In pydemull/activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE, echo = FALSE)
library(magrittr)
library(ggplot2)
library(patchwork)
df1_means <- params$results_summary_means
df1_medians <- params$results_summary_medians
df2 <- params$results_by_day
axis_mvpa_graph <- ifelse(params$axis_mvpa == "vertical axis", "axis1",
ifelse(params$axis_mvpa == "vector magnitude", "vm",
"na"))
height_factor1 = nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.8
height_factor1 <- ifelse(height_factor1 <= 6, nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.9, height_factor1)
height_factor2 = params$results_summary_means$valid_days * 0.75
height_factor2 <- ifelse(height_factor2 <= 6, nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.9, height_factor2)
height_factor3 = nlevels(as.factor(params$df_with_computed_metrics$date)) * 1.5
options("scipen"=100, "digits"=4)
Information
Patient: r params$patient_surname r params$patient_name | Date: r Sys.Date() | Assessor: r params$assessor_surname r params$assessor_name
Age: r params$age yr | Sex: r params$sex | Weight: r params$weight kg | Measurement period: r format(params$start_date, "%Y-%m-%d %H:%M:%S") to r format(params$end_date, "%Y-%m-%d %H:%M:%S") | Estimated Basal Metabolic Rate (BMR): r format(round(params$bmr_kcal_d, 2), nsmall = 2) kcal/day (Estimation based on Henry equations [2005, doi: 10.1079/PHN2005801])
Device: r params$device | Position: r params$position | Side: r params$side | Sampling rate: r params$sampling_rate Hz | Filter: r params$filter
Epoch: r params$epoch s | Nonwear time: Based on r params$axis_weartime, interval of r params$frame_size min with zero count for nonwear time detection, interval of r params$allowanceFrame_size min with nonzero counts allowed during a nonwear period, interval of r params$streamFrame_size min with zero count around detected activity to confirm nonwear time | MET equation: r params$equation_mets | Axis used for PA intensity categorization: r params$axis_mvpa | Cut-points: <r params$sed_cutpoint counts/min for SED, $\geqslant$ r params$mpa_cutpoint counts/min for MPA, $\geqslant$ r params$vpa_cutpoint counts/min for VPA
Period of the day considered to count wear time: from r params$start_day_analysis to r params$end_day_analysis
Minimum number of hours with wear time to validate a day: r params$minimum_wear_time_for_analysis hours
Abbreviations: SED = sedentary, LPA = light physical activity, MPA = moderate physical activity, VPA = vigorous physical activity, MVPA = moderate-to-vigorous physical activity, PAL = physical activity level
Results
Activity volume metrics
Temporal distribution of activity intensity
plot_data_with_intensity(
data = params$df_with_computed_metrics,
metric = axis_mvpa_graph,
valid_wear_time_start = params$start_day_analysis,
valid_wear_time_end = params$end_day_analysis
)
if (params$rendered_by_shiny)
shiny::setProgress(0.25) # set progress to 25%
Results by day
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "en",
metrics = "volume"
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary(
results_summary_means = df1_means,
results_summary_medians = df1_medians,
language = "en",
metrics = "volume",
epoch_label = paste0(params$epoch, "s")
)
if (params$rendered_by_shiny)
shiny::setProgress(0.5) # set progress to 50%
Comparisons with norms and recommendations
# PAL
g_pal <- create_fig_pal(score = df1_means[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm"))
# Steps
g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm"))
# MVPA
g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# SED
g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# MVPA/SED ratio
g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm"))
# Whole figure
(g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) +
plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Comments
# Determining physical activity guidelines status
guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "en")
# Determining PAL status
pal_status <- get_pal_status(df1_means[["pal"]], language = "en")
According to the Food and Agriculture Organization of the United Nations (FAO), the PAL of the patient could be characterized as r pal_status. After converting moderate and vigorous physical activity minutes to MET-hours, the estimate of the daily mean of MVPA volume for the patient is r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, which could correspond to falling r guidelines_status (i.e., 7.5-15.0 MET-hr a week, or 1.07-2.14 MET-hr a day).
