In pydemull/activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
```{css, echo=FALSE}
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```r
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)) * 0.75
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: Tabular view
reactable::reactable(params$results_by_day %>% dplyr::select(date:total_steps) %>%
dplyr::rename(
Date = date,
"Wear time (min)" = wear_time,
"Axis 1 total counts" = total_counts_axis1,
"VM total counts" = total_counts_vm,
"Axis 1 mean (counts/min)" = axis1_per_min,
"VM mean (counts/min)" = vm_per_min,
"SED time (min)" = minutes_SED,
"LPA time (min)" = minutes_LPA,
"MPA time (min)" = minutes_MPA,
"VPA time (min)" = minutes_VPA,
"MVPA time (min)" = minutes_MVPA,
"SED wear time proportion (%)" = percent_SED,
"LPA wear time proportion (%)" = percent_LPA,
"MPA wear time proportion (%)" = percent_MPA,
"VPA wear time proportion (%)" = percent_VPA,
"MVPA wear time proportion (%)" = percent_MVPA,
"Ratio MVPA / SED" = ratio_mvpa_sed,
"Total MVPA MET-hr" = mets_hours_mvpa,
"Total kcal" = total_kcal,
"PAL" = pal,
"Total steps" = total_steps
),
striped = TRUE,
list(
"Date" = reactable::colDef(
style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1),
headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1)
),
"Wear time (min)" = reactable::colDef(minWidth = 140),
"Axis 1 total counts" = reactable::colDef(minWidth = 160),
"VM total counts" = reactable::colDef(minWidth = 160),
"Axis 1 mean (counts/min)" = reactable::colDef(minWidth = 160),
"VM mean (counts/min)" = reactable::colDef(minWidth = 160),
"SED time (min)" = reactable::colDef(minWidth = 160),
"LPA time (min)" = reactable::colDef(minWidth = 160),
"MPA time (min)" = reactable::colDef(minWidth = 160),
"VPA time (min)" = reactable::colDef(minWidth = 160),
"MVPA time (min)" = reactable::colDef(minWidth = 160),
"SED wear time proportion (%)" = reactable::colDef(minWidth = 160),
"LPA wear time proportion (%)" = reactable::colDef(minWidth = 160),
"MPA wear time proportion (%)" = reactable::colDef(minWidth = 160),
"VPA wear time proportion (%)" = reactable::colDef(minWidth = 160),
"MVPA wear time proportion (%)" = reactable::colDef(minWidth = 160),
"Ratio MVPA / SED" = reactable::colDef(minWidth = 160),
"Total MVPA MET-hr" = reactable::colDef(minWidth = 160),
"Total kcal" = reactable::colDef(minWidth = 160),
"PAL" = reactable::colDef(minWidth = 160),
"Total steps" = reactable::colDef(minWidth = 160)
)
)
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable(
tibble::tibble(
Metric = c("Number of valid days",
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps"
),
"Daily mean | median" = c(
# Number of valid days
paste0(df1_means[["valid_days"]]),
# Wear time
paste0(format(round(df1_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["wear_time"]]), ") | ",
format(round(df1_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["wear_time"]]), ")"),
# Total counts Axis 1
paste0(format(round(df1_means[["total_counts_axis1"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["total_counts_axis1"]], 1), nsmall = 1)),
# Total counts VM
paste0(format(round(df1_means[["total_counts_vm"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["total_counts_vm"]], 1), nsmall = 1)),
# Axis 1 per min
paste0(format(round(df1_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM per min
paste0(format(round(df1_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["vm_per_min"]], 1), nsmall = 1)),
# SED time
paste0(format(round(df1_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_SED"]]), ") | ",
format(round(df1_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_SED"]]), ")"),
# LPA time
paste0(format(round(df1_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_LPA"]]), ") | ",
format(round(df1_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_LPA"]]), ")"),
# MPA time
paste0(format(round(df1_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_MPA"]]), ") | ",
format(round(df1_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_MPA"]]), ")"),
# VPA time
paste0(format(round(df1_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_VPA"]]), ") | ",
format(round(df1_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_VPA"]]), ")"),
# MVPA time
paste0(format(round(df1_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_MVPA"]]), ") | ",
format(round(df1_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_MVPA"]]), ")"),
