diff --git a/.Rbuildignore b/.Rbuildignore
index f58723f3..f02115e1 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -32,3 +32,4 @@ $run_dev.*
 ^Meta$
 _\.new\.png$
 ^temp$
+^\.github$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index c5d9dadd..c81d6459 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -1,4 +1,4 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/master/examples
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
@@ -19,16 +19,15 @@ jobs:
       matrix:
         config:
           - {os: windows-latest, r: 'release'}
-          - {os: ubuntu-latest,   r: 'release'}
-          - {os: windows-latest, r: 'devel'}
-          - {os: ubuntu-latest, r: 'devel'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -37,26 +36,13 @@ jobs:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
-          
-      - name: Install sessioninfo
-        run: |
-          install.packages("sessioninfo")
-        shell: Rscript {0}
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck
-          
-      - uses: r-lib/actions/check-r-package@v2
-
-      - name: Show testthat output
-        if: always()
-        run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true
-        shell: bash
+          needs: check
 
-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - uses: r-lib/actions/check-r-package@v2
         with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
+          upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index dc6c1d4b..bf6c7519 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -1,4 +1,4 @@
-# Workflow derived from https://github.com/rstudio/shinytest2/tree/main/actions/test-app/example-test-app-description.yaml
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
@@ -6,41 +6,45 @@ on:
   pull_request:
     branches: [main, master]
 
-name: Test app w/ {renv}
+name: test-coverage
 
 jobs:
-  test-app:
-    runs-on: ${{ matrix.config.os }}
-
-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
-
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          - {os: ubuntu-latest, r: release}
-
+  test-coverage:
+    runs-on: windows-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-      R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v3
-
-      - uses: r-lib/actions/setup-pandoc@v2
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
         with:
-          r-version: ${{ matrix.config.r }}
           use-public-rspm: true
 
-      - uses: r-lib/actions/setup-renv@v2
-
-      - uses: rstudio/shinytest2/actions/test-app@actions/v1
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          app-dir: "."
-          
+          extra-packages: any::covr
+          needs: coverage
+
       - name: Test coverage
-        run: covr::codecov(quiet = FALSE, clean = FALSE)
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
         shell: Rscript {0}
-          
\ No newline at end of file
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package
diff --git a/R/compute_accumulation_metrics.R b/R/compute_accumulation_metrics.R
index 3ce733c7..b9a5081c 100644
--- a/R/compute_accumulation_metrics.R
+++ b/R/compute_accumulation_metrics.R
@@ -98,18 +98,16 @@ compute_accumulation_metrics <- function(
     zoom_to = "23:59:59"
     ){
   
-# Filtering data based on selected dates and time periods, and adding a 
-# column containing "SED", NON-SED", or "Nonwear" labels
+# Getting selected dates
 if (is.null(dates)) {
   selected_dates <- attributes(as.factor(data$date))$levels
 } else {
     selected_dates <- attributes(as.factor(dates))$levels
 }
   
-  # Fix bug: Convert Nas to "Nonwear"
-  data[[col_cat_int]] <- dplyr::if_else(is.na(data[[col_cat_int]]), "Nonwear", data[[col_cat_int]])
-  
-  data <-
+# Filtering data based on selected dates and time periods, and adding a 
+# column 'new_intensity_category' containing "SED", PA, or "Nonwear" values 
+data <-
     data %>%
     dplyr::filter(
       date %in% as.Date(selected_dates) &
@@ -127,28 +125,28 @@ if (is.null(dates)) {
             "SED",
             dplyr::if_else(
               .data[[col_cat_int]] == "Nonwear",
-              "Nonwear",
-              dplyr::if_else(is.na(.data[[col_cat_int]]), "Nonwear", "Nonwear")
+              "Nonwear", NA
             )
           )
         )
     )
 
-# Updating bouts IDs
+# Setting IDs for the new PA/SED/Nonwear bouts
 data$new_intensity_category <- as.factor(data$new_intensity_category)
 data$new_intensity_category_num <- as.numeric(as.character(forcats::fct_recode(data$new_intensity_category , "0" = "Nonwear", "1" = "SED", "2" = "PA")))
 data$new_bout <- cumsum(c(1, as.numeric(diff(data$new_intensity_category_num))!= 0))
 