Step accumulation metrics
Results by day
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "en",
metrics = "step_acc"
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary(
df1_means,
df1_medians,
language = "en",
metrics = "step_acc",
epoch_label = paste0(params$epoch, "s")
)
if (params$rendered_by_shiny)
shiny::setProgress(0.75) # set progress to 75%
Intensity distribution metrics
Results by day
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "en",
metrics = "int_distri",
epoch_label = paste0(params$epoch, "s")
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary(
df1_means,
df1_medians,
language = "en",
metrics = "int_distri",
epoch_label = paste0(params$epoch, "s")
)
Radar view based on the means of the valid days
cor_factor <- 60 / params$epoch
create_fig_mx_summary(
data = df1_means,
labels = NULL,
mpa_cutpoint = params$mpa_cutpoint / cor_factor,
vpa_cutpoint = params$vpa_cutpoint / cor_factor
) + theme(legend.position = c(0.15, 0.08))
\newpage
Sedentary behaviour accumulation metrics based on valid days
Temporal distribution of sedentary bouts
if(length(params$p_breaks_sed) == 1) {
cat("No results. Results can be obtained using 60-s epochs only.")
} else {
params$p_breaks_sed
}
Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index
if(length(params$p_breaks_sed) == 1) {
cat("No results. Results can be obtained using 60-s epochs only.")
} else {
p1 <- params$p_alpha_sed + guides(color = "none", fill = "none")
p2 <- params$p_MBD_sed + guides(color = "none", fill = "none")
p3 <- params$p_UBD_sed
p4 <- params$p_gini_sed
(p1 | p2 | p3 | p4) + plot_layout(guides = "collect") &
theme(legend.position = 'bottom',
axis.title = element_text(size = 15),
legend.title = element_text(size = 17),
legend.text = element_text(size = 15)
)
}
\newpage
Physical activity accumulation metrics based on valid days
Temporal distribution of physical activity bouts
if(length(params$p_breaks_pa) == 1) {
cat("No results. Results can be obtained using 60-s epochs only.")
} else {
params$p_breaks_pa
}
if (params$rendered_by_shiny)
shiny::setProgress(1) # set progress to 100%
Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index
if(length(params$p_breaks_pa) == 1) {
cat("No results. Results can be obtained using 60-s epochs only.")
} else {
p1 <- params$p_alpha_pa + guides(color = "none", fill = "none")
p2 <- params$p_MBD_pa + guides(color = "none", fill = "none")
p3 <- params$p_UBD_pa
p4 <- params$p_gini_pa
(p1 | p2 | p3 | p4) + plot_layout(guides = "collect") &
theme(legend.position = 'bottom',
axis.title = element_text(size = 15),
legend.title = element_text(size = 17),
legend.text = element_text(size = 15)
)
}
pydemull/activAnalyzer documentation built on June 1, 2025, 1:09 p.m.
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knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE, echo = FALSE) library(magrittr) library(ggplot2) library(patchwork) df1_means <- params$results_summary_means df1_medians <- params$results_summary_medians df2 <- params$results_by_day axis_mvpa_graph <- ifelse(params$axis_mvpa == "vertical axis", "axis1", ifelse(params$axis_mvpa == "vector magnitude", "vm", "na")) height_factor1 = nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.8 height_factor1 <- ifelse(height_factor1 <= 6, nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.9, height_factor1) height_factor2 = params$results_summary_means$valid_days * 0.75 height_factor2 <- ifelse(height_factor2 <= 6, nlevels(as.factor(params$df_with_computed_metrics$date)) * 0.9, height_factor2) height_factor3 = nlevels(as.factor(params$df_with_computed_metrics$date)) * 1.5 options("scipen"=100, "digits"=4)
Information
Patient: r params$patient_surname r params$patient_name | Date: r Sys.Date() | Assessor: r params$assessor_surname r params$assessor_name
Age: r params$age yr | Sex: r params$sex | Weight: r params$weight kg | Measurement period: r format(params$start_date, "%Y-%m-%d %H:%M:%S") to r format(params$end_date, "%Y-%m-%d %H:%M:%S") | Estimated Basal Metabolic Rate (BMR): r format(round(params$bmr_kcal_d, 2), nsmall = 2) kcal/day (Estimation based on Henry equations [2005, doi: 10.1079/PHN2005801])
Device: r params$device | Position: r params$position | Side: r params$side | Sampling rate: r params$sampling_rate Hz | Filter: r params$filter
Epoch: r params$epoch s | Nonwear time: Based on r params$axis_weartime, interval of r params$frame_size min with zero count for nonwear time detection, interval of r params$allowanceFrame_size min with nonzero counts allowed during a nonwear period, interval of r params$streamFrame_size min with zero count around detected activity to confirm nonwear time | MET equation: r params$equation_mets | Axis used for PA intensity categorization: r params$axis_mvpa | Cut-points: <r params$sed_cutpoint counts/min for SED, $\geqslant$ r params$mpa_cutpoint counts/min for MPA, $\geqslant$ r params$vpa_cutpoint counts/min for VPA
Period of the day considered to count wear time: from r params$start_day_analysis to r params$end_day_analysis
