activAnalyzer

activAnalyzer: An R Shiny app to analyse ActiGraph accelerometer data and to implement the use of the PROactive Physical Activity in COPD instruments - Published in JOSS (2022)

https://github.com/pydemull/activanalyzer

Keywords

Scientific Fields

Repository

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-",
  out.width = "100%"
)
```

# activAnalyzer


[![CRAN status](https://www.r-pkg.org/badges/version/activAnalyzer)](https://CRAN.R-project.org/package=activAnalyzer)
[![Codecov test coverage](https://codecov.io/gh/pydemull/activAnalyzer/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pydemull/activAnalyzer?branch=master)
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![R-CMD-check](https://github.com/pydemull/activAnalyzer/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/pydemull/activAnalyzer/actions/workflows/R-CMD-check.yaml)
[![](http://cranlogs.r-pkg.org/badges/grand-total/activAnalyzer?color=blue)](https://cran.r-project.org/package=activAnalyzer)
[![status](https://joss.theoj.org/papers/5d6659af1bf8ca2fb977c189039b8315/status.svg)](https://joss.theoj.org/papers/10.21105/joss.04741)


activAnalyzer is a Shiny app that has been developed to analyze daily physical behavior data recorded at the hip in adults using an ActiGraph accelerometer (.agd file from a **GT3X**, **GT3X+**, **wGT3X+** or **wGT3X-BT** device). Once analysis is completed, the app allows exporting results to .csv files and generating a report of the measurement (in either an .html format or a .pdf format). All the configured inputs relevant for interpreting the results are recorded in the report. Be sure that the inputs that are configured when generating the report correspond to the analysis that was actually performed (in other words, avoid modifying the inputs after generating satisfactory results). In addition to an analysis of physical behavior, the app also allows to implement the [Daily- and Clinical visit-PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments (D-PPAC and C-PPAC)](https://erj.ersjournals.com/content/early/2015/05/28/09031936.00183014). Please read the [user's guide](https://github.com/pydemull/activAnalyzer/blob/master/inst/guide/user_guide_en.pdf) for details about how the app works.

## Usage

There are three different ways to use the activAnalyzer app:

* [On the web via a shinyapps.io platform](https://pydemull.shinyapps.io/activAnalyzer/) (stable version). For information, as indicated by [RStudio](https://www.shinyapps.io/), "*shinyapps.io is secure-by-design. Each Shiny application runs in its own protected environment and access is always SSL encrypted*". Importantly, the app is hosted using a free account that allows to run apps for 25 hours per month. Thus, the availability of the app on the web is very dependent on the number of users as well as the time spent by each user on the app. Moreover, as computations when using the app can be quite intensive, it is possible that speed and stability of this online version of the app become sometimes compromised. For these reasons, this option should be considered as a way to have a quick look at how the app works. The other available options (please see below) will be more appropriate for working with the app on a regular basis. Of note, Google Chrome and Microsoft Edge browsers allow the app to work as expected but Mozilla Firefox does not seem to allow resetting all the inputs when required.
* [On your machine via a standalone desktop app](https://sourceforge.net/projects/activanalyzer)  that is downloadable from the SourceForge website (stable version, for Windows machines only). The standalone app has been developed using the framework *DesktopDeployR* made available by [W. Lee Pang](https://github.com/wleepang). Explanations related to this framework can be retrieved from [a dedicated GitHub repository](https://github.com/wleepang/DesktopDeployR). Once the app is installed on your PC, you will have to double-click on the desktop app icon (if you chose this option during the installation process), which will run the R-portable version embedded in the app and then will launch the app in your default web browser with 127.0.0.1 as the value for the host parameter. This means that only your current machine will can access the app. You will can open only one session at a time. As written above, Google Chrome and Microsoft Edge browsers allow the app to work as expected but Mozilla Firefox does not seem to allow resetting all the inputs when required. Due to the extra work required to maintain such a format of the app up to date, it is not planned for the moment to provide updates for version 2.0.2 and newer versions.
* On your machine via [R software](https://CRAN.R-project.org/) (version: $\ge$ 3.4.0), the [RStudio environment](https://posit.co/), and the [activAnalyzer package](https://github.com/pydemull/activAnalyzer) installable from CRAN (stable version) or from GitHub (development version). With version 2.0.1 and former versions, the app is launched in the RStudio window by default. For these versions, unfortunately only the RStudio version called Prairie Trillium [2022.02] and former versions allow to correctly quit the app from the RStudio window. Since version 2.0.2, the app is launched in the default web browser by default and could be used with the latest RStudio version (hopefully) without problems. Whatever the version used, to be able to generate a .pdf report, you will have to install the [TinyTeX distribution](https://yihui.org/tinytex/). The first time you will generate a .pdf report, you will have to wait some time so that the required packages are installed on your machine. In short, after installing R and RStudio, you can run the following command lines in the RStudio console:

``` {r, eval=FALSE}
# For CRAN version:

## Code for installing the activAnalyzer package (stable version)
install.packages("activAnalyzer")

## Code for installing the TinyTex distribution
install.packages("tinytex")
tinytex::install_tinytex()
```



```{r, eval=FALSE}
# For development version:

## Code for installing the activAnalyzer package (development version)
install.packages("devtools")
devtools::install_github("pydemull/activAnalyzer")

## Code for installing the TinyTex distribution
install.packages("tinytex")
tinytex::install_tinytex()
```

## Example

To launch the app using R:

```{r install, eval=FALSE}
library(activAnalyzer)
activAnalyzer::run_app()
```


```{r, include=FALSE, eval=FALSE}
You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date:
devtools::build_readme()

You could also use GitHub Actions to re-render `README.Rmd` every time you push. An example workflow can be found here: .
```

## Code of Conduct

Please note that the activAnalyzer project is released with a [Contributor Code of Conduct](https://pydemull.github.io/activAnalyzer/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

JOSS Publication

activAnalyzer: An R Shiny app to analyse ActiGraph accelerometer data and to implement the use of the PROactive Physical Activity in COPD instruments

Published

December 01, 2022

DOI

10.21105/joss.04741

Volume 7, Issue 80, Page 4741

Authors

Pierre-Yves de Müllenheim
Institut de formation en éducation physique et en sport d'Angers (IFEPSA-UCO), Les Ponts-de-Cé, France

Arnaud Chambellan
Groupement des Hôpitaux de l’Institut Catholique de Lille (GHICL), Hôpital Saint Philibert, Lomme, France

Editor

Samuel Forbes

Tags

physical activity sedentary behaviour accelerometer actigraph COPD PROactive instruments Shiny

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