README.md
In dipetkov/actigraph.sleepr: Detect Periods of Sleep and Non-Wear from 'ActiGraph' Data
actigraph.sleepr: Sleep and non-wear detection from ActiGraph data
The actigraph.sleepr package implements three sleep scoring/detection
algorithms: Sadeh (Sadeh, Sharkey, and Carskadon 1994), Cole-Kripke
(Cole et al. 1992) and Tudor-Locke (Tudor-Locke et al. 2014) as well as
two non-wear detection algorithms: Troiano (Troiano et al. 2008) and
Choi (Choi et al. 2011).
Installation
# install.packages("remotes")
remotes::install_github("dipetkov/actigraph.sleepr")
Read AGD file(s)
An AGD file is an SQLite database file exported by an ActiGraph device.
See the ActiLife 6 User
manual.
For illustration let’s use one day of sample data recorded by a GT3X+
monitor.
library("actigraph.sleepr")
file_10s <- system.file("extdata", "GT3XPlus-RawData-Day01.agd",
package = "actigraph.sleepr"
)
agdb_10s <- read_agd(file_10s)
The read_agd function loads the raw activity measurements into a
convenient format: a tibble (data frame) of timestamped activity counts,
whose attributes are the device settings. However, further manipulations
of the tibble using the dplyr verbs (e.g., mutate, inner_join)
might drop these non-standard attributes, i.e., the attributes are not
always inherited.
attributes(agdb_10s)[10:12]
#> $grouping
#> [1] ","
#>
#> $culturename
#> [1] "English (United States)"
#>
#> $finished
#> [1] "true"
Since the data is stored in a tibble, we can use the dplyr verbs
(mutate, select, filter, summarise, group_by, arrange) to manipulate the
data. For example, let’s compute the vector magnitude of the three-axis
counts (axis1 - vertical, axis2 - horizontal, axis3 - lateral).
suppressMessages(library("dplyr"))
agdb_10s <- agdb_10s %>% select(timestamp, starts_with("axis"))
agdb_10s %>%
mutate(magnitude = sqrt(axis1^2 + axis2^2 + axis3^2))
#> # A tibble: 8,999 × 5
#> timestamp axis1 axis2 axis3 magnitude
#> <dttm> <int> <int> <int> <dbl>
#> 1 2012-06-27 10:54:00 377 397 413 686.
#> 2 2012-06-27 10:54:10 465 816 1225 1544.
#> 3 2012-06-27 10:54:20 505 444 713 980.
#> 4 2012-06-27 10:54:30 73 91 106 158.
#> 5 2012-06-27 10:54:40 45 43 115 131.
#> 6 2012-06-27 10:54:50 0 0 0 0
#> 7 2012-06-27 10:55:00 0 0 0 0
#> 8 2012-06-27 10:55:10 207 218 270 404.
#> 9 2012-06-27 10:55:20 0 0 0 0
#> 10 2012-06-27 10:55:30 0 0 0 0
#> # … with 8,989 more rows
Reintegrate from 10s to 60s epochs
The Sadeh and Cole-Kripke algorithms for converting activity
measurements into asleep/awake indicators were developed for 60s epochs.
If the data is in smaller epochs, we need to collapse or aggregate the
epochs. The example data is in 10s epochs. So we aggregate the epochs
from 10s to 60s by adding the counts for the six consecutive 10s epochs
that fall into the same 60s epoch.
# Collapse epochs from 10 sec to 60 sec by summing
agdb_60s <- agdb_10s %>% collapse_epochs(60)
agdb_60s
#> # A tibble: 1,500 × 4
#> timestamp axis1 axis2 axis3
#> <dttm> <int> <int> <int>
#> 1 2012-06-27 10:54:00 1465 1791 2572
#> 2 2012-06-27 10:55:00 207 218 270
#> 3 2012-06-27 10:56:00 169 257 270
#> 4 2012-06-27 10:57:00 0 0 0
#> 5 2012-06-27 10:58:00 157 174 248
#> 6 2012-06-27 10:59:00 23 23 279
#> 7 2012-06-27 11:00:00 0 0 0
#> 8 2012-06-27 11:01:00 0 0 0
#> 9 2012-06-27 11:02:00 0 0 0
#> 10 2012-06-27 11:03:00 0 0 0
#> # … with 1,490 more rows
Sleep scoring with the Sadeh algorithm
The Sadeh algorithm is primarily used for younger adolescents as the
supporting research was performed on children and young adults. It takes
60s epochs and uses an 11-minute window that includes the five previous
and five future epochs. The apply_sadeh function implements the
algorithm as described in the ActiGraph user manual.
