Title:	A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
Version:	2.1.2
Description:	A tool to analyse 'ActiGraph' accelerometer data and to implement the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis is completed, the app allows to export results to .csv files and to generate a report of the measurement. All the configured inputs relevant for interpreting the results are recorded in the report. In addition to the existing 'R' packages that are fully integrated with the app, the app uses some functions from the 'actigraph.sleepr' package developed by Petkova (2021) https://github.com/dipetkov/actigraph.sleepr/.
License:	GPL (≥ 3)
Imports:	assertthat, dbplyr, dplyr, flextable, forcats, ggplot2, golem, hms, lubridate, magrittr, modelr, patchwork, PhysicalActivity, plyr, reactable, rmarkdown, RSQLite, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyFeedback, shinyjs, stringr, tidyr, zoo
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://pydemull.github.io/activAnalyzer/, https://github.com/pydemull/activAnalyzer
BugReports:	https://github.com/pydemull/activAnalyzer/issues
Suggests:	covr, knitr, spelling, testthat (≥ 3.0.0), processx, globals, config, tidyselect, DBI, htmltools, officer, pkgload, scales, tibble, rlang, tinytex, shinytest2, pkgdown, callr
Language:	en-US
Depends:	R (≥ 3.4.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-09-23 22:34:41 UTC; pydemullenheim
Author:	Pierre-Yves de Müllenheim [cre, aut]
Maintainer:	Pierre-Yves de Müllenheim <pydemull@uco.fr>
Repository:	CRAN
Date/Publication:	2024-09-23 23:40:02 UTC

Average results over valid days

Description

This function computes, using valid days only, the mean of each of the metrics obtained using the recap_by_day function. The median can also be obtained with an appropriate configuration of the function.

Usage

average_results(data, minimum_wear_time = 10, fun = c("mean", "median"))

Arguments

Value

A dataframe.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
summary_by_day <- recap_by_day(
    data = mydata_with_intensity_marks, 
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00"
    )$df_all_metrics
average_results(data = summary_by_day, minimum_wear_time = 10)

Compute activity accumulation metrics

Description

This function computes metrics that summarise the pattern of accumulation of either sedentary behaviour or physical activity (depending on the configuration of the function) over time:

• mean_breaks: mean daily number of transitions from a sedentary bout to a physical activity bout (or from a physical activity bout to a sedentary bout); this actually corresponds to the mean daily total number of sedentary (or physical activity) bouts detected.

• alpha: provides information on the relative proportion of short and long bouts. The higher the alpha coefficient, the more the individual tends to accumulate sedentary (or physical activity) time using relatively short bouts. Alpha is computed using all the bouts of the days and periods of the day considered for analysis. Alpha is computed using the following equation provided by Chastin et al. (2010; doi: 10.1016/j.gaitpost.2009.09.002): \alpha = 1 + n \left[\sum_{i = 1}^{n}{ln}\frac{x_{i}}{x_{min}}\right]^{-1}, with n the total number of bouts, x_{i} the duration of the bout i, and x_{min} the shortest recorded bout duration.

• median bout duration (MBD): refers to the median sedentary (or physical activity) bout duration. MBD is computed using all the bouts of the days and periods of the day considered for analysis.

• usual bout duration (UBD): refers to the bout duration under/above which 50% of sedentary (or physical activity) time is accumulated. UBD is computed using all the bouts of the days and periods of the day considered for analysis. UBD is determined as described in Belletiere al. (2017; doi:10.1371/journal.pone.0180119) supplementary file 1. More precisely, UBD is found using non-linear regression with the following model: y = \frac{t^n}{t^n + UBD^n}, with t the bout duration, n a free parameter, and y the fraction of total time accumulated in bouts \le t.

