diff --git a/README.md b/README.md
index 3ba088c..9d237de 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,30 @@
-# Functional-data-analysis-act-dist
-Code for functional and multivariate linear regression to analyse the association between socio-demographic, behavioural, and health-related factors with a functional (activity intensity distribution function) or a scalar (time spent in a specific activity intensity range) outcome, respectively
+# Cross-sectional and prospective association of socio-demographic, behavioural, and health-related factors with objectively-assessed physical activity and sedentary time in older adults
+
+Code for functional and multivariate linear regression to analyse the association between socio-demographic, behavioural, and health-related factors with a functional (activity intensity distribution function) or a scalar (time spent in a specific activity intensity range) outcome, respectively.
+
+Data were analysed using R 3.6.1 (http://www.r-project.org), analyses required downloading of the following packages:
+- GGIR for accelerometer data processing (version 2.0-0, https://cran.r-project.org/web/packages/GGIR/vignettes/GGIR.html)
+- ks for kernel smoothing (version 1.11.7, https://cran.r-project.org/web/packages/ks/ks.pdf)
+- REFUND for function-on-scalar regressions (version 0.1-21, https://cran.r-project.org/web/packages/refund/refund.pdf)
+- pracma for trapezoidal integration of functional coefficients (version 2.2.9, https://cran.r-project.org/web/packages/pracma/pracma.pdf)
+
+## Step 1 - Data  
+Data from Whitehall II accelerometer-substudy. 
+
+Data should include:
+- the functional outcome for functional data analysis: individual activity intensity distribution function (a matrix with N lines and P columns, N corresponding to the number of subjects, P the number of points of the functional outcome)
+- the scalar outcome for multivariate linear: individual daily duration of different activity behaviors (sedentary behaviour, ligh-intensity physical activity, moderate-to-vigorous physical activity)
+- the scalar exposures: mean daily waking time, socio-demographics factors, behavioural factors, health related factors, the interaction terms, if necessary 
+
+One dataset for each wave of data collection, excluding participants with missing covariates. One dataset restricted to participants with all measurements at each wave. 
+
+## Step 2 - Load functions
+Specific functions to fit the models, extract coefficients and p values, and to plot the associations (heatmap for function-on-scalar regressions, table of coefficients for multivariate linear regressions)
+
+## Step 3 - Model fitting
+Function-on-scalar regressions (conducted on the full study population, then stratified by sex)
+Multivariate linear regressions (conducted on the full study population, then stratified by sex)
+
+## Step 4 - Tables & Figures
+Ploting the association between exposures and functional outcome using heatmaps.
+