mctq provides a consistent and fast way to process Munich Chronotype Questionnaire (MCTQ) data in R for all of its three versions (standard, micro, and shift).

Please note that this package is currently on the development stage and have not been peer reviewed yet. That means that people can try it out and provide feedback, but it comes with no promises for long term stability.

If you’re familiar with MCTQ, you know that it’s not a simple questionnaire, as it requires a lot of date/time manipulation. That can be real challenging, especially if you’re dealing with a large set of data.

The main advantage to use the mctq package in your research is that you will have reliable tools, thoroughly tested, at your disposition, made and supported by a sleep science research group (GIPSO) from a well know university (USP). mctq also helps with research reproducibility, since it’s a free and open source package that anyone can find and use.

This package is also equipped with several utility functions that allows you to easily convert and visualize your MCTQ data. It also provides fictional datasets for testing and learning purposes.

The first stable mctq version is in its final development stage, we hope that it will be available on CRAN soon. Until that moment comes, you can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("gipsousp/mctq", dependencies = TRUE)

mctq works with a set of object classes specially created to hold time values. This classes can be found in the hms and lubridate package from tidyverse. If your data do not conform to the object classes required, don’t worry, just use convert() to convert it. You can always convert it back if you want.

Here are some examples of how to convert your data using convert():

# From decimal hours to `hms`
convert(6.5, "hms", input_unit = "H")
#> 06:30:00
# From radians to `Duration`
convert(1.308997, "Duration", input_unit = "rad")
#> [1] "18000s (~5 hours)"
# From radians to decimal minutes
convert(0.2617994, "numeric", input_unit = "rad", output_unit = "M")
#> [1] 60
# From `character` `HMS` to `Duration`
convert("19:55:17", "Duration", orders = "HMS")
#> [1] "71717s (~19.92 hours)"
# From `character` `HM AM/PM ` to `hms`
convert("10:00 PM", "hms", orders = "IMp")
#> 22:00:00

After your data is all set, just use the mctq functions below to process it.

Note that mctq uses a similar naming scheme as those used in the MCTQ articles, that make it easy to find and apply any computation necessary.

• fd() compute MCTQ work-free days
• so() compute MCTQ sleep onset
• gu() compute MCTQ local time of getting out of bed
• sd() compute MCTQ sleep duration
• napd() compute MCTQ nap duration (only for MCTQ Shift)
• tbt() compute MCTQ total time in bed
• ms() compute MCTQ mid-sleep
• sd24() compute MCTQ 24h sleep duration (only for MCTQ Shift)

Example:

# Local time of preparing to sleep on workdays
sprep_w <- c(hms::parse_hms("23:45:00"), hms::parse_hms("02:15:00"))
# Sleep latency on workdays
slat_w <- c(lubridate::dminutes(30), lubridate::dminutes(90))
# Sleep onset
so(sprep_w, slat_w)
#> 00:15:00
#> 03:45:00

For computations combining workdays and work-free days, use:

• sd_week() compute MCTQ average weekly sleep duration
• sd_overall() compute MCTQ overall sleep duration (only for MCTQ Shift)
• sjl() and sjl_rel() compute MCTQ social jet lag
• sjl_weighted() compute MCTQ absolute social jetlag across all shifts (only for MCTQ Shift)
• sloss_week() compute MCTQ weekly sleep loss
• msf_sc() compute MCTQ chronotype or corrected mid-sleep on work-free days
• le_week() compute MCTQ average weekly light exposure

Example:

# Mid-sleep on workdays
msw <- c(hms::parse_hms("02:05:00"), hms::parse_hms("04:05:00"))
# Mid-sleep on work-free days
msf <- c(hms::parse_hms("23:05:00"), hms::parse_hms("08:30:00"))
# Relative social jetlag
sjl_rel(msw, msf)
#> [1] "-10800s (~-3 hours)"  "15900s (~4.42 hours)"

In addition to convert(), mctq is also equipped with many other utilities functions.

All functions are well documented, showing all the guidelines behind the computations. Click here to view a list of them.

If you use mctq in your research, please consider citing it. We put a lot of work to build and maintain a free and open source R package. You can find mctq citation below.

citation("mctq")
#> 
#> To cite mctq in publications use:
#> 
#>   Vartanian D., Benedito-Silva, A. A., Pedrazzoli, M. (2021). mctq: a R
#>   package for the Munich ChronoType Questionnaire (MCTQ). Retrieved
#>   from https://gipsousp.github.io/mctq/ .
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Unpublished{,
#>     title = {mctq: a R package for the Munich ChronoType Questionnaire (MCTQ)},
#>     author = {Daniel Vartanian and Ana Amelia Benedito-Silva and Mario Pedrazzoli},
#>     year = {2021},
#>     url = {https://gipsousp.github.io/mctq/},
#>     note = {Lifecycle: maturing},
#>   }

mctq is a community project, anyone and everyone is welcome to contribute. Take a moment to review our Guidelines for Contributing.

Please note that mctq is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.