• Gini index: provides information on the equality with which bout durations contribute to total sedentary (or physical activity) time. A value of 1 reveals perfect inequality, and a value of 0 reveals perfect equality. Gini index is computed using all the bouts of the days and periods of the day considered for analysis. Gini index is computed following the procedure described at the following link: https://www.statology.org/gini-coefficient-excel/. This method provides similar results as the frequency method implemented in the Gini function from the DescTools R package.

The appearance of the graphics generated by the function has been inspired by the supplementary file provided by Belletiere al. (2017; doi:10.1371/journal.pone.0180119).

Usage

compute_accumulation_metrics(
  data,
  col_time = "time",
  col_cat_int = "intensity_category",
  behaviour = c("sed", "pa"),
  dates = NULL,
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59",
  zoom_from = "00:00:00",
  zoom_to = "23:59:59"
)

Arguments

Value

A list of numeric and graphic objects related to mean daily total breaks, alpha, MBD, UBD and Gini index. The list also contains the processed datasets that were used to provide these metrics: recap_bouts_by_day used to compute mean_break, recap_bouts used to compute alpha and MBD, summarised_bouts used to compute UBD, and summarised_bouts2 used to compute ⁠Gini index⁠.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
compute_accumulation_metrics(
   data = mydata_with_intensity_marks,
   behaviour = "sed",
   dates = c("2021-04-07", "2021-04-08", "2021-04-09", "2021-04-10", "2021-04-11"),
   valid_wear_time_start = "00:00:00",
   valid_wear_time_end = "23:59:59",
   zoom_from = "00:00:00",
   zoom_to = "23:59:59"
    )

Compute Basal Metabolic Rate (BMR)

Description

This function computes Basal Metabolic Rate in kcal/d using a Henry et al. (2005; doi: 10.1079/PHN2005801) equation. This function is wrapped within the mark_intensity and recap_by_day functions.

Usage

compute_bmr(
  age = 40,
  sex = c("male", "female", "intersex", "undefined", "prefer not to say"),
  weight = 70
)

Arguments

Value

A numeric value.

Examples

compute_bmr(age = 32, sex = "male", weight = 67)

Compute intensity distribution metrics

Description

This function computes metrics that describe the distribution of intensity for each day of a dataset. Computations are performed based on the daily periods set for analysis and on the detected wear time.

Usage

compute_intensity_distri_metrics(
  data,
  col_axis = "vm",
  col_time = "time",
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59",
  start_first_bin = 0,
  start_last_bin = 10000,
  bin_width = 500
)

Arguments

Value

A list of objects: metrics, p_band, and p_log. metrics is a dataframe containing the intensity gradients and the MX metrics (in counts/epoch duration used) as described in Rowlands et al. (2018; doi:10.1249/MSS.0000000000001561). The graphic p_band shows the distribution of time spent in the configured bins of intensity for each day of the dataset. The graphic p_log shows, for each day, the relationship between the natural log of time spent in each bin with the natural log of the middle values of the intensity bins.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male"
    )
compute_intensity_distri_metrics(
   data = mydata_with_intensity_marks,
   col_axis = "vm",
   col_time = "time",
   valid_wear_time_start = "00:00:00",
   valid_wear_time_end = "23:59:59",
   start_first_bin = 0,
   start_last_bin = 10000,
   bin_width = 500
    )
 

Compute metabolic equivalent of task (MET) values

Description

This function computes metabolic equivalent of task (METs) from weight, sex, accelerometer counts, and a published equation from one of the following scientific articles: Sasaki et al. (2011; doi:10.1016/j.jsams.2011.04.003); Santos-Lozano et al. (2013; 10.1055/s-0033-1337945); Freedson et al. (1998; doi: 10.1097/00005768-199805000-00021). This function is wrapped within the mark_intensity function.

Usage

compute_mets(
  data,
  equation = c("Sasaki et al. (2011) [Adults]", "Santos-Lozano et al. (2013) [Adults]",
    "Freedson et al. (1998) [Adults]", "Santos-Lozano et al. (2013) [Older adults]"),
  weight = 70,
  sex = c("male", "female", "intersex", "undefined", "prefer not to say")
)

Arguments

Value

A numeric vector.

