mctq (version 0.3.2)

sjl_weighted: Compute MCTQ absolute social jetlag across all shifts

Description

sjl_weighted() computes the absolute social jetlag across all shifts for the shift version of the Munich ChronoType Questionnaire (MCTQ).

Usage

sjl_weighted(sjl, n_w)

Value

A Duration object corresponding to the vectorized weighted mean of sjl with n_w as weights.

Arguments

sjl

A list object with Duration elements corresponding to the social jetlag in each shift from a shift version of the MCTQ questionnaire (you can use sjl() to compute it). sjl elements and values must be paired with n elements and values.

n_w

A list object with integerish integer or double elements corresponding to the number of days worked in each shift within a shift cycle from a shift version of the MCTQ questionnaire. n elements and values must be paired with sjl elements and values.

Operation

The shift version of the MCTQ was developed for shift-workers rotating through morning-, evening-, and night-shifts, but it also allows adaptations to other shift schedules (Juda, Vetter, & Roenneberg, 2013). For this reason, sjl_weighted() must operate with any shift combination.

Considering the requirement above, sjl_weighted() was developed to only accept list objects as arguments. For this approach to work, both sjl and n_w arguments must be lists with paired elements and values, i.e., the first element of sjl (e.g., sjl_m) must be paired with the first element of n_w (e.g., n_w_m). The function will do the work of combining them and output a weighted mean.

Guidelines

Juda, Vetter, & Roenneberg (2013) and The Worldwide Experimental Platform (n.d.) guidelines for sjl_weighted() (\(\emptyset SJL_{weighted}\)) computation are as follows.

Notes

• The absolute social jetlag across all shifts (\(\emptyset SJL_{weighted}\)) is the weighted average of all absolute social jetlags.

• The authors describe an equation for a three-shift schedule, but this may not be your case. That's why this function works a little bit differently (see the Operation section), allowing you to compute a weighted average with any shift combination.

• 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

$$\emptyset SJL_{weighted} = \frac{(| SJL^{M} | \times n_{W}^{M}) + (| SJL^{E} | \times n_{W}^{E}) + (| SJL^{N} | \times n_{W}^{N})}{n_{W}^{M} + n_{W}^{E} + n_{W}^{N}}$$

Where:

• \(\emptyset SJL_{weighted}\) = Absolute social jetlag across all shifts.

• \(SJL^{M/E/N}\) = Absolute social jetlag in each shift.

• \(n_{W}^{M/E/N}\) = Number of days worked in each shift within a shift cycle.

* \(W\) = Workdays; \(F\) = Work-free days, \(M\) = Morning shift; \(E\) = Evening shift; \(N\) = Night shift.

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(), le_week(), msf_sc(), msl(), napd(), sd24(), sd_overall(), sd_week(), sdu(), sjl_sc(), sjl(), so(), tbt()

Examples

## Scalar example

sjl <- list(sjl_m = lubridate::dhours(1.25),
            sjl_e = lubridate::dhours(0.5),
            sjl_n = lubridate::dhours(3))
n_w <- list(n_w_m = 3, n_w_e = 1, n_w_n = 4)
sjl_weighted(sjl, n_w)
#> [1] "7312.5s (~2.03 hours)" # Expected

sjl <- list(sjl_m = lubridate::dhours(1.25),
            sjl_e = lubridate::as.duration(NA),
            sjl_n = lubridate::dhours(3))
n_w <- list(n_w_m = 3, n_w_e = 1, n_w_n = 4)
sjl_weighted(sjl, n_w)
#> [1] NA # Expected

## Vector example

sjl <- list(sjl_m = c(lubridate::dhours(2), lubridate::dhours(2.45)),
            sjl_e = c(lubridate::dhours(3.21), lubridate::as.duration(NA)),
            sjl_n = c(lubridate::dhours(1.2), lubridate::dhours(5.32)))
n_w <- list(n_w_m = c(1, 3), n_w_e = c(4, 1), n_w_n = c(3, 3))
sjl_weighted(sjl, n_w)
#> [1] "8298s (~2.31 hours)" NA # Expected

## Checking the first output from vector example

if (requireNamespace("stats", quietly = TRUE)) {
    i <- 1
    x <- c(sjl[["sjl_m"]][i], sjl[["sjl_e"]][i], sjl[["sjl_n"]][i])
    w <- c(n_w[["n_w_m"]][i], n_w[["n_w_e"]][i], n_w[["n_w_n"]][i])
    lubridate::as.duration(stats::weighted.mean(x, w))
}
#> [1] "8298s (~2.31 hours)" # Expected

## Converting the output to hms

sjl <- list(sjl_m = lubridate::dhours(0.25),
            sjl_e = lubridate::dhours(1.2),
            sjl_n = lubridate::dhours(4.32))
n_w <- list(n_w_m = 4, n_w_e = 2, n_w_n = 1)

sjl_weighted(sjl, n_w)
#> [1] "3970.28571428571s (~1.1 hours)" # Expected

hms::as_hms(as.numeric(sjl_weighted(sjl, n_w)))
#> 01:06:10.285714 # Expected

## Rounding the output at the seconds level

round_time(sjl_weighted(sjl, n_w))
#> [1] "3970s (~1.1 hours)" # Expected

round_time(hms::as_hms(as.numeric(sjl_weighted(sjl, n_w))))
#> 01:06:10 # Expected