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Shell function for analysing an accelerometer dataset.

GGIR.Rd

This function is designed to help users operate all steps of the analysis. It helps to generate and structure milestone data, and produces user-friendly reports. The function acts as a shell with calls to g.part1, g.part2, g.part3, g.part4 and g.part5.

Usage

GGIR(mode = 1:5,
     datadir = c(),
     outputdir = c(),
     studyname = c(),
     f0 = 1, f1 = 0,
     do.report = c(2, 4, 5, 6),
     configfile = c(),
     myfun = c(),
     verbose = TRUE, ...)

Arguments

mode

Numeric (default = 1:5). Specify which of the five parts need to be run, e.g., mode = 1 makes that g.part1 is run; or mode = 1:5 makes that the whole GGIR pipeline is run, from g.part1 to g.part5. Optionally mode can also include the number 6 to tell GGIR to run g.part6 which is currently under development.

datadir

Character (default = c()). Directory where the accelerometer files are stored, e.g., "C:/mydata", or list of accelerometer filenames and directories, e.g. c("C:/mydata/myfile1.bin", "C:/mydata/myfile2.bin").

outputdir

Character (default = c()). Directory where the output needs to be stored. Note that this function will attempt to create folders in this directory and uses those folder to keep output.

studyname

Character (default = c()). If the datadir is a folder, then the study will be given the name of the data directory. If datadir is a list of filenames then the studyname as specified by this input argument will be used as name for the study.

f0

Numeric (default = 1). File index to start with (default = 1). Index refers to the filenames sorted in alphabetical order.

f1

Numeric (default = 0). File index to finish with (defaults to number of files available).

do.report

Numeric (default = c(2, 4, 5, 6)). For which parts to generate a summary spreadsheet: 2, 4, 5, and/or 6. Default is c(2, 4, 5, 6). A report will be generated based on the available milestone data. When creating milestone data with multiple machines it is advisable to turn the report generation off when generating the milestone data, value = c(), and then to merge the milestone data and turn report generation back on while setting overwrite to FALSE.

configfile

Character (default = c()). Configuration file previously generated by function GGIR. See details.

myfun

List (default = c()). External function object to be applied to raw data. See package vignette for detailed tutorial with examples on how to use the function embedding: https://cran.r-project.org/package=GGIR/vignettes/ExternalFunction.pdf

verbose

Boolean (default = TRUE). to indicate whether console message should be printed. Note that warnings and error are always printed and can be suppressed with suppressWarning() or suppressMessages().

...

Any of the parameters used GGIR. Given the large number of parameters used in GGIR we have grouped them in objects that start with "params_". These are documented in the details section. You cannot provide these objects as argument to function GGIR, but you can provide the parameters inside them as input to function GGIR.

Value

The function provides no values, it only ensures that other functions are called and that their output is stored. Further, a configuration file is stored containing all the argument values used to facilitate reproducibility.

Details

Once you have used function GGIR and the output directory (outputdir) will be filled with milestone data and results. Function GGIR stores all the explicitely entered argument values and default values for the argument that are not explicitely 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 GGIR with argument configfile to replace the specification of all the arguments, except datadir and outputdir.

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: When combining a configuration file with explicitely provided argument values, the explicitely provided argument values will overrule the argument values in the configuration file. If a parameter is neither provided via the configuration file nor as input then GGIR uses its default paramter values which can be inspected with command print(load_params()), and if you are specifically interested in a certain subgroup of parameters, e.g., physical activity, then you can do print(load_params()$params_phyact). These defaults are part of the GGIR code and cannot be changed by the user.

The parameters that can be used in GGIR are:

params_general

A list of parameters used across all GGIR parts that do not fall in any of the other categories.

overwrite

Boolean (default = FALSE). Do you want to overwrite analysis for which milestone data exists? If overwrite = FALSE, then milestone data from a previous analysis will be used if available and visual reports will not be created again.

dayborder

Numeric (default = 0). Hour at which days start and end (dayborder = 4 would mean 4 am).

do.parallel

Boolean (default = TRUE). Whether to use multi-core processing (only works if at least 4 CPU cores are available).

maxNcores

Numeric (default = NULL). Maximum number of cores to use when argument do.parallel is set to true. GGIR by default uses either the maximum number of available cores or the number of files to process (whichever is lower), but this argument allows you to set a lower maximum.

acc.metric

Character (default = "ENMO"). Which one of the acceleration metrics do you want to use for all acceleration magnitude analyses in GGIR part 5 and the visual report? For example: "ENMO", "LFENMO", "MAD", "NeishabouriCount_y", or "NeishabouriCount_vm". Only one acceleration metric can be specified and the selected metric needs to have been calculated in part 1 (see g.part1) via arguments such as do.enmo = TRUE or do.mad = TRUE.

part5_agg2_60seconds

Boolean (default = FALSE). Whether to use aggregate epochs to 60 seconds as part of the GGIR g.part5 analysis. Aggregation is doen by averaging. Note that when working with count metrics such as Neishabouri counts this means that the threshold can stay the same as in part 2, because again the threshold is expressed relative to the original epoch size, even if averaged per minute. For example if we want to use a cut-point 100 count per minute then we specify mvpathreshold = 100 * (5/60) as well as `threshold.mod = 100 * (5/60) regardless of whether we set part5_agg2_60seconds to TRUE or FALSE.

print.filename

Boolean (default = FALSE). Whether to print the filename before analysing it (in case do.parallel = FALSE). Printing the filename can be useful to investigate problems (e.g., to verify that which file is being read).

desiredtz

Character (default = "", i.e., system timezone). Timezone in which device was configured and experiments took place. If experiments took place in a different timezone, then use this argument for the timezone in which the experiments took place and argument configtz to specify where the device was configured. Use the "TZ identifier" as specified at https://en.wikipedia.org/wiki/Zone.tab to set desiredtz, e.g., "Europe/London".

configtz

Character (default = "", i.e., system timezone). At the moment only functional for GENEActiv .bin, AX3 cwa, ActiGraph .gt3x, and ad-hoc csv file format. Timezone in which the accelerometer was configured. Only use this argument if the timezone of configuration and timezone in which recording took place are different. Use the "TZ identifier" as specified at https://en.wikipedia.org/wiki/Zone.tab to set configtz, e.g., "Europe/London".

sensor.location

Character (default = "wrist"). To indicate sensor location, default is wrist. If it is hip, the HDCZA algorithm for sleep detection also requires longitudinal axis of sensor to be between -45 and +45 degrees.

windowsizes

Numeric vector, three values (default = c(5, 900, 3600)). To indicate the lengths of the windows as in c(window1, window2, window3): window1 is the short epoch length in seconds, by default 5, and this is the time window over which acceleration and angle metrics are calculated; window2 is the long epoch length in seconds for which non-wear and signal clipping are defined, default 900 (expected to be a multitude of 60 seconds); window3 is the window length of data used for non-wear detection and by default 3600 seconds. So, when window3 is larger than window2 we use overlapping windows, while if window2 equals window3 non-wear periods are assessed by non-overlapping windows.

