sd_overall | R Documentation |
sd_overall()
computes the overall sleep duration in a particular shift
for the shift version of the Munich ChronoType Questionnaire (MCTQ).
See sd_week()
to compute the average weekly sleep
duration for the standard and micro versions of the MCTQ.
sd_overall(sd_w, sd_f, n_w, n_f)
sd_w |
A |
sd_f |
A |
n_w |
An integerish
|
n_f |
An integerish
|
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.).
μMCTQ 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.
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.
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.
A Duration
object corresponding to the
vectorized weighted mean of sd_w
and sd_f
with n_w
and n_f
as
weights.
Juda, Vetter, & Roenneberg (2013) and The Worldwide Experimental Platform
(n.d.) guidelines for sd_overall()
(OSD) computation
are as follows.
This computation must be applied to each section of the questionnaire. If you're using the three-shift design proposed by the MCTQ authors, you need to compute three overall sleep duration (e.g., OSD_M; OSD_E; OSD_N).
The overall sleep duration is the weighted average of the shift-specific mean sleep durations.
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.
\emptyset SD^{M/E/N} = \frac{(SD_{W}^{M/E/N} \times n_{W}^{M/E/N}) + (SD_{F}^{M/E/N} \times n_{F}^{M/E/N})}{n_W^{M/E/N} + n_{F}^{M/E/N}}
Where:
\emptyset SD^{M/E/N} = Overall sleep duration in a particular shift.
SD_W^{M/E/N} = Sleep duration between two days in a particular shift.
SD_F^{M/E/N} = Sleep duration between two free days after a particular shift.
n_W^{M/E/N} = Number of days worked in a particular shift within a shift cycle.
n_F^{M/E/N} = Number of free days after a particular shift within a shift cycle.
* W = Workdays; F = Work-free days, M = Morning shift; E = Evening shift; N = Night shift.
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 μMCTQ: 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/
Other MCTQ functions:
fd()
,
gu()
,
le_week()
,
msf_sc()
,
msl()
,
napd()
,
sd24()
,
sd_week()
,
sdu()
,
sjl_sc()
,
sjl_weighted()
,
sjl()
,
so()
,
tbt()
## Scalar example sd_w <- lubridate::dhours(5) sd_f <- lubridate::dhours(9) n_w <- 2 n_f <- 2 sd_overall(sd_w, sd_f, n_w, n_f) #> [1] "25200s (~7 hours)" # Expected sd_w <- lubridate::dhours(3.45) sd_f <- lubridate::dhours(10) n_w <- 3 n_f <- 1 sd_overall(sd_w, sd_f, n_w, n_f) #> [1] "18315s (~5.09 hours)" # Expected sd_w <- lubridate::as.duration(NA) sd_f <- lubridate::dhours(12) n_w <- 4 n_f <- 4 sd_overall(sd_w, sd_f, n_w, n_f) #> [1] NA # Expected ## Vector example sd_w <- c(lubridate::dhours(4), lubridate::dhours(7)) sd_f <- c(lubridate::dhours(12), lubridate::dhours(9)) n_w <- c(3, 4) n_f <- c(2, 4) sd_overall(sd_w, sd_f, n_w, n_f) #> [1] "25920s (~7.2 hours)" "28800s (~8 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(n_w[i], n_f[i]) lubridate::as.duration(stats::weighted.mean(x, w)) } #> [1] "28800s (~8 hours)" # Expected ## Converting the output to 'hms' sd_w <- lubridate::dhours(4.75) sd_f <- lubridate::dhours(10) n_w <- 5 n_f <- 2 sd_overall(sd_w, sd_f, n_w, n_f) #> [1] "22500s (~6.25 hours)" # Expected hms::as_hms(as.numeric(sd_overall(sd_w, sd_f, n_w, n_f))) #> 06:15:00 # Expected ## Rounding the output at the seconds level sd_w <- lubridate::dhours(5.9874) sd_f <- lubridate::dhours(9.3) n_w <- 3 n_f <- 2 sd_overall(sd_w, sd_f, n_w, n_f) #> [1] "26324.784s (~7.31 hours)" # Expected round_time(sd_overall(sd_w, sd_f, n_w, n_f)) #> [1] "26325s (~7.31 hours)" # Expected
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