In pydemull/activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
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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
sex <- ifelse(params$sex == "male", "masculin",
ifelse(params$sex == "female", "féminin",
"non défini"))
position <- ifelse(params$position == "hip", "hanche",
ifelse(params$position == "wrist", "poignet",
ifelse(params$position == "thigh", "cuisse",
ifelse(params$position == "ankle", "cheville", "NA"))))
side <- ifelse(params$side == "right", "droit",
ifelse(params$side == "left", "gauche",
"non défini"))
axis_weartime <- ifelse(params$axis_weartime == "vertical axis", "l'axe vertical",
ifelse(params$axis_weartime == "vector magnitude", "le vecteur magnitude",
"non défini"))
axis_mvpa <- ifelse(params$axis_mvpa == "vertical axis", "axe vertical",
ifelse(params$axis_mvpa == "vector magnitude", "vecteur magnitude",
"non défini"))
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)
Informations
Patient : r params$patient_surname r params$patient_name | Date : r Sys.Date() | Évaluateur : r params$assessor_surname r params$assessor_name
Age : r params$age ans | Sexe : r sex | Masse : r params$weight kg | Période de mesure : r format(params$start_date, "%d/%m/%Y %H:%M:%S") au r format(params$end_date, "%d/%m/%Y %H:%M:%S") | Estimation du métabolisme de base : r format(round(params$bmr_kcal_d, 2), nsmall = 2) kcal/jour (Estimation à partir des équations de Henry [2005, doi: 10.1079/PHN2005801])
Appareil : r params$device | Position : r position | Côté : r side | Fréquence d'échantillonnage : r params$sampling_rate Hz | Filtre : r params$filter
'Epoch' : r params$epoch s | Temps de non-port : Basé sur r axis_weartime, intervalle de r params$frame_size min avec zéro count pour la détection du temps de non-port, intervalle de r params$allowanceFrame_size min avec counts différents de 0 autorisé durant une période de non-port, intervalle de r params$streamFrame_size min avec zéro count autour de la période d'activité détectée pour confirmer le non-port | Équation de prédiction des METs : r params$equation_mets | Axe utilisé pour catégoriser l'intensité d'activité physique : r axis_mvpa | Valeurs seuils : <r params$sed_cutpoint counts/min pour SED, $\geqslant$ r params$mpa_cutpoint counts/min pour MPA, $\geqslant$ r params$vpa_cutpoint counts/min pour VPA
Période journalière considérée pour compter le temps de port : de r params$start_day_analysis à r params$end_day_analysis
Nombre minimum d'heures avec temps de port pour valider un jour : r params$minimum_wear_time_for_analysis heures
Abréviations : SED = sédentaire, LPA = activité physique légère, MPA = activité physique modérée, VPA = activité physique vigoureuse, MVPA = activité physique modérée à vigoureuse, NAP = niveau d'activité physique
Résultats
Indicateurs de volume d'activité
Distribution temporelle de l'intensité
g <- 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$end_day_analysis
)
g + labs(x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)")
if (params$rendered_by_shiny)
shiny::setProgress(0.25) # set progress to 25%
Résultats par jour : Vue tabulaire
reactable::reactable(params$results_by_day %>% dplyr::select(date:total_steps) %>%
dplyr::rename(
Date = date,
"Temps de port (min)" = "wear_time",
"Total counts Axe 1 " = "total_counts_axis1",
"Total counts VM" = "total_counts_vm",
"Moyenne Axe 1 (counts/min)" = "axis1_per_min",
"Moyenne VM (counts/min)" = "vm_per_min",
"Temps SED (min)" = "minutes_SED",
"Temps LPA (min)" = "minutes_LPA",
"Temps MPA (min)" = "minutes_MPA",
"Temps VPA (min)" = "minutes_VPA",
"Temps MVPA (min)" = "minutes_MVPA",
"Proportion du temps de port SED (%)" = "percent_SED",
"Proportion du temps de port LPA (%)" = "percent_LPA",
"Proportion du temps de port MPA (%)" = "percent_MPA",
"Proportion du temps de port VPA (%)" = "percent_VPA",
"Proportion du temps de port MVPA (%)" = "percent_MVPA",
"Ratio MVPA / SED" = "ratio_mvpa_sed",
"Total MET-hr" = "mets_hours_mvpa",
"Total kcal" = "total_kcal",
"NAP" = "pal",
"Total pas" = "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)
),
"Temps de port (min)" = reactable::colDef(minWidth = 160),
"Total counts Axe 1 " = reactable::colDef(minWidth = 160),
"Total counts VM" = reactable::colDef(minWidth = 160),
"Moyenne Axe 1 (counts/min)" = reactable::colDef(minWidth = 160),
"Moyenne VM (counts/min)" = reactable::colDef(minWidth = 160),
