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 in raw accelerometry 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 day of measurement or per measurement, including estimates of physical activity, inactivity and sleep. This vignette provides a general introduction to GGIR, additionally you can find a general introduction video and a mini-tutorial on YouTube.

1.2 Who has been using GGIR?

GGIR is increasingly being used by a number of research groups acros the world. A non-exhaustive overview of academic publications related to GGIR can be found here.

A non-exhaustive list of academic institutes that have been using GGIR:

United Kingdom: University College London, Exeter University, Newcastle University, Edge Hill University, University of Cambridge, University of Chester, Edinburgh Napier University, University of Birmingham, Liverpool John Moores University | United States: University College San Diego, Iowa State University, University of Southern California, University of Michican, West Virginia University, Arizona State University, University of Utah, National Institute of Health | The Netherlands: Erasmus Medical Centre Rotterdam, VU University Medical Centre, Leiden University Medical Centre | France: French Institute for Health and Medical Research, Institute of Myology, University of Dijon, University of Montpellier | Brazil: Federal University of Pelotas | Australia: Newcastle University, Australian Catholic University, University of Sydney, Queensland University of Technology, University of Western Australia | Spain: University of Granada | Norway: University of Oslo | Sweden: University of Lund | Switszerland: Centre Hospitalier Universitaire Vaudois | Germany: Technische Universität Dresden, Technical University Munich

1.3 Acknowledgements

R package GGIR would not have been possible without the support of the following people, institutions, and funders:

People:

• Dr. Vincent van Hees as main developer
• Dr. Evgeny Mirkes and Dr. Jing Hua Zhao provided code to enable binary accelerometer data to be read in R
• Dr. Zhou Fang contributed to a part of the code for the auto-calibration functionality
• Jairo Migueles helps maintain and improve the code
• Joe Heywood contributed code to correctly extract specific time segments with correct timestamps from an GENEActiv recording
• Dr. Severine Sabia tested and provided feedback on numerous aspects of the package
• Dr. Soren Brage provided supervision for a number of studies that formed the basis for GGIR

Institutions:

• The MRC Epidemiology Unit at the University of Cambridge, Cambridge, UK
• Movelab at Newcastle University, Newcastle upon Tyne, UK
• Netherlands eScience Center, Amsterdam, NL
• University of Exeter, Exeter, UK
• Centre for Longitudinal Studies, London, UK

Funders:

• Medical Research Council (MRC) grants MR/P012167/1 and MC_UU_12015/3
• Economic and Social Research Council (ESRC) grant ES/K005987 for ‘Millennium Cohort Study Sweep 6 (Age 14) Survey’ (MCS6)
• Biotechnology and Biological Sciences Research Council (BBSRC) and Unilever Discover Ltd funded a CASE studentship

1.4 How can I contribute to the GGIR development?

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

2.1 Install R and RStudio

Download and install R

Download and install RStudio (optional, but recommended)

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

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

2.2 Prepare folder structure

1. GGIR works with the following accelerometer brands and formats:
   • GENEActiv .bin and .csv
   • ActiGraph .csv. Note for Actigraph users: In ActiLife you have the option to export data with timestamps. Please do not do this as this causes memory issues. To cope with the absense of timestamps GGIR will re-caculate timestamps from the sample frequency and the start time and date as presented in the file header.
   • Axivity .wav, .csv and .cwa
   • Genea (an accelerometer that is not available anymore, but which was used for some studies between 2007 and 2012) .bin and .csv
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

Copy paste the following code in a new R script (file ending with .R). It is a shell functions that will allow you to have all your decisions in one place without having to worry about separate scripts and configurations.

library(GGIR)
g.shell.GGIR(#=======================================
             # INPUT NEEDED:
             mode=c(1,2,3,4,5),
             datadir="C:/mystudy/mydata",
             outputdir="D:/myresults",
             f0=1, f1=2,
             #-------------------------------
             # Part 1:
             #-------------------------------
             # Key functions: reading file, auto-calibration, and extracting features
             do.enmo = TRUE,             do.anglez=TRUE,
             chunksize=1,                printsummary=TRUE,
             #-------------------------------
             # Part 2:
             #-------------------------------
             strategy = 2,               ndayswindow=7,
             hrs.del.start = 0,          hrs.del.end = 0,
             maxdur = 9,                 includedaycrit = 16,
             winhr = c(5,10),
             qlevels = c(c(1380/1440),c(1410/1440)),
             qwindow=c(0,24),
             ilevels = c(seq(0,400,by=50),8000),
             mvpathreshold =c(100,120),
             bout.metric = 4,
             closedbout=FALSE,
             #-------------------------------
             # Part 3:
             #-------------------------------
             # Key functions: Sleep detection
             timethreshold= c(5),        anglethreshold=5,
             ignorenonwear = TRUE,
             #-------------------------------
             # Part 4:
             #-------------------------------
             # Key functions: Integrating sleep log (if available) with sleep detection
             # storing day and person specific summaries of sleep
             excludefirstlast = TRUE,
             includenightcrit = 16,
             def.noc.sleep = c(),
             loglocation= "C:/mydata/sleeplog.csv",
             outliers.only = TRUE,
             criterror = 4,
             relyonsleeplog = FALSE,
             sleeplogidnum = TRUE,
             colid=1,
             coln1=2,
             do.visual = TRUE,
             nnights = 9,
             #-------------------------------
             # Part 5:
             # Key functions: Merging physical activity with sleep analyses
             #-------------------------------
             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),   timewindow = c("WW"),
             #-----------------------------------
             # Report generation
             #-------------------------------
             # Key functions: Generating reports based on meta-data
             do.report=c(2,4,5),
             visualreport=TRUE,     dofirstpage = TRUE,
             viewingwindow=1)