Examples

library(magrittr)
file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mydata %>% mark_wear_time() %>% 
dplyr::filter(days == 2 & time >= hms::as_hms("14:00:00") & time <= hms::as_hms("15:00:00")) 
mets <- compute_mets(
    data = mydata_with_wear_marks,
    equation = "Sasaki et al. (2011) [Adults]", 
    weight = 67, 
    sex = "male"
    )
mets

Compute MX metric

Description

An MX metric is the count/epoch value at and above which a given amount of the most active minutes is spent (continuously or discontinuously).

Usage

compute_mx(x, n)

Arguments

Value

A numeric value.

Compute mean step accumulation (per min) from a given number of the best continous or discontinuous minutes

Description

Compute mean step accumulation (per min) from a given number of the best continous or discontinuous minutes

Usage

compute_peak_step_acc(x, n)

Arguments

Value

A numeric value.

Compute PROactive monitor-based physical activity score for C-PPAC tool

Description

This function computes the PROactive activity score based on the daily median or mean of step count or vector magnitude unit (in counts/min) obtained using an ActiGraph accelerometer.

Usage

compute_pro_actigraph_score(
  x,
  quest = c("C-PPAC", "D-PPAC"),
  metric = c("steps", "vmu"),
  fun = c("median", "mean")
)

Arguments

Value

A numeric value.

Examples

compute_pro_actigraph_score(x = 3500, quest = "C-PPAC", metric = "steps", fun = "median")

compute_pro_actigraph_score(x = 340, quest = "C-PPAC", metric = "vmu",  fun = "mean")

Provide score for each question of the C-PPAC

Description

This function provides a score (from 0 to 4) in relation to the response to a given question from the C-PPAC questionnaire.

Usage

compute_pro_score_cppac(
  x,
  question = c("q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9", "q10", "q11", "q12"),
  language = c("en", "fr")
)

Arguments

Value

A numeric value.

Examples

compute_pro_score_cppac(
    x = "A lot (about 1 hour every day)", 
    question = "q1",
    language = "en"
    )
    

Provide score for each question of the D-PPAC

Description

This function provides a score (from 0 to 4) in relation to the response to a given question from the D-PPAC questionnaire.

Usage

compute_pro_score_dppac(
  x,
  question = c("q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9", "q10", "q11", "q12"),
  language = c("en", "fr")
)

Arguments

Value

A numeric value.

Examples

compute_pro_score_dppac(
    x = "Un petit peu (jusqu\u2019\u00e0 10 minutes au total)", 
    question = "q1",
    language = "fr"
    )
    

Create a figure showing the mean daily MVPA time

Description

The function generates a figure showing mortality hazard ratio in correspondence with daily MVPA minutes. The figure is based on data extracted from Ekelund et al. paper (2019; doi: 10.1136/bmj.l4570).

Usage

create_fig_mvpa(score, language = c("en", "fr", "de"))

Arguments

Value

A ggplot object.

Examples

create_fig_mvpa(score = 27)

Create a radar plot for MX metrics relating to each day of the measurement of physical behavior

Description

This function creates radar plots in relation to MX metrics as illustrated in Rowlands et al. (2018; doi:10.1249/MSS.0000000000001561) paper, here for each day of an accelerometer measurement.

Usage

create_fig_mx_by_day(
  data,
  labels = NULL,
  mpa_cutpoint = 2690,
  vpa_cutpoint = 6167
)

Arguments

Value

A ggplot object

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male"
    )
summary_by_day <- recap_by_day(
    data = mydata_with_intensity_marks, 
    col_axis = "vm",
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00",
    start_first_bin = 0,
    start_last_bin = 10000,
    bin_width = 500
    )$df_all_metrics
create_fig_mx_by_day(
    data = summary_by_day,
    labels = seq(2500, 12500, 2500)
)

Create a radar plot for the mean or median MX metrics relating to the measurement of physical behavior

Description

This function creates a radar plot in relation to MX metrics as illustrated in Rowlands et al. (2018; doi:10.1249/MSS.0000000000001561) paper.

