# GGIR configuration parameters

NOTE: If you are viewing this page via CRAN note that the main GGIR documentation has been migrated to the GGIR GitHub pages.

The GGIR shell function takes the input arguments and groups them into parameter objects. The first section below displays all optional GGIR input argument names, the GGIR part (1, 2, 3, 4 and/or 5) they are used in, and the parameter object they are stored in. As you will see, a few parameters are not part of any parameter object because they are direct arguments of the GGIR shell function.

In the second section of this vignette you will find a description and default value for all the arguments.

# 1 Input arguments/parameters overview

Argument (parameter) | Used in GGIR part | Parameter object
--- | --- | ---
datadir | 1, 2, 4, 5 | not in parameter objects
f0 | 1, 2, 3, 4, 5 | not in parameter objects
f1 | 1, 2, 3, 4, 5 | not in parameter objects
windowsizes | 1, 5 | params_general
desiredtz | 1, 2, 3, 4, 5 | params_general
overwrite | 1, 2, 3, 4, 5 | params_general
do.parallel | 1, 2, 3, 5 | params_general
maxNcores | 1, 2, 3, 5 | params_general
myfun | 1, 2, 3 | not in parameter objects
outputdir | 1 | not in parameter objects
studyname | 1 | not in parameter objects
chunksize | 1 | params_rawdata
do.enmo | 1 | params_metrics
do.lfenmo | 1 | params_metrics
do.en | 1 | params_metrics
do.bfen | 1 | params_metrics
do.hfen | 1 | params_metrics
do.hfenplus | 1 | params_metrics
do.mad | 1 | params_metrics
do.anglex | 1 | params_metrics
do.angley | 1 | params_metrics
do.angle | 1 | params_metrics
do.enmoa | 1 | params_metrics
do.roll_med_acc_x | 1 | params_metrics
do.roll_med_acc_y | 1 | params_metrics
do.roll_med_acc_z | 1 | params_metrics
do.dev_roll_med_acc_x | 1 | params_metrics
do.dev_roll_med_acc_y | 1 | params_metrics
do.dev_roll_med_acc_z | 1 | params_metrics
do.lfen | 1 | params_metrics
do.lfx | 1 | params_metrics
do.lfy | 1 | params_metrics
do.lfz | 1 | params_metrics
do.hfx | 1 | params_metrics
do.hfy | 1 | params_metrics
do.hfz | 1 | params_metrics
do.bfx | 1 | params_metrics
do.bfy | 1 | params_metrics
do.bfz | 1 | params_metrics
do.zcx | 1 | params_metrics
do.zcy | 1 | params_metrics
do.zcz | 1 | params_metrics
do.neishabouricounts | 1 | params_metrics
actilife_LFE | 1 | params_metrics
lb | 1 | params_metrics
hb | 1 | params_metrics
n | 1 | params_metrics
do.cal | 1 | params_rawdata
spherecrit | 1 | params_rawdata
minloadcrit | 1 | params_rawdata
printsummary | 1 | params_rawdata
print.filename | 1 | params_general
backup.cal.coef | 1 | params_rawdata
rmc.noise | 1 | params_rawdata
rmc.dec | 1 | params_rawdata
rmc.firstrow.acc | 1 | params_rawdata
rmc.firstrow.header | 1 | params_rawdata
rmc.col.acc | 1 | params_rawdata
rmc.col.temp | 1 | params_rawdata
rmc.col.time | 1 | params_rawdata
rmc.unit.acc | 1 | params_rawdata
rmc.unit.temp | 1 | params_rawdata
rmc.origin | 1 | params_rawdata
rmc.header.length | 1 | params_rawdata
mc.format.time | 1 | params_rawdata
rmc.bitrate | 1 | params_rawdata
rmc.dynamic_range | 1 | params_rawdata
rmc.unsignedbit | 1 | params_rawdata
rmc.desiredtz | 1 | params_rawdata
rmc.sf | 1 | params_rawdata
rmc.headername.sf | 1 | params_rawdata
rmc.headername.sn | 1 | params_rawdata
rmc.headername.recordingid | 1 | params_rawdata
rmc.header.structure | 1 | params_rawdata
rmc.check4timegaps | 1 | params_rawdata
rmc.col.wear | 1 | params_rawdata
rmc.doresample | 1 | params_rawdata
imputeTimegaps | 1 | params_rawdata
dayborder | 1, 2, 5 | params_general
dynrange | 1 | params_rawdata
nonwear_range_threshold | 1 | params_rawdata
configtz | 1 | params_general
minimumFileSizeMB | 1 | params_rawdata
interpolationType | 1 | params_rawdata
expand_tail_max_hours | deprecated | params_general
recordingEndSleepHour | 1 | params_general
maxRecordingInterval | 1 | params_general
nonwear_approach | 1 | params_general