"Temps SED (min)" = reactable::colDef(minWidth = 160),
"Temps LPA (min)" = reactable::colDef(minWidth = 160),
"Temps MPA (min)" = reactable::colDef(minWidth = 160),
"Temps VPA (min)" = reactable::colDef(minWidth = 160),
"Temps MVPA (min)" = reactable::colDef(minWidth = 160),
"Proportion du temps de port SED (%)" = reactable::colDef(minWidth = 160),
"Proportion du temps de port LPA (%)" = reactable::colDef(minWidth = 160),
"Proportion du temps de port MPA (%)" = reactable::colDef(minWidth = 160),
"Proportion du temps de port VPA (%)" = reactable::colDef(minWidth = 160),
"Proportion du temps de port MVPA (%)" = reactable::colDef(minWidth = 160),
"Ratio MVPA / SED" = reactable::colDef(minWidth = 160),
"Total MET-hr" = reactable::colDef(minWidth = 160),
"Total kcal" = reactable::colDef(minWidth = 160),
"NAP" = reactable::colDef(minWidth = 160),
"Total pas" = reactable::colDef(minWidth = 160)
)
)
Résultats par jour : Vue graphique
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "fr",
metrics = "volume"
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable(
tibble::tibble(
Indicateur = c(
"Nombre de jours valides",
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas"
),
"Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c(
# Nombre de jours valides
paste0(df1_means[["valid_days"]]),
# Temps de port
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 par min
paste0(format(round(df1_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM par min
paste0(format(round(df1_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(df1_medians[["vm_per_min"]], 1), nsmall = 1)),
# Minutes SED
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"]]), ")"),
# Minutes LPA
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"]]), ")"),
# Minutes MPA
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"]]), ")"),
# Minutes VPA
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"]]), ")"),
# Minutes MVPA
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"]]), ")"),
# % SED
paste0(format(round(df1_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_SED"]], 1), nsmall = 1)),
# % LPA
paste0(format(round(df1_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_LPA"]], 1), nsmall = 1)),
# % MPA
paste0(format(round(df1_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MPA"]], 1), nsmall = 1)),
# % VPA
paste0(format(round(df1_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_VPA"]], 1), nsmall = 1)),
# % 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)),
# Total METs-hr
paste0(format(round(df1_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(df1_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kcal
paste0(format(round(df1_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["total_kcal"]], 2), nsmall = 2)),
# NAP
paste0(format(round(df1_means[["pal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["pal"]], 2), nsmall = 2)),
# Total pas
paste0(round(df1_means[["total_steps"]], 0), " | ", round(df1_medians[["total_steps"]], 0))
)
), striped = TRUE, defaultPageSize = 21
)
if (params$rendered_by_shiny)
shiny::setProgress(0.5) # set progress to 50%
Comparaison avec des normes et des recommandations
# PAL
g_pal <- create_fig_pal(score = df1_means[["pal"]], "fr") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm"))
# Steps
g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "fr") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm"))
# MVPA
g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# SED
g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# MVPA/SED ratio
g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))
# Whole figure
(g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) +
plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Commentaires
# Determining physical activity guidelines status
guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "fr")
# Determining PAL status
pal_status <- get_pal_status(df1_means[["pal"]], language = "fr")
Selon la Food and Agriculture Organization of the United Nations (FAO), le NAP du patient pourrait être décrit comme r pal_status. Après avoir fait la conversion des minutes MPA et VPA vers les MET-heures, l'estimation du volume MVPA moyen journalier pour le patient est de r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, ce qui pourrait correspondre au fait de tomber r guidelines_status (i.e., 7.5-15.0 MET-hr par semaine, ou 1.07-2.14 MET-hr par jour).