agdb_60s %>% apply_sadeh()
#> # A tibble: 1,500 × 6
#> timestamp axis1 axis2 axis3 count sleep
#> <dttm> <int> <int> <int> <dbl> <chr>
#> 1 2012-06-27 10:54:00 1465 1791 2572 300 W
#> 2 2012-06-27 10:55:00 207 218 270 207 W
#> 3 2012-06-27 10:56:00 169 257 270 169 W
#> 4 2012-06-27 10:57:00 0 0 0 0 W
#> 5 2012-06-27 10:58:00 157 174 248 157 W
#> 6 2012-06-27 10:59:00 23 23 279 23 W
#> 7 2012-06-27 11:00:00 0 0 0 0 S
#> 8 2012-06-27 11:01:00 0 0 0 0 S
#> 9 2012-06-27 11:02:00 0 0 0 0 S
#> 10 2012-06-27 11:03:00 0 0 0 0 S
#> # … with 1,490 more rows
Sleep scoring with the Cole-Kripke algorithm
The Cole-Kripke algorithm is primarily used for adult populations as the
supporting research was performed on subjects ranging from 35 to 65
years of age. Like the Sadeh algorithm, it takes 60s epochs and uses a
7-minute window that includes the four previous and two future epochs.
The apply_cole function implements the algorithm as described in the
ActiGraph user manual.
agdb_60s %>% apply_cole_kripke()
#> # A tibble: 1,500 × 6
#> timestamp axis1 axis2 axis3 count sleep
#> <dttm> <int> <int> <int> <dbl> <chr>
#> 1 2012-06-27 10:54:00 1465 1791 2572 14.6 W
#> 2 2012-06-27 10:55:00 207 218 270 2.07 W
#> 3 2012-06-27 10:56:00 169 257 270 1.69 W
#> 4 2012-06-27 10:57:00 0 0 0 0 W
#> 5 2012-06-27 10:58:00 157 174 248 1.57 W
#> 6 2012-06-27 10:59:00 23 23 279 0.23 S
#> 7 2012-06-27 11:00:00 0 0 0 0 S
#> 8 2012-06-27 11:01:00 0 0 0 0 S
#> 9 2012-06-27 11:02:00 0 0 0 0 S
#> 10 2012-06-27 11:03:00 0 0 0 0 S
#> # … with 1,490 more rows
Sleep period detection with the Tudor-Locke algorithm
Once each one-minute epoch is labeled as asleep (S) or awake (W), we can
use the Tudor-Locke algorithm to detect periods of time in bed and, for
each period, to compute sleep quality metrics such as total minutes in
bed, total sleep time, number and average length of awakenings, movement
and fragmentation index.
agdb_60s %>%
apply_sadeh() %>%
apply_tudor_locke()
#> # A tibble: 1 × 15
#> in_bed_time out_bed_time onset latency efficiency
#> <dttm> <dttm> <dttm> <int> <dbl>
#> 1 2012-06-28 00:03:00 2012-06-28 07:38:00 2012-06-28 00:03:00 0 97.1
#> # … with 10 more variables: duration <int>, activity_counts <int>,
#> # nonzero_epochs <int>, total_sleep_time <int>, wake_after_onset <int>,
#> # nb_awakenings <int>, ave_awakening <dbl>, movement_index <dbl>,
#> # fragmentation_index <dbl>, sleep_fragmentation_index <dbl>
Non-wear period detection with the Troiano and Choi algorithms
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. The Troiano algorithm for detecting
periods of non-wear formalizes a technique used to analyze the 2003-2004
NHANES data, which allows a non-wear period to contain a few nonzero
epochs of artifactual movement (spikes). 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 a fixed number of
consecutive zero epochs.
agdb_60s %>% apply_troiano()
#> # A tibble: 3 × 3
#> period_start period_end length
#> <dttm> <dttm> <int>
#> 1 2012-06-28 00:00:00 2012-06-28 02:37:00 157
#> 2 2012-06-28 02:46:00 2012-06-28 03:59:00 73
#> 3 2012-06-28 05:50:00 2012-06-28 07:25:00 95
agdb_60s %>% apply_choi()
#> # A tibble: 1 × 3
#> period_start period_end length
#> <dttm> <dttm> <int>
#> 1 2012-06-28 00:00:00 2012-06-28 02:37:00 157
References
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.
Cole, Roger J, Daniel F Kripke, William Gruen, Daniel J Mullaney, and J
Christian Gillin. 1992. “Automatic Sleep/Wake Identification from Wrist
Activity.” *Sleep* 15 (5): 461–69.
Sadeh, Avi, Katherine M Sharkey, and Mary A Carskadon. 1994. “Activity
Based Sleep-Wake Identification: An Empirical Test of Methodological
Issues.” *Sleep* 17 (3): 201–7.
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.