dataFormat | 1 | params_general
extEpochData_timeformat | 1 | params_general
metadatadir | 2, 3, 4, 5 | not in parameter objects
minimum_MM_length.part5 | 5 | params_cleaning
strategy | 2, 5 | params_cleaning
hrs.del.start | 2, 5 | params_cleaning
hrs.del.end | 2, 5 | params_cleaning
maxdur | 2, 5 | params_cleaning
max_calendar_days | 2 | params_cleaning
includedaycrit | 2, 5 | params_cleaning
nonWearEdgeCorrection | 2 | params_cleaning
nonwearFiltermaxHours | 2 | params_cleaning
nonwearFilterWindow | 2 | params_cleaning
L5M5window | 2 | params_247
M5L5res | 2, 5 | params_247
winhr | 2, 5 | params_247
qwindow | 2 | params_247
qlevels | 2 | params_247
ilevels | 2 | params_247
mvpathreshold | 2 | params_phyact
boutcriter | 2 | params_phyact
ndayswindow | 2 | params_cleaning
idloc | 2, 4 | params_general
do.imp | 2 | params_cleaning
storefolderstructure | 2, 4, 5 | params_output
epochvalues2csv | 2 | params_output
do.part2.pdf | 2 | params_output
sep_reports | 2, 4, 5 | params_output
dec_reports | 2, 4, 5 | params_output
sep_config | 1, 2, 3, 4, 5 | params_output
dec_config | 1, 2, 3, 4, 5 | params_output
mvpadur | 2 | params_phyact
bout.metric | 2, 5 | params_phyact
closedbout | 2 | params_phyact
IVIS_windowsize_minutes | 2 | params_247
IVIS_epochsize_seconds | 2 | params_247
IVIS.activity.metric | 2 | params_247
iglevels | 2, 5 | params_247
TimeSegments2ZeroFile | 2 | params_cleaning
qM5L5 | 2 | params_247
MX.ig.min.dur | 2 | params_247
qwindow_dateformat | 2 | params_247
anglethreshold | 3 | params_sleep
timethreshold | 3 | params_sleep
ignorenonwear | 3 | params_sleep
HDCZA_threshold | 3 | params_sleep
acc.metric | 3, 5 | params_general
do.part3.pdf | 3 | params_output
sensor.location | 3, 4 | params_general
HASPT.algo | 3 | params_sleep
HASIB.algo | 3 | params_sleep
Sadeh_axis | 3 | params_sleep
longitudinal_axis | 3 | params_sleep
HASPT.ignore.invalid | 3 | params_sleep
loglocation | 4, 5 | params_sleep
colid | 4 | params_sleep
coln1 | 4 | params_sleep
possible_nap_window | 5 | params_sleep
possible_nap_dur | 5 | params_sleep
possible_nap_gap | 5 | params_sleep
do.visual | 4 | params_output
outliers.only | 4 | params_output
excludefirstlast | 4 | params_cleaning
criterror | 4 | params_output
includenightcrit | 4 | params_cleaning
relyonguider | 4 | params_sleep
relyonsleeplog | 4 | deprecated
sleepefficiency.metric | 4 | params_sleep
def.noc.sleep | 4 | params_sleep
sib_must_fully_overlap_with_TimeInBed | 4 | params_sleep
data_cleaning_file | 4, 5 | params_cleaning
excludefirst.part4 | 4 | params_cleaning
excludelast.part4 | 4 | params_cleaning
sleepwindowType | 4 | params_cleaning
excludefirstlast.part5 | 5 | params_cleaning
boutcriter.mvpa | 5 | params_phyact
boutcriter.in | 5 | params_phyact
boutcriter.lig | 5 | params_phyact
threshold.lig | 5 | params_phyact
threshold.mod | 5 | params_phyact
threshold.vig | 5 | params_phyact
boutdur.mvpa | 5 | params_phyact
boutdur.in | 5 | params_phyact
boutdur.lig | 5 | params_phyact
save_ms5rawlevels | 5 | params_output
part5_agg2_60seconds | 5 | params_general
includedaycrit.part5 | 5 | params_cleaning
includenight.part5 | 5 | params_cleaning
frag.metrics | 5 | params_phyact
LUXthresholds | 5 | params_247
LUX_cal_constant | 5 | params_247
LUX_cal_exponent | 5 | params_247
LUX_day_segments | 5 | params_247
timewindow | 5 | params_output
save_ms5raw_format | 5 | params_output
save_ms5raw_without_invalid | 5 | params_output
do.sibreport | 5 | params_output
includecrit.part6 | 6 | params_cleaning
part6_threshold_combi | 6 | params_phyact
part6CR | 6 | params_247
part6HCA | 6 | params_247
part6Window | 6 | params_247
part6DFA | 6 | params_247
require_complete_lastnight_part5 | 5 | params_output
visualreport_without_invalid | visualreport | params_output
dofirstpage | visualreport | params_output
visualreport | visualreport | params_output
viewingwindow | visualreport | params_output