Usage

create_fig_mx_summary(
  data,
  labels = NULL,
  mpa_cutpoint = 2690,
  vpa_cutpoint = 6167
)

Arguments

Value

A ggplot object

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male"
    )
summary_by_day <- recap_by_day(
    data = mydata_with_intensity_marks, 
    col_axis = "vm",
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00",
    start_first_bin = 0,
    start_last_bin = 10000,
    bin_width = 500
    )$df_all_metrics
recap <- average_results(data = summary_by_day, minimum_wear_time = 10, fun  = "median")
create_fig_mx_summary(
    data = recap,
    labels = seq(2500, 12500, 2500)
 )
 
 

Create a figure showing the mean daily Physical Activity Level (PAL)

Description

The function generates a figure showing the daily mean of PAL in correspondence with the FAO (2004; http://www.fao.org/3/y5686e/y5686e07.htm#bm07.3) categories.

Usage

create_fig_pal(score, language = c("en", "fr", "de"))

Arguments

Value

A ggplot object.

Examples

create_fig_pal(score = 1.8)

Create a figure showing the mean daily MVPA/SED ratio

Description

The function generates a figure showing mortality hazard ratio in correspondence with the daily mean of the MVPA/SED ratio. The figure is based on data extracted from Chastin et al. paper (2021; doi: 10.1123/jpah.2020-0635).

Usage

create_fig_ratio_mvpa_sed(score, language = c("en", "fr", "de"))

Arguments

Value

A ggplot object.

Examples

create_fig_ratio_mvpa_sed(score = 0.06)

Create a figure with metrics shown for each day

Description

The function generates a figure with several common metrics shown for each day of the physical behavior measurement.

Usage

create_fig_res_by_day(
  data,
  minimum_wear_time_for_analysis = 10,
  start_day_analysis = "00:00:00",
  end_day_analysis = "23:59:00",
  language = c("en", "fr", "de"),
  metrics = c("all", "volume", "step_acc", "int_distri"),
  epoch_label = "60s"
)

Arguments

Value

A ggplot object

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    cts  = "vm", 
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
summary_by_day <- recap_by_day(
    data = mydata_with_intensity_marks, 
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00"
    )$df_all_metrics
create_fig_res_by_day(summary_by_day, 
    minimum_wear_time_for_analysis = 10, 
    start_day_analysis = "00:00:00", 
    end_day_analysis = "23:59:00", 
    language = "en")

Create a figure showing the mean daily sedentary (SED) time

Description

The function generates a figure showing mortality hazard ratio in correspondence with daily SED hours. The figure is based on data extracted from Ekelund et al. paper (2019; doi: 10.1136/bmj.l4570).

Usage

create_fig_sed(score, language = c("en", "fr", "de"))

Arguments

Value

A ggplot object.

Examples

create_fig_sed(score = 400)

Create a figure showing the mean daily step count

Description

The function generates a figure showing the daily mean of the daily step count in correspondence with the Tudor-Locke et al. (2011; doi: 10.1186/1479-5868-8-79) categories.

Usage

create_fig_steps(score, language = c("en", "fr", "de"))

Arguments

Value

A ggplot object.

Examples

create_fig_steps(score = 12500)

Create a formatted table of results

Description

The function generates a formatted table with both means and medians of the metrics obtained following the physical behavior measurement.

