Determine event windows for activities — windows • pregnancy247

Determine sleep, nap, work, and monitor wear windows recorded by the Actiwatch or by the subject in their diary.

Usage

windows_sleep(sleep)

windows_nap(sleep, interval = "first")

windows_work(sleep)

windows_monitor(sleep)

Arguments

sleep: A data.sleep object produced by read_sleep().
interval: For windows_nap(), a character taking a value of "first" or "second" to denote if first or second napping windows. Default is "first".

Value

A list containing two numeric vectors named start and end that are formatted as date-time (POSIXct) based on UTC time-zone.

Details

These functions require that a data.sleep object is used as this type of object contains the specific variable names to determine the daily windows of sleep, naps, work, and monitor wear.

The sleep and nap windows may be soley on times recorded by the Actiwatch, which tracks information based on activity, light, and event markers. Variables that end with input will denote how the sleep and nap windows should be recorded. Below is a list of the input values and their relation to how the times are to be record.

1 denotes event marker time
2 denotes white light time
3 denotes diary record time
4 denotes activity time

Each of the windows are initialing created with diary dates recorded by the subject. The dates and times are altered based on time of day in relation to the initial date. As an example, suppose subject 0001-AB records information in their diary on 01-01-2023, including their sleep onset and wake times. With the Pregnancy 24/7 study, the start of each day begins with sleep onset. So, the sleep onset and wake times initially start with the same date and need to be altered so a wake time of 6:00 AM is actually recorded to occur on 01-02-2023 and not 01-01-2023. Or, if the sleep onset time was at 12:00 AM, the date is recorded as 01-02-2023.

windows_work() and windows_monitor() are soley based on times recorded by the subject in their log.

The window creation functions also may require user input if their is a missing date or time. It will prompt you with a question whether to proceed with processing the windows given a certain day has missing data. If it is known had of time that certain days will have invalid data the missing data may not be problem, as the invalid data will be excluded later.

The interval parameter for windows_nap allows for recording two naps a day during the wear period. It is assumed the second napping intervals have variable names that end in _b#, where # denotes the wear day.

Examples

if (FALSE) { # \dontrun{
wind_sleep <- windows_sleep(sleep = sleep)
sleep_start <- wind_sleep$start
sleep_end <- wind_sleep$end
} # }

if (FALSE) { # \dontrun{
wind_nap <- windows_nap(sleep = sleep)
nap_start <- wind_nap$start
nap_end <- wind_nap$end
} # }

if (FALSE) { # \dontrun{
wind_work <- windows_work(sleep = sleep)
work_start <- wind_work$start
work_end <- wind_work$end
} # }

if (FALSE) { # \dontrun{
wind_monitor <- windows_monitor(sleep = sleep)
monitor_off <- wind_monitor$start
monitor_on <- wind_monitor$end
} # }