process_nhanes: Process NHANES 2003-2006 Accelerometer Data
In vandomed/nhanesaccel: Process Accelerometer Data from NHANES 2003-2006

Description Usage Arguments Details Value References Examples

Calculates a variety of physical activity variables from the time-series accelerometer data in NHANES 2003-2006. A data dictionary for the variables created is available here: https://vandomed.github.io/process_nhanes_dictionary.

process_nhanes(
  waves = 3,
  directory = getwd(),
  nci_methods = FALSE,
  brevity = 1,
  hourly_var = "cpm",
  hourly_wearmin = 0,
  hourly_normalize = FALSE,
  valid_days = 1,
  valid_wk_days = 0,
  valid_we_days = 0,
  int_cuts = c(100, 760, 2020, 5999),
  youth_mod_cuts = rep(int_cuts[3], 12),
  youth_vig_cuts = rep(int_cuts[4], 12),
  cpm_nci = FALSE,
  days_distinct = FALSE,
  nonwear_window = 60,
  nonwear_tol = 0,
  nonwear_tol_upper = 99,
  nonwear_nci = FALSE,
  weartime_minimum = 600,
  weartime_maximum = 1440,
  active_bout_length = 10,
  active_bout_tol = 0,
  mvpa_bout_tol_lower = 0,
  vig_bout_tol_lower = 0,
  active_bout_nci = FALSE,
  sed_bout_tol = 0,
  sed_bout_tol_maximum = int_cuts[2] - 1,
  artifact_thresh = 25000,
  artifact_action = 1,
  weekday_weekend = FALSE,
  return_form = "averages",
  write_csv = FALSE
)

`waves`	Integer value for which wave of data to process. Choices are 1 for NHANES 2003-2004, 2 for NHANES 2005-2006 data, and 3 for both.
`directory`	Character string specifying directory in which to write .csv file, if `write_csv = TRUE`.
`nci_methods`	Logical value for whether to set all arguments so as to replicate the data processing methods used in the NCI's SAS programs. More specifically: `valid_days = 4` `valid_wk_days = 0` `valid_we_days = 0` `int_cuts = c(100, 760, 2020, 5999)` `youth_mod_cuts = c(1400, 1515, 1638, 1770, 1910, 2059, 2220, 2393, 2580, 2781, 3000, 3239)` `youth_vig_cuts = c(3758, 3947, 4147, 4360, 4588, 4832, 5094, 5375, 5679, 6007, 6363, 6751)` `cpm_nci = TRUE` `days_distinct = TRUE` `nonwear_window = 60` `nonwear_tol = 2` `nonwear_tol_upper = 100` `nonwear_nci = TRUE` `weartime_minimum = 600` `weartime_maximum = 1440` `active_bout_length = 10` `active_bout_tol = 2` `mvpa_bout_tol_lower = 0` `vig_bout_tol_lower = 0` `active_bout_nci = TRUE` `sed_bout_tol = 0` `sed_bout_tol_maximum = 759` `artifact_thresh = 32767` `artifact_action = 3` If `TRUE`, you can still specify non-default values for `brevity`, `weekday_weekend`, and `return_form`.
`brevity`	Integer value controlling the number of physical activity variables generated. Choices are 1 for basic indicators of physical activity volume, 2 for addditional indicators of activity intensities, activity bouts, sedentary behavior, and peak activity, and 3 for additional hourly count averages.
`hourly_var`	Character string specifying what hourly activity variable to record, if `brevity = 3`. Choices are "counts", "cpm", "sed_min", "sed_bouted_10min", and "sed_breaks".
`hourly_wearmin`	Integer value specifying minimum number of wear time minutes needed during a given hour to record a value for the hourly activity variable.
`hourly_normalize`	Logical value for whether to normalize hourly activity by number of wear time minutes.
`valid_days`	Integer value specifying minimum number of valid days to be considered valid for analysis.
`valid_wk_days`	Integer value specifying minimum number of valid weekdays to be considered valid for analysis.
`valid_we_days`	Integer value specifying minimum number of valid weekend days to be considered valid for analysis.
`int_cuts`	Numeric vector with four cutpoints from which five intensity ranges are derived. For example, `int_cuts = c(100, 760, 2020, 5999)` creates: 0-99 = intensity 1; 100-759 = intensity level 2; 760-2019 = intensity 3; 2020-5998 = intensity 4; >= 5999 = intensity 5. Intensities 1-5 are typically viewed as sedentary, light, lifestyle, moderate, and vigorous.
`youth_mod_cuts`	Integer vector of 12 count cutpoints for classifying moderate physical activity in youth, for ages 6, 7, ..., 17. To replicate the NCI's SAS programs, set `youth_mod_cuts = c(1400, 1515, 1638, 1770, 1910, 2059, 2220, 2393, 2580, 2781, 3000, 3239)`.
`youth_vig_cuts`	Integer vector of 12 count cutpoints for classifying vigorous physical activity in youth, for ages 6, 7, ..., 17. To replicate the NCI's SAS programs, set `youth_vig_cuts = c(3758, 3947, 4147, 4360, 4588, 4832, 5094, 5375, 5679, 6007, 6363, 6751)`.
`cpm_nci`	Logical value for whether to calculate average counts per minute by dividing average daily counts by average daily wear time, as opposed to taking the average of each day's counts per minute value. Strongly recommend leave as `FALSE` unless you wish to replicate the NCI's SAS programs.
`days_distinct`	Logical value for whether to treat each day of data as distinct, as opposed to analyzing the entire monitoring period as one continuous segment.
`nonwear_window`	Integer value specifying minimum length of a non-wear period.
`nonwear_tol`	Integer value specifying tolerance for non-wear algorithm, i.e. number of minutes with non-zero counts allowed during a non-wear interval.
`nonwear_tol_upper`	Integer value specifying maximum count value for a minute with non-zero counts during a non-wear interval.
`nonwear_nci`	Logical value for whether to use non-wear algorithm from NCI's SAS programs.
`weartime_minimum`	Integer value specifying minimum number of wear time minutes for a day to be considered valid.
`weartime_maximum`	Integer value specifying maximum number of wear time minutes for a day to be considered valid. The default is 1440, but you may want to use a lower value (e.g. 1200) if participants were instructed to remove devices for sleeping, but often did not.
`active_bout_length`	Integer value specifying minimum length of an active bout.
`active_bout_tol`	Integer value specifying number of minutes with counts outside the required range to allow during an active bout. If non-zero and `active_bout_nci = FALSE`, specifying non-zero values for `mvpa_bout_tol_lower` and `vig_bout_tol_lower` is highly recommended. Otherwise minutes immediately before and after an active bout will tend to be classified as part of the bout.
`mvpa_bout_tol_lower`	Integer value specifying lower cut-off for count values outside of required intensity range for an MVPA bout.
`vig_bout_tol_lower`	Integer value specifying lower cut-off for count values outside of required intensity range for a vigorous bout.
`active_bout_nci`	Logical value for whether to use algorithm from the NCI's SAS programs for classifying active bouts.
`sed_bout_tol`	Integer value specifying number of minutes with counts outside sedentary range to allow during a sedentary bout.
`sed_bout_tol_maximum`	Integer value specifying upper cut-off for count values outside sedentary range during a sedentary bout.
`artifact_thresh`	Integer value specifying the smallest count value that should be considered an artifact.
`artifact_action`	Integer value controlling method of correcting artifacts. Choices are 1 to exclude days with one or more artifacts, 2 to lump artifacts into non-wear time, 3 to replace artifacts with the average of neighboring count values, and 4 to take no action.
`weekday_weekend`	Logical value for whether to calculate averages for weekdays and weekend days separately (in addition to all valid days).
`return_form`	Character string controlling how variables are returned. Choices are "daily" for per-day summaries, "averages" for averages across all valid days, and "both" for a list containing both.
`write_csv`	Logical value for whether to write the results to a .csv file in `directory`.

