Package 'ActFrag'

Activity/Wear Data from 50 Subjects from NHANES 2003 - 2006

Description

A list of two data.frames containing the counts and the weartime for 50 NHANES subjects

Usage

example_activity_data

Format

A list of two data.frames with 1442 columns, which are in the following order:

ID

identifier of the person.

Day

numeric sequence 1,2,.. indicating the order of days within a subject.

MIN1-MIN1440

counts of activity of that specific minute.

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Fragmentation Metrics

Description

Fragmentation methods to study the transition between two states, e.g. sedentary v.s. active.

Usage

fragmentation(
  x,
  w,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all")
)

Arguments

x	integer vector of activity data.
w	vector of wear flag data with same dimension as x.
thresh	threshold to binarize the data.
bout.length	minimum duration of defining an active bout; defaults to 1.
metrics	What is the fragmentation metrics to exract. Can be "mean_bout","TP","Gini","power","hazard",or all the above metrics "all".

Details

Metrics include mean_bout (mean bout duration), TP (between states transition probability), Gini (gini index), power (alapha parameter for power law distribution) hazard (average hazard function)

Value

A list with elements

mean_r	mean sedentary bout duration
mean_a	mean active bout duration
SATP	sedentary to active transition probability
ASTP	bactive to sedentary transition probability
Gini_r	Gini index for active bout
Gini_a	Gini index for sedentary bout
h_r	hazard function for sedentary bout
h_a	hazard function for active bout
alpha_r	power law parameter for sedentary bout
alpha_a	power law parameter for active bout

References

Junrui Di, Andrew Leroux, Jacek Urbanek, Ravi Varadhan, Adam P. Spira, Jennifer Schrack, Vadim Zipunnikov. Patterns of sedentary and active time accumulation are associated with mortality in US adults: The NHANES study. bioRxiv 182337; doi: https://doi.org/10.1101/182337

Examples

data(example_activity_data)
count1 = c(t(example_activity_data$count[1,-c(1,2)]))
wear1 = c(t(example_activity_data$wear[1,-c(1,2)]))
frag = fragmentation(x = count1, w = wear1, thresh = 100, bout.length = 1, metrics = "mean_bout")
frag = fragmentation(x = count1, w = wear1, thresh = 100,
bout.length = 1, metrics = "all")
res = sapply(c("mean_bout","TP","Gini","power","hazard"), function(x) {
frag = fragmentation(x = count1, w = wear1,
thresh = 100, bout.length = 1, metrics = x)
})
data(example_activity_data)
count1 = c(t(example_activity_data$count[1,-c(1,2)]))
wear1 = c(t(example_activity_data$wear[1,-c(1,2)]))
count1[ !is.na(count1) & count1 != 0] = 0L
res = sapply(c("mean_bout","TP","Gini","power","hazard", "all"), function(x) {
frag = fragmentation(x = count1, w = wear1,
thresh = 100, bout.length = 1, metrics = x)
})

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Fragmentation Metrics for Whole Dataset

Description

Fragmentation methods to study the transition between two states, e.g. sedentary v.s. active.This function is a whole dataset wrapper for fragmentation

Usage

fragmentation_long(
  count.data,
  weartime,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all"),
  by = c("day", "subject")
)

Arguments

count.data	data.frame of dimension n*1442 containing the 1440 minutes of activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.
weartime	data.frame with dimension of count.data. The first two columns have to be ID and Day.ID can be either character or numeric. Day has to be numeric indicating the sequencey of days within each subject.
thresh	threshold to define the two states.
bout.length	minimum duration of defining an active bout; defaults to 1.
metrics	What is the fragmentation metrics to exract. Can be "mean_bout","TP","Gini","power","hazard",or all the above metrics "all".
by	Determine whether fragmentation is calcualted by day or by subjects (i.e. aggregate bouts across days). by-subject is recommended to gain more power.

Details

Metrics include mean_bout (mean bout duration), TP (between states transition probability), Gini (gini index), power (alapha parameter for power law distribution) hazard (average hazard function)

Value

A dataframe with some of the following columns

ID	identifier of the person
Day	numeric vector indicating the sequencey of days within each subject.
mean_r	mean sedentary bout duration
mean_a	mean active bout duration
SATP	sedentary to active transition probability
ASTP	bactive to sedentary transition probability
Gini_r	Gini index for active bout
Gini_a	Gini index for sedentary bout
h_r	hazard function for sedentary bout
h_a	hazard function for active bout
alpha_r	power law parameter for sedentary bout
alpha_a	power law parameter for active bout

Examples

data(example_activity_data)
count = example_activity_data$count
wear = example_activity_data$wear
frag_by_day = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = "all",by = "day")
tp_by_subject = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = "TP",by = "subject")


res = sapply(c("mean_bout","TP","Gini","power","hazard", "all"), function(x) {
frag_by_day = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = x,by = "day")
})
res = sapply(c("mean_bout","TP","Gini","power","hazard", "all"), function(x) {
tp_by_subject = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = x,by = "subject")
})

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Create Wear/Nonwear Flags

Description

Determine during which time period, subject should wear the device. It is preferable that user provide their own wear/non wear flag which should has the same dimension as the activity data. This function provide wear/non wear flag based on time of day.

Usage

wear_flag(count.data, start = "05:00", end = "23:00")

Arguments

count.data	data.frame of dimension n*1442 containing the 1440 minute activity data for all n subject days. The first two columns have to be ID and Day.
start	start time, a string in the format of 24hr, e.g. "05:00"; defaults to "05:00".
end	end time, a string in the format of 24hr, e.g. "23:00"; defaults to "23:00"

Details

Fragmentation metrics are usually defined when subject is awake. The weartime provide time periods on which those features should be extracted. This can be also used as indication of wake/sleep.

Value

A data.frame with same dimension and column name as the count.data, with 0/1 as the elments reprensting wear, nonwear respectively.

Examples

data(example_activity_data)
count = example_activity_data$count
weartime = wear_flag(count.data = count)
testthat::expect_error({
weartime = wear_flag(count.data = count, start = "10:00PM")
})

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