--- title: "arctools_intro" output: rmarkdown::html_vignette: toc: true toc_depth: 3 vignette: > %\VignetteIndexEntry{arctools_intro} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} 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. # 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. ```{r} 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. ```{r, fig.width=8, fig.height=3.5} ## 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 ```{r} 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*. - 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. # Additional`activity_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. ```{r} 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`. ```{r} 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. ```{r} # 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. ```{r} # 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. ```{r} 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: - `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. ```{r} ## 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. ```{r} 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: - `acc` - a numeric vector; minute-level activity counts data, - `acc_ts` - a `POSIXct` vector; minute-level time of `acc` data collection. ```{r} 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` - 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`). ```{r} 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. - 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. ```{r} 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. ```{r} 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. ```{r} ## 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. ```{r} summarize_PA(acc, acc_ts, wear_flag, valid_day_flag) ``` It returns the same results as the `activity_stats` function: ```{r} activity_stats(dat$vectormagnitude, ymd_hms(dat$timestamp)) ```