Minimum number of hours with wear time to validate a day: r params$minimum_wear_time_for_analysis hours
Abbreviations: SED = sedentary, LPA = light physical activity, MPA = moderate physical activity, VPA = vigorous physical activity, MVPA = moderate-to-vigorous physical activity, PAL = physical activity level
Results
Activity volume metrics
Temporal distribution of activity intensity
plot_data_with_intensity( data = params$df_with_computed_metrics, metric = axis_mvpa_graph, valid_wear_time_start = params$start_day_analysis, valid_wear_time_end = params$end_day_analysis ) if (params$rendered_by_shiny) shiny::setProgress(0.25) # set progress to 25%
Results by day
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "en", metrics = "volume" ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary( results_summary_means = df1_means, results_summary_medians = df1_medians, language = "en", metrics = "volume", epoch_label = paste0(params$epoch, "s") ) if (params$rendered_by_shiny) shiny::setProgress(0.5) # set progress to 50%
Comparisons with norms and recommendations
# PAL g_pal <- create_fig_pal(score = df1_means[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm")) # Steps g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm")) # MVPA g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # SED g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # MVPA/SED ratio g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm")) # Whole figure (g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) + plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Comments
# Determining physical activity guidelines status guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "en") # Determining PAL status pal_status <- get_pal_status(df1_means[["pal"]], language = "en")
According to the Food and Agriculture Organization of the United Nations (FAO), the PAL of the patient could be characterized as r pal_status. After converting moderate and vigorous physical activity minutes to MET-hours, the estimate of the daily mean of MVPA volume for the patient is r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, which could correspond to falling r guidelines_status (i.e., 7.5-15.0 MET-hr a week, or 1.07-2.14 MET-hr a day).
Step accumulation metrics
Results by day
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "en", metrics = "step_acc" ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary( df1_means, df1_medians, language = "en", metrics = "step_acc", epoch_label = paste0(params$epoch, "s") ) if (params$rendered_by_shiny) shiny::setProgress(0.75) # set progress to 75%
Intensity distribution metrics
Results by day
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "en", metrics = "int_distri", epoch_label = paste0(params$epoch, "s") ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"), axis.text.x = element_text(hjust = 1.1))
Means | Medians computed using valid days
create_flextable_summary( df1_means, df1_medians, language = "en", metrics = "int_distri", epoch_label = paste0(params$epoch, "s") )
Radar view based on the means of the valid days
cor_factor <- 60 / params$epoch create_fig_mx_summary( data = df1_means, labels = NULL, mpa_cutpoint = params$mpa_cutpoint / cor_factor, vpa_cutpoint = params$vpa_cutpoint / cor_factor ) + theme(legend.position = c(0.15, 0.08))
\newpage
Sedentary behaviour accumulation metrics based on valid days
Temporal distribution of sedentary bouts
if(length(params$p_breaks_sed) == 1) { cat("No results. Results can be obtained using 60-s epochs only.") } else { params$p_breaks_sed }
Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index
if(length(params$p_breaks_sed) == 1) { cat("No results. Results can be obtained using 60-s epochs only.") } else { p1 <- params$p_alpha_sed + guides(color = "none", fill = "none") p2 <- params$p_MBD_sed + guides(color = "none", fill = "none") p3 <- params$p_UBD_sed p4 <- params$p_gini_sed (p1 | p2 | p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom', axis.title = element_text(size = 15), legend.title = element_text(size = 17), legend.text = element_text(size = 15) ) }
\newpage
Physical activity accumulation metrics based on valid days
Temporal distribution of physical activity bouts
if(length(params$p_breaks_pa) == 1) { cat("No results. Results can be obtained using 60-s epochs only.") } else { params$p_breaks_pa } if (params$rendered_by_shiny) shiny::setProgress(1) # set progress to 100%
Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index
if(length(params$p_breaks_pa) == 1) { cat("No results. Results can be obtained using 60-s epochs only.") } else { p1 <- params$p_alpha_pa + guides(color = "none", fill = "none") p2 <- params$p_MBD_pa + guides(color = "none", fill = "none") p3 <- params$p_UBD_pa p4 <- params$p_gini_pa (p1 | p2 | p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom', axis.title = element_text(size = 15), legend.title = element_text(size = 17), legend.text = element_text(size = 15) ) }
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