# 2 Arguments/parameters description

All information as shown below has been auto-generated and is identical to the information provided in the GGIR package pdf manual.

## 2.1 GGIR function input 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.html

## 2.2 General Parameters

### 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.

### 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.

### 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.

### 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).

### do.parallel

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

### 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.

### 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 ://en.wikipedia.org/wiki/Zone.tabhttps://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 ://en.wikipedia.org/wiki/Zone.tabhttps://en.wikipedia.org/wiki/Zone.tab to set configtz, e.g., “Europe/London”.

### 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.

### dayborder

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

### 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.

### 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.

### 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”

## 2.3 Raw Data Parameters

### chunksize

Numeric (default = 1). Value 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. The value is constrained by GGIR to not be lower than 0.05. Please note that setting 0.05 will not produce output when 3rd value of parameter windowsizes is 3600.

### 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.

### do.cal

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

### 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”.

### dynrange

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

### 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.

### rmc.dec

Character (default = “.”). Decimal used for numbers, same as dec argument in [utils]read.csv and in [data.table]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), “UNIXmsec” (same as UNIXsec but in milliseconds), “character”, or “ActivPAL” (exotic timestamp format only used in the ActivPAL activity monitor).

### rmc.format.time

Character (default = “%Y-%m-%d %H:%M:%OS”). Character giving a date-time format as used by [base]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.noise

Numeric (default = 13). Noise level of acceleration signal in m-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.

### nonwear_range_threshold

Numeric (default 150) used to define maximum value range per axis for non-wear detection, used in combination with brand specific standard deviation per axis.

### 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.

### 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.

### 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.

### 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.

### 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.

## 2.4 Metrics Parameters

### do.anglex

Boolean (default = FALSE). If TRUE, calculates the angle of the X axis relative to the horizontal: = (^-1_rollmedian(x)(acc_rollmedian(y))^2 + (acc_rollmedian(z))^2) * 180/

### do.angley

Boolean (default = FALSE). If TRUE, calculates the angle of the Y axis relative to the horizontal: = (^-1_rollmedian(y)(acc_rollmedian(x))^2 + (acc_rollmedian(z))^2) * 180/

### do.anglez

Boolean (default = TRUE). If TRUE, calculates the angle of the Z axis relative to the horizontal: = (^-1_rollmedian(z)(acc_rollmedian(x))^2 + (acc_rollmedian(y))^2) * 180/

### 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: = _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: = _x^2 + acc_y^2 + acc_z^2

### do.mad

Boolean (default = FALSE). If TRUE, calculates the Mean Amplitude Deviation: = 1n|r_i - |

### do.enmoa

Boolean (default = FALSE). If TRUE, calculates the metric: = _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 Brønd 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.

