini Mitter Co, Inc Bend OR). Based on the correspondence with AMI we can conclude that Actical counts are not identical to AMI’s actigraph counts. Further, a publicly accessible complete description of the Actical calculation does not exist. Therefore, we can also conclude that methodological consistency cannot be guaranteed for Actical counts.

5.6.1 Guiders

SIBs (explained above) can occur anytime in the day. In order to differentiate SIBs that correspond to daytime rest/naps from SIBs that correspond to the main Sleep Period Time window (abbreviated as SPT), a guiding method referred as guider is used. All SIBs that overlap with the window defined by guider are considered as sleep within the SPT window. The start of the first SIB identified as sleep period and the end of the last SIB identified as sleep period define the beginning and the end of the SPT window. In this way the classification relies on the accelerometer for detecting the timing of sleep onset and waking up time, but the guider tells it in what part of the day it should look, as SPT window will be defined only if SIB is detected during the guider specified window.

If a guider reflects the Time in Bed the interpretation of the Sleep Period Time, Sleep onset time and Wakeup time remains unchanged. However, we can then also assess sleep latency and and sleep efficiency, which will be included in the report.

The guiding method as introduced above can be one of the following methods:

  • Guider = sleep log: As presented in before mentioned 2015 article.See section on sleep analysis related arguments for a discussion fo sleep log data formats. Specify argument sleepwindowType to clarify whether the sleeplog capture “TimeInBed” or “SPT”. If it is set to “TimeInBed”, GGIR will automatically expand its part 4 analyses with sleep latency and sleep efficiency assessment.
  • Guider = HDCZA: As presented in our 2018 article. The HDCZA algorithm does not require access to a sleep log, and is designed for studies where no sleep log is available. The time segment over which the HDCZA is derived are by default from noon to noon. However, if the HDCZA ends between 11am and noon then it will be applied again but to a 6pm-6pm window.
  • Guider = L5+/-12: As presented in our 2018 article. Twelve hour window centred around the least active 5 hours of the day.
  • Guider = setwindow: Window times are specified by user, constant at specific clock times with argument def.noc.sleep.
  • Guider = HorAngle: Only used if argument sensor.location="hip", because this will trigger the identification of the longitudinal axis based on 24-hour lagged correlation. You can also force GGIR to use a specific axis as longitudinal axis with argument longitudinal_axis. Next, it identifies when the horizontal axis is between -45 and 45 degrees and considers this a horizontal posture. Next, this is used to identify the largest time in bed period, by only considering horizontal time segments of at least 30 minutes, and then looking for longest horizontal period in the day where gaps of less than 60 minutes are ignored. Therefore, it is partially similar to the HDCZA algorithm. When “HorAngle” is used, sleepwindowType is automatically set to “TimeInBed”.

For all guiders other than “HorAngle” and “sleep log” argument sleepwindowType is automatically switched to “SPT”, such that no attempt is made to estimate sleep latency or sleep efficiency.

GGIR uses by default the sleep log, if the sleep log is not available it falls back on the HDCZA algorithm (or HorAngle if sensor.location="hip"). If HDCZA is not successful GGIR will falls back on the L5+/-12 definition, and if this is not available it will use the setwindow. The user can specify the priority with argument def.noc.sleep. So, when we refer to guider then we refer to one of these five methods.

5.6.2 Daysleepers (nights workers)

If the guider indicates that the person woke up after noon, the sleep analysis in part 4 is performed again on a window from 6pm-6pm. In this way our method is sensitive to people who have their main sleep period starting before noon and ending after noon, referred as daysleeper=1 in daysummary.csv file, which you can interpret as night workers. Note that the L5+/-12 algorithm is not configured to identify daysleepers, it will only consider the noon-noon time window.

