Package 'actigraph.sleepr'

Description

This package implements three standard algorithms for sleep detection from ActiGraph data: Sadeh, Cole-Kripke and Tudor-Locke.

Details

In addition to the help pages, see the README for examples.

Author(s)

Maintainer: Desislava Petkova [email protected]

Authors:

• Desislava Petkova [email protected]

Other contributors:

• John Muschelli [contributor]

See Also

Useful links:

• https://github.com/dipetkov/actigraph.sleepr

• Report bugs at https://github.com/dipetkov/actigraph.sleepr/issues

Description

The Choi algorithm detects periods of non-wear in activity data from an ActiGraph device. Such intervals are likely to represent invalid data and therefore should be excluded from downstream analysis.

Usage

apply_choi(
  agdb,
  min_period_len = 90,
  min_window_len = 30,
  spike_tolerance = 2,
  use_magnitude = FALSE
)

Arguments

Details

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 min_window_len consecutive "zero" epochs.

This implementation of the algorithm expects that the epochs are 60 second long.

Value

A summary tibble of the detected non-wear periods. If the activity data is grouped, then non-wear periods are detected separately for each group.

References

L Choi, Z Liu, CE Matthews and MS Buchowski. Validation of accelerometer wear and nonwear time classification algorithm. Medicine & Science in Sports & Exercise, 43(2):357–364, 2011.

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

See Also

apply_troiano(), collapse_epochs()

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_choi()

Description

The Cole-Kripke sleep scoring algorithm is primarily used for adult populations as the supporting research was performed on subjects ranging from 35 to 65 years of age.

Usage

apply_cole_kripke(agdb)

Arguments

Details

The original paper proposes three versions of the Cole-Kripke algorithm, optimized for 1-minute, 30-second and 10-second epochs. Here only the 1-min version is implemented and therefore the apply_cole_kripke() function requires that the activity data is in 60s epochs. Use the collapse_epochs() function to modify higher-frequency data, if necessary.

The Cole-Kripke algorithm uses the y-axis (axis 1) counts. First epoch counts are divided by 100 and any scaled counts over 300 are clipped to 300. This transformation is specific to ActiGraph devices. The sleep index (SI) is defined as

.001 * (106 * epoch_prev(4) + 54 * epoch_prev(3) +
         58 * epoch_prev(2) + 76 * epoch_prev(1) +
        230 * epoch +
         74 * epoch_next(1) + 67 * epoch_next(2))

where at epoch t, epoch_prev(i) is the scaled activity count i epochs before t. Similarly, epoch_next(i) is the scaled activity count i epochs after t. That is, the algorithm uses a 7-epoch window which includes the four preceding and the two subsequent epochs. The time series of activity counts is padded with zeros as necessary, at the beginning and at the end.

Finally, the sleep state is awake (W) if the sleep index SI is less than 1; otherwise the sleep state is asleep (S).

Value

A tibble of activity data. A new column sleep indicates whether each 60s epoch is scored as asleep (S) or awake (W).

References

RJ Cole, DF Kripke, W Gruen, DJ Mullaney and JC Gillin. Automatic sleep/wake identification from wrist activity. Sleep, 15(5):461–469, 1992.

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

See Also

collapse_epochs(), apply_sadeh(), apply_tudor_locke()

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_cole_kripke()

Description

The Sadeh sleep scoring algorithm is primarily used for younger adolescents as the supporting research was performed on children and young adults.

Usage

apply_sadeh(agdb)

Arguments

Details

The Sadeh algorithm requires that the activity data is in 60s epochs and uses an 11-minute window that includes the five previous and five future epochs. This function implements the algorithm as described in the ActiGraph user manual.

