Merge pull request #25 from pydemull/dev

Dev

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: activAnalyzer
 Title: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
-Version: 1.0.5.9000
+Version: 1.1.0
 Authors@R: person('Pierre-Yves', 'de Müllenheim', email = 'pydemull@uco.fr', role = c('cre', 'aut'), comment = c(ORCID = "0000-0001-9157-7371"))
 Description: A tool to analyse 'ActiGraph' accelerometer data and to implement 
     the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis

NEWS.md

@@ -1,4 +1,4 @@
-# activAnalyzer (development version)
+# activAnalyzer 1.1.0
 
 * Added the `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.
 * Updated in the guide the description of the computation of BMR: "If the patient considers their sex as `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).".

R/app_ui.R

@@ -32,7 +32,7 @@ app_ui <- function(request) {
                 tags$style(".main-header {vertical-align: middle;}"),
                 tags$style(".main-header .logo {vertical-align: middle;}")
         ),
-        title = span(img(src="www/favicon.png", width = 30), "activAnalyzer dev"), titleWidth = 237
+        title = span(img(src="www/favicon.png", width = 30), "activAnalyzer 1.1.0"), titleWidth = 237
                                       ),
       shinydashboardPlus::dashboardSidebar( 
         tags$style(HTML(".sidebar-menu li a {font-size: 17px;}")),

joss/paper.md

@@ -40,7 +40,7 @@ Due to the large size of data files to analyse when using accelerometry and due
 Other ways than ActiLife software to analyse activity counts include using programming languages. R [@rcoreteamLanguageEnvironmentStatistical2022] and Python [@pythonsoftwarefoundationPython2022] have been programming languages commonly used by scientists to build tools aiming at fostering physical activity data analysis. In R, the 'accelerometry' and 'nhanesaccel' packages by Van Domelen and Pittard [-@vandomelenFlexibleFunctionsProcessing2014], the 'actigraph.sleepr' package by Petkova [-@petkovaActigraphSleeprDetect2021], and the 'pawacc' package by Geraci [-@geraciPawaccPhysicalActivity2017], provide several functions to perform analyses of interest with activity counts. In Python, the 'pyActigraphy' library by Hammad and Reyt [-@hammadPyActigraphy2020] also allows, among various other features, to handle ActiGraph activity counts. While useful for research settings, these ressources may be of a little interest for other settings where people have no programming skills, because they do not propose a GUI (graphical user interface) to help people who do not code and who have no time to learn this skill. Beyond the lack of a free and simple interface to analyse ActiGraph activity counts data, there is, to our knowledge, no app that allows an easy implementation of the PROactive framework with COPD patients that would be based on an analysis of ActiGraph activity counts. This is why we have developed the 'activAnalyzer' app. For now, a first main interest of this app is to allow teaching large groups of students or professionnals, who have no programming skills, to analyse activity counts for assessing physical behaviour. A second main interest is to allow an easy implementation of the PROactive framework with COPD patients when working with an ActiGraph accelerometer, this by clinicians, healthcare providers and/or researchers, either in clinical routine or in research setting.
 
 #  Functionality and purpose of the software
-'activAnalyzer' is an app built as a package using R programming language. The app can be used in three different ways as explained elsewhere (https://pydemull.github.io/activAnalyzer/), including (i) a standalone desktop application for Windows machines only thanks to the [DesktopDeployR framework developed by Lee Pang](https://github.com/wleepang/DesktopDeployR); (ii) using [R](https://CRAN.R-project.org/) and [RStudio](https://www.rstudio.com/) software along with the CRAN version of the 'activAnalyzer' package (v1.0.5) or its development version from GitHub; (iii) using a [shinyapps.io plateform ](https://pydemull.shinyapps.io/activAnalyzer/). If used with R and RStudio, the app will require the installation of the [TinyTeX distribution](https://yihui.org/tinytex/) to generate .pdf reports, as explained on the [app website](https://pydemull.github.io/activAnalyzer/).
+'activAnalyzer' is an app built as a package using R programming language. The app can be used in three different ways as explained elsewhere (https://pydemull.github.io/activAnalyzer/), including (i) a standalone desktop application for Windows machines only thanks to the [DesktopDeployR framework developed by Lee Pang](https://github.com/wleepang/DesktopDeployR); (ii) using [R](https://CRAN.R-project.org/) and [RStudio](https://www.rstudio.com/) software along with the CRAN version of the 'activAnalyzer' package or its development version from GitHub; (iii) using a [shinyapps.io plateform ](https://pydemull.shinyapps.io/activAnalyzer/). If used with R and RStudio, the app will require the installation of the [TinyTeX distribution](https://yihui.org/tinytex/) to generate .pdf reports, as explained on the [app website](https://pydemull.github.io/activAnalyzer/).
 
 When the user opens the app, they has to deal with four ordered sections. The first section allows the user to complete information related to the measurement setup (patient's characteristics, device position, etc.). In the second section, the user must upload an .agd data file ('.agd' being the extension of the initial file generated by ActiLife software when the user wants to work with activity counts data). Then, the user has to configure the app to detect nonwear time. The results from this first analysis can be visualised by the user, as shown in \autoref{fig:nonwear}.