### hb

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

### lb

Numeric (default = 0.2). Lower 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.

## 2.5 Cleaning Parameters

### 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.

### 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.

### 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).

### maxdur

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

### 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.

### 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.

### 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.

### 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.

### 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.

### 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.

### minimum_MM_length.part5

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

### excludefirstlast

Boolean (default = FALSE). If TRUE then the first and 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.

### 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.

### 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.

### 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.

### 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 = “%d-%m-%Y”). To specify the date format used in the study_dates_file as used by [base]strptime.

### includecrit.part6

Numeric (default = c(2/3, 2/3)) Vector of two with the minimum fraction of valid data required for day and spt time, respectively. This criteria is only used for circadian rhythm analysis.

### includenightcrit.part5

Numeric (default = 0). Inclusion criteria used in part 5 for number of valid hours during the sleep period hours of a day (the night), when value is smaller than or equal to 1 used as fraction of sleep period hours, when value above 1 used as absolute number of valid hours required. Do not confuse this argument with argument includenightcrit which is only used in GGIR part 4 and applies to the entire 24 hour window from noon to noon or 6pm to 6pm.

### nonwearFiltermaxHours

Numeric (default = NULL). If not NULL, ignore detected nonwear periods that last shorter than nonwearFiltermaxHours during a window as defined by parameter NonwearFilterWindow. If NonwearFilterWindow is not provided (set to NULL as is the default) check whether qwindow is defined as activity diary file and whether it has timestamps that allow for defining a time in bed, SPT or lightsoff window. For this the code looks for column names in the diary with the terms inbed, sleeponset, or lightsout to define the start of the window. Further, to define the end of the window the code looks for columns outbed, wakeup, or lightsoff. If yes, it uses the largest of the windows that can be defined with these timestamps. If not, use midnight-6am as fall back setting. You may realise that these columns are the same as the advanced format for the sleelog accepted by GGIR part 4. So, if you already have an advanced format sleeplog for part 4 then you can provide this as value for the qwindow parameter. If you want to use qwindow to guide the nonwear filtering as described in here but do not want GGIR to use it for day segment analysis in part 2 or 5 then make sure the diary filename includes the word “onlyfilter” or “filteronly” to tell GGIR to only use qwindow for nonwear filtering. The main purpose of this functionality is to offer the option to ignore short lasting nonwear episodes during the night when there is suspicion that these are falsely detect, e.g. in individuals with extended motionless sleep periods caused by medication.

### nonwearFilterWindow

Numeric (default = NULL). Vector of length 2 to specify the start and end hour of the night to be used for the functionality as described above for NonwearFiltermaxHours.

## 2.6 Sleep Parameters

### 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).

### ignorenonwear

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

### 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 a rolling average of acceleration magnitude is below 5 per cent of its standard deviation, see Cookbook vignette in the Annexes of https://wadpac.github.io/GGIR/ for more detailed guidance on how to use “NotWorn”.

### 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”, “NotWorn”. See vignette ://wadpac.github.io/GGIR/articles/chapter8_SleepFundamentalsSibs.htmlchapter 8 for details.

### 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.

### 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”.

### HASPT.ignore.invalid

Boolean (default = FALSE). To indicate whether invalid time segments should be ignored in the heuristic guiders. If FALSE (default), the imputed angle or activity metric during the invalid time segments are used. If TRUE, invalid time segments are ignored (i.e., they cannot contribute to the guider). If NA, then invalid time segments are considered to be no movement segments and can contribute to the guider. Further, the guider name in the output will be shown with “+invalid” its end, e.g. “HDCZA+invalid”, to reflect the NA setting. When HASPT.algo is “NotWorn”, HASPT.ignore.invalid is automatically set to NA.

