15 minutes missing when analyzing UK biobank raw accelerometer data using GGIR

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CH Su

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Apr 5, 2021, 5:24:54 AM4/5/21

to R package GGIR

Dear all,

I’m trying to extract some physical activity parameters from the UK BioBank raw accelerometer data (AX3, .cwa format) using GGIR package (version 2.3.0). I’m wondering why the starting time of cwa file is different from the GGIR output. 15 minutes are missing of GGIR output. Does anyone has encountered a similar situation? Or maybe our parameter settings are wrong?

Take “accsamp.cwa” as an example.

(Sample datafile of accelerometer results “accsamp.cwa” is downloaded from https://biobank.ndph.ox.ac.uk/ukb/refer.cgi?id=131620)

I read the cwa file by g.cwaread function, and it shows that the start time is 2013-07-13 10:00:05.

t <- g.cwaread(fileName = dir, start = 0, end = 10, desiredtz = "Europe/London")

t$header$start

[1] "2013-07-13 10:00:05 BST"

t$data[1:3,]

# time x y z temp battery light

# 1 1373706006 -0.67187500 0.1250000 -1.2968750 17.9 4.101562 319

# 2 1373706006 -0.03182551 -0.1099505 0.8454765 17.9 4.101562 319

# 3 1373706006 -0.04572397 -0.1250000 0.8906250 17.9 4.101562 319

library(lubridate)

as_datetime(t$data[1,1], tz = "Europe/London")

# [1] "2013-07-13 10:00:05 BST"

But the start time of the g.shell.GGIR function output is 2013-07-13T10:15:00+0100.

“output_accsamp/meta/csv/accsamp.cwa.RData.csv” file:

GGIR output csv.png

The parameters are set as follows.

dir <- "D:/accsamp.cwa" #cwa file

o.dir <- "D:/" #output path

name <- "accsamp"

GGIR <- function(d.dir, o.dir, name){

mode= c(1,2,3,4,5) # g.part1:5

datadir= d.dir

outputdir= o.dir

studyname= name

f0 = 1 # number of file to start with

f1 = 1 # number of file to end with

desiredtz = "Europe/London" # time zone

g.shell.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,

desiredtz=desiredtz,

#start = 0,

#dayborder = 10,

#-------------------------------

# Part 1 parameters:

#-------------------------------

windowsizes = c(5,900,3600),

desiredtz=desiredtz,

do.cal=TRUE,

do.enmo=TRUE,

do.anglez=TRUE,

chunksize=1.5,

printsummary=TRUE,

do.hfen = FALSE,

#-------------------------------

# Part 2 parameters:

#-------------------------------

strategy = 1,

#ndayswindow = nday,

hrs.del.start = 0,

hrs.del.end = 0,

maxdur = 0,

includedaycrit = 2,

M5L5res = 10,

winhr = c(5,10),

qlevels = c(0.25,0.5,0.75),

qwindow=c(0,24),

ilevels = c(0,100,200),

mvpathreshold =c(100,120),

epochvalues2csv=TRUE,

L5M5window = c(0,24),

#-------------------------------

# Part 3 parameters:

#-------------------------------

timethreshold= c(5),

anglethreshold=5,

ignorenonwear = TRUE,

#-------------------------------

# Part 4 parameters:

#-------------------------------

excludefirstlast = FALSE,

includenightcrit = 2,

def.noc.sleep = c(1),

outliers.only = FALSE,

criterror= 0,

relyonsleeplog = FALSE,

sleeplogidnum = FALSE,

do.visual = TRUE,

nnights = c(),

excludefirstlast = FALSE,

#-------------------------------

# Part 5 parameters:

#-------------------------------

# Key functions: Merging physical activity with sleep analyses

excludefirstlast.part5 =FALSE,

threshold.lig = c(30),

threshold.mod = c(100),

threshold.vig = c(500),

boutcriter = 0.8,

boutcriter.in = 0.9,

boutcriter.lig = 0.8,

boutcriter.mvpa = 0.8,

boutdur.in = c(1,10,30),

boutdur.lig = c(1,5,10),

boutdur.mvpa = c(1,5,10),

timewindow = c("WW"),

#save_ms5rawlevels=FALSE

#includedaycrit.part5 = 0.1,

#-----------------------------------

# Report generation

#-------------------------------

do.report=c(2,4,5),

visualreport=TRUE,

dofirstpage = FALSE,

viewingwindow=1)

}

GGIR(dir, o.dir, name)

Thanks for all your help!

Best,
Chian-Heng

Vincent van Hees

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Apr 9, 2021, 4:37:48 AM4/9/21

to CH Su, R package GGIR

Dear Chian-Heng,

GGIR aggregates data per short, medium and long epoch. The default for these are 5, 900 and 3600 seconds respectively. The short epoch is used for quantifying behaviour, while the medium and long epochs are used for detecting non-wear detection.

To ease comparisons between recordings these epochs are extracted in a way that all medium length epochs align with the hour. So, if your recording starts at 10:07, the GGIR will extract epochs from 10:15 onward, because that is the next 900 second (15 minutes) point in the hour.

If you want to shorten this poch length then to let's say 300 seconds, then that is fine. I do not see any danger with that in terms of non-wear detection accuracy. The only disadvantage is that it could slow down GGIR a bit.

Best, Vincent

Dr. Vincent van Hees

Independent consultant

https://accelting.com/

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