Functions for Processing Accelerometer Data

Dane Van Domelen <br> vandomed@gmail.com 2018-09-21

<!-- README.md is generated from README.Rmd. Please edit that file -->

Introduction

The accelerometry package has functions for processing time series accelerometer data from research studies. It was primarily developed for uniaxial minute-to-minute count data. It is also compatible with triaxial data, and could potentially also be used with epochs other than 1 minute.

If you have "raw data," meaning your device recorded actual acceleration data at 30 Hz or some other sampling frequency, this package probably won't be very helpful. Then again, you can usually convert raw data to counts after data collection, e.g. using ActiLife software for ActiGraph devices.

C++ is used for most of the functions, through the Rcpp package (Eddelbuettel and François 2011), so they should be pretty fast. For example, the nhanesaccel package, which relies on accelerometry's functions to process data from the 2003-2006 National Health and Nutrition Examination Survey (NHANES), can process that dataset about 90 times faster than a SAS script provided by the National Cancer Institute (Van Domelen and Pittard 2014).

Installation

The package is available to download from either CRAN or GitHub. To install from CRAN:

install.packages("accelerometry")

And to install from GitHub (devtools package must be installed):

library("devtools")
install_github("vandomed/accelerometry")

Typical usage

Converting time series accelerometer data to meaningful physical activity variables typically requires 3 steps:

1. Identify periods in which the accelerometer was removed (assuming it could be removed), and determine which days have sufficient wear time.
2. Calculate physical activity variables of interest for each day.
3. Calculate averages across valid days, and perhaps for weekdays/weekends separately.

In using the accelerometry package, you can either call separate functions to implement these steps (e.g. nonwear for 1, bouts and intensities for 2, and personvars for 3) or call an umbrella function that does it all internally (e.g. process_uni or process_tri). The composite functions should be sufficient for most users.

I'll use data from the first 5 subjects in NHANES 2003-2004 (included with the package) to illustrate the various functions. Here's what the dataset looks like:

head(unidata)
#>       seqn paxday paxinten
#> [1,] 21005      1        0
#> [2,] 21005      1        0
#> [3,] 21005      1        0
#> [4,] 21005      1        0
#> [5,] 21005      1        0
#> [6,] 21005      1        0
dim(unidata)
#> [1] 50400     3

There are 3 variables -- seqn is the subject ID, paxday is the day of the week (1 = Sunday, ..., 7 = Saturday), and paxinten is the count value recorded for each minute -- and 50,400 observations (1 obs/min x 1,440 min/day x 7 days/subject x 5 subjects).

Here's what 7 days of data looks like, for the 3rd subject in the dataset, SEQN = 21007. You can see the 7 separate clouds of data corresponding to each day of the week.

seqn <- unidata[, "seqn"]
counts <- unidata[, "paxinten"]
counts.21007 <- counts[seqn == 21007]
plot(counts.21007, main = "Counts for Subject 21007")

Individual functions

weartime

There are two distinct approaches for identifying non-wear time:

1. Have subjects keep a journal, writing down the precise times that they take the device off and put it back on.
2. Identify non-wear periods based on the accelerometer signal itself, under the assumption that long periods of 0 counts are probably non-wear time.

Approach 2 is seriously flawed, in my opinion, because it is very possible for somebody wearing the device to record 0 counts for several hours consecutively. But this is the only option for studies like NHANES, where journals were not part of the protocol.

The weartime function is for implementing the signal-based approach of removing non-wear time. The default behavior is to classify as non-wear any interval 60 minutes or longer with 0 counts. To do that for our subject of interest:

wear1 <- weartime(counts.21007)

If you have reason to prefer a 90- rather than 60-minute window size, and perhaps want to allow for up 2 nonzero count values as long as they are below 100, you could use:

wear2 <- weartime(counts.21007, 
                  window = 90, 
                  tol = 2, 
                  tol_upper = 99)

Let's see how these two compare (multiplying 0/1 vectors by 5000 and 5100 just to make it easier to visualize):

par(mfrow = c(1, 2))

plot(counts.21007, main = "Subject 21007 (whole week)")
points(wear1 * 5000, type = "l", col = "blue")
points(wear2 * 5100, type = "l", col = "red")

plot(counts.21007, xlim = c(4321, 5760), main = "Subject 21007 (day 4)")
points(wear1 * 5000, type = "l", col = "blue")
points(wear2 * 5100, type = "l", col = "red")