Usage

create_flextable_summary(
  results_summary_means,
  results_summary_medians,
  language = c("en", "fr"),
  metrics = c("all", "volume", "step_acc", "int_distri"),
  epoch_label = "60s"
)

Arguments

Value

A flextable object

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    cts  = "vm", 
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
summary_by_day <- recap_by_day(
    data = mydata_with_intensity_marks, 
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00"
    )$df_all_metrics
results_summary_means <- average_results(
    data = summary_by_day, 
    minimum_wear_time = 10, 
    fun = "mean"
    )
results_summary_medians <- average_results(
    data = summary_by_day, 
    minimum_wear_time = 10, 
    fun = "median"
    )
create_flextable_summary(
    results_summary_means,
    results_summary_medians, 
    language = "en"
    )

Do all analyses at once

Description

This function performs all analyses successively using the default data file associated to the package. It is an internal function allowing the computation of the speed of the whole analysis process, from the data importation to the final line of the results.

Usage

do_all_analyses(to_epoch = 60)

Arguments

Value

A dataset (1 row) with all computed metrics.

Get WHO physical activity guidelines status

Description

Get WHO physical activity guidelines status

Usage

get_guidelines_status(value, language = c("en", "fr"))

Arguments

Value

A character string.

Examples

get_guidelines_status(value = 5)

Get intensity gradient values and graphics

Description

The values and graphics are respectively computed and created from the daily periods set for analysis and are based on detected wear time.

Usage

get_ig_results(
  data,
  col_axis = "vm",
  col_time = "time",
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59",
  start_first_bin = 0,
  start_last_bin = 10000,
  bin_width = 500,
  cor_factor = 1
)

Arguments

Value

A list of objects.

Get relevant missing physical activity information indicated by the user of the app

Description

Get relevant missing physical activity information indicated by the user of the app

Usage

get_pa_period_info(period)

Arguments

Value

A dataframe

Get FAO physical activity level (PAL) status (http://www.fao.org/3/y5686e/y5686e07.htm#bm07.3)

Description

Get FAO physical activity level (PAL) status (http://www.fao.org/3/y5686e/y5686e07.htm#bm07.3)

Usage

get_pal_status(value, language = c("en", "fr", "de"))

Arguments

Value

A character string.

Examples

get_pal_status(value = 1.8)

Get comment about the MVPA/SED ratio

Description

Get comment about the MVPA/SED ratio

Usage

get_ratio_mvpa_sed_comment(value, language = c("en", "fr"))

Arguments

Value

A character string.

Examples

get_ratio_mvpa_sed_comment(value = 0.03)

Add intensity metrics

Description

This function adds several columns to a dataset that contains accelerometer counts data. These columns concern respectively sedentary time (SED), light physical activity time (LPA), moderate physical activity time (MPA), vigorous physical activity time (VPA), metabolic equivalent of task (METs), kilocalories (kcal), and MET-hours when time is spent in moderate-to-vigorous physical activity. For the SED, LPA, MPA, and VPA columns, the function provides, for each epoch, the numeric value 1 when the value of the configured counts variable respectively fulfills the criteria of the SED, LPA, MPA, and VPA category (e.g., for the SED column, 1 may be provided if VM counts are <150 counts/min); otherwise 0 is provided. METs are computed using the compute_mets function. METs are computed using a published equation from one of the following scientific articles: Sasaki et al. (2011; doi:10.1016/j.jsams.2011.04.003); Santos-Lozano et al. (2013; 10.1055/s-0033-1337945); Freedson et al. (1998; doi: 10.1097/00005768-199805000-00021). Kilocalories are computed as follows. For non-SED epochs, MET values are multiplied by BMR expressed in kcal/min when using the Santos-Lozano et al. (2013) equations since, in that study, METs were multiples of the measured (not standard) resting metabolic rate. When using the Sasaki et al. (2011) and Freedson et al. (1998) equations, the MET values are multiplied by weight and 1/60 since, in those studies, METs were multiples of standard resting metabolic rate (i.e., 3.5 mLO2/min/kg) and a standard MET is approximately equivalent to 1 kcal/kg/h (Butte et al., 2012; doi: 10.1249/MSS.0b013e3182399c0e). For SED epochs, BMR expressed in kcal/min is directly used. BMR is computed using the compute_bmr function that uses sex, age, and weight inputs, and one of the equations retrieved from the paper by Henry et al. (2005; doi: 10.1079/PHN2005801). MET-hours are obtained by multiplying METs by time related to each epoch (e.g., 1/60e of an hour for 1-min epochs), only when the MET value is >=3. Of note, kilocalories and MET-hours are initially computed on a 1-min basis, and are then adjusted using a correction factor to correspond to the epoch duration chosen to analyse the accelerometer dataset.