Tudor-Locke, Catrine, Tiago V. Barreira, John M. Schuna, Emily F. Mire,
and Peter T. Katzmarzyk. 2014. “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.
dipetkov/actigraph.sleepr documentation built on March 25, 2022, 2:33 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
actigraph.sleepr: Sleep and non-wear detection from ActiGraph data
The actigraph.sleepr package implements three sleep scoring/detection
algorithms: Sadeh (Sadeh, Sharkey, and Carskadon 1994), Cole-Kripke
(Cole et al. 1992) and Tudor-Locke (Tudor-Locke et al. 2014) as well as
two non-wear detection algorithms: Troiano (Troiano et al. 2008) and
Choi (Choi et al. 2011).
Installation
# install.packages("remotes")
remotes::install_github("dipetkov/actigraph.sleepr")
Read AGD file(s)
An AGD file is an SQLite database file exported by an ActiGraph device. See the ActiLife 6 User manual. For illustration let’s use one day of sample data recorded by a GT3X+ monitor.
library("actigraph.sleepr")
file_10s <- system.file("extdata", "GT3XPlus-RawData-Day01.agd",
package = "actigraph.sleepr"
)
agdb_10s <- read_agd(file_10s)
The read_agd function loads the raw activity measurements into a
convenient format: a tibble (data frame) of timestamped activity counts,
whose attributes are the device settings. However, further manipulations
of the tibble using the dplyr verbs (e.g., mutate, inner_join)
might drop these non-standard attributes, i.e., the attributes are not
always inherited.
attributes(agdb_10s)[10:12]
#> $grouping
#> [1] ","
#>
#> $culturename
#> [1] "English (United States)"
#>
#> $finished
#> [1] "true"
Since the data is stored in a tibble, we can use the dplyr verbs (mutate, select, filter, summarise, group_by, arrange) to manipulate the data. For example, let’s compute the vector magnitude of the three-axis counts (axis1 - vertical, axis2 - horizontal, axis3 - lateral).
suppressMessages(library("dplyr"))
agdb_10s <- agdb_10s %>% select(timestamp, starts_with("axis"))
agdb_10s %>%
mutate(magnitude = sqrt(axis1^2 + axis2^2 + axis3^2))
#> # A tibble: 8,999 × 5
#> timestamp axis1 axis2 axis3 magnitude
#> <dttm> <int> <int> <int> <dbl>
#> 1 2012-06-27 10:54:00 377 397 413 686.
#> 2 2012-06-27 10:54:10 465 816 1225 1544.
#> 3 2012-06-27 10:54:20 505 444 713 980.
#> 4 2012-06-27 10:54:30 73 91 106 158.
#> 5 2012-06-27 10:54:40 45 43 115 131.
#> 6 2012-06-27 10:54:50 0 0 0 0
#> 7 2012-06-27 10:55:00 0 0 0 0
#> 8 2012-06-27 10:55:10 207 218 270 404.
#> 9 2012-06-27 10:55:20 0 0 0 0
#> 10 2012-06-27 10:55:30 0 0 0 0
#> # … with 8,989 more rows
Reintegrate from 10s to 60s epochs
The Sadeh and Cole-Kripke algorithms for converting activity measurements into asleep/awake indicators were developed for 60s epochs. If the data is in smaller epochs, we need to collapse or aggregate the epochs. The example data is in 10s epochs. So we aggregate the epochs from 10s to 60s by adding the counts for the six consecutive 10s epochs that fall into the same 60s epoch.
# Collapse epochs from 10 sec to 60 sec by summing
agdb_60s <- agdb_10s %>% collapse_epochs(60)
agdb_60s
#> # A tibble: 1,500 × 4
#> timestamp axis1 axis2 axis3
#> <dttm> <int> <int> <int>
#> 1 2012-06-27 10:54:00 1465 1791 2572
#> 2 2012-06-27 10:55:00 207 218 270
#> 3 2012-06-27 10:56:00 169 257 270
#> 4 2012-06-27 10:57:00 0 0 0
#> 5 2012-06-27 10:58:00 157 174 248
#> 6 2012-06-27 10:59:00 23 23 279
#> 7 2012-06-27 11:00:00 0 0 0
#> 8 2012-06-27 11:01:00 0 0 0
#> 9 2012-06-27 11:02:00 0 0 0
#> 10 2012-06-27 11:03:00 0 0 0
#> # … with 1,490 more rows
Sleep scoring with the Sadeh algorithm
The Sadeh algorithm is primarily used for younger adolescents as the
supporting research was performed on children and young adults. It takes
60s epochs and uses an 11-minute window that includes the five previous
and five future epochs. The apply_sadeh function implements the
algorithm as described in the ActiGraph user manual.