# Percent time SED
paste0(format(round(df1_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_SED"]], 1), nsmall = 1)),
# Percent time LPA
paste0(format(round(df1_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_LPA"]], 1), nsmall = 1)),
# Percent time MPA
paste0(format(round(df1_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MPA"]], 1), nsmall = 1)),
# Percent time VPA
paste0(format(round(df1_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_VPA"]], 1), nsmall = 1)),
# Percent time MVPA
paste0(format(round(df1_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MVPA"]], 1), nsmall = 1)),
# Ratio MVPA/SED
paste0(format(round(df1_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(df1_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)),
# MET-hr MVPA
paste0(format(round(df1_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(df1_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kilocalories
paste0(format(round(df1_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["total_kcal"]], 2), nsmall = 2)),
# Physical activity level (PAL)
paste0(format(round(df1_means[["pal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["pal"]], 2), nsmall = 2)),
# Total number of steps
paste0(round(df1_means[["total_steps"]], 0), " | ", round(df1_medians[["total_steps"]], 0))
)
), striped = TRUE, defaultPageSize = 21
)
if (params$rendered_by_shiny)
shiny::setProgress(0.5) # set progress to 50%
Comparisons with norms and recommendations
# PAL
g_pal <- create_fig_pal(score = df1_means[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm"))
# Steps
g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm"))
# MVPA
g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# SED
g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# MVPA/SED ratio
g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm"))
# Whole figure
(g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) +
plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Comments
# Determining physical activity guidelines status
guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "en")
# Determining PAL status
pal_status <- get_pal_status(df1_means[["pal"]], language = "en")
According to the Food and Agriculture Organization of the United Nations (FAO), the PAL of the patient could be characterized as r pal_status. After converting moderate and vigorous physical activity minutes to MET-hours, the estimate of the daily mean of MVPA volume for the patient is r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, which could correspond to falling r guidelines_status (i.e., 7.5-15.0 MET-hr a week, or 1.07-2.14 MET-hr a day).
Step accumulation metrics
Results by day: Tabular view
reactable::reactable(params$results_by_day %>% dplyr::select(date, max_steps_60min:peak_steps_1min) %>%
dplyr::rename(
Date = date,
"Max step acc. 60 min (steps/min)" = max_steps_60min,
"Max step acc. 30 min (steps/min)" = max_steps_30min,
"Max step acc. 20 min (steps/min)" = max_steps_20min,
"Max step acc. 5 min (steps/min)" = max_steps_5min,
"Max step acc. 1 min (steps/min)" = max_steps_1min,
"Peak step acc. 60 min (steps/min)" = peak_steps_60min,
"Peak step acc. 30 min (steps/min)" = peak_steps_30min,
"Peak step acc. 20 min (steps/min)" = peak_steps_20min,
"Peak step acc. 5 min (steps/min)" = peak_steps_5min,
"Peak step acc. 1 min (steps/min)" = peak_steps_1min
),
striped = TRUE,
list(
"Date" = reactable::colDef(
style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1),
headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1)
),
"Max step acc. 60 min (steps/min)" = reactable::colDef(minWidth = 160),
"Max step acc. 30 min (steps/min)" = reactable::colDef(minWidth = 160),
"Max step acc. 20 min (steps/min)" = reactable::colDef(minWidth = 160),
"Max step acc. 5 min (steps/min)" = reactable::colDef(minWidth = 160),
"Max step acc. 1 min (steps/min)" = reactable::colDef(minWidth = 160),
"Peak step acc. 60 min (steps/min)" = reactable::colDef(minWidth = 160),
"Peak step acc. 30 min (steps/min)" = reactable::colDef(minWidth = 160),
"Peak step acc. 20 min (steps/min)" = reactable::colDef(minWidth = 160),
"Peak step acc. 5 min (steps/min)" = reactable::colDef(minWidth = 160),
"Peak step acc. 1 min (steps/min)" = reactable::colDef(minWidth = 160)
)
)
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable(
tibble::tibble(
Metric = c("Number of valid days",
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)"
),
"Daily mean | median" = c(
# Number of valid days
paste0(df1_means[["valid_days"]]),