The Sadeh algorithm uses the y-axis (axis 1) counts; epoch counts over 300 are set to 300. The sleep index (SI) is defined as

SI = 7.601 - (0.065 * AVG) - (1.08 * NATS) - (0.056 * SD) - (0.703 * LG)

where at epoch t

AVG

the arithmetic mean (average) of the activity counts in an 11-epoch window centered at t

NATS

the number of epochs in this 11-epoch window which have counts >= 50 and < 100

SD

the standard deviation of the counts in a 6-epoch window that includes t and the five preceding epochs

LG

the natural (base e) logarithm of the activity at epoch t. To avoid taking the log of 0, we add 1 to the count.

The time series of activity counts is padded with zeros as necessary, at the beginning and at the end, to compute the three functions AVG, SD, NATS within a rolling window.

Finally, the sleep state is asleep (S) if the sleep index SI is greater than -4; otherwise the sleep state is awake (W).

Value

A tibble of activity data. A new column sleep indicates whether each 60s epoch is scored as asleep (S) or awake (W).

References

A Sadeh, KM Sharkey and MA Carskadon. Activity based sleep-wake identification: An empirical test of methodological issues. Sleep, 17(3):201–207, 1994.

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

See Also

apply_cole_kripke(), apply_cole_kripke(), apply_tudor_locke()

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_sadeh()

Description

The Troiano algorithm detects periods of non-wear in activity data from an ActiGraph device. Such intervals are likely to represent invalid data and therefore should be excluded from downstream analysis. The algorithm 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/.

Usage

apply_troiano(
  agdb,
  activity_threshold = 0,
  min_period_len = 60,
  max_nonzero_count = Inf,
  spike_tolerance = 2,
  spike_stoplevel = 100,
  use_magnitude = FALSE,
  endat_nnz_seq = TRUE
)

Arguments

Details

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

This implementation of the algorithm expects 60s epochs.

Value

A summary tibble of the detected non-wear periods. If the activity data is grouped, then non-wear periods are detected separately for each group.

References

RP Troiano, D Berrigan, KW Dodd, LC Mâsse, T Tilert and M McDowell. Physical activity in the united states measured by accelerometer. Medicine & Science in Sports & Exercise, 40(1):181–188, 2008.

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

See Also

apply_choi(), collapse_epochs()

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_troiano()

Description

The Tudor-Locke algorithm detects periods of time in bed and, for each period, computes sleep quality metrics such as total minutes in bed, total sleep time, number and average length of awakenings, movement and fragmentation index.

Usage

apply_tudor_locke(
  agdb,
  n_bedtime_start = 5,
  n_wake_time_end = 10,
  min_sleep_period = 160,
  max_sleep_period = 1440,
  min_nonzero_epochs = 0
)

Arguments

Details

Once each one-minute epoch is labeled as asleep (S) or awake (W), we can use the Tudor-Locke algorithm to detect periods of bedtime and sleep time. By definition, sleep time < bedtime since one can be in bed and not sleeping.

Bedtime is (the first minute of) n_bedtime_start consecutive epochs/minutes labeled asleep (S). Similarly, wake time is (the first minute of) of n_wake_time_end consecutive epochs/minutes labeled awake (W), after a period of sleep. The block of time between bedtime and wake time is one sleep period, if the time elapsed is at least min_sleep_period minutes. There can be multiple sleep periods in 24 hours but a sleep period cannot be longer than max_sleep_period minutes.

For each sleep period, the algorithm calculates several measures of sleep quality such as time asleep and time awake, number and average length of awakenings, and movement and fragmentation indices.

This implementation of the Tudor-Locke algorithm detects all the sleep periods that ActiLife detects and, in some cases, it detects additional sleep periods. There are (at least) two such cases:

1. ActiLife filters out some sleep periods with exactly min_nonzero_epochs nonzero epochs. Alternatively, ActiLife computes the number of nonzero epochs in a sleep period differently and sometimes underestimates nonzero_epochs compared to apply_tudor_locke().

2. ActiLife filters out sleep periods that end when the activity data ends, i.e., when the out_bed_time is also the final timestamp in the agdb table.

