README.md

In martakarass/arcstats: Processing and Physical Activity Summaries of Minute Level Activity Data

arcstats

• Installation
• Documentation
• Using arcstats package to compute physical activity summaries
  • Reading PA data
  • Computing summaries with activity_stats method
  • Output explained
• Additionalactivity_stats method options
  • Summarizing PA only in a chosen time-of-day subset
  • Summarizing PA with a chosen time-of-day excluded
  • Summarizing PA excluding in-bed time
• Components of activity_stats method
  • Expand the length of minute-level AC vector to integer number of full 24-hour periods by NA-padding with midnight_to_midnight
  • Get wear/non-wear flag with get_wear_flag
  • Get valid/non-valid day flag with get_valid_day_flag
  • Impute missing data with impute_missing_data
  • Create PA characteristics with summarize_PA

The arcstats 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 all minute-level PA data with the corresponding timestamps vector.

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

Installation

You can install the released version of arcstats from GitHub. Note you may need to install devtools package if not yet installed (the line commented below).

# install.packages("devtools")
devtools::install_github("martakarass/arcstats")

Documentation

A PDF with detailed documentation of all methods can be accessed here.

Using arcstats package to compute physical activity summaries

Reading PA data

Four CSV data sets with minute-level activity counts data are attached to the arcstats package. The data file names are stored in extdata_fnames.

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

## Read one of the data sets
fpath <- system.file("extdata", extdata_fnames[1], package = "arcstats")
dat   <- as.data.frame(fread(fpath))
rbind(head(dat, 3), tail(dat, 3))
#>       Axis1 Axis2 Axis3 vectormagnitude           timestamp
#> 1      1021  1353  2170            2754 2018-07-13 10:00:00
#> 2      1656  1190  2212            3009 2018-07-13 10:01:00
#> 3      2540  1461  1957            3524 2018-07-13 10:02:00
#> 10078     0     0     0               0 2018-07-20 09:57:00
#> 10079     0     0     0               0 2018-07-20 09:58:00
#> 10080     0     0     0               0 2018-07-20 09:59:00

The data columns are:

• Axis1 - sensor’s X axis minute-level counts data,
• Axis2 - sensor’s Y axis minute-level counts data,
• Axis3 - sensor’s Z axis minute-level counts data,
• vectormagnitude - minute-level counts data defined as sqrt(Axis1^2 + Axis2^2 + Axis3^2),
• timestamp - time-stamps corresponding to minute-level measures.

## Plot activity counts
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)

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac tlac ltac 8 4 1440 2826648 6429.838 14.8546 astp satp time\_spent\_active time\_spent\_nonactive 0.1781782 0.0951621 499.5 940.5 no\_of\_active\_bouts no\_of\_nonactive\_bouts mean\_active\_bout mean\_nonactive\_bout 89 89.5 5.61236 10.50838

Output explained

To explain activity_stats method output, we define activity count, active/non-active minute, and active/non-active bout.

• Activity count (AC) - a minute-level metric of PA volume
• Active minute - AC labeled as active (non-sedentary). For Wrist-worn Actigraph we use AC>=1853 (defaut threshold).
• Active bout - sequence of >=1 consecutive active minute(s).
• Valid day - a day with less than 10% of the non-wear time (see ?get_wear_flag for wear/non-wear time detection details).

Meta information:

• n_days - number of days (unique day dates) of data collection.
• n_valid_days - number of days (unique day dates) of data collection determined as valid days.
• wear_time_on_valid_days - average number of wear-time minutes across valid days.

Summaries of PA volumes metrics:

• tac - TAC, Total activity counts per day - sum of AC measured on valid days divided by the number of valid days.
• tlac - TLAC, Total-log activity counts per day - sum of log(1+AC) measured on valid days divided by the number of valid days. Here ‘log’ denotes the natural logarithm.
• ltac - LTAC, Log-total activity counts - natural logarithm of TAC.
• time_spent_active - Average number of active minutes per valid day.
• time_spent_nonactive - Average number of sedentary minutes per valid day.