Indicateurs d'accumulation des pas
Résultats par jour : Vue tabulaire
reactable::reactable(params$results_by_day %>% dplyr::select(date, max_steps_60min:peak_steps_1min) %>%
dplyr::rename(
Date = date,
"Acc. pas max. 60 min (pas/min)" = max_steps_60min,
"Acc. pas max. 30 min (pas/min)" = max_steps_30min,
"Acc. pas max. 20 min (pas/min)" = max_steps_20min,
"Acc. pas max. 5 min (pas/min)" = max_steps_5min,
"Acc. pas max. 1 min (pas/min)" = max_steps_1min,
"Acc. pas pic 60 min (pas/min)" = peak_steps_60min,
"Acc. pas pic 30 min (pas/min)" = peak_steps_30min,
"Acc. pas pic 20 min (pas/min)" = peak_steps_20min,
"Acc. pas pic 5 min (pas/min)" = peak_steps_5min,
"Acc. pas pic 1 min (pas/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)
),
"Acc. pas max. 60 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas max. 30 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas max. 20 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas max. 5 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas max. 1 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas pic 60 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas pic 30 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas pic 20 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas pic 5 min (pas/min)" = reactable::colDef(minWidth = 160),
"Acc. pas pic 1 min (pas/min)" = reactable::colDef(minWidth = 160)
)
)
Résultats par jour : Vue graphique
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "fr",
metrics = "step_acc"
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable(
tibble::tibble(
Indicateur = c("Nombre de jours valides",
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)"
),
"Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c(
# Nombre de jours valides
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
)
Indicateurs de distribution de l'intensité
Distribution du temps dans les zones d'intensité
params$graph_int_distri_bands
Modèles log-log
params$graph_int_distri_models
Résultats par jour : Vue tabulaire
epoch_label <- as.name(paste0(params$epoch,"s"))
reactable::reactable(params$results_by_day %>% dplyr::select(date, ig:M5) %>%
dplyr::rename(
Date = date,
"Gradient d'intensité" = ig,
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := M120,
"M60 (counts/{{epoch_label}})" := M60,
"M30 (counts/{{epoch_label}})" := M30,
"M15 (counts/{{epoch_label}})" := M15,
"M5 (counts/{{epoch_label}})" := M5
),
striped = TRUE,
list(
"Date" = reactable::colDef(
style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1),
headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1)
)
)
)
if (params$rendered_by_shiny)
shiny::setProgress(0.75) # set progress to 75%
Résultats par jour : Vue graphique
create_fig_res_by_day(
df2,
params$minimum_wear_time_for_analysis,
params$start_day_analysis,
params$end_day_analysis,
language = "fr",
metrics = "int_distri",
epoch_label = paste0(params$epoch, "s")
) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable(
tibble::tibble(
Indicateur = c("Nombre de jours valides",
"Gradient d'intensité",
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)")
),
"Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c(
# Nombre de jours valides
paste0(df1_means[["valid_days"]]),
# Gradient d'intensité
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
)
Vue radar conçue à partir des moyennes des jours valides
cor_factor <- 60 / params$epoch
create_fig_mx_summary(
data = df1_means,
labels = NULL,
mpa_cutpoint = params$mpa_cutpoint / cor_factor,
vpa_cutpoint = params$vpa_cutpoint / cor_factor
)
Indicateurs d'accumulation du comportement sédentaire obtenus à partir des jours valides
Distribution temporelle des périodes de comportement sédentaire
if(length(params$p_breaks_sed) == 1) {
cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.")
