interpretablePA was created for researchers and clinicians interested
in measuring human movement using raw-acceleration accelerometers in
various populations or individuals.
This package supports the use of cut-point-free accelerometer metrics, namely daily average acceleration (AvAcc) and intensity gradient (IG), to assess physical activity. AvAcc and IG have been shown to be a viable alternative to traditional metrics and are strongly related to various health outcomes.
The interpretablePA package contains an application that can classify
physical activity levels based on age- and sex-specific reference values
and translate cut-point-free accelerometer metrics into meaningful
outcomes. Reference values are based on a population sample of 463
healthy adults aged 20 to 89 years in Switzerland who wore the GENEActiv
on their non-dominant wrist for up to 14 days.
interpretablePA requires data processed using the R-package GGIR in a
similar manner. GGIR supports the processing of multi-day raw
accelerometer data for physical activity and sleep research. See the
GGIR package
vignette
for further information.
You can install the package from GitHub by typing the following:
install.packages("remotes")
remotes::install_github("FSchwendinger/interpretablePA")After installation, load it in R using library(interpretablePA) and
start the application by running interpret.pa().
The below images will give you some insights into the package. A typical workflow could be:
-
The user run installs and loads
interpretablePAand runsinterpret.pa(). This starts the application. -
The user decides what data format they want to enter (data of an individual, pooled means/medians of a whole study cohort that are stratified by sex or not, or data of several individuals with sample size = N) and selects the respective option under panel “1) User data”.
-
Assuming the user decides to enter data of one individual (see Figure 1), they would fill in all the fields, i.e. sex, age, height, body weight, average acceleration, and intensity gradient; then press “Calculate”.
Figure 1. One of three data entry options the user can utilize.
-
Panels “2) View results” (see Figure 2) and “3) Translation of results” (see Figure 3) are now accessible.
-
The user can find out the exact percentile the individual is on compared to our reference values and download centile plots.
Figure 2. Example of graphical output. Green dots are the data entered by the user.
-
In panel “3) Translation of results”, the user is provided with information on a) what is necessary for the individual to reach the 50th percentile or increase their physical activity by 5%, b) which changes would be needed to achieve a clinically relevant improvement in cardiorespiratory fitness, and c) how much more physical activity would need to be performed to reduce the risk of death and disease.
Figure 3. Example of the translation of results.
If you are interested in contributing or have any queries regarding this package, feel free to reach out to:
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
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## Matrix products: default
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## locale:
## [1] LC_COLLATE=English_United Kingdom.1252
## [2] LC_CTYPE=English_United Kingdom.1252
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_4.1.0 fastmap_1.1.1 cli_3.6.1 tools_4.1.0
## [5] htmltools_0.5.5 rstudioapi_0.14 yaml_2.3.7 rmarkdown_2.21
## [9] knitr_1.42 xfun_0.38 digest_0.6.31 rlang_1.1.0
## [13] evaluate_0.20