5.6.3 Cleaningcode

To monitor possible problems with the sleep assessment, the variable cleaningcode is recorded for each night. Cleaningcode per night (noon-noon or 6pm-6pm as described above) can have one of the following values:

  • 0: no problem, sleep log available and SPT is identified;
  • 1: sleep log not available, thus HDCZA is used and SPT is identified,
  • 2: not enough valid accelerometer data based on the non-wear and clipping detection from part summarised over the present night where the argument includenightcrit indicates the minimum number of hours of valid data needed within those 24 hours.
  • 3: no accelerometer data available,
  • 4: there were no nights to be analysed for this person,
  • 5: SPT estimated based on guider only, because either no SIB was found during the entire guider window, which complicates defining the start and end of the SPT, or the user specified the ID number of the recording and the night number in the data_cleaning_file to tell GGIR to rely on the guider and not rely on the accelerometer data for this particular night
  • 6: no sleep log available and HDCZA also failed for this specific night then use average of HDCZA estimates from other nights in the recording as guider for this night. If HDCZA estimates are not available during the entire recording then use L5+/-12 estimate for this night. The last scenario seems highly unlikely in a recording where the accelerometer was worn for at least one day.

5.6.4 Difference between cleaned and full output

All the information for each night is stored in the results/QC folder allowing tracing of the data analysis and night selection. The cleaned results stored in the results folder. In part 4 a night is excluded from the ‘cleaned’ results based on the following criteria:

  • If the study proposed a sleep log to the individuals, then nights are excluded for which the sleep log was not used as a guider (i.o.w. nights with cleaningcode not equal to 0 or variable sleep log used equals FALSE).
  • If the study did not propose a sleep log to the individuals, then all nights are removed with cleaningcode higher than 1.

Be aware that if using the full output and working with wrist accelerometer data, then missing entries in a sleep log that asks for Time in Bed will be replaced by HDCZA estimates of SPT. Therefore, extra caution should be taken when working with the full output.

Notice that part 4 is focused on sleep research, by which the cleaned reported is the way it is. In the next section we will discuss the analysis done by part 5. There, the choice of guider may be considered less important, by which we use different criteria for including nights. So, you may see that a night that is excluded from the cleaned results in part 4 still appears in the cleaned results for part 5.

5.6.5 Data cleaning file

The package allows some adjustments to be made after data quality check. The data_cleaning_file argument allows you to specify individuals and nights for whom part4 should entirely rely on the guider (for example if we decide to use sleep log only information). The first column of this file should have header ID and there should be a column relyonguider_part4 to specify the night. The data_cleaning_file allows you to tell GGIR which person(s) and night(s) should be omitted in part 4. The the night numbers to be excluded should be listed in a column night_part4 as header.

5.7 Waking-waking or 24 hour time-use analysis

In part 5 the sleep estimates from part 4 are used to describe 24-hour time use. Part 5 allows you to do this in two ways: Literally 24 hours which start and end a calendar day (default midnight, but modifiable with argument dayborder) or from waking up to waking up. In GGIR we refer to the former as MM windows and to the latter as WW windows. The onset and waking times are guided by the estimates from part 4, but if they are missing part 5 will attempt to retrieve the estimate from the guider method, because even if the accelerometer was not worn during the night, or a sleep log is missing in a study where sleep log was proposed to the participants, estimates from a sleep log or HDCZA can still be considered a reasonable estimate of the SPT window in the context of 24-hour time use analysis.

If WW is used in combination with ignoring the first and last midnight, argument excludefirstlast, then the first wake-up time (on the second recording day) needs to be extracted for the first WW day. This is done with the guider method. This also means that the last WW window ends on the before last morning of the recording.

5.7.1 Time series output files

If you want to inspect the time series corresponding to these windows then see argument save_ms5rawlevels, which allows you to export the time series including behavioral classes and non-wear information to csv files. The behavioral classes are included as numbers, the legend for these classes is stored as a separate legend file in the meta/ms5.outraw folder named “behavioralcodes2020-04-26.csv” where the date will correspond to the date of analysis. A distinction is made between the full results stored in the results/QC folder and the cleaned results stored in the results folder.

5.7.2 Day inclusion criteria

The full part 5 output is stored in the results/QC folder. The default inclusion criteria for days in the cleaned output from part 5 (stored in the results folder) are:

  • For both MM and WW defined days: The valid (sensor worn) time fraction of the day needs to be above the fraction specified with argument includedaycrit.part5 (default 2/3).
  • For MM defined days only: The length of the day needs to be at least the number of hours as specified by minimum_MM_length.part5 (default 23). Note that if your experiment started and ended in the middle of the day then this default setting will exclude those incomplete first and last days. If you think including these days is still meaningful for your work then adjust the argument minimum_MM_length.part5.

