In dipetkov/actigraph.sleepr: Detect Periods of Sleep and Non-Wear from 'ActiGraph' Data

knitr::opts_chunk$set(
  echo = TRUE, warning = FALSE, message = FALSE, collapse = TRUE,
  comment = "#>", out.width = "75%", fig.asp = 1 / 1.6, fig.width = 5,
  dpi = 600, fig.retina = NULL
)
pckg <- c("actigraph.sleepr", "dplyr", "lubridate", "ggplot2")
inst <- suppressMessages(lapply(pckg, library, character.only = TRUE))
theme_set(theme_light())

The actigraph.sleepr package implements two non-wear detection algorithms: Troiano (Troiano et al. 2008) and Choi (Choi et al. 2011). For illustration let's use one day of sample data recorded by a GT3X+ monitor. See the sleep vignette for details on reading AGD files and collapsing epochs.

file_10s <- system.file("extdata", "ActiSleepPlus-RawData-Day01.agd",
  package = "actigraph.sleepr"
)
agdb_60s <- read_agd(file_10s) %>%
  collapse_epochs(60) %>%
  mutate(date = lubridate::as_date(timestamp)) %>%
  select(date, timestamp, starts_with("axis")) %>%
  group_by(date)
agdb_60s

Long stretches that consist almost entirely of zero counts (zero epochs) suggest that the device wasn't worn at all and therefore should be excluded from downstream analysis.

Non-wear detection with the Troiano algorithm

The Troiano algorithm for detecting periods of non-wear formalizes a technique used to analyze the 2003-2004 NHANES data; the original SAS source code can be found at https://riskfactor.cancer.gov/tools/nhanes_pam/. The method has some flexibility as a non-wear period can contain a few nonzero epochs of artifactual movement (spikes).

activity_threshold : Highest activity level to be considered "zero"; an epoch with activity exceeding the threshold is considered a "spike". The default threshold is 0.

min_period_len : Minimum number of consecutive zero epoch to start a non-wear period. The default is 60.

max_nonzero_count : Epochs with activity greater than max_nonzero_count are labeled "zero". The default is Inf.

spike_tolerance : Also known as artifactual movement interval. At most spike_tolerance "nonzero" epochs can occur in sequence during a non-wear period without interrupting it. The default is 2.

spike_stoplevel : An activity spike that exceeds spike_stoplevel counts ends a non-wear period, even if the spike tolerance has not been reached. The default is 100.

use_magnitude : If true, the magnitude of the vector (axis1, axis2, axis3) is used to measure activity; otherwise the axis1 value is used. The default is FALSE.

endat_nnz_seq : If true, a non-wear period ends with a run of nonzero epochs that is longer than spike_tolerance. This corresponds to the option "Require consecutive epochs outside of the activity threshold" in ActiLife's Wear Time Validation menu. The default is TRUE.

The Troiano algorithm specifies that a non-wear period starts with min_length consecutive epochs/minutes of zero activity and ends with more than spike_tolerance epochs/minutes of nonzero activity.

periods_nonwear <- agdb_60s %>% apply_troiano(min_period_len = 45)
periods_nonwear

The parameter settings of the Troiano algorithm are stored as attributes.

tail(attributes(periods_nonwear), 8)

Once non-wear periods are detected, we can further screen those intervals. For example, we can ignore non-wear periods that are too short.

periods_nonwear %>% filter(length >= 60)

Or we can flag 24-hour day as invalid if the device was worn only for a short period of time.

# Find the periods during which the device was worn
periods_wear <- complement_periods(
  periods_nonwear, agdb_60s,
  period_start, period_end
)
periods_wear

# Label each epoch with the period_id of the wear period it falls in
# or with NA if the epoch falls outside a wear period
agdb_wear <- combine_epochs_periods(
  agdb_60s, periods_wear,
  period_start, period_end
)
agdb_wear

# Once we add the wear/nonwear information, it is straightforward to
# compute summary statistics or join with external minute-by-minute data
agdb_wear %>%
  group_by(period_id, .add = TRUE) %>%
  summarise(
    time_worn = n(),
    ave_counts = mean(axis1),
    .groups = "drop_last"
  )

Non-wear detection with the Choi algorithm

The Choi algorithm extends the Troiano algorithm by requiring that short spikes of artifactual movement during a non-wear period are preceded and followed by \code{min_window_len} consecutive zero epochs.

min_period_len : Minimum number of consecutive zero epoch to start a non-wear period. The default is 90.

min_window_len : The minimum number of consecutive "zero" epochs immediately preceding and following a spike of artifactual movement. The default is 30.

spike_tolerance : Also known as artifactual movement interval. At most spike_tolerance "nonzero" epochs can occur in sequence during a non-wear period without interrupting it. The default is 2.

use_magnitude : If true, the magnitude of the vector (axis1, axis2, axis3) is used to measure activity; otherwise the axis1 value is used. The default is FALSE.

periods_nonwear <- agdb_60s %>% apply_choi(
  min_period_len = 45,
  min_window_len = 10
)
periods_nonwear

Again, the parameter settings are saved as attributes.

tail(attributes(periods_nonwear), 5)

References

Troiano, Richard P, David Berrigan, Kevin W Dodd, Louise C Mâsse, Timothy Tilert, and Margaret McDowell. 2008. “Physical Activity in the United States Measured by Accelerometer.” Medicine & Science in Sports & Exercise 40 (1): 181–88.

Choi, Leena, Zhouwen Liu, Charles E. Matthews, and Maciej S. Buchowski. 2011. “Validation of Accelerometer Wear and Nonwear Time Classification Algorithm.” Medicine & Science in Sports & Exercise 43 (2): 357–64.

dipetkov/actigraph.sleepr documentation built on March 25, 2022, 2:33 a.m.