Rest-Activity Bout Fragmentation

Summarizes how broken up the rest-activity rhythm is, from a per-epoch rest/active state: the mean and median rest and active bout durations, the number of state transitions, and transitions per day. These add a bout-length view of fragmentation to the transition probabilities of state.transitions (kRA/kAR) (Lim et al. 2011). It does not cover sedentary-bout distribution metrics (Gini, power law, hazard).

Usage

rest.activity.fragmentation(state, timestamps, epoch_length = 60)

Arguments

state

Per-epoch state: a logical vector (TRUE = active) or a character vector where "R"/"S"/"sleep"/"rest" mark rest.

timestamps

POSIXct timestamps, one per value.

epoch_length

Epoch length in seconds (default 60).

Value

An object of class actiRhythm_rafrag: mean/median rest and active bout durations (minutes), bout counts, transition count, and transitions per day. Never errors.

References

Lim ASP, Yu L, Costa MD, Buchman AS, Bennett DA, Leurgans SE, Saper CB (2011). “Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis.” Sleep, 34(11), 1569–1581. doi:10.5665/sleep.1400 .

See also

state.transitions

Examples

ts <- seq(as.POSIXct("2024-01-01", tz = "UTC"), by = 60, length.out = 3 * 1440)
h  <- as.numeric(format(ts, "%H"))
rest.activity.fragmentation(h >= 7 & h < 23, ts)
#> Rest-Activity Bout Fragmentation
#> 
#>   Active bouts: 3, mean 960 min (median 960)
#>   Rest bouts:   4, mean 360 min (median 450)
#>   Transitions:  6 (2.0 per day)
#>