idloc

Numeric (default = 1). If idloc = 1 the code assumes that ID number is stored in the obvious header field. Note that for ActiGraph data the ID is never stored in the file header. For value set to 2, 5, 6, and 7, GGIR looks at the filename and extracts the character string preceding the first occurance of a "_" (idloc = 2), " " (space, idloc = 5), "." (dot, idloc = 6), and "-" (idloc = 7), respectively. You may have noticed that idloc 3 and 4 are skipped, they were used for one study in 2012, and not actively maintained anymore, but because it is legacy code not omitted.

expand_tail_max_hours

Numeric (default = NULL). This parameter has been replaced by recordingEndSleepHour.

recordingEndSleepHour

Numeric (default = NULL). Time (in hours) at which the recording should end (or later) to expand the g.part1 output with synthetic data to trigger sleep detection for last night. Using argument recordingEndSleepHour implies the assumption that the participant fell asleep at or before the end of the recording if the recording ended at or after recordingEndSleepHour hour of the last day. This assumption may not always hold true and should be used with caution. The synthetic data for metashort entails: timestamps continuing regularly, zeros for acceleration metrics other than EN, one for EN. Angle columns are created in a way that it triggers the sleep detection using the equation: round(sin((1:length_expansion) / (900/epochsize))) * 15. To keep track of the tail expansion g.part1 stores the length of the expansion in the RData files, which is then passed via g.part2, g.part3, and g.part4 to g.part5. In g.part5 the tail expansion size is included as an additional variable in the csv-reports. In the g.part4 csv-report the last night is omitted, because we know that sleep estimates from the last night will not be trustworthy. Similarly, in the g.part5 output columns related to the sleep assessment will be omitted for the last window to avoid biasing the averages. Further, the synthetic data are also ignored in the visualizations and time series output to avoid biased output.

dataFormat

Character (default = "raw"). To indicate what the format is of the data in datadir. Alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls, which correspond to epoch level data files from, respecitively, UK Biobank in csv format, Actiwatch in csv format, Actiwatch in awd format, ActiGraph csv format, and Sensewear in xls format (also works with xlsx). Here, the assumed epoch size for UK Biobank csvdata is 5 seconds. The epoch size for the other non-raw data formats is flexible, but make sure that you set first value of argument windowsizes accordingly. Also when working with non-raw data formats specify argument extEpochData_timeformat as documented below. For ukbiobank_csv nonwear is a column in the data itself, for actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls non-wear is detected as 60 minute rolling zeros. The length of this window can be modified with the third value of argument windowsizes expressed in seconds.

maxRecordingInterval

Numeric (default = NULL). To indicate the maximum gap in hours between repeated measurements with the same ID for the recordings to be appended. So, the assumption is that the ID can be matched, make sure argument idloc is set correctly. If argument maxRecordingInterval is set to NULL (default) recordings are not appended. If recordings overlap then GGIR will use the data from the latest recording. If recordings are separated then the timegap between the recordings is filled with data points that resemble monitor not worn. The maximum value of maxFile gap is 504 (21 days). Only recordings from the same accelerometer brand are appended. The part 2 csv report will show number of appended recordings, sampling rate for each, time overlap or gap and the names of the filenames of the respective recording.

extEpochData_timeformat

Character (default = "%d-%m-%Y %H:%M:%S"). To specify the time format used in the external epoch level data when argument dataFormat is set to "actiwatch_csv", "actiwatch_awd", "actigraph_csv" or "sensewear_xls". For example "%Y-%m-%d %I:%M:%S %p" for "2023-07-11 01:24:01 PM" or "%m/%d/%Y %H:%M:%S" "2023-07-11 13:24:01"

params_rawdata

A list of parameters used to related to reading and pre-processing raw data, excluding parameters related to metrics as those are in the params_metrics object.

backup.cal.coef

Character (default = "retrieve"). Option to use backed-up calibration coefficient instead of deriving the calibration coefficients when analysing the same file twice. Argument backup.cal.coef has two usecase. Use case 1: If the auto-calibration fails then the user has the option to provide back-up calibration coefficients via this argument. The value of the argument needs to be the name and directory of a csv-spreadsheet with the following column names and subsequent values: "filename" with the names of accelerometer files on which the calibration coefficients need to be applied in case auto-calibration fails; "scale.x", "scale.y", and "scale.z" with the scaling coefficients; "offset.x", "offset.y", and "offset.z" with the offset coefficients, and; "temperature.offset.x", "temperature.offset.y", and "temperature.offset.z" with the temperature offset coefficients. This can be useful for analysing short lasting laboratory experiments with insufficient sphere data to perform the auto-calibration, but for which calibration coefficients can be derived in an alternative way. It is the users responsibility to compile the csv-spreadsheet. Instead of building this file the user can also Use case 2: The user wants to avoid performing the auto-calibration repeatedly on the same file. If backup.cal.coef value is set to "retrieve" (default) then GGIR will look out for the "data_quality_report.csv" file in the outputfolder QC, which holds the previously generated calibration coefficients. If you do not want this happen, then deleted the data_quality_report.csv from the QC folder or set it to value "redo".

minimumFileSizeMB

Numeric (default = 2). Minimum File size in MB required to enter processing. This argument can help to avoid having short uninformative files to enter the analyses. Given that a typical accelerometer collects several MBs per hour, the default setting should only skip the very tiny files.

do.cal

Boolean (default = TRUE). Whether to apply auto-calibration or not by g.calibrate. Recommended setting is TRUE.

imputeTimegaps

Boolean (default = TRUE). To indicate whether timegaps larger than 1 sample should be imputed. Currently only used for .gt3x data and ActiGraph .csv format, where timegaps can be expected as a result of Actigraph's idle sleep.mode configuration.

spherecrit

Numeric (default = 0.3). The minimum required acceleration value (in g) on both sides of 0 g for each axis. Used to judge whether the sphere is sufficiently populated

minloadcrit

Numeric (default = 168). The minimum number of hours the code needs to read for the autocalibration procedure to be effective (only sensitive to multitudes of 12 hrs, other values will be ceiled). After loading these hours only extra data is loaded if calibration error has not been reduced to under 0.01 g.

printsummary

Boolean (default = FALSE). If TRUE will print a summary of the calibration procedure in the console when done.

chunksize

Numeric (default = 1). Value between 0.2 and 1 to specify the size of chunks to be loaded as a fraction of an approximately 12 hour period for auto-calibration procedure and as fraction of 24 hour period for the metric calculation, e.g., 0.5 equals 6 and 12 hour chunks, respectively. For machines with less than 4Gb of RAM memory or with < 2GB memory per process when using do.parallel = TRUE a value below 1 is recommended.

dynrange

Numeric (default = NULL). Provide dynamic range of 8 gravity.

interpolationType

Integer (default = 1). To indicate type of interpolation to be used when resampling time series (mainly relevant for Axivity sensors), 1=linear, 2=nearest neighbour.