# Max step accum 60min
paste0(format(round(df1_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_60min"]], 2), nsmall = 2)),
# Max step accum 30min
paste0(format(round(df1_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_30min"]], 2), nsmall = 2)),
# Max step accum 20min
paste0(format(round(df1_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_20min"]], 2), nsmall = 2)),
# Max step accum 5min
paste0(format(round(df1_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_5min"]], 2), nsmall = 2)),
# Max step accum 1min
paste0(format(round(df1_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_1min"]], 2), nsmall = 2)),
# Peak step accum 60min
paste0(format(round(df1_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_60min"]], 2), nsmall = 2)),
# Peak step accum 30min
paste0(format(round(df1_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_30min"]], 2), nsmall = 2)),
# Peak step accum 20min
paste0(format(round(df1_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_20min"]], 2), nsmall = 2)),
# Peak step accum 5min
paste0(format(round(df1_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_5min"]], 2), nsmall = 2)),
# Peak step accum 1min
paste0(format(round(df1_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_1min"]], 2), nsmall = 2))
)
), striped = TRUE, defaultPageSize = 17
)
Intensity distribution metrics
Distribution of time spent in intensity bins
params$graph_int_distri_bands
Log-log models
params$graph_int_distri_models
Results by day: Tabular view
epoch_label <- as.name(paste0(params$epoch,"s"))
reactable::reactable(params$results_by_day %>% dplyr::select(date, ig:M5) %>%
dplyr::rename(
Date = date,
"Intensity gradient" = ig,
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := M120,
"M60 (counts/{{epoch_label}})" := M60,
"M30 (counts/{{epoch_label}})" := M30,
"M15 (counts/{{epoch_label}})" := M15,
"M5 (counts/{{epoch_label}})" := M5
),
striped = TRUE,
list(
"Date" = reactable::colDef(
style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1),
headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1)
)
)
)
if (params$rendered_by_shiny)
shiny::setProgress(0.75) # set progress to 75%
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable(
tibble::tibble(
Metric = c("Number of valid days",
"Intensity gradient",
paste0("M1/3", " (counts/", params$epoch, "s)"),
paste0("M120", " (counts/", params$epoch, "s)"),
paste0("M60", " (counts/", params$epoch, "s)"),
paste0("M30", " (counts/", params$epoch, "s)"),
paste0("M15", " (counts/", params$epoch, "s)"),
paste0("M5", " (counts/", params$epoch, "s)")
),
"Daily mean | median" = c(
# Number of valid days
paste0(df1_means[["valid_days"]]),
# Intensity gradient
paste0(format(round(df1_means[["ig"]], 2), nsmall = 2), " | ", format(round(df1_medians[["ig"]], 2), nsmall = 2)),
# M1/3
paste0(format(round(df1_means[["M1/3"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M1/3"]], 1), nsmall = 1)),
# M120
paste0(format(round(df1_means[["M120"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M120"]], 1), nsmall = 1)),
# M60
paste0(format(round(df1_means[["M60"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M60"]], 1), nsmall = 1)),
# M30
paste0(format(round(df1_means[["M30"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M30"]], 1), nsmall = 1)),
# M15
paste0(format(round(df1_means[["M15"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M15"]], 1), nsmall = 1)),
# M5
paste0(format(round(df1_means[["M5"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["M5"]], 1), nsmall = 1))
)
), striped = TRUE, defaultPageSize = 17
)
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$vpa_cutpoint / cor_factor
)
Sedentary behaviour accumulation metrics based on valid days
Temporal distribution of sedentary bouts
if(length(params$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)
)
}
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),
axis.text = element_text(size = 10),
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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```r 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)) * 0.75 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: Tabular view