It looks like the main difference here is an extra non-wear period for the 60-minute method on the 4th day of monitoring. Personally, I wouldn't go higher than 60 minutes. Even at 60, you'll have no chance of removing short non-wear periods, e.g. when subjects are showering or swimming for exercise. That's all going to be classified as sedentary time, unfortunately.

bouts

A "bout" is a sustained period of activity of some intensity for some time. Bouted MVPA (moderate-to-vigorous physical activity) is a popular bout variable because physical activity guidelines are often based on MVPA accumulated in bouts of g**e 10 minutes (Troiano et al. 2008). Bouted sedentary time can also be interesting, given recent enthusiasm for studying sedentary behaviors.

Here is how you would use bouts to identify bouted MVPA for our subject of interest. Note that 2020 is a common cutpoint used to classify MVPA (Troiano et al. 2008).

mvpa <- bouts(counts = counts.21007, 
              bout_length = 10, 
              thresh_lower = 2020)
sum(mvpa)
#> [1] 0

Zero bouted MVPA for the entire week - not good! That's actually not uncommon in NHANES. And we're using a strict definition of a bout, not allowing for any minutes to dip below 2020 counts during a 10-minute window. We could be more liberal and define bouted MVPA as any 10-minute interval with counts ≥ 2020, allowing for 1-2 minutes with counts < 2020, as long as they are still ≥ 100. To implement that algorithm:

mvpa <- bouts(counts = counts.21007, 
              bout_length = 10, 
              thresh_lower = 2020, 
              tol = 2, 
              tol_lower = 100)
sum(mvpa)
#> [1] 10075

With this more forgiving algorithm, the subject is considered to have 21 rather than 0 minutes of bouted MVPA over the course of the week.

intensities

This is a simple function that calculates the number of minutes spent in 5 different intensity levels, and the number of counts accumulated from each intensity level. Default behavior is to categorize counts as follows:

• 0-99 = intensity 1 (sedentary)
• 100-759 = intensity 2 (light)
• 760-2019 = intensity 3 (lifestyle)
• 2020-5998 = intensity 4 (moderate)
• ≥ 5999 = intensity 5 (vigorous)

It also lumps intensities 2-3, 4-5, and 2-5 together, for a total of 8 categories. Applied to our subject:

(intensity.data <- intensities(counts = counts.21007[wear1 == 1]))
#>  [1]    3379    2069     790     247      12    2859     259    3118
#>  [9]   62933  702722  953350  717948   91473 1656072  809421 2465493

I subsetted just the valid wear time for this subject, to avoid classifying non-wear time as sedentary time. The first 8 numbers are time spent in the various intensities (e.g. 3379 minutes of sedentary time), and the next 8 are counts accumulated from those intensities (e.g. 62933 counts accumulated during sedentary time).

sedbreaks

There is some evidence in the epidemiological literature that "sedentary breaks" are beneficial (Stamatakis et al. 2018), i.e. that breaking up prolonged periods of sedentary time can lead to improved health. To count the number of sedentary breaks over the 7-day period for our subject of interest:

breakcount <- sedbreaks(counts = counts.21007, 
                        weartime = wear1)
sum(breakcount)
#> [1] 729

This subject had 729 sedentary breaks over the 7-day period, or a little more than 100 a day. The definition of a sedentary break here is having a minute with counts ≥ 100 after a minute of counts < 100. Note that we had to give sedbreaks the wear time vector; this prevents the first minute after each non-wear periods being classified as a sedentary break.

Umbrella functions

process_uni

With uniaxial accelerometer data, you can use process_uni to calculate physical activity variables for each subject. To calculate a few basic activity variables for our subject of interest, you can run:

(averages.21007 <- process_uni(counts.21007))
#>      id valid_days valid_wk_days valid_we_days include valid_min   counts
#> [1,]  1          7             5             2       1  928.1429 361203.7
#>          cpm steps
#> [1,] 402.281   NaN

It looks like all 7 days were valid for analysis, meaning they had at least 10 hours of wear time. The subject averaged about 928 minutes (15.5 hours) of wear time per day. The variables counts and cpm (counts per minute of wear time) are probably the two most popular measures of total physical activity volume; this subject averaged about 361,000 counts per day, and 402.3 counts per minute of wear time.

All we spe