rmc.file

Character (default = NULL). Filename of file to be read if it is in the working directory, or full path to the file otherwise.

rmc.nrow

Numeric (default = NULL). Number of rows to read, same as nrow argument in read.csv and nrows in fread. The whole file is read by default (i.e., rmc.nrow = Inf).

rmc.skip

Numeric (default = 0). Number of rows to skip, same as skip argument in read.csv and in fread.

rmc.dec

Character (default = "."). Decimal used for numbers, same as dec argument in read.csv and in fread.

rmc.firstrow.acc

Numeric (default = NULL). First row (number) of the acceleration data.

rmc.firstrow.header

Numeric (default = NULL). First row (number) of the header. Leave blank if the file does not have a header.

rmc.header.length

Numeric (default = NULL). If file has header, specify header length (number of rows).

rmc.col.acc

Numeric, three values (default = c(1, 2, 3)). Vector with three column (numbers) in which the acceleration signals are stored.

rmc.col.temp

Numeric (default = NULL). Scalar with column (number) in which the temperature is stored. Leave in default setting if no temperature is available. The temperature will be used by g.calibrate.

rmc.col.time

Numeric (default = NULL). Scalar with column (number) in which the timestamps are stored. Leave in default setting if timestamps are not stored.

rmc.unit.acc

Character (default = "g"). Character with unit of acceleration values: "g", "mg", or "bit".

rmc.unit.temp

Character (default = "C"). Character with unit of temperature values: (K)elvin, (C)elsius, or (F)ahrenheit.

rmc.unit.time

Character (default = "POSIX"). Character with unit of timestamps: "POSIX", "UNIXsec" (seconds since origin, see argument rmc.origin), "character", or "ActivPAL" (exotic timestamp format only used in the ActivPAL activity monitor).

rmc.format.time

Character (default = " Character giving a date-time format as used by strptime. Only used for rmc.unit.time: character and POSIX.

rmc.bitrate

Numeric (default = NULL). If unit of acceleration is a bit then provide bit rate, e.g., 12 bit.

rmc.dynamic_range

Numeric or character (default = NULL). If unit of acceleration is a bit then provide dynamic range deviation in g from zero, e.g., +/-6g would mean this argument needs to be 6. If you give this argument a character value the code will search the file header for elements with a name equal to the character value and use the corresponding numeric value next to it as dynamic range.

rmc.unsignedbit

Boolean (default = TRUE). If unsignedbit = TRUE means that bits are only positive numbers. if unsignedbit = FALSE then bits are both positive and negative.

rmc.origin

Character (default = "1970-01-01"). Origin of time when unit of time is UNIXsec, e.g., 1970-1-1.

rmc.desiredtz

Character (default = NULL). Timezone in which experiments took place. This argument is scheduled to be deprecated and is now used to overwrite desiredtz if not provided.

rmc.configtz

Character (default = NULL). Timezone in which device was configured. This argument is scheduled to be deprecated and is now used to overwrite configtz if not provided.

rmc.sf

Numeric (default = NULL). Sample rate in Hertz, if this is stored in the file header then that will be used instead (see argument rmc.headername.sf).

rmc.headername.sf

Character (default = NULL). If file has a header: Row name under which the sample frequency can be found.

rmc.headername.sn

Character (default = NULL). If file has a header: Row name under which the serial number can be found.

rmc.headername.recordingid

Character (default = NULL). If file has a header: Row name under which the recording ID can be found.

rmc.header.structure

Character (default = NULL). Used to split the header name from the header value, e.g., ":" or " ".

rmc.check4timegaps

Boolean (default = FALSE). To indicate whether gaps in time should be imputed with zeros. Some sensing equipment provides accelerometer with gaps in time. The rest of GGIR is not designed for this, by setting this argument to TRUE the gaps in time will be filled with zeros.

rmc.col.wear

Numeric (default = NULL). If external wear detection outcome is stored as part of the data then this can be used by GGIR. This argument specifies the column in which the wear detection (Boolean) is stored.

rmc.doresample

Boolean (default = FALSE). To indicate whether to resample the data based on the available timestamps and extracted sample rate from the file header.

rmc.noise

Numeric (default = 13). Noise level of acceleration signal in mg-units, used when working ad-hoc .csv data formats using read.myacc.csv. The read.myacc.csv does not take rmc.noise as argument, but when interacting with GGIR or g.part1 rmc.noise is used.

rmc.scalefactor.acc

Numeric value (default 1) to scale the acceleration signals via multiplication. For example, if data is provided in m/s2 then by setting this to 1/9.81 we would derive gravitational units.

frequency_tol

Number (default = 0.1) as passed on to readAxivity from the GGIRread package. Represents the frequency tolerance as fraction between 0 and 1. When the relative bias per data block is larger than this fraction then the data block will be imputed by lack of movement with gravitational oriationed guessed from most recent valid data block. Only applicable to Axivity .cwa data.

params_metrics

A list of parameters used to specify the signal metrics that need to be extract in GGIR g.part1.

do.anglex

Boolean (default = FALSE). If TRUE, calculates the angle of the X axis relative to the horizontal: $$angleX = (\tan{^{-1}\frac{acc_{rollmedian(x)}}{(acc_{rollmedian(y)})^2 + (acc_{rollmedian(z)})^2}}) * 180/\pi$$

do.angley

Boolean (default = FALSE). If TRUE, calculates the angle of the Y axis relative to the horizontal: $$angleY = (\tan{^{-1}\frac{acc_{rollmedian(y)}}{(acc_{rollmedian(x)})^2 + (acc_{rollmedian(z)})^2}}) * 180/\pi$$

do.anglez

Boolean (default = TRUE). If TRUE, calculates the angle of the Z axis relative to the horizontal: $$angleZ = (\tan{^{-1}\frac{acc_{rollmedian(z)}}{(acc_{rollmedian(x)})^2 + (acc_{rollmedian(y)})^2}}) * 180/\pi$$

do.zcx

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for x-axis. For computation specifics see source code of function g.applymetrics

do.zcy

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for y-axis. For computation specifics see source code of function g.applymetrics

do.zcz

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for z-axis. For computation specifics see source code of function g.applymetrics

do.enmo

Boolean (default = TRUE). If TRUE, calculates the metric: $$ENMO = \sqrt{acc_x^2 + acc_y^2 + acc_z^2} - 1$$ (if ENMO < 0, then ENMO = 0).

do.lfenmo

Boolean (default = FALSE). If TRUE, calculates the metric ENMO over the low-pass filtered accelerations (for computation specifics see source code of function g.applymetrics). The filter bound is defined by the parameter hb.

do.en

Boolean (default = FALSE). If TRUE, calculates the Euclidean Norm of the raw accelerations: $$EN = \sqrt{acc_x^2 + acc_y^2 + acc_z^2}$$

do.mad

Boolean (default = FALSE). If TRUE, calculates the Mean Amplitude Deviation: $$MAD = \frac{1}{n}\Sigma|r_i - \overline{r}|$$

do.enmoa

Boolean (default = FALSE). If TRUE, calculates the metric: $$ENMOa = \sqrt{acc_x^2 + acc_y^2 + acc_z^2} - 1$$ (if ENMOa < 0, then ENMOa = |ENMOa|).