Usage

mark_intensity(
  data,
  col_axis = c("vm", "axis1"),
  col_time = "time",
  col_nonwear = "non_wearing_count",
  col_wear = "wearing_count",
  sed_cutpoint = 200,
  mpa_cutpoint = 2690,
  vpa_cutpoint = 6167,
  equation = c("Sasaki et al. (2011) [Adults]", "Santos-Lozano et al. (2013) [Adults]",
    "Freedson et al. (1998) [Adults]", "Santos-Lozano et al. (2013) [Older adults]"),
  age = 40,
  weight = 70,
  sex = c("male", "female", "intersex", "undefined", "prefer not to say"),
  dates = NULL
)

Arguments

Value

A dataframe.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
head(mydata_with_intensity_marks)

Mark dataset for nonwear/wear time

Description

This function wraps the dataCollapser and the wearingMarking functions from the PhysicalActivity package. After collapsing data, the function adds time and date columns. Then, the function analyzes the dataset for nonwear time detection. Finally, the function adds two variables to the dataset: the variable non_wearing_count that contains the number 1 when the device was not worn (otherwise, 0 is used), and the variable wearing_count that contains the number 1 when the device was worn (otherwise, 0 is used).

Usage

mark_wear_time(
  dataset,
  TS = "TimeStamp",
  to_epoch = 60,
  cts = "vm",
  frame = 90,
  allowanceFrame = 2,
  streamFrame = 30
)

Arguments

Value

A dataframe.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
head(mydata_with_wear_marks)

Plot accelerometer data for each day

Description

This function plots accelerometer data against time for each day of measurement, with the possibility to specify the metric to visualize.

Usage

plot_data(
  data,
  metric = "axis1",
  col_time = "time",
  col_nonwear = "non_wearing_count",
  col_wear = "wearing_count",
  zoom_from = "00:00:00",
  zoom_to = "23:59:59"
)

Arguments

Value

A ggplot object.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
plot_data(
    data = mydata_with_wear_marks, 
    metric = "vm", 
    col_time = "time", 
    col_nonwear = "non_wearing_count", 
    col_wear = "wearing_count",
    zoom_from = "02:00:00",
    zoom_to = "23:58:00"
    )

Plot accelerometer data for each day with both nonwear time and physical activity intensity categories

Description

This function plots accelerometer data with intensity categories against time for each day of measurement, with the possibility to specify the metric to visualize.

Usage

plot_data_with_intensity(
  data,
  metric = "axis1",
  col_time = "time",
  col_nonwear = "non_wearing_count",
  col_wear = "wearing_count",
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59",
  zoom_from = "00:00:00",
  zoom_to = "23:59:59"
)

Arguments

Value

A ggplot object.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male",
    )
plot_data_with_intensity(
    data = mydata_with_intensity_marks,
    metric = "vm",
    valid_wear_time_start = "00:00:00",
    valid_wear_time_end = "23:59:59",
    zoom_from = "02:00:00",
    zoom_to = "23:58:00"
    )

Prepare accelerometer data

Description

This function reads an .agd file and then creates the vector magnitude variable as follows: vm = \sqrt{axis1^{2} + axis2^{2} + axis3^{2}}. The .agd file must contain at least the following columns:

• axis1

• axis2

• axis3

• steps

Usage

prepare_dataset(data)

Arguments

Value

A dataframe.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
head(mydata)

Compute Rasch transformation for PROactive scores

Description

This function provides the 0-100 Rasch scaled score of a given C-PPAC or D-PPAC raw score (based on: Garcia-Aymerich J, et al. Thorax 2021;0:1–11. doi: 10.1136/thoraxjnl-2020-214554).