2.4 Updating the GGIR shell input arguments

The function arguments need to be tailored to your experimental protocol. There are many function arguments you can specify which are explained in the package tutorial. GGIR is structured in 5 parts and the arguments to g.shell.GGIR can be structured accordingly:

• 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: o 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.

By looking up the corresponding functions g.part1, g.part2, g.part3, g.part4, and g.part5 you can see what arguments are possible. All of these arguments are also accepted by the shell function g.shell.GGIR, because g.shell.GGIR is nothing more than a wrapper around those functions.

Below I have highlighted a few of the key arguments you may want to be aware of. Please see the package manual for more detailed documentation:

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

It is possible to run GGIR on a computing cluster to process multiple files in parallel. The way I did it is as follows, please note that some of these commands are specific to the computing cluster you are working on. Please consult your local cluster specilist to tailor this to your situation. In my case, I had three files:

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. 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. I have been able to process all 4000 files from the Whitehall II Study in just a couple of hours.

4.1 Output part 2

Part 2 generates:

• part2_summary.csv
• part2_daysummary.csv
• QC/data_quality_report.csv
• QC/plots to check data quality 1.pdf

4.2 Output part 4

Part 4 generates the following output:

• part4_nightsummary_sleep_cleaned.csv
• part4_summary_sleep_cleaned.csv
• visualisation_sleep.pdf (optional)

The csv. files contain variables that are explained in the documentation of GGIR function g.part4.

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. Colors are used to dinstinguish definitions of sustianed inacitivty bouts (2 definitions in this case) and to indicate existence or absense 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:

• 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

Here is a beta-version of the variable dictionary, see GGIR package manual for more details

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, so this will enable others to reproduce your analyses. 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. Keep a copy of your scripts with the functions calls
3. Keep a copy of your milestone data
4. Try to be explicit in referring to variable names

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. doi: 10.1152/japplphysiol.00421.2014. Epub 2014 Aug 7. 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 changing 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 was estimated on the basis of the standard deviation and the value range of the raw data from each accelerometer axis. Classification was done per 15 minute block and based on the characteristics of the 60 minute window centred at these 15 minutes. A block was classified as non-wear time if the standard deviation of the 60 minute window was less than 13.0 mg (1 mg = 0.00981 m·s−2) for at least two out of the three axes or if the value range, for at least two out of three axes, was less than 50 mg. 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;8(4):e61691. doi: 10.1371/journal.pone.0061691. Print 2013 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;8(4):e61691. doi: 10.1371/journal.pone.0061691. Print 2013 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 validaty of accelerometer data is so far are 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 Why use data metric ENMO?

In most studies I have been involved in we used metric ENMO (Euclidean Norm Minus One with negative values rounded to zero). In 2013 I wrote a paper in which I investigated different ways of summarising the raw acceleration data. In short, different metrics exist and there is very little literature to support the supperiority of any metric. As long as different studies use different metrics their findings will not be comparable. Therefore, the choice for metric ENMO is partially pragmatic. I am using ENMO because:

1. Has demonstrated value in describing variance in energy expenditure, correlated with questionnaire data, able to describe patterns in physical activity
2. Easy to describe mathematically and by that higher chances of reproducibility
3. Attempt to quantify the actual biomechanical acceleration in universal units. I prefer to avoid using abstract index for movement quantification
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 (which is essentially the same as the MAD metric recently used in literature). The few studies I am aware of who contested this, did not use auto-calibration by which ENMO was evalauted under unrealistic conditions. Metric ENMO depends by design on well calibrated acceleration signals, while metrics like HFEN and MAD are not affected by calibration offset.

5.7 What does GGIR stand for?

I wanted a short name and not to spend too much time finding it. At the time when I created GGIR I was working with GENEActiv and GENEA data In R, and that’s how the name GGIR was born.