do.roll_med_acc_x

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.roll_med_acc_y

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.roll_med_acc_z

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.dev_roll_med_acc_x

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.dev_roll_med_acc_y

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.dev_roll_med_acc_z

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.bfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.hfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.hfenplus

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.lfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.lfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.lfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.lfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.hfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.hfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.hfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.bfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.bfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.bfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

do.brondcounts

Boolean (default = FALSE). this option has been deprecated (October 2022) due to issues with the activityCounts package that we used as a dependency. If TRUE, calculated the metric via R package activityCounts. We called them BrondCounts because there are large number of activity counts in the physical activity and sleep research field. By calling them _brondcounts_ we clarify that these are the counts proposed by Jan Br&#248;nd and implemented in R by Ruben Brondeel. The _brondcounts_ are intended to be an imitation of the counts produced by one of the closed source ActiLife software by ActiGraph.

do.neishabouricounts

Boolean (default = FALSE). If TRUE, calculates the metric via R package actilifecounts, which is an implementation of the algorithm used in the closed-source software ActiLife by ActiGraph (methods published in doi: 10.1038/s41598-022-16003-x). We use the name of the first author (instead of ActiLifeCounts) of the paper and call them NeishabouriCount under the uncertainty that ActiLife will implement this same algorithm over time. To use the Neishabouri counts for the physical activity intensity classification in part 5 (i.e., metric over the threshold.lig, threshold.mod, and threshold.vig would be applied), the acc.metric argument needs to be set as one of the following: "NeishabouriCount_x", "NeishabouriCount_y", "NeishabouriCount_z", "NeishabouriCount_vm" to use the counts in the x-, y-, z-axis or vector magnitude, respectively.

lb

Numeric (default = 0.2). Lower boundary of the frequency filter (in Hertz) as used in the filter-based metrics.

hb

Numeric (default = 15). Higher boundary of the frequency filter (in Hertz) as used in the filter-based metrics.

n

Numeric (default = n). Order of the frequency filter as used in the filter-based metrics.

zc.lb

Numeric (default = 0.25). Used for zero-crossing counts only. Lower boundary of cut-off frequency filter.

zc.hb

Numeric (default = 3). Used for zero-crossing counts only. Higher boundary of cut-off frequencies in filter.

zc.sb

Numeric (default = 0.01). Stop band used for calculation of zero crossing counts. Value is the acceleration threshold in g units below which acceleration will be rounded to zero.

zc.order

Numeric (default = 2). Used for zero-crossing counts only. Order of frequency filter.

zc.scale

Numeric (default = 1) Used for zero-crossing counts only. Scaling factor to be applied after counts are calculated (GGIR part 3).

actilife_LFE

Boolean (default = FALSE). If TRUE, calculates the NeishabouriCount metric with the low-frequency extension filter as proposed in the closed source ActiLife software by ActiGraph. Only applicable to the metric NeishabouriCount.

params_cleaning

A list of parameters used across all GGIR parts releated to masking or imputing data, abbreviated as "cleaning".

do.imp

Boolean (default = TRUE). Whether to impute missing values (e.g., suspected of monitor non-wear or clippling) or not by g.impute in GGIR g.part2. Recommended setting is TRUE.

TimeSegments2ZeroFile

Character (default = NULL). Takes path to a csv file that has columns "windowstart" and "windowend" to refer to the start and end time of a time windows in format "2024-10-12 20:00:00", and "filename" of the GGIR milestone data file without the "meta_" segment of the name. GGIR part 2 uses this to set all acceleration values to zero and the non-wear classification to zero (meaning sensor worn). Motivation: When the accelerometer is not worn during the night GGIR automatically labels them as invalid, while the user may like to treat them as zero movement. Disclaimer: This functionality was developed in 2019. With hindsight it is not generic enough and in need for revision. Please contact GGIR maintainers if you would like us to invest time in improving this functionality.

data_cleaning_file

Character (default = NULL). Optional path to a csv file you create that holds four columns: ID, day_part5, relyonguider_part4, and night_part4. ID should hold the participant ID. Columns day_part5 and night_part4 allow you to specify which day(s) and night(s) need to be excluded from g.part5 and g.part4, respectively. When including multiple day(s)/night(s) create a new line for each day/night. So, this will be done regardless of whether the rest of GGIR thinks those day(s)/night(s) are valid. Column relyonguider_part4 allows you to specify for which nights g.part4 should fully rely on the guider. See also package vignette.

excludefirstlast.part5

Boolean (default = FALSE). If TRUE then the first and last window (waking-waking, midnight-midnight, or sleep onset-onset) are ignored in g.part5.

excludefirstlast

Boolean (default = FALSE). If TRUE then the first and last night of the measurement are ignored for the sleep assessment in g.part4.

excludefirst.part4

Boolean (default = FALSE). If TRUE then the first night of the measurement are ignored for the sleep assessment in g.part4.

excludelast.part4

Boolean (default = FALSE). If TRUE then the last night of the measurement are ignored for the sleep assessment in g.part4.

includenightcrit

Numeric (default = 16). Minimum number of valid hours per night (24 hour window between noon and noon), used for sleep assessment in g.part4.

minimum_MM_length.part5

Numeric (default = 23). Minimum length in hours of a MM day to be included in the cleaned g.part5 results.

study_dates_file

Character (default = c()). Full path to csv file containing the first and last date of the expected wear period for every study participant (dates are provided per individual). Expected format of the activity diary is: First column headers followed by one row per recording. There should be three columns: first column is recording ID, which needs to match with the ID GGIR extracts from the accelerometer file; second column should contain the first date of the study; and third column the last date of the study. Date columns should be by default in format "23-04-2017", or in the date format specified by argument study_dates_dateformat (below). If not specified (default), then GGIR would use the first and last day of the recording as beginning and end of the study. Note that these dates are used on top of the data_masking_strategy selected.

study_dates_dateformat

Character (default = " To specify the date format used in the study_dates_file as used by strptime.

strategy

Deprecated and replaced by data_masking_strategy. If strategy is specified then its value is passed on and used for data_masking_strategy.

data_masking_strategy

Numeric (default = 1). How to deal with knowledge about study protocol. data_masking_strategy = 1 means select data based on hrs.del.start and hrs.del.end. data_masking_strategy = 2 makes that only the data between the first midnight and the last midnight is used. data_masking_strategy = 3 selects the most active X days in the file where X is specified by argument ndayswindow, where the days are a series of 24-h blocks starting any time in the day (X hours at the beginning and end of this period can be deleted with arguments hrs.del.start and hrs.del.end) data_masking_strategy = 4 to only use the data after the first midnight. data_masking_strategy = 5 is similar to data_masking_strategy = 3, but it selects X complete calendar days where X is specified by argument ndayswindow (X hours at the beginning and end of this period can be deleted with arguments hrs.del.start and hrs.del.end).