} else {
params$p_breaks_sed + labs(title = paste("Nombre journalier moyen de 'breaks':", params$mean_breaks_sed), x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)")
}
Coefficient alpha, durée médiane des périodes (MBD), durée usuelle des périodes (UBD), et indice Gini
if(length(params$p_breaks_pa) == 1) {
cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.")
} else {
p1 <- params$p_alpha_sed + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none")
p2 <- params$p_MBD_sed + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none")
p3 <- params$p_UBD_sed + labs(x = "Durée de période (min)", y = "Fraction cumulée du temps total sédentaire", color = "Durée (min)", fill = "Durée (min)")
p4 <- params$p_gini_sed + labs(x = "Fraction du nombre de périodes de durée > x", y = "Fraction cumulée du temps total sédentaire", color = "Durée (min)", fill = "Durée (min)")
(p1 | p2 | p3 | p4) + plot_layout(guides = "collect") &
theme(legend.position = 'bottom',
axis.title = element_text(size = 15),
legend.title = element_text(size = 17),
legend.text = element_text(size = 15)
)
}
Indicateurs d'accumulation de l'activité physique obtenus à partir des jours valides
Distribution temporelle des périodes d'activité physique
if(length(params$p_breaks_pa) == 1) {
cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.")
} else {
params$p_breaks_pa + labs(title = paste("Nombre journalier moyen de 'breaks':", params$mean_breaks_pa), x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)")
}
if (params$rendered_by_shiny)
shiny::setProgress(1) # set progress to 100%
Coefficient alpha, durée médiane des périodes (MBD), durée usuelle des périodes (UBD), et indice Gini
if(length(params$p_breaks_pa) == 1) {
cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.")
} else {
p1 <- params$p_alpha_pa + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none")
p2 <- params$p_MBD_pa + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none")
p3 <- params$p_UBD_pa + labs(x = "Durée de période (min)", y = "Fraction cumulée du temps total d'activité physique", color = "Durée (min)", fill = "Durée (min)")
p4 <- params$p_gini_pa + labs(x = "Fraction du nombre de périodes de durée > x", y = "Fraction cumulée du temps total d'activité physique", color = "Durée (min)", fill = "Durée (min)")
(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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```{css, echo=FALSE} .title { font-weight: bold; }
h1 { font-size: 25px; font-weight: bold; } h2 { font-size: 23px; font-weight: bold; } h3 { font-size: 21px; } p.caption { color: #777; margin-top: 10px; } p code { white-space: inherit; } pre { word-break: normal; word-wrap: normal; } pre code { white-space: inherit; }
```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 sex <- ifelse(params$sex == "male", "masculin", ifelse(params$sex == "female", "féminin", "non défini")) position <- ifelse(params$position == "hip", "hanche", ifelse(params$position == "wrist", "poignet", ifelse(params$position == "thigh", "cuisse", ifelse(params$position == "ankle", "cheville", "NA")))) side <- ifelse(params$side == "right", "droit", ifelse(params$side == "left", "gauche", "non défini")) axis_weartime <- ifelse(params$axis_weartime == "vertical axis", "l'axe vertical", ifelse(params$axis_weartime == "vector magnitude", "le vecteur magnitude", "non défini")) axis_mvpa <- ifelse(params$axis_mvpa == "vertical axis", "axe vertical", ifelse(params$axis_mvpa == "vector magnitude", "vecteur magnitude", "non défini")) 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)
Informations
Patient : r params$patient_surname r params$patient_name | Date : r Sys.Date() | Évaluateur : r params$assessor_surname r params$assessor_name
