weartime {accelerometry} | R Documentation |

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

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

`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 |

`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 |

`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. |

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`

.

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

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.

```
# 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)
```

[Package *accelerometry* version 3.1.2 Index]