Accelerometer data processing with GGIR

NOTE: With the growing amount of functionality in GGIR we have decided to migrate the narrative documentation to the GitHub pages of GGIR. This to ease maintenance and accessibility. Therefore, many of the sections in this vignette have been replaced by a link to their new location.

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.

Training in R essentials and GGIR We offer frequent online GGIR training courses. Check our dedicated training website with more details and the option to book your training. Do you have questions about the training or the booking process? Do not hesitate to contact us via: training@accelting.com.

Also of interest may be the brief free R introduction tutorial.

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

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")

Additionally, in some use-cases you will need to install one or multiple additional packages:

• If you are working with Axivity, GENEActiv, or GENEA files, install the GGIRread package with install.packages("GGIRread")
• If you are working with ActiGraph gt3x files, install the read.gt3x package with install.packages("read.gt3x")
• If you want to derive Neishabouricounts (with do.neishabouricounts = TRUE), install the actilifecounts package with install.packages("actilifecounts")
• If you want to derive circadian rhythm indicators using the Cosinor analysis and Extended Cosinor analysis (with cosinor = TRUE), install the ActCR package with install.packages("ActCR")

2.2 Prepare folder structure

1. GGIR works with the following accelerometer brands and formats:
   • GENEActiv .bin
   • Axivity AX3 and AX6 .cwa
   • ActiGraph .csv and .gt3x (.gt3x only the newer format generated with firmware versions above 2.5.0. Serial numbers that start with “NEO” or “MRA” and have firmware version of 2.5.0 or earlier use an older format of the .gt3x file). Note for Actigraph users: If you want to work with .csv exports via the commercial ActiLife software 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 calculate timestamps from the sample frequency, the start time and start date as presented in the file header.
   • Movisens .bin files with data stored in folders. GGIR expects that each participant’s folder contains at least a file named acc.bin.
   • 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). Note that functionality for the following file formats was part of GGIR but has been deprecated as it required a significant maintenance effort without a clear use case or community support: (1) .bin for the Genea monitor by Unilever Discover, an accelerometer that was used for some studies between 2007 and 2012) .bin, and (2) .wav files as can be exported by the Axivity Ltd OMGUI software. Please contact us if you think these data formats should be facilitated by GGIR again and if you are interested in supporting their ongoing maintenance.
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

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for 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 GGIR is:

library(GGIR)
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 with a sixth part under development.
• 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)
• data_masking_strategy - informs GGIR how to consider the design of the experiment.
  • If data_masking_strategy is set to value 1, then check out arguments hrs.del.start and hrs.del.end.
  • If data_masking_strategy is set to value 3 or 5, then check out arguments ndayswindow, hrs.del.start and hrs.del.end.
• 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. Note that the visual report was initially developed to provide something to show to study participants and not for data quality checking purposes. Over time we have improved the visual report to also be useful for QC-ing the data. however, some of the scorings as shown in the visual report are created for the visual report only and may not reflect the scorings in the main GGIR analyses as reported in the quantitative csv-reports. Most of our effort in the past 10 years has gone into making sure that the csv-report are correct, while the visualreport has mostly been a side project. This is unfortunate and we hope to find funding in the future to design a new report specifically for the purpose of QC-ing the anlayses done by GGIR.
• maxRecordingInterval - if specified controls whether neighboring or overlapping recordings with the same participant ID and brand are appended at epoch level. This can be useful when the intention is to monitor behaviour over larger periods of time but accelerometers only allow for a few weeks of data collection. GGIR will never append or alter the raw input file, this operation is preformed on the derived data.
• study_dates_file - if specified trims the recorded data to the first and last date in which the study took place. This is relevant for studies that started the recording several days before the accelerometers were actually worn by participants. This is used on the top of data_masking_strategy, so that it may be combined with the strategies in GGIR.

2.3.2 Key arguments related to sleep analysis

For an explanation on how sleep is detected and the specific role of the various function arguments see section Sleep analysis.