reactable::reactable(params$results_by_day %>% dplyr::select(date:total_steps) %>% dplyr::rename( Date = date, "Wear time (min)" = wear_time, "Axis 1 total counts" = total_counts_axis1, "VM total counts" = total_counts_vm, "Axis 1 mean (counts/min)" = axis1_per_min, "VM mean (counts/min)" = vm_per_min, "SED time (min)" = minutes_SED, "LPA time (min)" = minutes_LPA, "MPA time (min)" = minutes_MPA, "VPA time (min)" = minutes_VPA, "MVPA time (min)" = minutes_MVPA, "SED wear time proportion (%)" = percent_SED, "LPA wear time proportion (%)" = percent_LPA, "MPA wear time proportion (%)" = percent_MPA, "VPA wear time proportion (%)" = percent_VPA, "MVPA wear time proportion (%)" = percent_MVPA, "Ratio MVPA / SED" = ratio_mvpa_sed, "Total MVPA MET-hr" = mets_hours_mvpa, "Total kcal" = total_kcal, "PAL" = pal, "Total steps" = total_steps ), striped = TRUE, list( "Date" = reactable::colDef( style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1), headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1) ), "Wear time (min)" = reactable::colDef(minWidth = 140), "Axis 1 total counts" = reactable::colDef(minWidth = 160), "VM total counts" = reactable::colDef(minWidth = 160), "Axis 1 mean (counts/min)" = reactable::colDef(minWidth = 160), "VM mean (counts/min)" = reactable::colDef(minWidth = 160), "SED time (min)" = reactable::colDef(minWidth = 160), "LPA time (min)" = reactable::colDef(minWidth = 160), "MPA time (min)" = reactable::colDef(minWidth = 160), "VPA time (min)" = reactable::colDef(minWidth = 160), "MVPA time (min)" = reactable::colDef(minWidth = 160), "SED wear time proportion (%)" = reactable::colDef(minWidth = 160), "LPA wear time proportion (%)" = reactable::colDef(minWidth = 160), "MPA wear time proportion (%)" = reactable::colDef(minWidth = 160), "VPA wear time proportion (%)" = reactable::colDef(minWidth = 160), "MVPA wear time proportion (%)" = reactable::colDef(minWidth = 160), "Ratio MVPA / SED" = reactable::colDef(minWidth = 160), "Total MVPA MET-hr" = reactable::colDef(minWidth = 160), "Total kcal" = reactable::colDef(minWidth = 160), "PAL" = reactable::colDef(minWidth = 160), "Total steps" = reactable::colDef(minWidth = 160) ) )
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable( tibble::tibble( Metric = c("Number of valid days", "Wear time (min)", "Axis 1 total counts", "VM total counts", "Axis 1 mean (counts/min)", "VM mean (counts/min)", "SED time (min)", "LPA time (min)", "MPA time (min)", "VPA time (min)", "MVPA time (min)", "SED wear time proportion (%)", "LPA wear time proportion (%)", "MPA wear time proportion (%)", "VPA wear time proportion (%)", "MVPA wear time proportion (%)", "Ratio MVPA / SED", "Total MVPA MET-hr", "Total kcal", "PAL", "Total steps" ), "Daily mean | median" = c( # Number of valid days paste0(df1_means[["valid_days"]]), # Wear time paste0(format(round(df1_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["wear_time"]]), ") | ", format(round(df1_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["wear_time"]]), ")"), # Total counts Axis 1 paste0(format(round(df1_means[["total_counts_axis1"]], 1), nsmall = 1), " | ", format(round(df1_medians[["total_counts_axis1"]], 1), nsmall = 1)), # Total counts VM paste0(format(round(df1_means[["total_counts_vm"]], 1), nsmall = 1), " | ", format(round(df1_medians[["total_counts_vm"]], 1), nsmall = 1)), # Axis 1 per min paste0(format(round(df1_means[["axis1_per_min"]], 1), nsmall = 1), " | ", format(round(df1_medians[["axis1_per_min"]], 1), nsmall = 1)), # VM per min paste0(format(round(df1_means[["vm_per_min"]], 1), nsmall = 1), " | ", format(round(df1_medians[["vm_per_min"]], 1), nsmall = 1)), # SED time paste0(format(round(df1_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_SED"]]), ") | ", format(round(df1_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_SED"]]), ")"), # LPA time paste0(format(round(df1_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_LPA"]]), ") | ", format(round(df1_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_LPA"]]), ")"), # MPA time paste0(format(round(df1_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_MPA"]]), ") | ", format(round(df1_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_MPA"]]), ")"), # VPA time paste0(format(round(df1_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_VPA"]]), ") | ", format(round(df1_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_VPA"]]), ")"), # MVPA time paste0(format(round(df1_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_means[["minutes_MVPA"]]), ") | ", format(round(df1_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = df1_medians[["minutes_MVPA"]]), ")"), # Percent time SED paste0(format(round(df1_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_SED"]], 