hrs.del.start

Numeric (default = 0). How many HOURS after start of experiment did wearing of monitor start? Used in GGIR g.part2 when data_masking_strategy = 1.

hrs.del.end

Numeric (default = 0). How many HOURS before the end of the experiment did wearing of monitor definitely end? Used in GGIR g.part2 when data_masking_strategy = 1.

maxdur

Numeric (default = 0). How many DAYS after start of experiment did experiment definitely stop? (set to zero if unknown).

ndayswindow

Numeric (default = 7). If data_masking_strategy is set to 3 or 5, then this is the size of the window as a number of days. For data_masking_strategy 3 value can be fractional, e.g. 7.5, while for data_masking_strategy 5 it needs to be an integer.

includedaycrit.part5

Numeric (default = 2/3). Inclusion criteria used in part 5 for number of valid hours during the waking hours of a day, when value is smaller than or equal to 1 used as fraction of waking hours, when value above 1 used as absolute number of valid hours required. Do not confuse this argument with argument includedaycrit which is only used in GGIR part 2 and applies to the entire day.

segmentWEARcrit.part5

Numeric (default = 0.5). Fraction of qwindow segment expected to be valid in part 5, where 0.3 indicates that at least 30 percent of the time should be valid.

segmentDAYSPTcrit.part5

Numeric vector or length 2 (default = c(0.9, 0)). Inclusion criteria for the proportion of the segment that should be classified as day (awake) and spt (sleep period time) to be considered valid. If you are interested in comparing time spent in behaviour then it is better to set one of the two numbers to 0, and the other defines the proportion of the segment that should be classified as day or spt, respectively. The default setting would focus on waking hour segments and includes all segments that overlap for at least 90 percent with waking hours. In order to shift focus to the SPT you could use c(0, 0.9) which ensures that all segments that overlap for at least 90 percent with the SPT are included. Setting both to zero would be problematic when comparing time spent in behaviours between days or individuals: A complete segment would be averaged with an incomplete segments (someone going to bed or waking up in the middle of a segment) by which it is no longer clear whether the person is less active or sleeps more during that segment. Similarly it is not clear whether the person has more wakefulness during SPT for a segment or woke up or went to bed during the segment.

includedaycrit

Numeric (default = 16). Minimum required number of valid hours in calendar day specific to analysis in part 2. If you specify two values as in c(16, 16) then the first value will be used in part 2 and the second value will be used in part 5 and applied as a criterion on the full part 5 window. Note that this is then applied in addition to parameter includedaycrit.part5 which only looks at valid data during waking hours.

max_calendar_days

Numeric (default = 0). The maximum number of calendar days to include (set to zero if unknown).

nonWearEdgeCorrection

Boolean (default = TRUE). If TRUE then the non-wear detection around the edges of the recording (first and last 3 hours) are corrected following description in vanHees2013 as has been the default since then. This functionality is advisable when working with sleep clinic or exercise lab data typically lasting less than a day.

nonwear_approach

Character (default = "2023"). Whether to use the traditional version of the non-wear detection algorithm (nonwear_approach = "2013") or the new version (nonwear_approach = "2023"). The 2013 version would use the longsize window (windowsizes[3], one hour as default) to check the conditions for nonwear identification and would flag as nonwear the mediumsize window (windowsizes[2], 15 min as default) in the middle. The 2023 version differs in which it would flag as nonwear the full longsize window. For the 2013 method the longsize window is centered in the centre of the mediumsize window, while in the 2023 method the longsizewindow is aligned with its left edge to the left edge of the mediumsize window.

params_phyact

A list of parameters releated to physical activity as used in GGIR g.part2 and GGIR g.part5.

mvpathreshold

Numeric (default = 100). Acceleration threshold for MVPA estimation in GGIR g.part2. This can be a single number or an vector of numbers, e.g., mvpathreshold = c(100, 120). In the latter case the code will estimate MVPA separately for each threshold. If this variable is left blank, e.g., mvpathreshold = c(), then MVPA is not estimated.

mvpadur

Numeric (default = 10). The bout duration(s) for which MVPA will be calculated. Only used in GGIR g.part2.

boutcriter

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the mvpathreshold, only used in GGIR g.part2.

threshold.lig

Numeric (default = 40). In g.part5: Threshold for light physical activity to separate inactivity from light. Value can be one number or an vector of multiple numbers, e.g., threshold.lig =c(30,40). If multiple numbers are entered then analysis will be repeated for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

threshold.mod

Numeric (default = 100). In g.part5: Threshold for moderate physical activity to separate light from moderate. Value can be one number or an vector of multiple numbers, e.g., threshold.mod = c(100, 120). If multiple numbers are entered then analysis will be repeated for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

threshold.vig

Numeric (default = 400). In g.part5: Threshold for vigorous physical activity to separate moderate from vigorous. Value can be one number or an vector of multiple numbers, e.g., threshold.vig =c(400,500). If multiple numbers are entered then analysis will be repeated for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

boutdur.mvpa

Numeric (default = c(1, 5, 10)). Duration(s) of MVPA bouts in minutes to be extracted. It will start with the identification of the longest to the shortest duration. In the default setting, it will start with the 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.

boutdur.in

Numeric (default = c(10, 20, 30)). Duration(s) of inactivity bouts in minutes to be extracted. Inactivity bouts are detected in the segments of the data which were not labelled as sleep or MVPA bouts. It will start with the identification of the longest to the shortest duration. In the default setting, it will start with the identification of 30 minute bouts, followed by 20 minute bouts in the rest of the data, and followed by 10 minute bouts in the rest of the data. Note that we use the term inactivity instead of sedentary behaviour for the lowest intensity level of behaviour. The reason for this is that GGIR does not attempt to classifying the activity type sitting at the moment, by which we feel that using the term sedentary behaviour would fail to communicate that.

boutdur.lig

Numeric (default = c(1, 5, 10)). Duration(s) of light activity bouts in minutes to be extracted. Light activity bouts are detected in the segments of the data which were not labelled as sleep, MVPA, or inactivity bouts. It will start with the identification of the longest to the shortest duration. In the default setting, this will start with the identification of 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.

boutcriter.mvpa

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the threshold.mod.

boutcriter.in

Numeric (default = 0.9). A number between 0 and 1, it defines what fraction of a bout needs to be below the threshold.lig.

boutcriter.lig

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be between the threshold.lig and the threshold.mod.

frag.metrics

Character (default = NULL). Fragmentation metric to extract. Can be "mean", "TP", "Gini", "power", or "CoV", "NFragPM", or all the above metrics with "all". See package vignette for description of fragmentation metrics.

part6_threshold_combi

Character (default = "40_100_120") to indicate the threshold combination derived in part 5 to be used for part 6

params_sleep

A list of parameters used to configure the sleep analysis as performend in GGIR g.part3 and g.part4.

relyonguider

Boolean (default = FALSE). If TRUE, then sleep onset and waking time are defined based on timestamps derived from the guider. If participants were instructed NOT to wear the accelerometer during waking hours then set to TRUE, in all other scenarios set to FALSE.

