M30: the
count value (in counts/epoch duration) at and above
which the most active 30 minutes are accumulated over the day.compute_peak_step_acc()) to
compute peak step accumulation only when there is the minimum number of
minutes required to perform computation. It returns NA otherwise.compute_mx()) to compute MX
metrics only when there is the minimum amount of time required to
perform the targetted computation. It returns NA otherwise.compute_accumulation_metrics()
function.dplyr::left_join() function
following the release of dplyr package v1.1.0.recap_by_day() function now returns a list, not a
dataframe.tbl_agd.create_fig_res_by_day() now allows to
visualise all metrics by day (activity volume, step accumulation,
intensity distribution).intersex and prefer not to say
categories to provide a more inclusive classification of sex. As it
seems there is no scientific study about what should be the calculation
of resting and activity energy expenditures for intersex people, the
values provided for Basal metabolic rate (BMR) and METs are the averages
of two values: the value that would be computed for a male, and the
value that would be computed for a female. For people reporting
prefer not to say, computations for females are used by
default.undefined or chooses the
prefer not to say option, then an equation for females is
used. If the patient falls into the intersex category, then
the average of the results for a male and for a female of the considered
age is used (WARNING: At the time of writing this guide, there is no
scientific data to justify any calculation for intersex people).”.undefined or chooses the
prefer not to say option, then equations including sex
information, when selected, are used as if the patient were a female;
when the intersex category is used, an average of the METs
related respectively to a male and to a female is used with the
equations using sex information; of note, at the time of writing this
guide, there is no scientific data to justify any calculation for
intersex people);”.compute_bmr() and compute_mets()
functions now use the dplyr::case_when() function to
determine the appropriate value of BMR and METs, respectively.prepare_dataset() function imports data using
the read_agd() function (instead of the
PhysicalActivity::readActigraph() function). This
modification now allows to import data from the GT3X device (previously
only data from GT3X+ and newer devices could be used). This was not
possible before because the structure of the .agd file obtained with a
GT3X device is not accepted by the
PhysicalActivity::readActigraph() function.size arguments of the internal
geom_line(), geom_segment() and
geom_rect() functions by linewidth arguments
in relation to the v3.4.0 {ggplot2} update.as.character() by format() in the
mark_wear_time() function so that there is no more error
when checking for R dev versions.verify_fa = FALSE to
icon() functions in the UI to remove an error message that
appeared when running the app.mark_intensity() function: the intensity
category numbers associated to the Nonwear, SED, LPA, and MVPA
categories (that are present only in the exported marked whole dataset
when using the app) were not as expected because they were obtained by
converting a factor vector to a numeric vector. Now the conversion is
done from a character vector to a numeric vector, which keeps the
numerical order as expected. This error had no impact on the results,
nor on the figures provided by the package/app. The exported marked
dataset has now the corrected intensity category numbers, that is: 0 for
Nonwear, 1 for SED, 2 for LPA, and 3 for MVPA.NEWS.md file to track changes to the
package.
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