• Arguments related to configuring the sleep detection algorithm: anglethreshold, timethreshold, HASPT.algo, HASIB.algo, Sadeh_axis, and HASPT.ignore.invalid.
• ignorenonwear if set to TRUE then ignore detected monitor non-wear periods in the detection of sustained inactivity bouts to avoid confusion between monitor non-wear time.
• If you want to create a visualisation of how sleep period time and sustained inactivity bouts match throughout a day then consider arguments do.visual, outliers.only, and criterror.
• If you want to exclude the first and last night from the sleep analysis then used excludefirstlast.
• def.noc.sleep specifies how the sleep period time window should be estimated if no sleeplog is used.
• includenightcrit Minimum number of valid hours per night (24 hour window between noon and noon or 6pm-6pm).
• data_cleaning_file to ginore specific nights for specific individuals, see also section Data cleaning file.
• If you want the sleep analysis to be guided by a sleeplog (diary) then check out arguments loglocation which specifies the location of the spreadsheet (csv) with sleep log information. Further, use arguments colid and coln1 to specify the details of your sleep log structure.

GGIR facilitates two possible sleeplog file structures:

2.3.2.1 Basic sleep log

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

2.3.2.2 Advanced sleep log

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

2.3.3 Key arguments related to time use analysis

For an explanation on how time use analysis is performed see section Waking-waking or 24 hour time-use analysis.

• excludefirstlast.part5 - whether to ignore the last and first day.
• includedaycrit.part5 - tell GGIR what fraction of the waking hours in a day (value below 1) is acceptable.
• minimum_MM_length.part5 - tell GGIR what the minimum length (hours) should be of the MM window in part 5.
• Configure thresholds for acceleration levels (some may want to interpret this as intensity levels): threshold.lig, threshold.mod, threshold.vig.
• Configure what fraction of a bout needs to meet the threshold (cut-point) crtieria boutcriter.in, boutcriter.lig, boutcriter.mvpa. Note that boutcriter.mvpa = 0.8 means that an MVPA bout can have interruptions (i.e., the time out of MVPA intensity) that meet the following criteria:
  1. A single interruption can last < 1 min
  2. Repeated interruptions are allowed provided that their total time does not exceed 20% of the bout duration
  3. The time spent in the interruptions is included in the duration of the MVPA bout. For example: A 25-minute bout can have two 1 minute interruption, but not a single 2-minute interruption. Here, the full 25 minutes would count towards the duration of the MVPA bout.
• timewindow to specify whether days should be defined from midnight to midnight "MM", from waking-up to waking-up "WW", from sleep onset to sleep onset "OO", or any combination of them.
• Configure durations of bouts: boutdur.mvpa, boutdur.in, and boutdur.lig. Note that this can be a vector of multiple values indicating the minimum and maximum duration of subsequent bout types, e.g. 1-5 minutes MVPA, 5-10 minutes MVPA, and longer than 10 minutes MVPA.

2.3.4 Published cut-points and how to use them

This section has been rewritten and moved. Please, visit the vignette Published cut-points and how to use them in GGIR for more details on the cut-points available, how to use them, and some additional reflections on the use of cut-points in GGIR.

2.3.5 Example call

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

library(GGIR)
GGIR(mode=c(1,2,3,4,5),
      datadir="C:/mystudy/mydata",
      outputdir="D:/myresults",
      do.report=c(2,4,5),
      #=====================
      # Part 2
      #=====================
      data_masking_strategy = 1,
      hrs.del.start = 0,          hrs.del.end = 0,
      maxdur = 9,                 includedaycrit = 16,
      qwindow=c(0,24),
      mvpathreshold =c(100),
      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 GGIR and the output directory (outputdir) will be filled with milestone data and results.

2.3.6 Configuration file

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

3 Time for action: How to run your analysis?

3.1 From the R console on your own desktop/laptop

Create an R-script and put the GGIR call in it. Next, you can source the R-script with the source function in R:

source("pathtoscript/myshellscript.R")

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

3.2 In a cluster

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

3.3 Processing time

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

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

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.1.1 Person level summary

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.1.2 Day level summary

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.1.3 Data_quality_report

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.2 Output part 4

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.2.1 Night level summaries

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.2.1.1 Non-default variables in part 4 csv report

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.2.2 visualisation_sleep.pdf

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.3 Output part 5

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.3.1 Day level summary

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

4.3.2 Person level summary

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

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

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.3 Non-wear detection

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.4 Clipping score

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

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.