-# Getting arguments
+# Getting the type of activity bout to be analyzed
 behaviour <- match.arg(behaviour)
 if(behaviour == "sed") {BEHAV <- "SED"; color_fill = c("#D9DBE5", "#A6ADD5", "#6A78C3", "#3F51B5"); auto_text = "sedentary"} 
 if(behaviour == "pa") {BEHAV <- "PA"; color_fill = c("#EDD3DD", "#F38DB6", "#FA3B87", "#FF0066"); auto_text = "physical activity"} 
   
-# Getting correction factor related to the epoch length (reference epoch = 60 s);
+# Getting the correction factor related to the epoch length (reference epoch = 60 s);
 # bout durations are computed in minutes
 cor_factor = 60 / (as.numeric(data[[col_time]][2] - data[[col_time]][1]))
  
-# Summarising bout durations (in minutes) of interest by day
+# Getting all the identified bouts of the kind of interest and their respective 
+# durations (in minutes) 
 recap_bouts_by_day <-
   data %>%
   dplyr::group_by(date, new_bout, new_intensity_category) %>%
@@ -166,7 +164,7 @@ recap_bouts_by_day <-
   )
 
 
-# Computing mean daily number of breaks
+# Computing the mean of the daily number of breaks
 mean_breaks <-
   recap_bouts_by_day %>%
   dplyr::ungroup(new_bout, new_intensity_category) %>%
@@ -206,19 +204,21 @@ mean_breaks <-
          fill = dur_cat
          )
      ) +
-     geom_rect(aes(
+     annotate(
+       geom = "rect",
        xmin = hms::as_hms(0), 
        xmax =  hms::as_hms(valid_wear_time_start), 
        ymin = -Inf, 
-       ymax = Inf), 
+       ymax = Inf, 
        color = "grey",
        fill = "grey"
      ) +
-     geom_rect(aes(
+     annotate(
+       geom = "rect",
        xmin = hms::as_hms(valid_wear_time_end), 
        xmax =  hms::as_hms("23:59:59"),
        ymin = -Inf, 
-       ymax = Inf), 
+       ymax = Inf, 
        color = "grey",
        fill = "grey"
      ) +
@@ -266,7 +266,8 @@ mean_breaks <-
      geom_vline(aes(xintercept = 3600*22),   linetype = "dotted", color = "grey50") +
      geom_vline(aes(xintercept = 3600*23),   linetype = "dotted", color = "grey50")
 
-# Summarising bout durations (in minutes) of interest without grouping by day
+# Getting all the identified bouts of the kind of interest and their respective 
+# durations (in minutes) without grouping by day
 recap_bouts <-
   data %>%
   dplyr::group_by(new_bout, new_intensity_category) %>%
@@ -308,7 +309,7 @@ summarised_bouts <-
   )
     
 
-# Fitting cumulated fraction of time vs bout duration
+# Fitting cumulated fraction of time vs bout duration relationship
 model <- nls(
   cum_frac_time ~ duration^x / (duration^x + UBD^x), 
   data = summarised_bouts, 
@@ -333,26 +334,26 @@ max_bout_duration <- max(summarised_bouts$duration)
       duration = seq(xmin, max_bout_duration, 0.1)
     ) %>% 
     dplyr::mutate(
-      pred = duration ^ (-alpha) ,
+      pred = duration ^ (-alpha),
       pred = duration ^ (-alpha) / max(pred) * max(summarised_bouts$n, na.rm = TRUE)
       ) 
   
   # Building the graphic
    p_alpha <-
-     ggplot(data = recap_bouts) + 
+     ggplot(data = recap_bouts |> dplyr::ungroup(new_bout)) + 
      geom_histogram(aes(x = duration, fill = dur_cat), binwidth = xmin) +
      scale_fill_manual(values = color_fill) +
      labs(x = "Bout duration (min)", y = "n", fill = "Duration (min)") +
-     geom_line(data = df_pred_alpha, aes(x = duration, y = pred), linewidth = 0.8, color = "grey10") +
+     geom_line(data = df_pred_alpha, aes(x = duration, y = pred), linewidth = 0.5, color = "grey10") +
      annotate("text", x = max_bout_duration/2, y = max(summarised_bouts$n, na.rm = TRUE)/2, label = paste("alpha =", round(alpha, 2)), hjust = 0.5, size = 6, vjust = 0.5) +
      theme_bw() +
      theme(legend.position = "bottom")
 