relyonsleeplog

Boolean (default = FALSE). Do not use, now replaced by argument relyonguider. Values provided to argument relyonsleeplog will be passed on to argument relyonguider to not preserve functionality of old R scripts.

def.noc.sleep

Numeric (default = 1). The time window during which sustained inactivity will be assumed to represent sleep, e.g., def.noc.sleep = c(21, 9). This is only used if no sleep log entry is available. If left blank def.noc.sleep = c() then the 12 hour window centred at the least active 5 hours of the 24 hour period will be used instead. Here, L5 is hardcoded and will not change by changing argument winhr in function g.part2. If def.noc.sleep is filled with a single integer, e.g., def.noc.sleep=c(1) then the window will be detected with based on built in algorithms. See argument HASPT.algo from HASPT for specifying which of the algorithms to use.

sleepwindowType

Character (default = "SPT"). To indicate type of information in the sleeplog, "SPT" for sleep period time. Set to "TimeInBed" if sleep log recorded time in bed to enable calculation of sleep latency and sleep efficiency.

nnights

Numeric (default = NULL). This argument has been deprecated.

loglocation

Character (default = NULL). Path to csv file with sleep log information. See package vignette for how to format this file.

colid

Numeric (default = 1). Column number in the sleep log spreadsheet in which the participant ID code is stored.

coln1

Numeric (default = 2). Column number in the sleep log spreadsheet where the onset of the first night starts.

ignorenonwear

Boolean (default = TRUE). If TRUE then ignore detected monitor non-wear periods to avoid confusion between monitor non-wear time and sustained inactivity.

constrain2range

Deprecated, used to be a Boolean (default = TRUE) Whether or not to constrain the range of threshold used in the diary free sleep period time window detection.

HASPT.algo

Character (default = "HDCZA"). To indicate what algorithm should be used for the sleep period time detection. Default "HDCZA" is Heuristic algorithm looking at Distribution of Change in Z-Angle as described in van Hees et al. 2018. Other options included: "HorAngle", which is based on HDCZA but replaces non-movement detection of the HDCZA algorithm by looking for time segments where the angle of the longitudinal sensor axis has an angle relative to the horizontal plane between -45 and +45 degrees. And "NotWorn" which is also the same as HDCZA but looks for time segments when the 5 minute rolling average of counts is below 20 per cent of its standard deviation.

HDCZA_threshold

Numeric (default = c()) If HASPT.algo is set to "HDCZA" and HDCZA_threshold is NOT NULL, (e.g., HDCZA_threshold = 0.2), then that value will be used as threshold in the 6th step in the diagram of Figure 1 in van Hees et al. 2018 Scientific Report (doi: 10.1038/s41598-018-31266-z). However, doing so has not been supported by research yet and is only intended to facilitate methodological research, so we advise sticking with the default in line with the publication.

HASPT.ignore.invalid

Boolean (default = FALSE). To indicate whether invalid time segments should be ignored in the Sleep Period Time detection. If FALSE (default), the imputed angle or activity metric during the invalid time segments is used in the Sleep Period Time detection. If TRUE, invalid time segments are ignored for the Sleep Period Time detection (i.e., considered to be out of the Sleep Period Time). If NA, then invalid time segments are considered to be no movement segments.

HASIB.algo

Character (default = "vanHees2015"). To indicate which algorithm should be used to define the sustained inactivity bouts (i.e., likely sleep). Options: "vanHees2015", "Sadeh1994", "Galland2012".

Sadeh_axis

Character (default = "Y"). To indicate which axis to use for the Sadeh1994 algorithm, and other algortihms that relied on count-based Actigraphy such as Galland2012.

sleeplogsep

Character (default = NULL). This argument is deprecated.

nap_model

Character (default = NULL). To specify classification model. Currently the only option is "hip3yr", which corresponds to a model trained with hip data in 3-3.5 olds trained with parent diary data.

longitudinal_axis

Integer (default = NULL). To indicate which axis is the longitudinal axis. If not provided, the function will estimate longitudinal axis as the axis with the highest 24 hour lagged autocorrelation. Only used when sensor.location = "hip" or HASPT.algo = "HorAngle".

anglethreshold

Numeric (default = 5). Angle threshold (degrees) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., anglethreshold = c(5,10).

timethreshold

Numeric (default = 5). Time threshold (minutes) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., timethreshold = c(5,10).

possible_nap_window

Numeric (default = c(9, 18)). Numeric vector of length two with range in clock hours during which naps are assumed to take place, e.g., possible_nap_window = c(9, 18). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.

possible_nap_dur

Numeric (default = c(15, 240)). Numeric vector of length two with range in duration (minutes) of a nap, e.g., possible_nap_dur = c(15, 240). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.

sleepefficiency.metric

Numeric (default = 1). If 1 (default), sleep efficiency is calculated as detected sleep time during the SPT window divided by log-derived time in bed. If 2, sleep efficiency is calculated as detected sleep time during the SPT window divided by detected duration in sleep period time plus sleep latency (where sleep latency refers to the difference between time in bed and sleep onset). sleepefficiency.metric is only considered when argument sleepwindowType = "TimeInBed"

possible_nap_edge_acc

Numeric (default = Inf). Maximum acceleration before or after the SIB for the nap to be considered. By default this will allow all possible naps.

params_247

A list of parameters releated to description of 24/7 behaviours that do not fall under conventional physical activity or sleep outcomes, these parameters are used in GGIR g.part2 and GGIR g.part5:

qwindow

Numeric or character (default = c(0, 24)). To specify windows over which all variables are calculated, e.g., acceleration distribution, number of valid hours, LXMX analysis, MVPA. If numeric, qwindow should have length two, e.g., qwindow = c(0, 24), all variables will only be calculated over the full 24 hours in a day. If qwindow = c(8, 24) variables will be calculated over the window 0-8, 8-24 and 0-24. All days in the recording will be segmented based on these values. If you want to use a day specific segmentation in each day then you can set qwindow to be the full path to activity diary file (character). Expected format of the activity diary is: First column headers followed by one row per recording, first column is recording ID, which needs to match with the ID GGIR extracts from the accelerometer file. Followed by date column in format "23-04-2017", where date format is specified by argument qwindow_dateformat (below). Use the character combination date, Date or DATE in the column name. This is followed by one or multiple columns with start times for the activity types in that day format in hours:minutes:seconds. The header of the column will be used as label for each activity type. Insert a new date column before continuing with activity types for next day. Leave missing values empty. If an activity log is used then individuals who do not appear in the activity log will still be processed with value qwindow = c(0, 24). Dates with no activity log data can be skipped, no need to have a column with the date followed by a column with the next date. If times in the activity diary are not multiple of the short window size (epoch length), the next epoch is considered (e.g., with epoch of 5 seconds, 8:00:02 will be redefined as 8:00:05 in the activity log). When using the qwindow functionality in combination with GGIR part 5 then make sure to check that arguments segmentWEARcrit.part5 and segmentDAYSPTcrit.part5 are specified to your research needs.

qwindow_dateformat

Character (default = " To specify the date format used in the activity log as used by strptime.

