`sd_week()`

computes the **average weekly sleep duration** for the standard
and micro versions of the Munich ChronoType Questionnaire (MCTQ).

See `sd_overall()`

to compute the overall sleep
duration of a particular shift for the shift version of the MCTQ.

## Arguments

- sd_w
A

`Duration`

object corresponding to the**sleep duration on workdays**from a standard or micro version of the MCTQ questionnaire. You can use`sdu()`

to compute it.- sd_f
A

`Duration`

object corresponding to the**sleep duration on work-free days**from a standard or micro version of the MCTQ questionnaire. You can use`sdu()`

to compute it.- wd
An integerish

`numeric`

object or an`integer`

object corresponding to the**number of workdays per week**from a standard or micro version of the MCTQ questionnaire.

## Value

A `Duration`

object corresponding to the
vectorized weighted mean of `sd_w`

and `sd_f`

with `wd`

and `fd(wd)`

as
weights.

## Details

**Standard MCTQ** functions were created following the guidelines in
Roenneberg, Wirz-Justice, & Merrow (2003), Roenneberg, Allebrandt, Merrow, &
Vetter (2012), and from The Worldwide Experimental Platform (theWeP, n.d.).

**\(\mu\)MCTQ** functions were created following the guidelines in Ghotbi
et al. (2020), in addition to the guidelines used for the standard MCTQ.

**MCTQ\(^{Shift}\)** functions were created following the
guidelines in Juda, Vetter, & Roenneberg (2013), in addition to the
guidelines used for the standard MCTQ.

See the References section to learn more.

### Class requirements

The `mctq`

package works with a set of object classes specially created to
hold time values. These classes can be found in the
lubridate and hms
packages. Please refer to those package documentations to learn more about
them.

### Rounding and fractional time

Some operations may produce an output with fractional time (e.g.,
`"19538.3828571429s (~5.43 hours)"`

, `01:15:44.505`

). If you want, you
can round it with `mctq:::round_time()`

.

Our recommendation is to avoid rounding, but, if you do, make sure that you only round your values after all computations are done. That way you avoid round-off errors.

## Guidelines

Roenneberg, Allebrandt, Merrow, & Vetter (2012), Ghotbi et al. (2020), and
The Worldwide Experimental Platform (n.d.) guidelines for `sd_week()`

(\(SD_{week}\)) computation are as follows.

### Notes

The average weekly sleep duration is the weighted average of the sleep durations on work and work-free days in a week.

If you are visualizing this documentation in plain text, you may have some trouble understanding the equations. You can see this documentation on the package website.

### Computation

$$SD_{week} = \frac{(SD_{W} \times WD) + (SD_{F} \times FD)}{7}$$

Where:

\(SD_{week}\) = Average weekly sleep duration.

\(SD_{W}\) = Sleep duration on workdays.

\(SD_{F}\) = Sleep duration on work-free days.

\(WD\) = Number of workdays per week ("I have a regular work schedule and work ___ days per week").

\(FD\) = Number of work-free days per week.

***** \(W\) = Workdays; \(F\) = Work-free days.

## References

Ghotbi, N., Pilz, L. K., Winnebeck, E. C., Vetter, C., Zerbini, G., Lenssen,
D., Frighetto, G., Salamanca, M., Costa, R., Montagnese, S., & Roenneberg, T.
(2020). The \(\mu\)MCTQ: an ultra-short version of the Munich ChronoType
Questionnaire. *Journal of Biological Rhythms*, *35*(1), 98-110.
doi:10.1177/0748730419886986

Juda, M., Vetter, C., & Roenneberg, T. (2013). The Munich ChronoType
Questionnaire for shift-workers (MCTQ\(^{Shift}\)). *Journal of
Biological Rhythms*, *28*(2), 130-140. doi:10.1177/0748730412475041

Roenneberg T., Allebrandt K. V., Merrow M., & Vetter C. (2012). Social jetlag
and obesity. *Current Biology*, *22*(10), 939-43.
doi:10.1016/j.cub.2012.03.038

Roenneberg, T., Wirz-Justice, A., & Merrow, M. (2003). Life between clocks:
daily temporal patterns of human chronotypes. *Journal of Biological
Rhythms*, *18*(1), 80-90. doi:10.1177/0748730402239679

The Worldwide Experimental Platform (n.d.). MCTQ. https://www.thewep.org/documentations/mctq/

## Examples

```
## Scalar example
sd_w <- lubridate::dhours(4)
sd_f <- lubridate::dhours(8)
wd <- 5
sd_week(sd_w, sd_f, wd)
#> [1] "18514.2857142857s (~5.14 hours)"
#> [1] "18514.2857142857s (~5.14 hours)" # Expected
sd_w <- lubridate::dhours(7)
sd_f <- lubridate::dhours(7)
wd <- 4
sd_week(sd_w, sd_f, wd)
#> [1] "25200s (~7 hours)"
#> [1] "25200s (~7 hours)" # Expected
sd_w <- lubridate::as.duration(NA)
sd_f <- lubridate::dhours(10)
wd <- 6
sd_week(sd_w, sd_f, wd)
#> [1] NA
#> [1] NA # Expected
## Vector example
sd_w <- c(lubridate::dhours(4.5), lubridate::dhours(5.45))
sd_f <- c(lubridate::dhours(8), lubridate::dhours(7.3))
wd <- c(3, 7)
sd_week(sd_w, sd_f, wd)
#> [1] "23400s (~6.5 hours)" "19620s (~5.45 hours)"
#> [1] "23400s (~6.5 hours)" "19620s (~5.45 hours)" # Expected
## Checking second output from vector example
if (requireNamespace("stats", quietly = TRUE)) {
i <- 2
x <- c(sd_w[i], sd_f[i])
w <- c(wd[i], fd(wd[i]))
lubridate::as.duration(stats::weighted.mean(x, w))
}
#> [1] "19620s (~5.45 hours)"
#> [1] "19620s (~5.45 hours)" # Expected
## Converting the output to 'hms'
sd_w <- lubridate::dhours(5.45)
sd_f <- lubridate::dhours(9.5)
wd <- 5
x <- sd_week(sd_w, sd_f, wd)
x
#> [1] "23785.7142857143s (~6.61 hours)"
#> [1] "23785.7142857143s (~6.61 hours)" # Expected
hms::as_hms(as.numeric(x))
#> 06:36:25.714286
#> 06:36:25.714286 # Expected
## Rounding the output at the seconds level
sd_w <- lubridate::dhours(4.5)
sd_f <- lubridate::dhours(7.8)
wd <- 3
sd_week(sd_w, sd_f, wd)
#> [1] "22988.5714285714s (~6.39 hours)"
#> [1] "22988.5714285714s (~6.39 hours)" # Expected
mctq:::round_time(sd_week(sd_w, sd_f, wd))
#> [1] "22989s (~6.39 hours)"
#> [1] "22989s (~6.39 hours)" # Expected
```