 # Building a graphic for MBD
 p_MBD <-
-  ggplot(data = recap_bouts) + 
+  ggplot(data = recap_bouts |> dplyr::ungroup(new_bout)) + 
   geom_histogram(aes(x = duration, fill = dur_cat), binwidth = xmin) +
-  geom_segment(aes(x = MBD, xend = MBD, y = 0, yend = max(summarised_bouts$n, na.rm = TRUE)), linetype = "dashed") +
+  annotate(geom = "segment", x = MBD, xend = MBD, y = 0, yend = max(summarised_bouts$n, na.rm = TRUE), linetype = "dashed") +
   scale_fill_manual(values = color_fill) +
   labs(x = "Bout duration (min)", y = "n", fill = "Duration (min)") +
   geom_segment(
@@ -377,10 +378,10 @@ p_MBD <-
      p_UBD <-
        ggplot(data = summarised_bouts, aes(x = duration, y = cum_frac_time)) +
        geom_point(aes(color = dur_cat), size = 6) +
-       geom_segment(aes(x = 0, y = 0.5, xend = UBD, yend = 0.5), linetype = "dashed", linewidth = 0.5) +
-       geom_segment(aes(x = UBD, y = 0.5, xend = UBD, yend = 0), linetype = "dashed", linewidth = 0.5) +
-       geom_line(data = df_pred_UBD, aes(x = duration, y = pred), linewidth = 0.8, color = "grey10") +
-       geom_segment(aes(x = max_bout_duration/2, y = 0.4, xend = UBD, yend = 0), arrow = arrow(length = unit(0.02, "npc"))) +
+       annotate(geom = "segment", x = 0, y = 0.5, xend = UBD, yend = 0.5, linetype = "dashed", linewidth = 0.5) +
+       annotate(geom = "segment", x = UBD, y = 0.5, xend = UBD, yend = 0, linetype = "dashed", linewidth = 0.5) +
+       geom_line(data = df_pred_UBD, aes(x = duration, y = pred), linewidth = 0.5, color = "grey10") +
+       annotate(geom = "segment", x = max_bout_duration/2, y = 0.4, xend = UBD, yend = 0, arrow = arrow(length = unit(0.02, "npc"))) +
        annotate("text", x = max_bout_duration/2, y = 0.4, label = paste(" UBD =", round(UBD, 1), "min"), hjust = 0, size = 6, vjust = 0) +
        labs(x = "Bout duration (min)", y = paste("Cumulated fraction of total", auto_text, "time"), color = "Duration (min)") +
        scale_color_manual(values = color_fill) +
@@ -417,7 +418,7 @@ p_gini <-
   geom_ribbon(aes(x = cum_frac_bout, ymin = cum_frac_time, ymax = cum_frac_bout), fill = alpha(color_fill[[2]], 0.3)) +
   geom_point(data = summarised_bouts2, aes(color = dur_cat), size = 6) +
   geom_segment(x = 0, xend = 1, y = 0, yend = 1, linewidth = 0.3) +
-  geom_line(linewidth = 0.6) +
+  geom_line(linewidth = 0.5) +
   scale_color_manual(values = color_fill) +
   coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
   labs(
diff --git a/R/compute_intensity_distri_metrics.R b/R/compute_intensity_distri_metrics.R
index c6bf2e24..cafe4794 100644
--- a/R/compute_intensity_distri_metrics.R
+++ b/R/compute_intensity_distri_metrics.R
@@ -154,7 +154,7 @@ df_mx <-
     M60 = compute_mx(x = .data[[col_axis]], n = 60 * cor_factor),
     M30 = compute_mx(x = .data[[col_axis]], n = 30 * cor_factor),
     M15 = compute_mx(x = .data[[col_axis]], n = 15 * cor_factor),
-    M5 = compute_mx(x = .data[[col_axis]], n = 15 * cor_factor)
+    M5 = compute_mx(x = .data[[col_axis]], n = 5 * cor_factor)
   ) 
   
 
diff --git a/R/create_fig_mx_summary.R b/R/create_fig_mx_summary.R
index e729c51e..e36aa295 100644
--- a/R/create_fig_mx_summary.R
+++ b/R/create_fig_mx_summary.R
@@ -126,7 +126,7 @@ p <-
     axis.text.x = element_text(size = 15),
     legend.text = element_text(size = 12),
     legend.key.width = unit(1.5,"cm"),
-    legend.position = c(0.13, 0.07)
+    legend.position.inside = c(0.13, 0.07)
   ) +
   guides(color = "none", fill = "none")
 