Age : r params$age ans | Sexe : r sex | Masse : r params$weight kg | Période de mesure : r format(params$start_date, "%d/%m/%Y %H:%M:%S") au r format(params$end_date, "%d/%m/%Y %H:%M:%S") | Estimation du métabolisme de base : r format(round(params$bmr_kcal_d, 2), nsmall = 2) kcal/jour (Estimation à partir des équations de Henry [2005, doi: 10.1079/PHN2005801])
Appareil : r params$device | Position : r position | Côté : r side | Fréquence d'échantillonnage : r params$sampling_rate Hz | Filtre : r params$filter
'Epoch' : r params$epoch s | Temps de non-port : Basé sur r axis_weartime, intervalle de r params$frame_size min avec zéro count pour la détection du temps de non-port, intervalle de r params$allowanceFrame_size min avec counts différents de 0 autorisé durant une période de non-port, intervalle de r params$streamFrame_size min avec zéro count autour de la période d'activité détectée pour confirmer le non-port | Équation de prédiction des METs : r params$equation_mets | Axe utilisé pour catégoriser l'intensité d'activité physique : r axis_mvpa | Valeurs seuils : <r params$sed_cutpoint counts/min pour SED, $\geqslant$ r params$mpa_cutpoint counts/min pour MPA, $\geqslant$ r params$vpa_cutpoint counts/min pour VPA
Période journalière considérée pour compter le temps de port : de r params$start_day_analysis à r params$end_day_analysis
Nombre minimum d'heures avec temps de port pour valider un jour : r params$minimum_wear_time_for_analysis heures
Abréviations : SED = sédentaire, LPA = activité physique légère, MPA = activité physique modérée, VPA = activité physique vigoureuse, MVPA = activité physique modérée à vigoureuse, NAP = niveau d'activité physique
Résultats
Indicateurs de volume d'activité
Distribution temporelle de l'intensité
g <- 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$end_day_analysis )
g + labs(x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)") if (params$rendered_by_shiny) shiny::setProgress(0.25) # set progress to 25%
Résultats par jour : Vue tabulaire
reactable::reactable(params$results_by_day %>% dplyr::select(date:total_steps) %>% dplyr::rename( Date = date, "Temps de port (min)" = "wear_time", "Total counts Axe 1 " = "total_counts_axis1", "Total counts VM" = "total_counts_vm", "Moyenne Axe 1 (counts/min)" = "axis1_per_min", "Moyenne VM (counts/min)" = "vm_per_min", "Temps SED (min)" = "minutes_SED", "Temps LPA (min)" = "minutes_LPA", "Temps MPA (min)" = "minutes_MPA", "Temps VPA (min)" = "minutes_VPA", "Temps MVPA (min)" = "minutes_MVPA", "Proportion du temps de port SED (%)" = "percent_SED", "Proportion du temps de port LPA (%)" = "percent_LPA", "Proportion du temps de port MPA (%)" = "percent_MPA", "Proportion du temps de port VPA (%)" = "percent_VPA", "Proportion du temps de port MVPA (%)" = "percent_MVPA", "Ratio MVPA / SED" = "ratio_mvpa_sed", "Total MET-hr" = "mets_hours_mvpa", "Total kcal" = "total_kcal", "NAP" = "pal", "Total pas" = "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) ), "Temps de port (min)" = reactable::colDef(minWidth = 160), "Total counts Axe 1 " = reactable::colDef(minWidth = 160), "Total counts VM" = reactable::colDef(minWidth = 160), "Moyenne Axe 1 (counts/min)" = reactable::colDef(minWidth = 160), "Moyenne VM (counts/min)" = reactable::colDef(minWidth = 160), "Temps SED (min)" = reactable::colDef(minWidth = 160), "Temps LPA (min)" = reactable::colDef(minWidth = 160), "Temps MPA (min)" = reactable::colDef(minWidth = 160), "Temps VPA (min)" = reactable::colDef(minWidth = 160), "Temps MVPA (min)" = reactable::colDef(minWidth = 160), "Proportion du temps de port SED (%)" = reactable::colDef(minWidth = 160), "Proportion du temps de port LPA (%)" = reactable::colDef(minWidth = 160), "Proportion du temps de port MPA (%)" = reactable::colDef(minWidth = 160), "Proportion du temps de port VPA (%)" = reactable::colDef(minWidth = 160), "Proportion du temps de port MVPA (%)" = reactable::colDef(minWidth = 160), "Ratio MVPA / SED" = reactable::colDef(minWidth = 160), "Total MET-hr" = reactable::colDef(minWidth = 160), "Total kcal" = reactable::colDef(minWidth = 160), "NAP" = reactable::colDef(minWidth = 160), "Total pas" = reactable::colDef(minWidth = 160) ) )