Summaries of PA fragmentation metrics:

• astp - ASTP, active to sedentary transition probability on valid days.
• satp - SATP, sedentary to active transition probability on valid days.
• no_of_active_bouts - Average number of active minutes per valid day.
• no_of_nonactive_bouts - Average number of sedentary minutes per valid day.
• mean_active_bout - Average duration (in minutes) of an active bout on valid days.
• mean_nonactive_bout - Average duration (in minutes) of a sedentary bout on valid days.

Additionalactivity_stats method options

Summarizing PA only in a chosen time-of-day subset

The subset_minutes argument allows to specify 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) 

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac\_0to6only tlac\_0to6only ltac\_0to6only 8 4 1440 65477.5 322.1523 11.08946 astp\_0to6only satp\_0to6only time\_spent\_active\_0to6only time\_spent\_nonactive\_0to6only 0.5581395 0.0200429 10.75 349.25 no\_of\_active\_bouts\_0to6only no\_of\_nonactive\_bouts\_0to6only mean\_active\_bout\_0to6only mean\_nonactive\_bout\_0to6only 6 7 1.791667 49.89286

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

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac tlac ltac 12am-6am 8 4 1440 65477.5 322.1523 11.08946 6am-12pm 8 4 1440 1089788.5 2139.4534 13.90149 12pm-6pm 8 4 1440 994104.8 2194.8539 13.80960 6pm-12am 8 4 1440 677277.5 1773.3781 13.42584 astp satp time\_spent\_active time\_spent\_nonactive 12am-6am 0.5581395 0.0200429 10.75 349.25 6am-12pm 0.1501377 0.1540616 181.50 178.50 12pm-6pm 0.1751337 0.1864162 187.00 173.00 6pm-12am 0.2037422 0.1032325 120.25 239.75 no\_of\_active\_bouts no\_of\_nonactive\_bouts mean\_active\_bout mean\_nonactive\_bout 12am-6am 6.00 7.00 1.791667 49.892857 6am-12pm 27.25 27.50 6.660551 6.490909 12pm-6pm 32.75 32.25 5.709924 5.364341 6pm-12am 24.50 24.75 4.908163 9.686869

Summarizing PA with a chosen time-of-day excluded

The exclude_minutes argument allows to specify 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) 

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac\_23to5removed tlac\_23to5removed ltac\_23to5removed 8 4 1440 2735749 6052.84 14.82192 astp\_23to5removed satp\_23to5removed time\_spent\_active\_23to5removed time\_spent\_nonactive\_23to5removed 0.1702018 0.1395057 483.25 596.75 no\_of\_active\_bouts\_23to5removed no\_of\_nonactive\_bouts\_23to5removed mean\_active\_bout\_23to5removed mean\_nonactive\_bout\_23to5removed 82.25 83.25 5.87538 7.168168

Summarizing PA excluding in-bed time

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 arcstats package. The sleep data columns relevant are,

• Subject Name - subject IDs coressponding to AC data, stored in extdata_fnames,
• In Bed Time - ActiLife-estimated start of in-bed interval for each day of the measurement,
• Out Bed Time - ActiLife-estimated end of in-bed interval.

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

## Filter sleep details data to keep data correcponding to current counts data file
SleepDetails_sub <-
    SleepDetails %>%
    filter(`Subject Name` == "ID_1") %>%
    mutate(`In Bed Time`  = mdy_hms(`In Bed Time`),
           `Out Bed Time` = mdy_hms(`Out Bed Time`)) %>%
    select(`Subject Name`, `In Bed Time`, `Out Bed Time`) 
SleepDetails_sub
#>   Subject Name         In Bed Time        Out Bed Time
#> 1         ID_1 2018-07-13 21:18:00 2018-07-14 04:50:00
#> 2         ID_1 2018-07-14 22:41:00 2018-07-15 05:40:00
#> 3         ID_1 2018-07-16 19:46:00 2018-07-17 04:32:00
#> 4         ID_1 2018-07-17 23:30:00 2018-07-18 06:32:00
#> 5         ID_1 2018-07-18 22:16:00 2018-07-19 07:17:00
#> 6         ID_1 2018-07-19 22:30:00 2018-07-20 06:40:00

Finally, we use in/out-bed time POSIXct vectors in activity_stats method.