diff --git a/R/create_fig_pal.R b/R/create_fig_pal.R
index fe59b92c..da85975d 100644
--- a/R/create_fig_pal.R
+++ b/R/create_fig_pal.R
@@ -60,7 +60,7 @@ if (language == "en") {
     theme(axis.ticks = element_blank(),
           axis.text.y = element_blank(),
           axis.text.x = element_text(size = 13),
-          legend.position = c(0.5, 1.4),
+          legend.position.inside = c(0.5, 1.4),
           legend.title = element_text(face = "bold" , size = 10),
           legend.text = element_text(face = "bold", size = 17),
           legend.background = element_rect(fill = "beige"),
@@ -105,7 +105,7 @@ if (language == "fr") {
     theme(axis.ticks = element_blank(),
           axis.text.y = element_blank(),
           axis.text.x = element_text(size = 13),
-          legend.position = c(0.5, 1.4),
+          legend.position.inside = c(0.5, 1.4),
           legend.title = element_text(face = "bold" , size = 10),
           legend.text = element_text(face = "bold", size = 17),
           legend.background = element_rect(fill = "beige"),
@@ -151,7 +151,7 @@ if (language == "de") {
     theme(axis.ticks = element_blank(),
           axis.text.y = element_blank(),
           axis.text.x = element_text(size = 13),
-          legend.position = c(0.5, 1.4),
+          legend.position.inside = c(0.5, 1.4),
           legend.title = element_text(face = "bold" , size = 10),
           legend.text = element_text(face = "bold", size = 17),
           legend.background = element_rect(fill = "beige"),
diff --git a/R/do_all_analyses.R b/R/do_all_analyses.R
index bf5c0979..fb54dc98 100644
--- a/R/do_all_analyses.R
+++ b/R/do_all_analyses.R
@@ -5,6 +5,9 @@
 #'     the package. It is an internal function allowing the computation of the speed of the whole
 #'     analysis process, from the data importation to the final line of the results.
 #'
+#' @param to_epoch A numeric value to set the epoch required to collapse counts 
+#'     in seconds.
+#'
 #' @return
 #' A dataset (1 row) with all computed metrics.
 
diff --git a/R/get_ig_results.R b/R/get_ig_results.R
index a308018f..1ff5be85 100644
--- a/R/get_ig_results.R
+++ b/R/get_ig_results.R
@@ -54,7 +54,7 @@ get_ig_results <- function(
   df_bins$bin_label <- paste0(round(df_bins$bin_start, 0),"-", round(df_bins$bin_end, 0))
   
   # Correcting the value of the upper bound of the last bin (the value has been arbitrarily set so that it is very high)
-  df_bins[nrow(df_bins), "bin_end"] <- 50000
+  df_bins[nrow(df_bins), "bin_end"] <- 1000000
   
   # Correcting the label of the last bin
   df_bins[nrow(df_bins), "bin_label"] <- paste0(">", round(df_bins[nrow(df_bins), "bin_start"]-1, 0))
diff --git a/R/mark_wear_time.R b/R/mark_wear_time.R
index c67e260b..a106e5ee 100644
--- a/R/mark_wear_time.R
+++ b/R/mark_wear_time.R
@@ -108,10 +108,12 @@ mark_wear_time <- function(
       streamFrame = streamFrame
       ) %>%
     dplyr::mutate(
+      wearing = dplyr::if_else(is.na(wearing), "nw", wearing),
       non_wearing_count = dplyr::if_else(wearing == "nw", 1, 0),
       wearing_count = dplyr::if_else(wearing == "w", 1, 0)
       )
   
+  
   return(df2)
   