Résultats par jour : Vue graphique
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "fr", metrics = "volume" ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable( tibble::tibble( Indicateur = c( "Nombre de jours valides", "Temps de port (min)", "Total counts Axe 1 ", "Total counts VM", "Moyenne Axe 1 (counts/min)", "Moyenne VM (counts/min)", "Temps SED (min)", "Temps LPA (min)", "Temps MPA (min)", "Temps VPA (min)", "Temps MVPA (min)", "Proportion du temps de port SED (%)", "Proportion du temps de port LPA (%)", "Proportion du temps de port MPA (%)", "Proportion du temps de port VPA (%)", "Proportion du temps de port MVPA (%)", "Ratio MVPA / SED", "Total MET-hr", "Total kcal", "NAP", "Total pas" ), "Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c( # Nombre de jours valides paste0(df1_means[["valid_days"]]), # Temps de port 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 par min paste0(format(round(df1_means[["axis1_per_min"]], 1), nsmall = 1), " | ", format(round(df1_medians[["axis1_per_min"]], 1), nsmall = 1)), # VM par min paste0(format(round(df1_means[["vm_per_min"]], 1), nsmall = 1), " | ", format(round(df1_medians[["vm_per_min"]], 1), nsmall = 1)), # Minutes SED 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"]]), ")"), # Minutes LPA 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"]]), ")"), # Minutes MPA 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"]]), ")"), # Minutes VPA 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"]]), ")"), # Minutes MVPA 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"]]), ")"), # % SED paste0(format(round(df1_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_SED"]], 1), nsmall = 1)), # % LPA paste0(format(round(df1_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_LPA"]], 1), nsmall = 1)), # % MPA paste0(format(round(df1_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_MPA"]], 1), nsmall = 1)), # % VPA paste0(format(round(df1_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(df1_medians[["percent_VPA"]], 1), nsmall = 1)), # % 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)), # Total METs-hr paste0(format(round(df1_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(df1_medians[["mets_hours_mvpa"]], 2), nsmall = 2)), # Total kcal paste0(format(round(df1_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["total_kcal"]], 2), nsmall = 2)), # NAP paste0(format(round(df1_means[["pal"]], 2), nsmall = 2), " | ", format(round(df1_medians[["pal"]], 2), nsmall = 2)), # Total pas paste0(round(df1_means[["total_steps"]], 0), " | ", round(df1_medians[["total_steps"]], 0)) ) ), striped = TRUE, defaultPageSize = 21 ) if (params$rendered_by_shiny) shiny::setProgress(0.5) # set progress to 50%
Comparaison avec des normes et des recommandations
# PAL g_pal <- create_fig_pal(score = df1_means[["pal"]], "fr") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm")) # Steps g_steps <- create_fig_steps(score = df1_means[["total_steps"]], "fr") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm")) # MVPA g_mvpa <- create_fig_mvpa(score = df1_means[["minutes_MVPA"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # SED g_sed <- create_fig_sed(score = df1_means[["minutes_SED"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # MVPA/SED ratio g_ratio <- create_fig_ratio_mvpa_sed(score = df1_means[["ratio_mvpa_sed"]], "fr") + theme(plot.margin = margin(0, 1, 0, 1, "cm")) # Whole figure (g_pal + theme(legend.position = "top")) / g_steps / (g_mvpa | g_sed | g_ratio) + plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")
Commentaires