### 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.

### nnights

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

### relyonguider

Boolean (default = FALSE). Sustained inactivity bouts (sib) that overlap with the guider are labelled as sleep. If relyonguider = FALSE and the sib overlaps only partially with the guider then it is the sib that defines the edge of the SPT window and not the guider. If relyonguider = TRUE and the sib overlaps only partially with the guider then it is the guider that defines the edge of the SPT window and not the sib. If participants were instructed NOT to wear the accelerometer during waking hours and ignorenonware=FALSE then set to relyonguider=TRUE, in all other scenarios set to FALSE.

### 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.

### sleeplogsep

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

### 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.

### possible_nap_window

Numeric (default = NULL). 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). The nap classification is only applied if both possible_nap_window and possible_nap_dur are both specified. More documentation to follow in 2025 when development work has completed.

### possible_nap_dur

Numeric (default = NULL). Numeric vector of length two with range in duration (minutes) of a nap, e.g., possible_nap_dur = c(15, 240). The nap classification is only applied if both possible_nap_window and possible_nap_dur are both specified. More documentation to follow in 2025 when development work has completed.

### possible_nap_gap

Numeric (default = 0). Time gap expressed in seconds that is allowed between the sustained inactivity bouts that form the naps.

### 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.

### 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. This functionality is currently superseded by nap detection which is triggered by possible_nap_window and possible_nap_dur. Leave nap_model as NULL when using the new functionality. More documentation to follow in 2025 when development work has completed.

### 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 parameter sleepwindowType = “TimeInBed”

### 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. Further, if HDCZA_threshold is set to a numeric vector of length 2, e.g. c(10, 15), that will be used as percentile and multiplier for the above mentioned 6th step.

### sib_must_fully_overlap_with_TimeInBed

Boolean (default = c(TRUE, TRUE)). To indicate whether sib must fully overlap with TimeInBed to be considered sleep for the start and end of time in bed, respectively. Only considered when parameter sleepwindowType = “TimeInBed”. Note that negative sleep latency, if any, will be reported in the night summary report (part4_nightsummary_sleep csv files) but these negative sleep latency and corresponding sleep efficiency values when calculating the person summary aggregate as stored in the person level report (part4_summary_sleep csv files).

## 2.7 Physical activity Parameters

### mvpathreshold

Numeric (default = NULL). Legacy parameter, if not provided GGIR uses the value of threshold.mod for this. 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.

### boutcriter

Numeric (default = NULL). Legacy parameter, if not provided GGIR uses the value of boutcriter.mvpa for this. 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.

### mvpadur

Numeric (default = c(1, 5, 10)). The bout duration(s) for which MVPA will be calculated. Only used in GGIR g.part2.

### 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.

### 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.

### 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.

### 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 = NULL) to indicate the threshold combination derived in part 5 to be used for part 6. For example, “40_100_120”. If left in default value GGIR will use the first threshold value from parameters threshold.lig, threshold.mod, and threshold.vig.

## 2.8 24/7 Parameters

### 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 parameter 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 parameters segmentWEARcrit.part5 and segmentDAYSPTcrit.part5 are specified to your research needs. When using an activity diary be aware that any column name including the words “impute” or “uncertain” will be ignored. This means you can, for you own convenience, add columns to log which timestamps have been manually imputed or are considered uncertain.

### 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. Parameter qlevels can for example be used for the MX-metrics (e.g. Rowlands et al) as discussed in the ://cran.r-project.org/package=GGIR/vignettes/GGIR.htmlmain package vignette

### qwindow_dateformat

Character (default = “%d-%m-%Y”). To specify the date format used in the activity log as used by [base]strptime.

### ilevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in m, 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.

### IVIS_windowsize_minutes

This argument has been deprecated.

### IVIS_epochsize_seconds

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

### IVIS.activity.metric

This argument has been deprecated.

### IVIS_acc_threshold

This argument has been deprecated.

### 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.

### 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.

### iglevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in mused for intensity gradient calculation (according to the method by Rowlands 2018). By default this is parameter 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.

### 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

Has been deprecated after version 1.5-24. This parameter 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 parameter qwindow.

