PhysicalActivity (version 0.2-4)

PhysicalActivity-package: Process Accelerometer Data for Physical Activity Measurement

Description

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.

Details

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.

References

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.

Examples

# NOT RUN {
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")
# }