Usage

rasch_transform(
  x,
  quest = c("C-PPAC", "D-PPAC"),
  score = c("difficulty", "quantity")
)

Arguments

Value

A numeric value.

Examples

rasch_transform(33, quest = "C-PPAC", score = "difficulty")

Read activity counts from an *.agd file

Description

Read ActiGraph sleep watch data from a database stored in an AGD file. Return a tibble. (Code is from actigraph.sleepr package https://github.com/dipetkov/actigraph.sleepr/. See LICENSE.note file in the app skeleton.)

Usage

read_agd(file, tz = "UTC")

Arguments

Value

A tibble of activity data with at least two columns: timestamp and axis1 counts. Optional columns include axis2, axis2, steps, lux and inclinometer indicators (incline off, standing, sitting and lying). The device settings are stored as attributes, which include epochlength.

References

The AGD file format is described in the ActiLife 6 Manual. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://6407355.fs1.hubspotusercontent-na1.net/hubfs/6407355/Product%20Manuals/ActiLife%206%20Users%20Manual%20-%20Rev-A-110315.pdf/

See Also

read_agd_raw()

Examples

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

Read an *.agd file, with no post-processing

Description

Read ActiGraph sleep watch data from an SQLite database stored in an AGD file and return a list with (at least) five tables: data, sleep, filters, settings, awakenings. The tables have the schema described in the ActiLife 6 User manual and the timestamps are converted from Unix time format to human-readable POSIXct representation. Code is from actigraph.sleepr package https://github.com/dipetkov/actigraph.sleepr/. See LICENCE.note file in the app skeleton.

Usage

read_agd_raw(file, tz = "UTC")

Arguments

Details

Some ActiGraph devices contain a capacitive sensor to detect monitor removal when worn against the skin. If that data is available, the return list includes a capsense table as well.

Value

A list of five tables: settings, data, filters, sleep, awakenings and, if available, capsense.

References

ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.

covertagd: R package for converting agd files from ActiGraph into data.frames.

See Also

read_agd()

Examples

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

Summarize results by day

Description

This function summarizes various accelerometer metrics for each day of the measurement period.

Usage

recap_by_day(
  data,
  col_axis = "vm",
  col_time = "time",
  col_nonwear = "non_wearing_count",
  col_wear = "wearing_count",
  valid_wear_time_start = "00:00:00",
  valid_wear_time_end = "23:59:59",
  age = 40,
  weight = 70,
  sex = c("male", "female", "intersex", "undefined", "prefer not to say"),
  start_first_bin = 0,
  start_last_bin = 10000,
  bin_width = 500
)

Arguments

Details

The following metrics are computed from epochs corresponding to valid wear time:

• wear_time: total wear time computed using the daily period defined in the function

• total_counts_axis1: total counts for the vertical axis

• total_counts_vm: total counts for the vector magnitude

• axis1_per_min: mean of the counts per minute for the vertical axis

• vm_per_min: mean of the counts per minute for the vector magnitude

• minutes_SED: total minutes spent in SED behavior

• minutes_LPA: total minutes spent in LPA behavior

• minutes_MPA: total minutes spent in MPA behavior

• minutes_VPA: total minutes spent in VPA behavior

• minutes_MVPA: total minutes spent in MVPA behavior

• percent_SED: proportion of wear time spent in SED behavior

• percent_LPA: proportion of wear time spent in LPA behavior

• percent_MPA: proportion of wear time spent in MPA behavior

• percent_VPA: proportion of wear time spent in VPA behavior

• percent_MVPA: proportion of wear time spent in MVPA behavior

• ratio_mvpa_sed: ratio between MVPA and SED times (minutes_MVPA / minutes_SED)