### cosinor

Boolean (default = FALSE). Whether to apply the cosinor analysis from the ActCR package in part 2. In part 6 cosinor analysis is applied by default and cannot be turned off.

### part6CR

Boolean (default = FALSE) to indicate whether circadian rhythm analysis should be run by part 6, this includes: cosinor analysis, extended cosinor analysis, IS, IV, and phi. Optionally this can be expanded with detrended fluctutation analysis which is controlled by parameter `part6DFA`.

### 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.

### part6DFA

Boolean (default = FALSE) to indicate whether to perform Detrended Fluctuation Analysis. Turned off by default because it can be time consuming.

### clevels

Not fully operational yet, to be actived in 2025. Numeric vector (default = c(30, 150)) with length 2 to indicate cadence ranges used when summarising step counts, if available. When set to NULL, step counts will not be summarised in part 5 output.

## 2.9 Output Parameters

### 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.

### save_ms5rawlevels

Boolean (default = TRUE). 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”). If you are not using GGIR part 6, are not interested in the visualreport generation, and not interested in time series then you may want to consider setting this parameter to FALSE.

### save_ms5raw_format

Character (default = “RData”). In g.part5: To specify how data should be stored: “csv”, “RData”, or both via c(“csv”, “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.

### storefolderstructure

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

### 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.

### viewingwindow

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

### dofirstpage

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

### visualreport

Boolean (default = TRUE). If TRUE, store two visualreports in outputfolder ‘results/file summary reports’ with names starting with “old_report_…” and “report…”. The report “old_report_…” has been in GGIR for years and is based on combined output from GGIR part 2 and 4. It was initially only a quick development attempt to have something to show to study participants back in 2015. This report is not designed for data quality checking purposes as it makes no attempt to exactly visualise the classifications made in GGIR part 5. Given the number of inaccuracies, this report will eventually be deprecated from GGIR. To turn off the old report use old_visualreport = FALSE. “report_…”, is a new report as of GGIR 3.1-8 and is specifically aimed at supporting data quality checks as it shows the time series data as generated and used in GGIR part 5. .

### 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.

### do.part3.pdf

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

### 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 parameter 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.

### 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.

### do.sibreport

Boolean (default = TRUE). 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 parameter possible_nap_edge_acc and the first value of possible_nap_dur

### do.part2.pdf

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

### sep_reports

Character (default = “,”). Value used as sep parameter in [data.table]fwrite for writing csv reports.

### sep_config

Character (default = “,”). Value used as sep parameter in [data.table]fwrite for writing csv config file.

### dec_reports

Character (default = “.”). Value used as dec parameter in [data.table]fwrite for writing csv reports.

### dec_config

Character (default = “.”). Value used as dec parameter in [data.table]fwrite for writing csv config file.

### visualreport_without_invalid

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

### old_visualreport

Boolean (default = TRUE). If TRUE, then generate old visual report in addition to new visualreport. The old visualreport will eventually be deprecated

### visualreport_hrsPerRow

Numeric (default = 36). Width of the plots in the new visualreport (named “report_…”) expressed in hours. Expected to be in the range 24-48. If more than 24 then the extra time overlaps with the beginning of the next plot.

### visualreport_focus

Character (default = “day”) Whether new visual report (names start with “report…”) focuss on day or night. If set ot “night” the focus will be on the night.

### visualreport_validcrit

Numeric (default = 0). Value between 0 and 1 representing the fraction of data in a plot that is expected to be valid. If this criteria is not met the row is skipped. Only used in new visual report (names start with “report…”).

### require_complete_lastnight_part5

Boolean (default = FALSE). When set to TRUE: The last WW window is excluded if the recording ends between midnight and 3pm, and starts on a date that is on or one day before the recording end date; The last OO and MM window are excluded if recording ends between midnight and 9am, and starts on a date that is on or one day before the recording end date. This to avoid risk that recording end biases the sleep estimates for the last night.

### method_research_vars

Character (default = NULL). Vector with names of methodological variable categories to store in the csv output files. Currently on “nap” is available which affects the part5 output. The variables are intended for methodological research only and are by default turned off.