activAnalyzer"

In activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The activAnalyzer package was primarily built for working through a Shiny app. The procedure for using the app is explained in the related user's guide. The functions used in this app can also be used to analyze data outside the app, as shown below.

Loading packages

library(activAnalyzer)
library(magrittr)
library(ggplot2)
library(patchwork)
library(dplyr)

Getting file

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")

Preparing dataset

mydata <- prepare_dataset(data = file)

Getting nonwear/wear time marks

mydata_with_wear_marks <- 
  mydata %>%
  mark_wear_time(
    to_epoch = 60,
    cts = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )

height_factor <- nlevels(as.factor(mydata_with_wear_marks$date)) * 0.80

Viewing data with nonwear/wear time marks

#| fig.alt: >
#|   Plot with nonwear/wear time marks
plot_data(data = mydata_with_wear_marks, metric = "vm")

Viewing data with nonwear/wear time marks and with a zoom in on the figure

#| fig.alt: >
#|   Plot with nonwear/wear time marks and with a zoom in on the figure
plot_data(
  data = mydata_with_wear_marks, 
  metric = "vm",
  zoom_from = "16:00:00",
  zoom_to = "18:00:00"
  )

Getting activity intensity marks

mydata_with_intensity_marks <- 
  mark_intensity(
     data = mydata_with_wear_marks, 
     col_axis = "vm", 
     equation = "Sasaki et al. (2011) [Adults]",
     sed_cutpoint = 200, 
     mpa_cutpoint = 2690, 
     vpa_cutpoint = 6167, 
     age = 32,
     weight = 67,
     sex = "male"
    )

Viewing data with activity intensity marks

#| fig.alt: >
#|   Plot with activity intensity marks
plot_data_with_intensity(
  mydata_with_intensity_marks, 
  metric = "vm",
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59"
  )

Viewing data with activity intensity marks and grey bands to highlight the daily period considered for analysis

#| fig.alt: >
#|   Plot with activity intensity marks and grey bands to highlight the daily period considered for analysis
plot_data_with_intensity(
  mydata_with_intensity_marks, 
  metric = "vm",
  valid_wear_time_start = "07:00:00",
  valid_wear_time_end = "22:00:00"
  )

Viewing data with activity intensity marks and a zoom in on the figure

#| fig.alt: >
#|   Plot with activity intensity marks and a zoom in on the figure
plot_data_with_intensity(
  mydata_with_intensity_marks, 
  metric = "vm",
  zoom_from = "13:00:00",
  zoom_to = "16:30:00"
  )

Getting activity metrics by day

results_by_day <-
  mydata_with_intensity_marks %>%
  recap_by_day(
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00",
    start_first_bin = 0,
    start_last_bin = 10000,
    bin_width = 500
    )

Computing means of the metrics using the valid days

mean_results <-
  results_by_day$df_all_metrics  %>%
  average_results(minimum_wear_time = 10, fun = "mean")

Computing medians of the metrics using the valid days

median_results <-
  results_by_day$df_all_metrics  %>%
  average_results(minimum_wear_time = 10, fun = "median")

Showing activity volume metrics

Results by day: Tabular view

results_by_day$df_all_metrics %>% 
  dplyr::select(date:total_steps) %>%
  reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

Results by day: Graphical view

#| fig.alt: >
#|   Graphical view of the results by day for the activity volume metrics
create_fig_res_by_day(
  results_by_day$df_all_metrics, 
  minimum_wear_time_for_analysis = 10, 
  start_day_analysis = "00:00:00", 
  end_day_analysis = "23:59:00", 
  metrics = "volume",
  epoch_label = "60s"
  ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

Means | Medians computed using valid days

create_flextable_summary(
  results_summary_means = mean_results, 
  results_summary_medians = median_results, 
  metrics = "volume",
  epoch_label = "60s"
  )

Comparisons with norms and recommendations

#| fig.alt: >
#|   Comparisons with norms and recommendations
# PAL
g_pal <- create_fig_pal(score = mean_results[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm"))