5.5.1 Why does the first epoch not allign with the original start of the recording

GGIR uses short (default 5 seconds) and long epochs (default 15 minutes). The epochs are aligned to the hour in the day, and to each other. For example, if a recording starts at 9:52:00 then the GGIR will work with epochs derived from 10:00:00 onward. If the recording starts at 10:12 then GGIR will work with epochs derived from 10:15:00 onward.

Motivation:

• This allows us to have a standardised time grid across recordings to describe behaviour.
• This allows us to calculate behaviour exactly per day or per specified time interval in a day.

If the first 15 minute epochs would start at 9:52 then the next one would start at 10:07, which makes it impossible to make statement about behaviour between 10:00 and 13:00.

5.6 Sleep analysis

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.1 Notes on sleep classification algorithms designed for count data

5.6.1.1 Replication of the movement counts needed

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.1.2 Missing information for replicating movement counts

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.1.3 An educated guess and how you can to help optimise the implementation

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.2 Guiders

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.3 Daysleepers (nights workers)

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.4 Cleaningcode

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.5 Difference between cleaned and full output

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.6.6 Data cleaning file

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.7 Waking-waking or 24 hour time-use analysis

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.7.1 Time series output files

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

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

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

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. In contrast, for fragments we may group LIPA and MVPA together when refering to the fragmentation of PA.

Differences with R package ActFrag:

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.8 Why use data metric ENMO as default?

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.9 What does GGIR stand for?

I wanted a short name and not to spend too much time finding it. The abbreviation has lost its functional meaning, which is why we now only use GGIR as the name.

5.10 Circadian Rhythm analyses

5.10.1 MXLX

Detection of the continuous least (LX) and most (MX) active X hours in a day, where X is defined by argument winhr. For both GGIR calculates the average acceleration, the start time, and if argument iglevels is specified also the intensity gradient. If argument winhr is a vector then descriptive values for LX and MX are derived per value in winhr. Within GGIR part 2 MXLX is calculated per calendar day and, if argument qwindow is specified, per segment of the day. Within GGIR part 5 MXLX is calculated per window, and if used in combination with the GENEActiv or Axivity accelerometer brand LUX estimates per LX and MX are included.

The MX metric describe here should not be confused by the MX metrics as proposed by Rowlands et al. which looks at accumulated most active time which may not always be continuous in time. The MX metrics by Rowlands et all are discussed further down.

5.10.2 Cosinor analysis and Extended Cosinor analysis

The (Extended) Cosinor analysis quantifies the circadian 24 hour cycle. To do this GGIR uses R package ActCR as a dependency. Specify argument cosinor = TRUE to perform these analysis.

The implementation within GGIR part 2 is as follows:

• Acceleration values are averaged per minute, and then log transformation as log(acceleration converted to _mg_ + 1).
• Invalid data points such as caused by non-wear are set to missing (NA) in order to prevent the imputation approach used elsewhere in GGIR to influence the Cosinor analysis. We do this because imputation technique generally come with some assumptions about circadian rhythm.
• GGIR looks for the first valid data point in the recording and then selects the maximum integer number of recording days following this data point and feeds these to the ActCosinor and ActExtendCosinor functions of ActCR. The time offset between the start and the following midnight is then used to reverse offset the ActCR results, to ensure that acrophase and acrotime can be interpreted relative to midnight.
• In relation to Day Saving Time: Duplicated time stamps when clock moves backward are ignored and missing time stamps when clock moves forward are inserted as missing values.
• Time series corresponding to the fitted models are stored inside the part 2 milestone data to facilitate visual inspection. For the moment they are not used in any GGIR visualisation, but you may want to look them up and try to plot them yourself.

5.10.3 Intradaily Variability (IV) and Interdaily Stability (IS)

5.10.3.1 IV and IS - Default

The original implementation (argument IVIS.activity.metric = 1) uses the continuous numeric acceleration values. However, as we later realised this is not compatible with the original approach by van Someren and colleagues, which uses a binary distinction between active and inactive. Therefore, a second option was added (argument IVIS.activity.metric = 2), which needs to be used in combination with accelerometer metric ENMO, and collapses the acceleration values into a binary score of rest versus active. This is the current default.

5.10.3.2 IV and IS - Cosinor analysis compatible

Disclaimer: The following has been implemented 2022, but is currently undergoing critical evaluation. As a result, we may update this algorithm during the course of 2023.