M5L5res

Numeric (default = 10). Resolution of L5 and M5 analysis in minutes.

winhr

Numeric (default = 5). Vector of window size(s) (unit: hours) of LX and MX analysis, where look for least and most active consecutive number of X hours.

qlevels

Numeric (default = NULL). Vector of percentiles for which value needs to be extracted. These need to be expressed as a fraction of 1, e.g., c(0.1, 0.5, 0.75). There is no limit to the number of percentiles. If left empty then percentiles will not be extracted. Distribution will be derived from short epoch metric data. Argument qlevels can for example be used for the MX-metrics (e.g. Rowlands et al) as discussed in the main package vignette

ilevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in mg, e.g., ilevels = c(0,100,200). There is no limit to the number of levels. If left empty then the intensity levels will not be extracted. Distribution will be derived from short epoch metric data.

iglevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in mg used for intensity gradient calculation (according to the method by Rowlands 2018). By default this is argument is empty and the intensity gradient calculation is not done. The user can either provide a single value (any) to make the intensity gradient use the bins iglevels = c(seq(0,4000,by=25), 8000) or the user could specify their own distribution. There is no constriction to the number of levels.

IVIS_windowsize_minutes

Numeric (default = 60). Window size of the Intradaily Variability (IV) and Interdaily Stability (IS) metrics in minutes, needs to be able to add up to 24 hours.

IVIS_epochsize_seconds

Numeric (default = NULL). This argument is deprecated.

IVIS.activity.metric

Numeric (default = 2). Metric used for activity calculation. Value = 1, uses continuous scaled acceleration. Value = 2, tries to collapse acceleration into a binary score of rest versus active to try to simulate the original approach.

IVIS_acc_threshold

Numeric (default = 20). Acceleration threshold to distinguish inactive from active.

qM5L5

Numeric (default = NULL). Percentiles (quantiles) to be calculated over L5 and M5 window.

MX.ig.min.dur

Numeric (default = 10). Minimum MX duration needed in order for intensity gradient to be calculated.

LUXthresholds

Numeric (default = c(0, 100, 500, 1000, 3000, 5000, 10000)). Vector with numeric sequence corresponding to the thresholds used to calculate time spent in LUX ranges.

LUX_cal_constant

Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX values are converted based on formula y = constant * exp(x * exponent)

LUX_cal_exponent

Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX LUX values are converted based on formula y = constant * exp(x * exponent)

LUX_day_segments

Numeric (default = NULL). Vector with hours at which the day should be segmented for the LUX analysis.

L5M5window

Argument deprecated after version 1.5-24. This argument used to define the start and end time, in 24 hour clock hours, over which L5M5 needs to be calculated. Now this is done with argument qwindow.

cosinor

Boolean (default = FALSE). Whether to apply the cosinor analysis from the ActCR package.

part6CR

Boolean (default = FALSE) to indicate whether circadian rhythm analysis should be run by part 6.

part6HCA

Boolean (default = FALSE) to indicate whether Household Co Analysis should be run by part 6.

part6Window

Character vector with length two (default = c("start", "end")) to indicate the start and the end of the time series to be used for circadian rhythm analysis in part 6. In other words, this parameters is not used for Household co-analysis. Alternative values are: "Wx", "Ox", "Hx", where "x" is a number to indicat the xth wakeup, onset or hour of the recording. Negative values for "x" are also possible and will count relative to the end of the recording. For example, c("W1", "W-1") goes from the first till the last wakeup, c("H5", "H-5") ignores the first and last 5 hours, and c("O2", "W10") goes from the second onset till the 10th wakeup time.

params_output

A list of parameters used to specify whether and how GGIR stores its output at various stages of the process.

storefolderstructure

Boolean (default = FALSE). Store folder structure of the accelerometer data.

do.part2.pdf

Boolean (default = TRUE). In g.part2: Whether to generate a pdf for g.part2.

do.part3.pdf

Boolean (default = TRUE). In g.part3: Whether to generate a pdf for g.part3.

timewindow

Character (default = c("MM", "WW")). In g.part5: Timewindow over which summary statistics are derived. Value can be "MM" (midnight to midnight), "WW" (waking time to waking time), "OO" (sleep onset to sleep onset), or any combination of them.

save_ms5rawlevels

Boolean (default = FALSE). In g.part5: Whether to save the time series classification (levels) as csv or RData files (as defined by save_ms5raw_format). Note that time stamps will be stored in the column timenum in UTC format (i.e., seconds from 1970-01-01). To convert timenum to time stamp format, you need to specify your desired time zone, e.g., as.POSIXct(mdat$timenum, tz = "Europe/London").

save_ms5raw_format

Character (default = "csv"). In g.part5: To specify how data should be stored: either "csv" or "RData". Only used if save_ms5rawlevels = TRUE.

save_ms5raw_without_invalid

Boolean (default = TRUE). In g.part5: To indicate whether to remove invalid days from the time series output files. Only used if save_ms5rawlevels = TRUE.

epochvalues2csv

Boolean (default = FALSE). In g.part2: If TRUE then epoch values are exported to a csv file. Here, non-wear time is imputed where possible.

do.sibreport

Boolean (default = FALSE). In g.part4: To indicate whether to generate report for the sustained inactivity bouts (SIB). If set to TRUE and when an advanced sleep diary is available in part 4 then part 5 will use this to generate summary statistics on the overlap between self-reported nonwear and napping with SIB. Here, SIB can be filter based on argument possible_nap_edge_acc and the first value of possible_nap_dur

do.visual

Boolean (default = TRUE). In g.part4: If TRUE, the function will generate a pdf with a visual representation of the overlap between the sleeplog entries and the accelerometer detections. This can be used to visually verify that the sleeplog entries do not come with obvious mistakes.

outliers.only

Boolean (default = FALSE). In g.part4: Only used if do.visual = TRUE. If FALSE, all available nights are included in the visual representation of the data and sleeplog. If TRUE, then only nights with a difference in onset or waking time larger than the variable of argument criterror will be included.

criterror

Numeric (default = 3). In g.part4: Only used if do.visual = TRUE and outliers.only = TRUE. criterror specifies the number of minimum number of hours difference between sleep log and accelerometer estimate for the night to be included in the visualisation.

visualreport

Boolean (default = TRUE). If TRUE, then generate visual report based on combined output from g.part2 and g.part4. Please 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 analyses done by GGIR.

viewingwindow

Numeric (default = 1). Centre the day as displayed around noon (viewingwindow = 1) or around midnight (viewingwindow = 2) in the visual report generated with visualreport = TRUE.

week_weekend_aggregate.part5

Boolean (default = FALSE). In g.part5: To indicate whether week and weekend-days aggregates should be stored. This is turned off by default as it generates a large number of extra columns in the output report.