# Steps
g_steps <- create_fig_steps(score = mean_results[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm"))

# MVPA
g_mvpa <- create_fig_mvpa(score = mean_results[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))

# SED
g_sed <- create_fig_sed(score = mean_results[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))

# MVPA/SED ratio
g_ratio <- create_fig_ratio_mvpa_sed(score = mean_results[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm"))

# Whole figure
(g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) + 
    plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")

Showing step accumulation metrics

Results by day: Tabular view

results_by_day$df_all_metrics %>% 
  dplyr::select(date, max_steps_60min:peak_steps_1min) %>%
  reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

Results by day: Graphical view

#| fig.alt: >
#|   Graphical view of the results by day for the step accumulation metrics
create_fig_res_by_day(
  results_by_day$df_all_metrics, 
  minimum_wear_time_for_analysis = 10, 
  start_day_analysis = "00:00:00", 
  end_day_analysis = "23:59:00", 
  metrics = "step_acc",
  epoch_label = "60s"
  ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

Means | Medians computed using valid days

create_flextable_summary(
  results_summary_means = mean_results, 
  results_summary_medians = median_results,
  metrics = "step_acc",
  epoch_label = "60s"
  )

Showing intensity gradient and MX metrics

Distribution of time spent in intensity bins

#| fig.alt: >
#|   Distribution of time spent in intensity bins | Bar plot
  results_by_day$p_band

#| fig.alt: >
#|   Distribution of time spent in intensity bins | Log-log plot
  results_by_day$p_log

Results by day: Tabular view

results_by_day$df_all_metrics %>% 
  dplyr::select(date, ig:M5) %>%
  reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

Results by day: Graphical view

#| fig.alt: >
#|   Graphical view of the results by day for the intensity gradient and MX metrics
create_fig_res_by_day(
  results_by_day$df_all_metrics, 
  minimum_wear_time_for_analysis = 10, 
  start_day_analysis = "00:00:00", 
  end_day_analysis = "23:59:00", 
  metrics = "int_distri",
  epoch_label = "60s"
  ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

Means | Medians computed using valid days

create_flextable_summary(
  results_summary_means = mean_results, 
  results_summary_medians = median_results,
  metrics = "int_distri",
  epoch_label = "60s"
  )

Radar view based on the means of valid days

#| fig.alt: >
#|   Radar plot for the MX metrics
create_fig_mx_summary(
data = mean_results,
labels = NULL,
mpa_cutpoint = 2690, 
vpa_cutpoint = 6167
)

Showing sedentary behaviour accumulation metrics based on valid days

Getting results

accum_metrics_sed <- 
  compute_accumulation_metrics(
    data = mydata_with_intensity_marks, 
    behaviour = "sed",
    dates = c("2021-04-07", "2021-04-08", "2021-04-09", "2021-04-10", "2021-04-11")
    )

Temporal distribution of sedentary bouts

#| fig.alt: >
#|    Temporal distribution of sedentary bouts
accum_metrics_sed$p_breaks

Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index

#| fig.alt: >
#|    Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index related to sedentary behaviour
p1 <- accum_metrics_sed$p_alpha  + guides(color = "none", fill = "none")
p2 <- accum_metrics_sed$p_MBD    + guides(color = "none", fill = "none")
p3 <- accum_metrics_sed$p_UBD
p4 <- accum_metrics_sed$p_gini

(p1 | p2) / (p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom') 

Showing physical activity accumulation metrics based on valid days

Getting results

accum_metrics_pa <- 
  compute_accumulation_metrics(
    mydata_with_intensity_marks, 
    behaviour = "pa",
    dates = c("2021-04-07", "2021-04-08", "2021-04-09", "2021-04-10", "2021-04-11")
    )

Temporal distribution of physical activity bouts

#| fig.alt: >
#|   Temporal distribution of physical activity bouts
accum_metrics_pa$p_breaks

Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index

#| fig.alt: >
#|   Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index related to physical activity
p1 <- accum_metrics_pa$p_alpha  + guides(color = "none", fill = "none")
p2 <- accum_metrics_pa$p_MBD    + guides(color = "none", fill = "none")
p3 <- accum_metrics_pa$p_UBD
p4 <- accum_metrics_pa$p_gini

(p1 | p2) / (p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom') 

activAnalyzer documentation built on Sept. 24, 2024, 1:07 a.m.