This article provides notes on
different approaches to computing social jetlag (SJL) for the
Munich ChronoType Questionnaire (MCTQ). It also explains how the
method
argument in the sjl_rel()
,
sjl()
, sjl_sc_rel()
, and sjl_sc()
functions works.
It’s helpful to have the standard MCTQ questionnaire and the guidelines for standard MCTQ variable computation open while reading this article. This will enhance your understanding of the data objects discussed. You can download the MCTQ full standard version here and the guidelines for standard MCTQ variables here.
According to Roenneberg et al. (2012), the relative social jetlag (SJLrel), i.e., the discrepancy between social and biological time, must be computed as the difference between MSF (local time of mid-sleep on work-free days) and MSW (local time of mid-sleep on workdays).
SJLrel = MSF − MSW
This simple equation seems trivial until you consider that it deals with two time values detached from a timeline. In other words, MSW and MSF represent two moments in different contexts (workdays and work-free days).
If you dive into the MCTQ articles, you can see that this computation have two objectives:
Represent the distance between MSW and MSF (i.e., the discrepancy).
Establish what value comes before or after the other, representing that with a +/− signal. That is, when MSW comes before MSF, SJLrel must be positive, and when MSW comes after MSF, SJLrel must be negative.
To represent the distance between MSW and MSF (i.e., the discrepancy).
To establish what value comes before or after the other, representing that with a +/− signal. That is, when MSW comes before MSF, SJLrel must be positive, and when MSW comes after MSF, SJLrel must be negative.
You can find the rationale about the SJLrel signal in Roenneberg et al. (2019) (see item “3.2 Social Jetlag Computation”).
Most people have some trouble understanding this. To illustrate what we mean, let’s visualize a timeline overlapping an MSW and MSF value:
day 1 day 2
MSF MSW MSF MSW
05:00 21:00 05:00 21:00
-----|------------------|---------|------------------|----->
16h 8h 16h
longer int. shorter int. longer int.
Note that, while doing the representation above, we’re dealing with the assumption that MSW and MSF can be represented in a two-day timeline since people don’t usually sleep more than 24 hours (basic assumption).
As you can see, by overlapping two time values in a two-day timeline, we need to make a choice of what interval to use. For most people MSF and MSW are close to each other, so, usually, we are looking for the shorter interval between the two. But, in some extreme cases, usually when dealing with shift workers, MSW and MSF distance can surpass 12 hours, making the longer interval the correct answer.
To obtain the SJLrel signal we must check the start value of the interval. If the interval between MSW and MSF starts with MSW, that means that MSW comes before MSF, hence, the signal must be positive. Else, if the interval between MSW and MSF starts with MSF, that means that MSW comes after MSF, hence, the signal must be negative.
day 1 day 2
MSW MSF
21:00 05:00
------------------------|---------|------------------------>
day 1 day 2
MSF MSW
21:00 05:00
------------------------|---------|------------------------>
We call this the two intervals problem. It represents an unsolvable mathematical scenario, if you deprive it of the respondent context. That can generate minor errors when computing SJL, especially if you’re dealing with large datasets.
the sjl_rel()
, sjl()
,
sjl_sc_rel()
, and sjl_sc()
functions provides
an argument called method
that allows you to choose three
different methods to deal with the two intervals problem. Here’s how
they work.
The sjl()
function will be used in the examples, but the
same logic apply to the other sjl
functions.
method = "difference"
By using method = "difference"
, sjl()
will
do the exact computation proposed by the MCTQ authors, i.e., SJL will be
computed as the linear difference between MSF and MSW.
Let’s see some examples using this method.
"difference"
methodMSW = 04:00
MSF = 06:00
Real difference: + 02:00
MSF − MSW = 06:00 − 04:00 = + 02:00 (right)
"difference"
methodMSW = 23:00
MSF = 03:00
Real difference: + 04:00
MSF − MSW = 03:00 − 23:00 = - 20:00 (wrong)
As you can see with the second example, the "difference"
method uses a linear time frame approach, creating problems regarding
the circularity of time.
method = "shorter"
(default method)By using method = "shorter"
, sjl()
uses the
shorter interval between MSW and MSF.
This is the most reliable method we found to compute SJL, considering
the context of the MCTQ data. However, it comes with a limitation: when
MSW and
MSF values
distance themselves by more than 12 hours, sjl()
can return
a wrong output. From our experience with MCTQ data, a SJL greater than
12 hours is highly improbable.
Let’s see some examples using this method.
"shorter"
methodMSW = 04:00
MSF = 06:00
Real difference: + 02:00
day 1 day 2
MSF MSW MSF MSW
06:00 04:00 06:00 04:00
-----|------------------|---------|------------------|----->
22h 2h 22h
longer int. shorter int. longer int.
By using the shorter interval, MSW comes before MSF, so SJLrel must be equal to + 02:00 (right).
"shorter"
methodMSW = 23:00
MSF = 03:00
Real difference: + 04:00
day 1 day 2
MSF MSW MSF MSW
03:00 23:00 03:00 23:00
-----|------------------|---------|------------------|----->
20h 4h 20h
longer int. shorter int. longer int.
By using the shorter interval, MSW comes before MSF, so SJLrel must be equal to + 04:00 (right).
"shorter"
method failsMSW = 12:00
MSF = 23:00
Real difference: - 13:00
day 1 day 2
MSW MSF MSW
12:00 23:00 12:00
-----|-----------------------|------------------------|----->
11h 13h
shorter int. longer int.
By using the shorter interval, MSW comes before MSF, so SJLrel must be equal to + 11:00 (wrong).
You can see example 7 in the shift_mctq
dataset provided
by the mctq
package (ID 39, on and after night shifts).
That’s the only MCTQShift
case in shift_mctq
where we think that the
"shorter"
method would fail.
method = "longer"
By using method = "longer"
, sjl()
uses the
longer interval between MSW and MSF. It’s just the
opposite of the "shorter"
method showed above.
We recommend that you always use the "shorter"
method
when computing SJLrel
or SJL (the
default sjl()
method).
In our tests, the "shorter"
method demonstrated to be
almost fail-safe. You just need to worry about the SJL computation if
you are dealing with shift workers.
When dealing with a large MCTQShift dataset, it will be very difficult to identify SJL errors, unless you look case by case and check the results with your respondents. This is usually not a viable option. We recommend that you mention which method you use to compute SJL and add it as a possible limitation of your results.