mctq (version 0.3.2)

le_week: Compute MCTQ average weekly light exposure

Description

le_week() computes the average weekly light exposure for the standard version of the Munich ChronoType Questionnaire (MCTQ).

Usage

le_week(le_w, le_f, wd)

Value

A Duration object corresponding to the vectorized weighted mean of le_w and le_f with wd and fd(wd) as weights.

Arguments

le_w

A Duration object corresponding to the light exposure on workdays from a standard version of the MCTQ questionnaire.

le_f

A Duration object corresponding to the light exposure on work-free days from a standard version of the MCTQ questionnaire.

wd

An integerish numeric object or an integer object corresponding to the number of workdays per week from a standard version of the MCTQ questionnaire.

Guidelines

Roenneberg, Allebrandt, Merrow, & Vetter (2012) and The Worldwide Experimental Platform (n.d.) guidelines for le_week() (\(LE_{week}\)) computation are as follows.

Notes

• The average weekly light exposure (\(LE_{week}\)) is the weighted average of the light exposure 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

$$LE_{week} = \frac{(LE_W \times WD) + (LE_F \times FD)}{7}$$

Where:

• \(LE_{week}\) = Average weekly light exposure.

• \(LE_W\) = Light exposure on workdays.

• \(LE_F\) = Light exposure 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.

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 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.

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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1177/0748730412475041")

Roenneberg T., Allebrandt K. V., Merrow M., & Vetter C. (2012). Social jetlag and obesity. Current Biology, 22(10), 939-43. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1177/0748730402239679")

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

See Also

Other MCTQ functions: fd(), gu(), msf_sc(), msl(), napd(), sd24(), sd_overall(), sd_week(), sdu(), sjl_sc(), sjl_weighted(), sjl(), so(), tbt()

Examples

## Scalar example

le_w <- lubridate::dhours(1.5)
le_f <- lubridate::dhours(3.7)
wd <- 5
le_week(le_w, le_f, wd)
#> [1] "7662.85714285714s (~2.13 hours)" # Expected

le_w <- lubridate::dhours(3)
le_f <- lubridate::dhours(1.5)
wd <- 6
le_week(le_w, le_f, wd)
#> [1] "10028.5714285714s (~2.79 hours)" # Expected

le_w <- lubridate::dhours(5.6)
le_f <- lubridate::as.duration(NA)
wd <- 3
le_week(le_w, le_f, wd)
#> [1] NA # Expected

## Vector example

le_w <- c(lubridate::dhours(3), lubridate::dhours(2.45))
le_f <- c(lubridate::dhours(3), lubridate::dhours(3.75))
wd <- c(4, 5)
le_week(le_w, le_f, wd)
#> [1] "10800s (~3 hours)" # Expected
#> [2] "10157.1428571429s (~2.82 hours)" # Expected

## Checking second output from vector example

if (requireNamespace("stats", quietly = TRUE)) {
    i <- 2
    x <- c(le_w[i], le_f[i])
    w <- c(wd[i], fd(wd[i]))
    lubridate::as.duration(stats::weighted.mean(x, w))
}
#> [1] "10157.1428571429s (~2.82 hours)" # Expected

## Converting the output to `hms`

le_w <- lubridate::dhours(1.25)
le_f <- lubridate::dhours(6.23)
wd <- 3
le_week(le_w, le_f, wd)
#> [1] "14744.5714285714s (~4.1 hours)" # Expected

hms::hms(as.numeric(le_week(le_w, le_f, wd)))
#> 04:05:44.571429 # Expected

## Rounding the output at the seconds level

le_w <- lubridate::dhours(3.4094)
le_f <- lubridate::dhours(6.2345)
wd <- 2
le_week(le_w, le_f, wd)
#> [1] "19538.3828571429s (~5.43 hours)" # Expected

round_time(le_week(le_w, le_f, wd))
#> [1] "19538s (~5.43 hours)" # Expected