GGIR logo

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 Therefore, many of the sections in this vignette have been replaced by a link to their new location.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?

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

Cite GGIR:

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

1.2 Contributing, Support, and Keeping up to date

2 Setting up your work environment

2.1 Install R and RStudio

2.2 Prepare folder structure

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.

2.3.4 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

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

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)

Example visualreport

4.1 Output part 2

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4.1.1 Person level summary

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4.1.2 Day level summary

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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

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4.2.1 Night level summaries

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4.2.1.1 Non-default variables in part 4 csv report

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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

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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.

4.4 Output part 6

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

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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

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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

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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

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

5.11 ActiGraph’s idle sleep mode

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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

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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

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

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

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.