weartime {accelerometry}

Wear Time Classification

Description

Classifies wear time vs. non-wear time based on a vector of accelerometer count values.

Usage

weartime(counts, window = 60L, tol = 0L, tol_upper = 99L, nci = FALSE,
  days_distinct = FALSE, units_day = 1440L)

Arguments

counts

Integer vector with accelerometer count values.

window

Integer value specifying minimum length of a non-wear period.

tol

Integer value specifying tolerance for non-wear algorithm, i.e. number of seconds/minutes with non-zero counts allowed during a non-wear interval.

tol_upper

Integer value specifying maximum count value for a second/minute with non-zero counts during a non-wear interval.

nci

Logical value for whether to use algorithm from NCI's SAS programs. See Details.

days_distinct

Logical value for whether to treat each day of data as distinct, as opposed to analyzing the entire monitoring period as one continuous segment. For minute-to-minute counts, strongly recommend setting to FALSE to correctly classify time near midnight.

units_day

Integer value specifying how many data point are in a day. Typically either 1440 or 86400 depending on whether count values are minute-to-minute or second-to-second.

Details

If nci = FALSE, the algorithm uses a moving window to go through every possible interval of length window in counts. Any interval in which no more than tol counts are non-zero, and those are still < tol.upper, is classified as non-wear time.

If nci = TRUE, non-wear time is classified according to the algorithm used in the NCI's SAS programs. Briefly, this algorithm defines a non-wear period as an interval of length window that starts with a count value of 0, does not contain any periods with (tol + 1) consecutive non-zero count values, and does not contain any counts > tol.upper. If these criteria are met, the non-wear period continues until there are (tol + 1) consecutive non-zero count values or a single count value > tol.upper.

Value

Integer vector with 1's for valid wear time and 0's for non-wear time.

References

National Cancer Institute. Risk factor monitoring and methods: SAS programs for analyzing NHANES 2003-2004 accelerometer data. Available at: http://riskfactor.cancer.gov/tools/nhanes_pam. Accessed Aug. 19, 2018.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

Examples

# Load accelerometer data for first 5 participants in NHANES 2003-2004
data(unidata)

# Get data from ID number 21005
counts.part1 <- unidata[unidata[, "seqn"] == 21005, "paxinten"]

# Identify periods of valid wear time
weartime.flag <- weartime(counts = counts.part1)