# Determining physical activity guidelines status guidelines_status <- get_guidelines_status(df1_means[["mets_hours_mvpa"]], language = "fr") # Determining PAL status pal_status <- get_pal_status(df1_means[["pal"]], language = "fr")
Selon la Food and Agriculture Organization of the United Nations (FAO), le NAP du patient pourrait être décrit comme r pal_status. Après avoir fait la conversion des minutes MPA et VPA vers les MET-heures, l'estimation du volume MVPA moyen journalier pour le patient est de r paste0(format(round(df1_means[["mets_hours_mvpa"]], 1), nsmall = 1)) MET-hr, ce qui pourrait correspondre au fait de tomber r guidelines_status (i.e., 7.5-15.0 MET-hr par semaine, ou 1.07-2.14 MET-hr par jour).
Indicateurs d'accumulation des pas
Résultats par jour : Vue tabulaire
reactable::reactable(params$results_by_day %>% dplyr::select(date, max_steps_60min:peak_steps_1min) %>% dplyr::rename( Date = date, "Acc. pas max. 60 min (pas/min)" = max_steps_60min, "Acc. pas max. 30 min (pas/min)" = max_steps_30min, "Acc. pas max. 20 min (pas/min)" = max_steps_20min, "Acc. pas max. 5 min (pas/min)" = max_steps_5min, "Acc. pas max. 1 min (pas/min)" = max_steps_1min, "Acc. pas pic 60 min (pas/min)" = peak_steps_60min, "Acc. pas pic 30 min (pas/min)" = peak_steps_30min, "Acc. pas pic 20 min (pas/min)" = peak_steps_20min, "Acc. pas pic 5 min (pas/min)" = peak_steps_5min, "Acc. pas pic 1 min (pas/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) ), "Acc. pas max. 60 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas max. 30 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas max. 20 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas max. 5 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas max. 1 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas pic 60 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas pic 30 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas pic 20 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas pic 5 min (pas/min)" = reactable::colDef(minWidth = 160), "Acc. pas pic 1 min (pas/min)" = reactable::colDef(minWidth = 160) ) )
Résultats par jour : Vue graphique
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "fr", metrics = "step_acc" ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable( tibble::tibble( Indicateur = c("Nombre de jours valides", "Acc. pas max. 60 min (pas/min)", "Acc. pas max. 30 min (pas/min)", "Acc. pas max. 20 min (pas/min)", "Acc. pas max. 5 min (pas/min)", "Acc. pas max. 1 min (pas/min)", "Acc. pas pic 60 min (pas/min)", "Acc. pas pic 30 min (pas/min)", "Acc. pas pic 20 min (pas/min)", "Acc. pas pic 5 min (pas/min)", "Acc. pas pic 1 min (pas/min)" ), "Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c( # Nombre de jours valides 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 )
Indicateurs de distribution de l'intensité
Distribution du temps dans les zones d'intensité
params$graph_int_distri_bands
Modèles log-log
params$graph_int_distri_models
Résultats par jour : Vue tabulaire
epoch_label <- as.name(paste0(params$epoch,"s")) reactable::reactable(params$results_by_day %>% dplyr::select(date, ig:M5) %>% dplyr::rename( Date = date, "Gradient d'intensité" = ig, "M1/3 (counts/{{epoch_label}})" := "M1/3", "M120 (counts/{{epoch_label}})" := M120, "M60 (counts/{{epoch_label}})" := M60, "M30 (counts/{{epoch_label}})" := M30, "M15 (counts/{{epoch_label}})" := M15, "M5 (counts/{{epoch_label}})" := M5 ), striped = TRUE, list( "Date" = reactable::colDef( style = list(position = "sticky", left = 0, background = "#CCCCCC", zIndex = 1), headerStyle = list(position = "sticky", left = 0, background = "#fff", zIndex = 1) ) ) ) if (params$rendered_by_shiny) shiny::setProgress(0.75) # set progress to 75%
