arctools_intro

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The arctools package allows to generate summaries of the minute-level physical activity (PA) data. The default parameters are chosen for the Actigraph activity counts collected with a wrist-worn device; however, the package can be used for other minute-level PA data with the corresponding timepstamps vector.

Below, we demonstrate the use of arctools with the attached, exemplary minute-level Actigraph PA counts data.

Using arctools package to compute physical activity summaries

The arctools functions process one file with accelerometry data at a time.

Reading PA data

Four CSV data sets with minute-level activity counts data are attached to the arctools package. The data file names are stored in extdata_fnames object that becomes available once the arctools package is loaded.

Below, we defined fpath to be a path to one of the minute-level activity counts data files. fread() reads minute-level activity counts data file while conveniently skipping first few rows with meta data, and then as.data.frame() converts the read data into a data frame object. The read-in data is assigned to dat variable. head() and tail() get first few and last few rows of dat, respectively.

library(arctools)
library(data.table)
library(dplyr)
library(ggplot2)
library(lubridate)

## Read one of the data sets
fpath <- system.file("extdata", extdata_fnames[1], package = "arctools")
dat   <- as.data.frame(fread(fpath))
rbind(head(dat, 3), tail(dat, 3))

The data columns are:

## Plot activity counts
## Format timestamp data column from character to POSIXct object
ggplot(dat, aes(x = ymd_hms(timestamp), y = vectormagnitude)) + 
  geom_line(size = 0.3, alpha = 0.8) + 
  labs(x = "Time", y = "Activity counts") + 
  theme_gray(base_size = 10) + 
  scale_x_datetime(date_breaks = "1 day", date_labels = "%b %d")

Computing summaries with activity_stats method

acc    <- dat$vectormagnitude
acc_ts <- ymd_hms(dat$timestamp)

activity_stats(acc, acc_ts)

Output explained

To explain activity_stats method output, we first define the terms activity count, active/non-active minute, active/non-active bout, and valid day.

Meta information:

Summaries of PA volumes metrics:

Summaries of PA fragmentation metrics:

Additionalactivity_stats method options

Summarizing PA within a fixed set of minutes only

The subset_minutes argument allows to specify a subset of a day's minutes where activity summaries should be computed. There are 1440 minutes in a 24-hour day where 1 denotes 1st minute of the day (from 00:00 to 00:01), and 1440 denotes the last minute (from 23:59 to 00:00).

Here, we summarize PA observed between 12:00 AM and 6:00 AM.

subset_12am_6am <- 1 : (6 * 1440/24)
activity_stats(acc, acc_ts, subset_minutes = subset_12am_6am) 

By default, column names have a suffix added to denote that a subset of minutes was used (here, _0to6only). This can be disabled by setting adjust_out_colnames to FALSE.

subset_12am_6am = 1 : (6/24 * 1440)
subset_6am_12pm = (6/24 * 1440 + 1) : (12/24 * 1440) 
subset_12pm_6pm = (12/24 * 1440 + 1) : (18/24 * 1440) 
subset_6pm_12am = (18/24 * 1440 + 1) : (24/24 * 1440) 
out <- rbind(
  activity_stats(acc, acc_ts, subset_minutes = subset_12am_6am, adjust_out_colnames = FALSE),
  activity_stats(acc, acc_ts, subset_minutes = subset_6am_12pm, adjust_out_colnames = FALSE),
  activity_stats(acc, acc_ts, subset_minutes = subset_12pm_6pm, adjust_out_colnames = FALSE),
  activity_stats(acc, acc_ts, subset_minutes = subset_6pm_12am, adjust_out_colnames = FALSE))
rownames(out) <- c("12am-6am", "6am-12pm", "12pm-6pm", "6pm-12am")
out

Summarizing PA within a subset of weekdays only

The subset_weekdays argument allows to specify days of a week within which activity summaries are to be computed; it takes values between 1 (Sunday) to 7 (Saturday). Default is NULL (all days of a week are used).

Here, we summarize PA within weekday days only. Note that in the method output, the n_days and n_valid_days columns only count the days from the selected week days subset; for example, below, n_days number of unique day dates in data is 6 despite the range of data collection without subsetting ranges 8 days.

# day of a week indices 2,3,4,5,6 correspond to Mon,Tue,Wed,Thu,Fri 
subset_weekdays <- c(2:6)
activity_stats(acc, acc_ts, subset_weekdays = subset_weekdays) 

Note the subset_weekdays argument can be combined with other arguments, i.e. subset_minutes to subset of a day's minutes where activity summaries should be computed.

# day of a week indices 7,1 correspond to Sat,Sun
subset_weekdays <- c(7,1)
activity_stats(acc, acc_ts, subset_weekdays = subset_weekdays, subset_minutes = subset_6am_12pm) 

Summarizing PA with a fixed set of minutes excluded

The exclude_minutes argument allows specifying a subset of a day's minutes excluded for computing activity summaries.