• mets_hours_mvpa: total MET-hours spent in MVPA behavior

• total_kcal: total kilocalories

• PAL: physical activity level

• total_steps: total step count

• max_steps_60min: best step accumulation per minute averaged over a window of 60 continuous minutes

• max_steps_30min: best step accumulation per minute averaged over a window of 30 continuous minutes

• max_steps_20min: best step accumulation per minute averaged over a window of 20 continuous minutes

• max_steps_5min: best step accumulation per minute averaged over a window of 5 continuous minutes

• max_steps_1min: best step accumulation per minute over a window of 1 minute

• peak_steps_60min: step accumulation per minute averaged over the best 60 continuous or discontinuous minutes

• peak_steps_30min: step accumulation per minute averaged over the best 30 continuous or discontinuous minutes

• peak_steps_20min: step accumulation per minute averaged over the best 20 continuous or discontinuous minutes

• peak_steps_5min: step accumulation per minute averaged over the best 5 continuous or discontinuous minutes

• peak_steps_1min: step accumulation per minute over the best minute (same result as for max_steps_1min)

• ig: intensity gradient

• M1/3: the count value (in counts/epoch duration) at and above which the most active 8h were accumulated over the day

• M120: the count value (in counts/epoch duration) at and above which the most active 120 minutes were accumulated over the day

• M60: the count value (in counts/epoch duration) at and above which the most active 60 minutes were accumulated over the day

• M30: the count value (in counts/epoch duration) at and above which the most active 30 minutes were accumulated over the day

• M15: the count value (in counts/epoch duration) at and above which the most active 15 minutes were accumulated over the day

• M5: the count value (in counts/epoch duration) at and above which the most active 5 minutes were accumulated over the day

PAL is computed by dividing total energy expenditure (TEE) by BMR. TEE is obtained by summing the kilocalories computed for wear time epochs and the kilocalories related to BMR theoretically expended during nonwear time epochs (it is assumed that the periods where the device was not worn corresponded to sleeping periods, during which energy expenditure is near of BMR), and by multiplying this sum by 10/9 to take into account the thermic effect of food. Of course, such calculations may conduct to underestimate TEE and PAL if the device was removed during prolonged periods of physical activity. Moreover, even if the device was correctly worn, the estimate of PAL is very approximate since both BMR and kilocalories are estimated using methods that may not be accurate at the individual level.

The intensity gradient and the MX metrics are obtained using the compute_intensity_distri_metrics function.

Value

A list of objects: df_all_metrics, p_band, and p_log. df_all_metrics is a dataframe containing all the metrics for each day. p_band is a figure that shows the distribution of time spent in the configured bins of intensity for each day of the dataset. p_log is a figure that shows, for each day, the relationship between the natural log of time spent in each intensity bin with the natural log of the middle values of the intensity bins.

Examples

file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
mydata <- prepare_dataset(data = file)
mydata_with_wear_marks <- mark_wear_time(
    dataset = mydata, 
    TS = "TimeStamp", 
    to_epoch = 60,
    cts  = "vm",
    frame = 90, 
    allowanceFrame = 2, 
    streamFrame = 30
    )
mydata_with_intensity_marks <- mark_intensity(
    data = mydata_with_wear_marks, 
    col_axis = "vm", 
    equation = "Sasaki et al. (2011) [Adults]",
    sed_cutpoint = 200, 
    mpa_cutpoint = 2690, 
    vpa_cutpoint = 6167, 
    age = 32,
    weight = 67,
    sex = "male"
    )
recap_by_day(
    data = mydata_with_intensity_marks, 
    col_axis = "vm",
    age = 32, 
    weight = 67, 
    sex = "male",
    valid_wear_time_start = "07:00:00",
    valid_wear_time_end = "22:00:00",
    start_first_bin = 0,
    start_last_bin = 10000,
    bin_width = 500
    )

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(launch.browser = TRUE),
  enableBookmarking = NULL,
  uiPattern = "/",
  ...
)

Arguments

Value

No return value, called for side effects.