Résultats par jour : Vue graphique
create_fig_res_by_day( df2, params$minimum_wear_time_for_analysis, params$start_day_analysis, params$end_day_analysis, language = "fr", metrics = "int_distri", epoch_label = paste0(params$epoch, "s") ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))
Moyennes | Médianes calculées à partir des jours valides
reactable::reactable( tibble::tibble( Indicateur = c("Nombre de jours valides", "Gradient d'intensité", 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)") ), "Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c( # Nombre de jours valides paste0(df1_means[["valid_days"]]), # Gradient d'intensité 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 )
Vue radar conçue à partir des moyennes des jours valides
cor_factor <- 60 / params$epoch create_fig_mx_summary( data = df1_means, labels = NULL, mpa_cutpoint = params$mpa_cutpoint / cor_factor, vpa_cutpoint = params$vpa_cutpoint / cor_factor )
Indicateurs d'accumulation du comportement sédentaire obtenus à partir des jours valides
Distribution temporelle des périodes de comportement sédentaire
if(length(params$p_breaks_sed) == 1) { cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.") } else { params$p_breaks_sed + labs(title = paste("Nombre journalier moyen de 'breaks':", params$mean_breaks_sed), x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)") }
Coefficient alpha, durée médiane des périodes (MBD), durée usuelle des périodes (UBD), et indice Gini
if(length(params$p_breaks_pa) == 1) { cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.") } else { p1 <- params$p_alpha_sed + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none") p2 <- params$p_MBD_sed + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none") p3 <- params$p_UBD_sed + labs(x = "Durée de période (min)", y = "Fraction cumulée du temps total sédentaire", color = "Durée (min)", fill = "Durée (min)") p4 <- params$p_gini_sed + labs(x = "Fraction du nombre de périodes de durée > x", y = "Fraction cumulée du temps total sédentaire", color = "Durée (min)", fill = "Durée (min)") (p1 | p2 | p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom', axis.title = element_text(size = 15), legend.title = element_text(size = 17), legend.text = element_text(size = 15) ) }
Indicateurs d'accumulation de l'activité physique obtenus à partir des jours valides
Distribution temporelle des périodes d'activité physique
if(length(params$p_breaks_pa) == 1) { cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.") } else { params$p_breaks_pa + labs(title = paste("Nombre journalier moyen de 'breaks':", params$mean_breaks_pa), x = "Temps (hh:mm)", color = "Durée de période (min)", fill = "Durée de période (min)") } if (params$rendered_by_shiny) shiny::setProgress(1) # set progress to 100%
Coefficient alpha, durée médiane des périodes (MBD), durée usuelle des périodes (UBD), et indice Gini
if(length(params$p_breaks_pa) == 1) { cat("Aucun résultat. Les résultats ne peuvent être obtenus qu'en utilisant des périodes de 60 s.") } else { p1 <- params$p_alpha_pa + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none") p2 <- params$p_MBD_pa + labs(x = "Durée de période (min)", color = "Durée (min)", fill = "Durée (min)") + guides(color = "none", fill = "none") p3 <- params$p_UBD_pa + labs(x = "Durée de période (min)", y = "Fraction cumulée du temps total d'activité physique", color = "Durée (min)", fill = "Durée (min)") p4 <- params$p_gini_pa + labs(x = "Fraction du nombre de périodes de durée > x", y = "Fraction cumulée du temps total d'activité physique", color = "Durée (min)", fill = "Durée (min)") (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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