dofirstpage

Boolean (default = TRUE). To indicate whether a first page with histograms summarizing the whole measurement should be added in the file summary reports generated with visualreport = TRUE.

sep_reports

Character (default = ","). Value used as sep argument in fwrite for writing csv reports.

dec_reports

Character (default = "."). Value used as dec argument in fwrite for writing csv reports.

sep_config

Character (default = ","). Value used as sep argument in fwrite for writing csv config file.

dec_config

Character (default = "."). Value used as dec argument in fwrite for writing csv config file.

visualreport_without_invalid

Boolean (default = TRUE). If TRUE, then reports generated with visualreport = TRUE only show the windows with sufficiently valid data according to includedaycrit when viewingwindow = 1 or includenightcrit when viewingwindow = 2

Author

Vincent T van Hees <v.vanhees@accelting.com>

References

  • van Hees VT, Gorzelniak L, Dean Leon EC, Eder M, Pias M, et al. (2013) Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE 8(4): e61691. doi:10.1371/journal.pone.0061691

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

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

Examples

if (FALSE) {
  mode = c(1,2,3,4,5)
  datadir = "C:/myfolder/mydata"
  outputdir = "C:/myresults"
  studyname ="test"
  f0 = 1
  f1 = 2
  GGIR(#-------------------------------
       # General parameters
       #-------------------------------
       mode = mode,
       datadir = datadir,
       outputdir = outputdir,
       studyname = studyname,
       f0 = f0,
       f1 = f1,
       overwrite = FALSE,
       do.imp = TRUE,
       idloc = 1,
       print.filename = FALSE,
       storefolderstructure = FALSE,
       #-------------------------------
       # Part 1 parameters:
       #-------------------------------
       windowsizes = c(5,900,3600),
       do.cal = TRUE,
       do.enmo = TRUE,
       do.anglez = TRUE,
       chunksize = 1,
       printsummary = TRUE,
       #-------------------------------
       # Part 2 parameters:
       #-------------------------------
       data_masking_strategy = 1,
       ndayswindow = 7,
       hrs.del.start = 1,
       hrs.del.end = 1,
       maxdur = 9,
       includedaycrit = 16,
       L5M5window = c(0,24),
       M5L5res = 10,
       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),
       #-------------------------------
       # Part 3 parameters:
       #-------------------------------
       timethreshold = c(5,10),
       anglethreshold = 5,
       ignorenonwear = TRUE,
       #-------------------------------
       # Part 4 parameters:
       #-------------------------------
       excludefirstlast = FALSE,
       includenightcrit = 16,
       def.noc.sleep = 1,
       loglocation = "D:/sleeplog.csv",
       outliers.only = FALSE,
       criterror = 4,
       relyonguider = FALSE,
       colid = 1,
       coln1 = 2,
       do.visual = TRUE,
       #-------------------------------
       # Part 5 parameters:
       #-------------------------------
       # Key functions: Merging physical activity with sleep analyses
       threshold.lig = c(30,40,50),
       threshold.mod = c(100,120),
       threshold.vig = c(400,500),
       excludefirstlast = FALSE,
       boutcriter = 0.8,
       boutcriter.in = 0.9,
       boutcriter.lig = 0.8,
       boutcriter.mvpa = 0.8,
       boutdur.in = c(10,20,30),
       boutdur.lig = c(1,5,10),
       boutdur.mvpa = c(1,5,10),
       timewindow = c("WW"),
       #-----------------------------------
       # Report generation
       #-------------------------------
       do.report = c(2,4,5))

       # For externally derived Actiwatch data in .AWD format:
       GGIR(datadir = "/media/actiwatch_awd", # folder with epoch level .AWD file
          outputdir = "/media/myoutput",
          dataFormat = "actiwatch_awd",
          extEpochData_timeformat = "%m/%d/%Y %H:%M:%S",
          mode = 1:5,
          do.report = c(2, 4, 5),
          windowsizes = c(60, 900, 3600), # 60 is the expected epoch length
          visualreport = FALSE,
          outliers.only = FALSE,
          overwrite = TRUE,
          HASIB.algo = "Sadeh1994",
          def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY

       # For externally derived Actiwatch data in .CSV format:
       GGIR(datadir = "/media/actiwatch_csv", # folder with epoch level .AWD file
          outputdir = "/media/myoutput",
          dataFormat = "actiwatch_csv",
          extEpochData_timeformat = "%m/%d/%Y %H:%M:%S",
          mode = 1:5,
          do.report = c(2, 4, 5),
          windowsizes = c(15, 900, 3600), # 15 is the expected epoch length
          visualreport = FALSE,
          outliers.only = FALSE,
          HASIB.algo = "Sadeh1994",
          def.noc.sleep = c()) # <= because we cannot use HDCZA for ZCY

       # For externally derived UK Biobank data in .CSV format:
       GGIR(datadir = "/media/ukbiobank",
           outputdir = "/media/myoutput",
           dataFormat = "ukbiobank_csv",
           extEpochData_timeformat = "%m/%d/%Y %H:%M:%S",
           mode = c(1:2),
           do.report = c(2),
           windowsizes = c(5, 900, 3600), # We know that data was stored in 5 second epoch
           desiredtz = "Europe/London", # We know that data was collected in the UK
           visualreport = FALSE,
           overwrite = TRUE)
        
       # For externally derived ActiGraph count data in .CSV format assuming
       # a study protocol where sensor was not worn during the night:
       GGIR(datadir = "/examplefiles",
           outputdir = "",
           dataFormat = "actigraph_csv",
           mode = 1:5,
           do.report = c(2, 4, 5),
           windowsizes = c(5, 900, 3600),
           threshold.in = round(100 * (5/60), digits = 2),
           threshold.mod = round(2500 * (5/60), digits = 2),
           threshold.vig = round(10000 * (5/60), digits = 2),
           extEpochData_timeformat = "%m/%d/%Y %H:%M:%S",
           do.neishabouricounts = TRUE,
           acc.metric = "NeishabouriCount_x",
           HASPT.algo = "NotWorn",
           HASIB.algo = "NotWorn",
           do.visual = TRUE,
           includedaycrit = 10,
           includenightcrit = 10,
           visualreport = FALSE,
           outliers.only = FALSE,
           save_ms5rawlevels = TRUE,
           ignorenonwear = FALSE,
           HASPT.ignore.invalid = FALSE,
           save_ms5raw_without_invalid = FALSE)
           
           
       # For externally derived Sensear data in .xls format:  
        GGIR(datadir = "C:/yoursenseweardatafolder",
            outputdir = "D:/youroutputfolder",
            mode = 1:5,
            windowsizes = c(60, 900, 3600),
            threshold.in = 1.5,
            threshold.mod = 3,
            threshold.vig = 6,
            dataFormat = "sensewear_xls",
            extEpochData_timeformat = "%d-%b-%Y %H:%M:%S",
            HASPT.algo = "NotWorn",
            desiredtz = "America/New_York",
            overwrite = TRUE,
            do.report = c(2, 4, 5),
            visualreport = FALSE)
           
  }