knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = "100%", echo = TRUE, cache = FALSE, message = FALSE )

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.

`arctools`

package to compute physical activity summariesThe `arctools`

functions process one file with accelerometry data at a time.

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:

`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 ## 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")

`activity_stats`

methodacc <- dat$vectormagnitude acc_ts <- ymd_hms(dat$timestamp) activity_stats(acc, acc_ts)

To explain `activity_stats`

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

- Activity count (AC) - a minute-level metric of PA volume.
- Active minute - a minute with AC equal or above a fixed threshold; for wrist-worn Actigraph

we use AC>=1853 (method's default). - Non-active (sedentary) minute - a minute with AC below a fixed threshold; for wrist-worn Actigraph

we use AC<1853 (method's default). - Active bout - a sequence of 1 or more consecutive active minute(s).
- Non-active bout - a sequence of 1 or more consecutive non-active minute(s).
- Valid day - a day with no more than 10% of the non-wear time (see
*Details*in`?activity_stats`

).

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.

`activity_stats`

method optionsThe `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

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)

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)

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 `arctools`

package. The sleep data columns include:

`Subject Name`

- subject IDs corresponding 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 = "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)

`activity_stats`

methodThe 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`

- a numeric vector; minute-level activity counts data,`acc_ts`

- a`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))

`midnight_to_midnight`

- In the returned vector, the first observation corresponds to the minute of
`00:00-00:01`

on the first day of data collection, and the last observation corresponds to the 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))

`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.

- 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)

`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`

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)]))

`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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