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

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

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac\_inbedremoved tlac\_inbedremoved ltac\_inbedremoved 8 4 1440 2746582 6062.753 14.82587 astp\_inbedremoved satp\_inbedremoved time\_spent\_active\_inbedremoved time\_spent\_nonactive\_inbedremoved 0.1703551 0.1580934 485.75 529.75 no\_of\_active\_bouts\_inbedremoved no\_of\_nonactive\_bouts\_inbedremoved mean\_active\_bout\_inbedremoved mean\_nonactive\_bout\_inbedremoved 82.75 83.75 5.870091 6.325373

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:

• acc - numeric vector; minute-level activity counts data,
• acc_ts - POSIXct vector; minute-level time of acc data collection.

df <- data.frame(acc = acc, acc_ts = acc_ts)
rbind(head(df, 3), tail(df, 3))
#>        acc              acc_ts
#> 1     2754 2018-07-13 10:00:00
#> 2     3009 2018-07-13 10:01:00
#> 3     3524 2018-07-13 10:02:00
#> 10078    0 2018-07-20 09:57:00
#> 10079    0 2018-07-20 09:58:00
#> 10080    0 2018-07-20 09:59:00

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

• In the returned vector, the first observation corresponds to minute of 00:00-00:01 on the first day of data collection, and last observation corresponds to minute of 23:50-00:00 on the last day of data collection.
• Entries corresponding to non-measured minutes are filled with NA.

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))
#> [1] 10080 11520

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 proposed by Choi et al. (2011).

• The returned vector has value 1 for wear and 0 for non-wear flagged minute.
• If there is an NA entry in a data input vector, then the returned vector will have a corresponding entry set to NA too.

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)
#> [1] 0.583 1.000 0.874 0.679 1.000 1.000 1.000 0.338

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.

Here, 4 out of 8 days have more that 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)
#> [1] 0 1 0 0 1 1 1 0

Impute missing data with impute_missing_data

Here, all four valid days have 100% of wear time. We hence demonstrate the impute_missing_data method by artificially replacing 1h (4%) of a valid day with non-wear and running impute_missing_data function then.

Function impute_missing_data imputes missing data from the “average day profile”. An “average day profile” is computed as a minute-wise average of AC across valid days.

## 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)]))
#> [1] 1955.521 1957.186

Create PA characteristics with summarize_PA

Finally, method summarize_PA computes physical all PA characteristics.

• Same as activity_stats, it returns a data frame with physical activity summaries.
• Same as activity_stats, it accepts arguments to compute physical activity summaries
  • within a fixed subset of minutes (arg. subset_minutes),
  • excluding a fixed subset of minutes (arg. exclude_minutes),
  • excluding day-specific in-bed intervals (args in_bed_time, out_bed_time). See ?summarize_PA for more details.

Here, we summarize PA with the default parameters, across all valid days.

summarize_PA(acc, acc_ts, wear_flag, valid_day_flag) 

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac tlac ltac 8 4 1440 2826648 6429.838 14.8546 astp satp time\_spent\_active time\_spent\_nonactive 0.1781782 0.0951621 499.5 940.5 no\_of\_active\_bouts no\_of\_nonactive\_bouts mean\_active\_bout mean\_nonactive\_bout 89 89.5 5.61236 10.50838

It returns the same results as the activity_stats function:

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

n\_days n\_valid\_days wear\_time\_on\_valid\_days tac tlac ltac 8 4 1440 2826648 6429.838 14.8546 astp satp time\_spent\_active time\_spent\_nonactive 0.1781782 0.0951621 499.5 940.5 no\_of\_active\_bouts no\_of\_nonactive\_bouts mean\_active\_bout mean\_nonactive\_bout 89 89.5 5.61236 10.50838

martakarass/arcstats documentation built on Sept. 10, 2020, 5:29 a.m.