1), nsmall = 1)), # Percent time LPA paste0(format(round(df1_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_LPA"]], 1), nsmall = 1)), # Percent time MPA paste0(format(round(df1_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MPA"]], 1), nsmall = 1)), # Percent time VPA paste0(format(round(df1_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_VPA"]], 1), nsmall = 1)), # Percent time MVPA paste0(format(round(df1_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MVPA"]], 1), nsmall = 1)), # Ratio MVPA/SED paste0(format(round(df1_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(df1_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)), # MET-hr MVPA paste0(format(round(df1_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(df1_medians[["mets_hours_mvpa"]], 2), nsmall = 2)), # Total kilocalories paste0(format(round(df1_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["total_kcal"]], 2), nsmall = 2)), # Physical activity level (PAL) paste0(format(round(df1_means[["pal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["pal"]], 2), nsmall = 2)), # Total number of steps paste0(round(df1_means[["total_steps"]], 0), " | ", round(df1_medians[["total_steps"]], 0)) ) ), striped = TRUE, defaultPageSize = 21 ) if (params$rendered_by_shiny) shiny::setProgress(0.5) # set progress to 50%
Comparisons with norms and recommendations
# PAL g_pal <- create_fig_pal(score = df1_means[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm")) # Steps g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm")) # MVPA g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # SED g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # MVPA/SED ratio g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm")) # Whole figure (g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) + plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Comments
# Determining physical activity guidelines status guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "en") # Determining PAL status pal_status <- get_pal_status(df1_means[["pal"]], language = "en")
According to the Food and Agriculture Organization of the United Nations (FAO), the PAL of the patient could be characterized as r pal_status. After converting moderate and vigorous physical activity minutes to MET-hours, the estimate of the daily mean of MVPA volume for the patient is r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, which could correspond to falling r guidelines_status (i.e., 7.5-15.0 MET-hr a week, or 1.07-2.14 MET-hr a day).
Step accumulation metrics
Results by day: Tabular view
reactable::reactable(params$results_by_day %>% dplyr::select(date, max_steps_60min:peak_steps_1min) %>% dplyr::rename( Date = date, "Max step acc. 60 min (steps/min)" = max_steps_60min, "Max step acc. 30 min (steps/min)" = max_steps_30min, "Max step acc. 20 min (steps/min)" = max_steps_20min, "Max step acc. 5 min (steps/min)" = max_steps_5min, "Max step acc. 1 min (steps/min)" = max_steps_1min, "Peak step acc. 60 min (steps/min)" = peak_steps_60min, "Peak step acc. 30 min (steps/min)" = peak_steps_30min, "Peak step acc. 20 min (steps/min)" = peak_steps_20min, "Peak step acc. 5 min (steps/min)" = peak_steps_5min, "Peak step acc. 1 min (steps/min)" = peak_steps_1min ), striped = TRUE, list( "Date" = reactable::colDef( style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1), headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1) ), "Max step acc. 60 min (steps/min)" = reactable::colDef(minWidth = 160), "Max step acc. 30 min (steps/min)" = reactable::colDef(minWidth = 160), "Max step acc. 20 min (steps/min)" = reactable::colDef(minWidth = 160), "Max step acc. 5 min (steps/min)" = reactable::colDef(minWidth = 160), "Max step acc. 1 min (steps/min)" = reactable::colDef(minWidth = 160), "Peak step acc. 60 min (steps/min)" = reactable::colDef(minWidth = 160), "Peak step acc. 30 min (steps/min)" = reactable::colDef(minWidth = 160), "Peak step acc. 20 min (steps/min)" = reactable::colDef(minWidth = 160), "Peak step acc. 5 min (steps/min)" = reactable::colDef(minWidth = 160), "Peak step acc. 1 min (steps/min)" = reactable::colDef(minWidth = 160) ) )