agdb_60s %>% apply_sadeh()
#> # A tibble: 1,500 × 6
#> timestamp axis1 axis2 axis3 count sleep
#> <dttm> <int> <int> <int> <dbl> <chr>
#> 1 2012-06-27 10:54:00 1465 1791 2572 300 W
#> 2 2012-06-27 10:55:00 207 218 270 207 W
#> 3 2012-06-27 10:56:00 169 257 270 169 W
#> 4 2012-06-27 10:57:00 0 0 0 0 W
#> 5 2012-06-27 10:58:00 157 174 248 157 W
#> 6 2012-06-27 10:59:00 23 23 279 23 W
#> 7 2012-06-27 11:00:00 0 0 0 0 S
#> 8 2012-06-27 11:01:00 0 0 0 0 S
#> 9 2012-06-27 11:02:00 0 0 0 0 S
#> 10 2012-06-27 11:03:00 0 0 0 0 S
#> # … with 1,490 more rows
Sleep scoring with the Cole-Kripke algorithm
The Cole-Kripke algorithm is primarily used for adult populations as the
supporting research was performed on subjects ranging from 35 to 65
years of age. Like the Sadeh algorithm, it takes 60s epochs and uses a
7-minute window that includes the four previous and two future epochs.
The apply_cole function implements the algorithm as described in the
ActiGraph user manual.
agdb_60s %>% apply_cole_kripke()
#> # A tibble: 1,500 × 6
#> timestamp axis1 axis2 axis3 count sleep
#> <dttm> <int> <int> <int> <dbl> <chr>
#> 1 2012-06-27 10:54:00 1465 1791 2572 14.6 W
#> 2 2012-06-27 10:55:00 207 218 270 2.07 W
#> 3 2012-06-27 10:56:00 169 257 270 1.69 W
#> 4 2012-06-27 10:57:00 0 0 0 0 W
#> 5 2012-06-27 10:58:00 157 174 248 1.57 W
#> 6 2012-06-27 10:59:00 23 23 279 0.23 S
#> 7 2012-06-27 11:00:00 0 0 0 0 S
#> 8 2012-06-27 11:01:00 0 0 0 0 S
#> 9 2012-06-27 11:02:00 0 0 0 0 S
#> 10 2012-06-27 11:03:00 0 0 0 0 S
#> # … with 1,490 more rows
Sleep period detection with the Tudor-Locke algorithm
Once each one-minute epoch is labeled as asleep (S) or awake (W), we can use the Tudor-Locke algorithm to detect periods of time in bed and, for each period, to compute sleep quality metrics such as total minutes in bed, total sleep time, number and average length of awakenings, movement and fragmentation index.
agdb_60s %>%
apply_sadeh() %>%
apply_tudor_locke()
#> # A tibble: 1 × 15
#> in_bed_time out_bed_time onset latency efficiency
#> <dttm> <dttm> <dttm> <int> <dbl>
#> 1 2012-06-28 00:03:00 2012-06-28 07:38:00 2012-06-28 00:03:00 0 97.1
#> # … with 10 more variables: duration <int>, activity_counts <int>,
#> # nonzero_epochs <int>, total_sleep_time <int>, wake_after_onset <int>,
#> # nb_awakenings <int>, ave_awakening <dbl>, movement_index <dbl>,
#> # fragmentation_index <dbl>, sleep_fragmentation_index <dbl>
Non-wear period detection with the Troiano and Choi algorithms
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. The Troiano algorithm for detecting periods of non-wear formalizes a technique used to analyze the 2003-2004 NHANES data, which allows a non-wear period to contain a few nonzero epochs of artifactual movement (spikes). 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 a fixed number of consecutive zero epochs.
agdb_60s %>% apply_troiano()
#> # A tibble: 3 × 3
#> period_start period_end length
#> <dttm> <dttm> <int>
#> 1 2012-06-28 00:00:00 2012-06-28 02:37:00 157
#> 2 2012-06-28 02:46:00 2012-06-28 03:59:00 73
#> 3 2012-06-28 05:50:00 2012-06-28 07:25:00 95
agdb_60s %>% apply_choi()
#> # A tibble: 1 × 3
#> period_start period_end length
#> <dttm> <dttm> <int>
#> 1 2012-06-28 00:00:00 2012-06-28 02:37:00 157
References
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.
Cole, Roger J, Daniel F Kripke, William Gruen, Daniel J Mullaney, and J
Christian Gillin. 1992. “Automatic Sleep/Wake Identification from Wrist
Activity.” *Sleep* 15 (5): 461–69.
Sadeh, Avi, Katherine M Sharkey, and Mary A Carskadon. 1994. “Activity
Based Sleep-Wake Identification: An Empirical Test of Methodological
Issues.” *Sleep* 17 (3): 201–7.
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.
Tudor-Locke, Catrine, Tiago V. Barreira, John M. Schuna, Emily F. Mire,
and Peter T. Katzmarzyk. 2014. “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.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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