PhysicalActivity-package: Process Accelerometer Data for Physical Activity Measurement
In PhysicalActivity: Process Accelerometer Data for Physical Activity Measurement

Description Details Author(s) References Examples

It provides a function wearingMarking for classification of monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity. The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days.

The revised package version 0.2-2 improved the functions in the previous version regarding speed and robustness. In addition, several functions were added: markDelivery can classify days for ActiGraph delivery by mail; markPAI can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with readActigraph and queryActigraph functions. The package also better supports time zones and daylight saving.

Classify wear and nonwear time status for accelerometer data by epoch-by-epoch basis by wearingMarking.

Classify mail delivery and non-delivery day status for accelerometer data by markDelivery.

Three options are available for the package: pa.validCut=600, pa.timeStamp='TimeStamp', and pa.cts='axis1'. When these options are specified (as in markDelivery), the other functions will automatically respect these values as defaults. For instance, the count variable in data(dataSec) is "counts". Running options(pa.cts='counts') allows the user to avoid specifying the "cts" argument in wearingMarking. The options for validCut and timeStamp are rarely changed.

Shiny app called Actigraph can be used to visualize accelerometer data and summarize the data. Please see https://github.com/couthcommander/PhysicalActivityShiny.

Leena Choi leena.choi@Vanderbilt.Edu, Cole Beck cole.beck@vumc.org, Zhouwen Liu zhouwen.liu@vumc.org, Charles E. Matthews Charles.Matthews2@nih.gov, and Maciej S. Buchowski maciej.buchowski@Vanderbilt.Edu

Maintainer: Leena Choi leena.choi@Vanderbilt.Edu

Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011 Feb;43(2):357-64.

Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012 Oct;44(10):2009-16.

Choi L, Chen KY, Acra SA, Buchowski MS. Distributed lag and spline modeling for predicting energy expenditure from accelerometry in youth. J Appl Physiol. 2010 Feb;108(2):314-27.

data(dataSec)

mydata1m = dataCollapser(dataSec, TS = "TimeStamp", col = "counts", by = 60)
options(pa.cts = 'counts') # change cnt variable from "axis1" to "counts"
data1m = wearingMarking(dataset = mydata1m, frame = 90)

sumVct(data1m, id="sdata1m")

plotData(data=data1m)

summaryData(data=data1m, validCut=600, perMinuteCts=1, markingString = "w")

       id      startTimeStamp        endTimeStamp days   weekday start  end
1 sdata1m 2007-08-01 07:01:00 2007-08-01 23:59:00    1 Wednesday     1 1019
2 sdata1m 2007-08-02 00:00:00 2007-08-02 23:59:00    2  Thursday  1020 2459
3 sdata1m 2007-08-03 00:00:00 2007-08-03 01:04:00    3    Friday  2460 2524
4 sdata1m 2007-08-03 05:52:00 2007-08-03 23:59:00    3    Friday  2812 3899
5 sdata1m 2007-08-04 00:00:00 2007-08-04 01:09:00    4  Saturday  3900 3969
  duration
1     1019
2     1440
3       65
4     1088
5       70
$unit
[1] "1 min"

$totalNumDays
[1] 4

$totalNumWeekWeekend
weekday weekend 
      3       1 

$validCut
[1] 600

$totalValidNumDays
[1] 3

$totalValidNumWeekWeekend
weekday weekend 
      3       0 

$wearTimeByDay
   1    2    3    4 
1019 1440 1153   70 

$deliveryDays
NULL

$validWearTimeByDay
   1    2    3 
1019 1440 1153 

$meanWeartimeValidDays
weekday weekend 
   1204      NA 

$meanWeartimeOverallValidDays
[1] 1204

$dayInfo
     days   weekday weekend minutes  obs wearTime cnt.mean
1       1 Wednesday weekday    1019 1019     1019   1044.8
1020    2  Thursday weekday    1440 1440     1440   1169.1
2460    3    Friday weekday    1440 1440     1153   1834.3
3900    4  Saturday weekend      70   70       70   1456.5