Important notes:

  • No criteria is set for the amount of valid data during the SPT window, because all we are interested in part 5 is knowing the borders of the night and we trust that this was sufficiently estimated by part 4. If you disagree then please notice that all the days are included in the full report available in results/QC folder.
  • This means that argument includenightcrit as used for part 4 is not used in part 5.

The data_cleaning_file argument discussed in Data_cleaning_file also allows you to tell GGIR which person(s) and day(s) should be omitted in part 5. The the day numbers to be excluded should be listed in a column day_part5 as header.

5.7.3 Fragmentation metrics

When setting input argument as frag.metrics="all" GGIR part 5 will perform daytime behavioural fragmentation analysis. Do this in combination with argument part5_agg2_60seconds=TRUE as that will aggregate the time series to 1 minute resolution as is common in behavioural fragmentation literature.

In GGIR, a fragment is a defined as a sequence of epochs that belong to one of the four categories:

  1. Inactivity
  2. Light Physical Activity (LIPA)
  3. Moderate or Vigorous Physical Acitivty (MVPA)
  4. Physical activity (can be either LIPA or MVPA)

Each of these categories represents the combination of bouted and unbouted time in the respective categories. Inactivity and physical activity add up to a full day, as well as inactivity, LIPA and MVPA. The fragmentation metrics are applied in function g.fragmentation.

Literature about these metrics:

  • Coefficient of Variance (CoV) is calculated according to Blikman et al. 2014.
  • Transition probability (TP) from Inactivity (IN) to Physical activity (IN2PA) and from Physical activity to inactivity (PA2IN) are calculated as 1 divided by the mean fragment duration. The transition probability from Inactivity to LIPA and MVPA are calculated as: (Total duration in IN followed by LIPA or MVPA, respectively, divided by total duration in IN) divided by the average duration in IN.
  • Gini index is calculated with function Gini from the ineq R package, and with it’s argument corr set to TRUE.
  • Power law exponent metrics: Alpha, x0.5, and W0.5 are calculated according to Chastin et al. 2010.
  • Number of fragment per minutes (NFragPM) is calculated identical to metric fragmentation in Chastin et al. 2012, but it is renamed here to be a more specific reflection of the calculation. The term fragmentation appears too generic given that all fragmentation metrics inform us about fragmentation. Please not that this is effectively the same metric as the transition probability, because total number divided by total sum in duration equals 1 divided by average duration. This is just different terminology for the same construct.

Conditions for calculation and value when condition is not met:

  • Metrics Gini and CoV are only calculated if there are at least 10 fragments (e.g. 5 inactive and 5 active). If this condition is not met the metric value will be set to missing.
  • Metrics related to power law exponent alpha are also only calculated when there are at least 10 fragments, but with the additional condition that the standard deviation in fragment duration is not zero. If these conditions are not met the metric value will be set to missing.
  • Other metrics related to binary fragmentation (mean_dur_PA and mean_dur_IN), are calculated when there are at least 2 fragments (1 inactive, 1 active). If this condition is not met the value will is set to zero.
  • Metrics related to TP are calculated if: There is at least 1 inactivity fragment AND (1 LIPA OR 1 MVPA fragment). If this condition is not met the TP metric value is set to zero.

To keep an overview of which recording days met the criteria for non-zero standard deviation and at least ten fragments, GGIR part5 stores variable Nvaliddays_AL10F at person level (=Number of valid days with at least 10 fragments), and SD_dur (=standard deviation of fragment durations) at day level as well as aggregated per person.

Difference between fragments and blocks:

Elsewhere in the part5 we use the term block. A block is a sequence of epochs that belong to the same behavioural class. This may sound similar to the definition of a fragment, but for blocks we distinguish every behavioural class, which includes the subcategories such as bouted and unbouted behaviour. This means that variables Nblock_day_total_IN and Nblock_day_total_LIG are identical to Nfrag_IN_day and Nfrag_LIPA_day, respectively.