As an alternative to using this function programmatically, you can use the process_nhanes_app function to access a GUI. Just run process_nhanes_app() in R.

Data frame or list of two data frames, depending on return_form.

Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2003-6. https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Accessed Jan. 7, 2019.

National Cancer Institute. Risk factor monitoring and methods: SAS programs for analyzing NHANES 2003-2004 accelerometer data. Available at: http://riskfactor.cancer.gov/tools/nhanes_pam. Accessed Jan. 7, 2019.

Van Domelen, D.R. (2018) accelerometry: Functions for processing accelerometer data. R package version 3.1.2. http://CRAN.R-project.org/package=accelerometry.

# Process NHANES 2003-2006 data using default settings
nhanes1 <- process_nhanes()

# Process NHANES 2003-2004 with following non-default settings: require >= 4
# valid days, use 90- rather than 60-minute window for non-wear algorithm,
# and request averages across all days and for weekdays/weekends separately
nhanes2 <- process_nhanes(
  waves = 1,
  valid_days = 4,
  nonwear_window = 90,
  weekday_weekend = TRUE
)

# Process data according to methods used in NCI's SAS programs
youth_mod_cuts <- c(1400, 1515, 1638, 1770, 1910, 2059, 2220, 2393, 2580,
                    2781, 3000, 3239)
youth_vig_cuts <- c(3758, 3947, 4147, 4360, 4588, 4832, 5094, 5375, 5679,
                    6007, 6363, 6751)
nhanes3 <- process_nhanes(
  waves = 3,
  brevity = 2,
  valid_days = 4,
  youth_mod_cuts = youth_mod_cuts,
  youth_vig_cuts = youth_vig_cuts,
  cpm_nci = TRUE,
  days_distinct = TRUE,
  nonwear_tol = 2,
  nonwear_tol_upper = 100,
  nonwear_nci = TRUE,
  weartime_maximum = 1440,
  active_bout_tol = 2,
  active_bout_nci = TRUE,
  artifact_thresh = 32767,
  artifact_action = 3
)

# Repeat, but use nci_methods input for convenience
nhanes4 <- process_nhanes(
  waves = 3,
  brevity = 2,
  nci_methods = TRUE
)

# Results are identical
all.equal(nhanes3, nhanes4)