 }
diff --git a/R/recap_by_day.R b/R/recap_by_day.R
index d4f45033..977baad1 100644
--- a/R/recap_by_day.R
+++ b/R/recap_by_day.R
@@ -35,12 +35,12 @@
 #'   \item \strong{peak_steps_5min:} step accumulation per minute averaged over the best 5 continuous or discontinuous minutes
 #'   \item \strong{peak_steps_1min:} step accumulation per minute over the best minute (same result as for `max_steps_1min`)
 #'   \item \strong{ig:} intensity gradient
-#'   \item \strong{M1/3:} the count value (in counts/epoch duration) above which the most active 8h were accumulated over the day
-#'   \item \strong{M120:} the count value (in counts/epoch duration) above which the most active 120 minutes were accumulated over the day
-#'   \item \strong{M60:} the count value (in counts/epoch duration) above which the most active 60 minutes were accumulated over the day
-#'   \item \strong{M30:} the count value (in counts/epoch duration) above which the most active 30 minutes were accumulated over the day
-#'   \item \strong{M15:} the count value (in counts/epoch duration) above which the most active 15 minutes were accumulated over the day
-#'   \item \strong{M5:} the count value (in counts/epoch duration) above which the most active 5 minutes were accumulated over the day
+#'   \item \strong{M1/3:} the count value (in counts/epoch duration) at and above which the most active 8h were accumulated over the day
+#'   \item \strong{M120:} the count value (in counts/epoch duration) at and above which the most active 120 minutes were accumulated over the day
+#'   \item \strong{M60:} the count value (in counts/epoch duration) at and above which the most active 60 minutes were accumulated over the day
+#'   \item \strong{M30:} the count value (in counts/epoch duration) at and above which the most active 30 minutes were accumulated over the day
+#'   \item \strong{M15:} the count value (in counts/epoch duration) at and above which the most active 15 minutes were accumulated over the day
+#'   \item \strong{M5:} the count value (in counts/epoch duration) at and above which the most active 5 minutes were accumulated over the day
 #'}
 #'
 #'PAL is computed by dividing total energy expenditure (TEE) by BMR. TEE is obtained by summing 
diff --git a/README.Rmd b/README.Rmd
index 971ad0ba..178c40fc 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 [![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/workflows/R-CMD-check/badge.svg)](https://github.com/pydemull/activAnalyzer/actions)
+[![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)
 <!-- badges: end -->
diff --git a/README.md b/README.md
index 3833c197..e6be1c17 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ status](https://www.r-pkg.org/badges/version/activAnalyzer)](https://CRAN.R-proj
 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/workflows/R-CMD-check/badge.svg)](https://github.com/pydemull/activAnalyzer/actions)
+[![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)
 <!-- badges: end -->
diff --git a/man/do_all_analyses.Rd b/man/do_all_analyses.Rd
index 0af15260..11678a68 100644
--- a/man/do_all_analyses.Rd
+++ b/man/do_all_analyses.Rd
@@ -4,7 +4,11 @@
 \alias{do_all_analyses}
 \title{Do all analyses at once}
 \usage{
-do_all_analyses()
+do_all_analyses(to_epoch = 60)
+}
+\arguments{
+\item{to_epoch}{A numeric value to set the epoch required to collapse counts
+in seconds.}
 }
 \value{
 A dataset (1 row) with all computed metrics.
diff --git a/man/recap_by_day.Rd b/man/recap_by_day.Rd
index d13ee2cb..1318a869 100644
--- a/man/recap_by_day.Rd
+++ b/man/recap_by_day.Rd
@@ -91,12 +91,12 @@ The following metrics are computed from epochs corresponding to valid wear time:
 \item \strong{peak_steps_5min:} step accumulation per minute averaged over the best 5 continuous or discontinuous minutes
 \item \strong{peak_steps_1min:} step accumulation per minute over the best minute (same result as for \code{max_steps_1min})
 \item \strong{ig:} intensity gradient
-\item \strong{M1/3:} the count value (in counts/epoch duration) above which the most active 8h were accumulated over the day
-\item \strong{M120:} the count value (in counts/epoch duration) above which the most active 120 minutes were accumulated over the day
-\item \strong{M60:} the count value (in counts/epoch duration) above which the most active 60 minutes were accumulated over the day
-\item \strong{M30:} the count value (in counts/epoch duration) above which the most active 30 minutes were accumulated over the day
-\item \strong{M15:} the count value (in counts/epoch duration) above which the most active 15 minutes were accumulated over the day
-\item \strong{M5:} the count value (in counts/epoch duration) above which the most active 5 minutes were accumulated over the day
+\item \strong{M1/3:} the count value (in counts/epoch duration) at and above which the most active 8h were accumulated over the day
+\item \strong{M120:} the count value (in counts/epoch duration) at and above which the most active 120 minutes were accumulated over the day
+\item \strong{M60:} the count value (in counts/epoch duration) at and above which the most active 60 minutes were accumulated over the day
+\item \strong{M30:} the count value (in counts/epoch duration) at and above which the most active 30 minutes were accumulated over the day
+\item \strong{M15:} the count value (in counts/epoch duration) at and above which the most active 15 minutes were accumulated over the day
+\item \strong{M5:} the count value (in counts/epoch duration) at and above which the most active 5 minutes were accumulated over the day
 }
 
 PAL is computed by dividing total energy expenditure (TEE) by BMR. TEE is obtained by summing