Value

A summary tibble of the detected sleep periods. If the activity data is grouped, then sleep periods are detected separately for each group.

in_bed_time

The first minute of the bedtime.

out_bed_time

The first minute of wake time.

onset

The first minute that the algorithm scores "asleep".

latency

The time elapsed between bedtime and sleep onset. By the definition of Tudor-Locke, latency is 0.

efficiency

The number of sleep minutes divided by the bedtime minutes.

duration

The duration of the sleep period, in minutes.

activity_counts

The sum of activity counts for the entire sleep period.

total_sleep_time

The number of minutes scored as "asleep" during the sleep period.

wake_after_onset

The number of minutes scored as "awake", minus the sleep latency, during the sleep period.

nb_awakenings

The number of awakening episodes.

ave_awakening

The average length, in minutes, of all awakening episodes.

movement_index

Proportion of awake time out of the total time in bed, in percentages.

fragmentation_index

Proportion of one-minute sleep bouts out of the number of sleep bouts of any length, in percentages.

sleep_fragmentation_index

The sum of the movement and fragmentation indices.

nonzero_epochs

The number of epochs with activity > 0 (nonzero epochs).

References

C Tudor-Locke, TV Barreira, JM Schuna Jr, EF Mire and PT Katzmarzyk. Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors. Applied Physiology, Nutrition, and Metabolism, 39(1):53–57, 2014.

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

See Also

apply_sadeh(), apply_cole_kripke()

Examples

library("dplyr")
library("lubridate")
data("gtxplus1day")

# Detect sleep periods using Sadeh as the sleep/awake algorithm
# and Tudor-Locke as the sleep period algorithm
agdb <- gtxplus1day %>%
  collapse_epochs(60) %>%
  filter(day(timestamp) == 28)
periods_sleep <- agdb %>%
  apply_sadeh() %>%
  apply_tudor_locke(min_sleep_period = 60)
periods_sleep

# Group and summarize by an extra varible (hour < 6 or not), which
# splits one long sleep period in two
agdb <- agdb %>%
  mutate(hour_lt6 = hour(timestamp) < 6) %>%
  group_by(hour_lt6)
periods_sleep <- agdb %>%
  apply_sadeh() %>%
  apply_tudor_locke(min_sleep_period = 60)
periods_sleep

Description

Collapse post-filtered activity counts into larger epoch "buckets".

Usage

collapse_epochs(agdb, epoch_len_out, use_incomplete = TRUE)

Arguments

Details

Activity counts cannot be reintegrated into shorter epochs, e.g., 60s -> 10s. Currently, collapse_epochs() integrates into 60s epochs only. This is not general but is sufficient for sleep analysis because the standard Sadeh and Cole-Kripke sleep algorithms were developed for 60s epoch data.

Suppose we want to collapse from 15 to 60 seconds. A complete 60s epoch consists of four 15s epochs: 00, 15, 45 and 60. However, the first and last epochs would be incomplete if the device started/stopped collecting data mid-minute. ActiLife 6 uses these epochs anyway. For example, if only 45 and 60 are available for the first minute, then ActiLife will aggregate across these two epochs only. This is a reasonable approach to sleep analysis with the Sadeh and the Cole-Kripke algorithms which pad the beginning and the end of the time series with zeros anyway.

Value

A tibble of activity data collapsed into one-minute epochs.

References

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60)

Description

Suppose we have used apply_tudor_locke() to detect sleep periods or apply_troiano()/apply_choi() to detect non-wear periods. It might be useful to combine the epochs data with the periods data, so that each epoch is labeled according to which period it falls into, if any. Then we can easily slice the epochs data by sleep/non-sleep or wear/non-wear.

Usage

combine_epochs_periods(epochs, periods, start_var, end_var)

Arguments

Value

A tibble of activity data with one additional column, period_id, which indicates the period each epoch falls into.