Differences with R package ActFrag:

The fragmentation functionality is loosely inspired on the great work done by Dr. Junrui Di and colleages in R package ActFrag, as described in Junrui Di et al. 2017.

However, we made a couple of a different decisions that may affect comparability:

  • GGIR derives fragmentation metrics for day. This allows us to avoid the issue of dealing with appending days. Further, it allows us to test for behavioural differences between days of the week. It is well known that human behaviour can be driven by weekly rhythms, e.g. work days versus weekend. Estimating fragmentation per day of the week allows us to study and account for such possible variation. As with all other GGIR variables we also report recording level aggregates of the daily estimates.
  • Power law alpha exponent metrics were calculated according to Chastin et al. 2010 using the theoretical minimum fragment duration instead of the observed minimum fragment duration.

5.8 Why use data metric ENMO as default?

GGIR offers a range of acceleration metrics to choose from, but only one metric can be the default. Acceleration metric ENMO (Euclidean Norm Minus One with negative values rounded to zero) has been the default metric in GGIR. In 2013 we wrote a paper in which we investigated different ways of summarising the raw acceleration data. In short, different metrics exist and there is very little literature to support the superiority of any metric at the time. As long as different studies use different metrics their findings will not be comparable. Therefore, the choice for metric ENMO is partially pragmatic. GGIR uses ENMO as default because:

  1. ENMO has demonstrated value in describing variance in energy expenditure, correlated with questionnaire data, able to describe patterns in physical activity
  2. ENMO is easy to describe mathematically and by that improves reproducibility across studies and software tools
  3. ENMO attempts to quantify the actual biomechanical acceleration in universal units.
  4. The 2013 paper showed that when ENMO is used in combination with auto-calibration it has similar validity to filter-based metrics like HFEN and BFEN, which are conceptually similar to metrics proposed later such as MIMSunit, MAD, AI0.
  5. Studies who have criticised ENMO consistently failed to apply auto-calibration, or attempted to apply auto-calibration in a lab setting, ignoring the fact that the auto-calibration is not designed for short lasting lab settings. It needs free-living data to work properly. Further, studies are often not clear about how the problematic zero imputation during the idle sleep mode in ActiGraph devices is dealt with. See for a more detailed discussion on this the paragraph: Published cut-points and how to use them.

See also this blog post on this topic.

5.9 What does GGIR stand for?

I wanted a short name and not to spend too much time finding it. At the time I was primarily working with GENEActiv and GENEA data In R, and that’s how the name GGIR was born: Short, easy to remember, and as acronym sufficiently vague to not be tight up with a specific functionality. However, later the functionality expanded to other sensor brands, so the abbreviation has lost its functional meaning.

6 Other Resources

7 Citing GGIR

A correct citation of research software is important to make your research reproducible and to acknowledge the effort that goes into the development of open-source software.

To do so, please report the GGIR version you used in the text. Additionally, please also cite:

  1. Migueles JH, Rowlands AV, et al. GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data. Journal for the Measurement of Physical Behaviour. 2(3) 2019. doi: 10.1123/jmpb.2018-0063.

If your work depends on the quantification of physical activity then also cite:

  1. van Hees VT, Gorzelniak L, et al. Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE 8(4) 2013. link
  2. Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G, Elbaz A, Kivimaki M, Singh-Manoux A. Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am J Epidemiol. 2014 Mar 15;179(6):781-90. doi: 10.1093/aje/kwt330. Epub 2014 Feb 4. PMID: 24500862 link

If you used the auto-calibration functionality then also cite:

  1. van Hees VT, Fang Z, et al. Auto-calibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol 2014. link

If you used the sleep detection then also cite:

  1. van Hees VT, Sabia S, et al. A novel, open access method to assess sleep duration using a wrist-worn accelerometer, PLoS ONE, 2015 link

If you used the sleep detection without relying on sleep diary then also cite:

  1. van Hees VT, Sabia S, et al. Estimating sleep parameters using an accelerometer without sleep diary. Scientific Reports 2018. doi: 10.1038/s41598-018-31266-z. link

If you used the sleep regularity index then also cite:

  1. Andrew J. K. Phillips, William M. Clerx, et al. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Scientific Reports. 2017 June 12 link.