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable( tibble::tibble( Metric = c("Number of valid days", "Max step acc. 60 min (steps/min)", "Max step acc. 30 min (steps/min)", "Max step acc. 20 min (steps/min)", "Max step acc. 5 min (steps/min)", "Max step acc. 1 min (steps/min)", "Peak step acc. 60 min (steps/min)", "Peak step acc. 30 min (steps/min)", "Peak step acc. 20 min (steps/min)", "Peak step acc. 5 min (steps/min)", "Peak step acc. 1 min (steps/min)" ), "Daily mean | median" = c( # Number of valid days paste0(df1_means[["valid_days"]]), # Max step accum 60min paste0(format(round(df1_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_60min"]], 2), nsmall = 2)), # Max step accum 30min paste0(format(round(df1_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_30min"]], 2), nsmall = 2)), # Max step accum 20min paste0(format(round(df1_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_20min"]], 2), nsmall = 2)), # Max step accum 5min paste0(format(round(df1_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_5min"]], 2), nsmall = 2)), # Max step accum 1min paste0(format(round(df1_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["max_steps_1min"]], 2), nsmall = 2)), # Peak step accum 60min paste0(format(round(df1_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_60min"]], 2), nsmall = 2)), # Peak step accum 30min paste0(format(round(df1_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_30min"]], 2), nsmall = 2)), # Peak step accum 20min paste0(format(round(df1_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_20min"]], 2), nsmall = 2)), # Peak step accum 5min paste0(format(round(df1_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_5min"]], 2), nsmall = 2)), # Peak step accum 1min paste0(format(round(df1_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(df1_medians[["peak_steps_1min"]], 2), nsmall = 2)) ) ), striped = TRUE, defaultPageSize = 17 )
Intensity distribution metrics
Distribution of time spent in intensity bins
params$graph_int_distri_bands
Log-log models
params$graph_int_distri_models
Results by day: Tabular view
epoch_label <- as.name(paste0(params$epoch,"s")) reactable::reactable(params$results_by_day %>% dplyr::select(date, ig:M5) %>% dplyr::rename( Date = date, "Intensity gradient" = ig, "M1/3 (counts/{{epoch_label}})" := "M1/3", "M120 (counts/{{epoch_label}})" := M120, "M60 (counts/{{epoch_label}})" := M60, "M30 (counts/{{epoch_label}})" := M30, "M15 (counts/{{epoch_label}})" := M15, "M5 (counts/{{epoch_label}})" := M5 ), striped = TRUE, list( "Date" = reactable::colDef( style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1), headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1) ) ) ) if (params$rendered_by_shiny) shiny::setProgress(0.75) # set progress to 75%
Results by day: Graphical view
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"))
Means | Medians computed using valid days
reactable::reactable( tibble::tibble( Metric = c("Number of valid days", "Intensity gradient", paste0("M1/3", " (counts/", params$epoch, "s)"), paste0("M120", " (counts/", params$epoch, "s)"), paste0("M60", " (counts/", params$epoch, "s)"), paste0("M30", " (counts/", params$epoch, "s)"), paste0("M15", " (counts/", params$epoch, "s)"), paste0("M5", " (counts/", params$epoch, "s)") ), "Daily mean | median" = c( # Number of valid days paste0(df1_means[["valid_days"]]), # Intensity gradient paste0(format(round(df1_means[["ig"]], 2), nsmall = 2), " | ", format(round(df1_medians[["ig"]], 2), nsmall = 2)), # M1/3 paste0(format(round(df1_means[["M1/3"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M1/3"]], 1), nsmall = 1)), # M120 paste0(format(round(df1_means[["M120"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M120"]], 1), nsmall = 1)), # M60 paste0(format(round(df1_means[["M60"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M60"]], 1), nsmall = 1)), # M30 paste0(format(round(df1_means[["M30"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M30"]], 1), nsmall = 1)), # M15 paste0(format(round(df1_means[["M15"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M15"]], 1), nsmall = 1)), # M5 paste0(format(round(df1_means[["M5"]], 1), nsmall = 1), " | ", format(round(df1_medians[["M5"]], 1), nsmall = 1)) ) ), striped = TRUE, defaultPageSize = 17 )
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$vpa_cutpoint / cor_factor )
Sedentary behaviour accumulation metrics based on valid days
Temporal distribution of sedentary bouts
if(length(params$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) ) }
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), axis.text = element_text(size = 10), legend.title = element_text(size = 17), legend.text = element_text(size = 15) ) }
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