Examples

library("dplyr")
data("gtxplus1day")

agdb <- gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_sadeh()
periods <- agdb %>%
  apply_tudor_locke(min_sleep_period = 60)

agdb_with_periods <- combine_epochs_periods(
  agdb, periods,
  in_bed_time, out_bed_time
)

# How many sleep periods were detected and what is their duration,
# in minutes?
periods %>% select(in_bed_time, out_bed_time, duration)
# What is the assignment of epochs to periods?
agdb_with_periods %>% count(period_id)

Description

Find the complement of a set of time periods in a set of epochs. For illustration, let's use integers instead of time periods and epochs. Suppose we have two intervals/periods, ⁠{[1,3], [8,10]}⁠; their complement in the set ⁠{[0, ..., 12]}⁠ is ⁠{[0,0], [4,7], [11,12]}⁠.

Usage

complement_periods(periods, epochs, start_var, end_var)

Arguments

Value

A tibble of time periods with three columns: period_id (a sequential identifier), start_var (first epoch in period) and end_var (last epoch in period).

Examples

library("lubridate")
library("dplyr")
periods <- tibble(
  start = ymd_hm("2017-01-01 00:01"),
  end = ymd_hm("2017-01-01 00:05")
)
epochs <- tibble(timestamp = ymd_hm("2017-01-01 00:00") +
  minutes(0:12))
complement_periods(periods, epochs, start, end)

Description

Expand time periods into a tibble of equally spaced time points

Usage

expand_periods(periods, start_var, end_var, units = "1 min")

Arguments

Value

A tibble with two columns: period_id identifies the input period and timestamp is a POSIXct vector of time points within that period, equally spaced by units. Any grouping of periods is preserved.

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day %>%
  collapse_epochs(60) %>%
  apply_choi(min_period_len = 45) %>%
  expand_periods(period_start, period_end, units = "30 mins")

Description

Given the start time and the end time of a period, expand it into a vector of equally spaced time points.

Usage

expand_timestamp(start, end, units = "1 min")

Arguments

Value

A POSIXct vector of equally spaced time points starting at start, ending no later than end, and separated by units.

Examples

start <- as.POSIXct("2017-01-01")
end <- as.POSIXct("2017-01-01 01:00:00")
expand_timestamp(start, end, "15 mins")

Description

Guess the epoch length (in seconds) from the timestamp column

Usage

get_epoch_length(epochs)

Arguments

Value

The epoch length in seconds, inferred from the spacing between consecutive timestamps. Throws an error if the time points are not equally spaced.

Examples

data("gtxplus1day")

gtxplus1day %>%
  get_epoch_length()

gtxplus1day %>%
  collapse_epochs(60) %>%
  get_epoch_length()

Description

24 hours of actigraphy data collected with a GT3X+ device, which uses a tri-axial accelerometer to record activity in vertical (axis 1), horizontal (axis 2) and lateral (axis 3) directions.

Usage

gtxplus1day

Format

An object of class tbl_agd (inherits from tbl_df, tbl, data.frame) with 8999 rows and 4 columns.

Description

The timestamps in the agd time series should run from first(timestamp) to last(timestamp) in increments of epochlength seconds. This function checks whether this holds or not. If the data is grouped (e.g., by subject), the check is performed for each group separately.

Usage

has_missing_epochs(agdb)

Arguments

Value

TRUE or FALSE

Description

Trim leading and trailing NAs. Fill in the rest of the NAs using cubic spline interpolation.

Usage

impute_epochs(agdb, ...)

Arguments

Value

Atibble of activity data. Each variable in ... is imputed.

See Also

zoo::na.spline(), zoo::na.trim()

Examples

library("dplyr")
data("gtxplus1day")

gtxplus1day$axis1[5:10] <- NA
gtxplus1day %>%
  impute_epochs(axis1)

Description

Plot a time series of activity values (by default, the counts on the vertical axis axis1).

