- Installation
- Documentation
- Using
arcstatspackage to compute physical activity summaries - Additional
activity_statsmethod options - Components of
activity_statsmethod- 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
- Expand the length of minute-level AC vector to integer number
of full 24-hour periods by NA-padding with
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.
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")A PDF with detailed documentation of all methods can be accessed here.
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 09:59:00The 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 assqrt(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")
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 |
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_flagfor 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.
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 |
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 |
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.
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 inextdata_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 06:40:00Finally, 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 |
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 ofaccdata 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 09:59:00Expand 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
00:00-00:01on the first day of data collection, and last observation corresponds to minute of23:50-00:00on 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 11520Function 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
1for wear and0for non-wear flagged minute. - If there is an
NAentry in a data input vector, then the returned vector will have a corresponding entry set toNAtoo.
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 0.338Function 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 0Here, 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 1957.186Finally, 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_PAfor more details.
- within a fixed subset of minutes (arg.
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 |