Here, we summarize PA while excluding observations between 11:00 PM and 5:00 AM.

subset_11pm_5am <- c(
  (23 * 1440/24 + 1) : 1440,   ## 11:00 PM - midnight
  1 : (5 * 1440/24)            ## midnight - 5:00 AM
) 
activity_stats(acc, acc_ts, exclude_minutes = subset_11pm_5am) 

Summarizing PA with in-bed time excluded

The in_bed_time and out_bed_time arguments allow to provide day-specific in-bed periods to be excluded from analysis.

Here, we summarize PA excluding in-bed time estimated by ActiLife software.

ActiLife-estimated in-bed data

The ActiLife-estimated in-bed data file is attached to the arctools package. The sleep data columns include:

## Read sleep details data file
SleepDetails_fname <- "BatchSleepExportDetails_2020-05-01_14-00-46.csv"
SleepDetails_fpath <- system.file("extdata", SleepDetails_fname, package = "arctools")
SleepDetails       <- as.data.frame(fread(SleepDetails_fpath))

## Filter sleep details data to keep ID1 file 
SleepDetails_sub <-
    SleepDetails %>%
    filter(`Subject Name` == "ID_1") %>%
    select(`Subject Name`, `In Bed Time`, `Out Bed Time`) 
str(SleepDetails_sub)

We transform dates stored as character into POSIXct object, and then use in/out-bed dates vectors in activity_stats method.

in_bed_time  <- mdy_hms(SleepDetails_sub[, "In Bed Time"])
out_bed_time <- mdy_hms(SleepDetails_sub[, "Out Bed Time"])

activity_stats(acc, acc_ts, in_bed_time = in_bed_time, out_bed_time = out_bed_time) 

Components of activity_stats method

The primary method activity_stats is composed of several steps implemented in their respective functions. Below, we demonstrate how to produce activity_stats results step by step with these functions.

We reuse the objects:

df <- data.frame(acc = acc, acc_ts = acc_ts)
rbind(head(df, 3), tail(df, 3))

Expand the length of minute-level AC vector to integer number of full 24-hour periods by NA-padding with midnight_to_midnight

Here, collected data cover total of 7*24*1440 = 10080 minutes (from 2018-07-13 10:00:00 to 2018-07-20 09:59:00), but spans 8*24*1440 = 11520 minutes of full midnight-to-midnight days (from 2018-07-13 00:00:00 to 2018-07-20 23:59:00).

acc <- midnight_to_midnight(acc = acc, acc_ts = acc_ts)

## Vector length on non NA-obs, vector length after acc 
c(length(acc[!is.na(acc)]), length(acc))

Get wear/non-wear flag with get_wear_flag

Function get_wear_flag computes wear/non-wear flag (1/0) for each minute of activity counts data. Method implements wear/non-wear detection algorithm closely following that of Choi et al. (2011). See ?get_wear_flag for more details and function arguments.

wear_flag <- get_wear_flag(acc)

## Proportion of wear time across the days
wear_flag_mat <- matrix(wear_flag, ncol = 1440, byrow = TRUE)
round(apply(wear_flag_mat, 1, sum, na.rm = TRUE) / 1440, 3)

Get valid/non-valid day flag with get_valid_day_flag

Function get_valid_day_flag computes valid/non-valid day flag (1/0) for each minute of activity counts data. See ?get_valid_day_flag for more details and function arguments.

Here, 4 out of 8 days have more than 10% (144 minutes) of missing data.

valid_day_flag <- get_valid_day_flag(wear_flag)

## Compute number of valid days
valid_day_flag_mat <- matrix(valid_day_flag, ncol = 1440, byrow = TRUE)
apply(valid_day_flag_mat, 1, mean, na.rm = TRUE)

Impute missing data with impute_missing_data

Function impute_missing_data imputes missing data in valid days based on the "average day profile", a minute-wise average of wear-time AC across valid days. See ?get_valid_day_flag for more details and function arguments.

## Copies of original objects for the purpose of demonstration
acc_cpy  <- acc
wear_flag_cpy <- wear_flag

## Artificially replace 1h (4%) of a valid day with non-wear 
repl_idx <- seq(from = 1441, by = 1, length.out = 60)
acc_cpy[repl_idx] <- 0
wear_flag_cpy[repl_idx] <- 0

## Impute data for minutes identified as non-wear in days identified as valid
acc_cpy_imputed <- impute_missing_data(acc_cpy, wear_flag_cpy, valid_day_flag)

## Compare mean activity count on valid days before and after imputation
c(mean(acc_cpy[which(valid_day_flag == 1)]), 
  mean(acc_cpy_imputed[which(valid_day_flag == 1)]))

Create PA characteristics with summarize_PA

Finally, method summarize_PA computes PA summaries. Similarly as activity_stats, it accepts arguments to subset/exclude minutes. See ?activity_stats for more details and function arguments.

summarize_PA(acc, acc_ts, wear_flag, valid_day_flag) 

It returns the same results as the activity_stats function:

activity_stats(dat$vectormagnitude, ymd_hms(dat$timestamp))


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arctools documentation built on July 21, 2021, 1:06 a.m.