Usage

plot_activity(agdb, var, color = "black", nrow = NULL, ncol = NULL)

Arguments

Value

A ggplot object. It shows the activity values in column var as a time series, faceted by group when the input agdb is grouped.

Examples

data("gtxplus1day")
sub_gt3x <- gtxplus1day %>%
  dplyr::filter(timestamp <= lubridate::as_datetime("2012-06-27 18:00:00"))

data <- sub_gt3x %>%
  collapse_epochs(60) %>%
  apply_cole_kripke()

plot_activity(data, axis1, color = "gray")
plot_activity(data, axis1, color = "sleep")

Description

Plot activity values as a time series and periods as polygons.

Usage

plot_activity_period(
  agdb,
  periods,
  act_var,
  start_var,
  end_var,
  color = "black",
  fill = "#525252",
  ncol = NULL,
  nrow = NULL
)

Arguments

Value

A ggplot object. It shows the activity values as a time series and each period as a rectangle from start_var to end_var.

Examples

data("gtxplus1day")

sub_gt3x <- gtxplus1day %>%
  dplyr::filter(timestamp <= lubridate::as_datetime("2012-06-27 18:00:00"))
# Detect sleep periods using Sadeh as the sleep/awake algorithm
# and Tudor-Locke as the sleep period algorithm
periods_sleep <- sub_gt3x %>%
  collapse_epochs(60) %>%
  apply_cole_kripke() %>%
  apply_tudor_locke(min_sleep_period = 60)

plot_activity_period(
  sub_gt3x, periods_sleep, axis1,
  in_bed_time, out_bed_time
)

Description

Read ActiGraph sleep watch data from a database stored in an AGD file. Return a tibble.

Usage

read_agd(file, tz = "UTC")

Arguments

Value

A tibble of activity data with at least two columns: timestamp and axis1 counts. Optional columns include axis2, axis2, steps, lux and inclinometer indicators (incline off, standing, sitting and lying). The device settings are stored as attributes, which include epochlength.

References

The AGD file format is described in the ActiLife 6 User's Manual by the ActiGraph Software Department, 04/03/2012 (Document SFT12DOC13, Revision A).

See Also

read_agd_raw()

Examples

file <- system.file("extdata", "GT3XPlus-RawData-Day01.agd",
  package = "actigraph.sleepr"
)
read_agd(file)

library("dplyr")
library("purrr")

# Read ActiGraph sleep watch data from the AGD files in a directory
# and bind the data into one data frame indexed by `.filename`.
path <- system.file("extdata", package = "actigraph.sleepr")

list.files(path, pattern = "*.agd", full.names = TRUE) %>%
  map_dfr(read_agd, .id = ".filename")

Description

Read ActiGraph sleep watch data from an SQLite database stored in an AGD file and return a list with (at least) five tables: data, sleep, filters, settings, awakenings. The tables have the schema described in the ActiLife 6 User manual and the timestamps are converted from Unix time format to human-readable POSIXct representation.

Usage

read_agd_raw(file, tz = "UTC")

Arguments

Details

Some ActiGraph devices contain a capacitive sensor to detect monitor removal when worn against the skin. If that data is available, the return list includes a capsense table as well.

Value

A list of five tables: settings, data, filters, sleep, awakenings and, if available, capsense.

References

ActiLife 6 User's Manual by the ActiGraph Software Department, 04/03/2012 (Document SFT12DOC13, Revision A).

covertagd: R package for converting agd files from ActiGraph into data.frames.

See Also

read_agd()

Examples

file <- system.file("extdata", "GT3XPlus-RawData-Day01.agd",
  package = "actigraph.sleepr"
)
str(read_agd_raw(file))

Description

This tibble has several attributes, most importantly, epochlength.

Usage

tbl_agd(data, settings)

Arguments

Value

A tibble of activity data. It has a time index, timestamp, one or more activity value columns, and attributes holding settings such as epochlength.

Examples

data("gtxplus1day")
gtxplus1day
attr(gtxplus1day, "epochlength")