IS is sometimes used as a measure of behavioural robustness when conducting Cosinor analysis. However, to work with the combination of the two outcomes it seems important that IS is calculated from the same time series. Therefore, when cosinor = TRUE IV and IS are calculated twice: Once as part of the default IV and IS analysis as discussed above, and once as part of the Cosinor analysis using the same log transformed time series. More specifically, the IV and IS algorithm is applied with IVIS.activity.metric = 2 and IVIS_acc_threshold = log(20 + 1) to make the binary distinction between active and inactive, and IVIS_per_daypair = TRUE. The setting IVIS_per_daypair was specifically designed for this context to handle the potentially missing values in the time series as used for Cosinor analysis. Applying the default IVIS algorithm would not be able to handle the missing values and would result in a loss of information if all non-matching epochs across the entire recording were excluded. Instead, IV and IS are calculated as follows:

1. Per day pair based on matching valid epochs only IV and IS and calculated. Here, a log is kept of the number of valid epoch per day pair.
2. Omit day pairs where the fraction of valid epoch pairs is below 0.66 (0.66 is hard-coded at the moment).
3. Calculate average IS across days weighted by fraction of valid epochs per day pairs.

The new Cosinor-compatible IV and IS estimates are stored as output variables cosinorIV and cosinorIS.

5.11 ActiGraph’s idle sleep mode

The idle sleep mode is explained on the manufacturer’s website. In short, idle sleep mode is a setting that can be turned on or off by the user. When it is turned on the device will fall asleep during periods of no movement, resulting in time gaps in the data. This functionality was probably introduced to safe battery life and minimize data size. However, it also means that we end up with time gaps that need to be accounted for.

5.11.1 Time gap imputation

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.11.2 The importance of reporting idle.sleep.mode usage

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.12 MX metrics (minimum intensity of most active X minutes)

This section has been migrated to this section in the GGIR github-pages, which is now the main documentation resource for GGIR.

5.13 Minimum recording duration

GGIR has been designed to process multi-day recordings. The minimum recording duration considered by GGIR depends on the type of analysis:

Running part 1 and 2

• File size; At least 2MB, where 2MB can be adjusted with argument minimumFileSizeMB. This should not be changed unless you have good reason to believe that a smaller file size is also acceptable.

• Recording duration: At least two long epoch windows (default 60 minutes) in g.readaccfile. The size of this epoch can be altered with the second and third value of vector argument windowsizes, where the third should not be smaller than the second. For example, in short lasting lab-experiments you may find it easier to set this to windowsizes = c(5, 600, 600) as non-wear detection is usually not necessary in lab studies.

Running part 3 and 4

• At least one night of data is expected, where a night is expected to have at least the timestamp for midnight. If midnight is not found the sleep detection is skipped.

Running part 5

• Ideally two valid consecutive nights and the waking hours in between.

5.14 LUX sensor data processing

Although GGIR focuses on accelerometer data a few brands come with LUX data.

In part 1 GGIR calculates the peak lux per long epoch at a default resolution of 15 minutes, which can be modified with argument windowsizes. Peak light offers a more reliable estimate of light exposure per time window compared with taking the average. Further, LUX is used in the auto-calibration.

In GGIR part 2 we visualise the LUX values in the qc plot. In part 3 and 4 LUX is not used for sleep classification because relation between light exposure and sleep is weak.

In part 5 we calculate the mean and maximum of the peak LUX per epoch across all waking hours of the day. Here, the mean (peak per epoch) LUX would then indicate average light exposure per time segment, while max peak would indicate the maximum light exposure per day. Further, we calculate the max and mean peak LUX per most active consecutive X hour of the day. This is intended to offer an alternative to LUX exposure during waking hours which relies on correct sleep classification. LUX exposure during M10 may be seen as an alternative if you are unsure whether you can trust the sleep classification in your data set.

6 Other Resources

• The GGIR package manual provides documentation on individual functions.
• For general questions about how to use GGIR join our google group/mailing list.
• For bug reports please post them here.

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:

2. 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
3. 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:

4. 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:

5. 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:

6. 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:

7. 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.

7.1 Copyright for GGIR logo

The copyright of the GGIR logo lies with Accelting (Almere, The Netherlands), please contact v.vanhees@acceleting.com to ask for permission to use this logo.