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mapnhanespa maps physical activity summaries from a
study sample onto population-level quantiles estimated from NHANES
accelerometer data.
You can install the development version of mapnhanespa from GitHub with:
# install.packages("pak")
pak::pak("jhuwit/mapnhanespa")Map one row per participant-measure observation with
map_nhanes_pa_quantiles():
library(mapnhanespa)
study_data <- data.frame(
id = c("P01", "P02", "P03"),
age = c(25, 62, 84),
sex = c("Female", "Male", "Female"),
measure = c("mims", "ssl_steps", "AC"),
value = c(15000, 7500, 1000000)
)
map_nhanes_pa_quantiles(study_data, id = "id")
#> id age sex measure value age_category_pa_map nhanes_quantile
#> 1 P01 25 Female mims 15000 [20,30) 0.5349443
#> 2 P02 62 Male ssl_steps 7500 [60,70) 0.3527381
#> 3 P03 84 Female AC 1000000 [80,85) 0.1322205The returned age_category_pa_map column shows the NHANES
age category used to select each participant’s CDF. The following
example covers all nine supported summaries:
all_measures <- data.frame(
id = rep("P01", 9),
age = 25,
sex = "Female",
measure = c("total_activity_counts", "total_log10AC", "total_PAXMTSM",
"total_log10PAXMTSM", "total_scsslsteps", "total_scrfsteps",
"total_oaksteps", "total_vssteps", "total_vsrevsteps"),
value = c(2773035, 3039, 14653, 1043, 8694, 11421, 12055, 9505, 9631)
)
map_nhanes_pa_quantiles(all_measures, id = "id")
#> id age sex measure value age_category_pa_map
#> 1 P01 25 Female total_activity_counts 2773035 [20,30)
#> 2 P01 25 Female total_log10AC 3039 [20,30)
#> 3 P01 25 Female total_PAXMTSM 14653 [20,30)
#> 4 P01 25 Female total_log10PAXMTSM 1043 [20,30)
#> 5 P01 25 Female total_scsslsteps 8694 [20,30)
#> 6 P01 25 Female total_scrfsteps 11421 [20,30)
#> 7 P01 25 Female total_oaksteps 12055 [20,30)
#> 8 P01 25 Female total_vssteps 9505 [20,30)
#> 9 P01 25 Female total_vsrevsteps 9631 [20,30)
#> nhanes_quantile
#> 1 0.4852706
#> 2 0.5714842
#> 3 0.4873396
#> 4 0.5564848
#> 5 0.5872852
#> 6 0.4938108
#> 7 0.4892558
#> 8 0.5019565
#> 9 0.5158600The complete list of accepted names and the summary used for each CDF
is available from nhanes_pa_measure_map(). Other names
return NA with a warning listing the unmapped measures.
Each value should be calculated using the summary named in
the map and supplied on that summary’s scale.
nhanes_pa_measure_map()
#> measure summary
#> 1 AC AC
#> 2 activity_counts AC
#> 3 activitycounts AC
#> 4 counts AC
#> 5 total_AC AC
#> 6 total_activity_counts AC
#> 7 log10AC log10AC
#> 8 log10_activity_counts log10AC
#> 9 log10_counts log10AC
#> 10 total_log10AC log10AC
#> 11 PAXMTSM PAXMTSM
#> 12 mims PAXMTSM
#> 13 mims_unit PAXMTSM
#> 14 total_PAXMTSM PAXMTSM
#> 15 log10PAXMTSM log10PAXMTSM
#> 16 log10_mims log10PAXMTSM
#> 17 total_log10PAXMTSM log10PAXMTSM
#> 18 log10_mims_unit log10PAXMTSM
#> 19 scsslsteps scsslsteps
#> 20 ssl_steps scsslsteps
#> 21 sslstepcount scsslsteps
#> 22 sslstepcounts scsslsteps
#> 23 total_ssl_steps scsslsteps
#> 24 total_scsslsteps scsslsteps
#> 25 steps_stepcount_ssl scsslsteps
#> 26 steps_stepcounts_ssl scsslsteps
#> 27 scrfsteps scrfsteps
#> 28 rf_steps scrfsteps
#> 29 rfstepcount scrfsteps
#> 30 rfstepcounts scrfsteps
#> 31 total_rf_steps scrfsteps
#> 32 total_scrfsteps scrfsteps
#> 33 steps_stepcount_rf scrfsteps
#> 34 steps_stepcounts_rf scrfsteps
#> 35 oaksteps oaksteps
#> 36 foreststeps oaksteps
#> 37 steps_oak oaksteps
#> 38 steps_forest oaksteps
#> 39 nsteps_oak oaksteps
#> 40 nsteps_forest oaksteps
#> 41 total_oaksteps oaksteps
#> 42 steps_stepcount_forest oaksteps
#> 43 steps_stepcounts_forest oaksteps
#> 44 vssteps vssteps
#> 45 vssteps_original vssteps
#> 46 steps_vs_original vssteps
#> 47 total_vssteps vssteps
#> 48 vsrevsteps vsrevsteps
#> 49 vssteps_revised vsrevsteps
#> 50 steps_vs_revised vsrevsteps
#> 51 total_vsrevsteps vsrevstepsThe measure column accepts common aliases, including
total_ac and total_activity_counts for the
AC CDF:
measures <- data.frame(
id = rep("P01", 4),
age = 25,
sex = "Female",
measure = c("mims", "PAXMTSM", "total_ac", "total_activity_counts"),
value = c(15000, 15000, 2773035, 2773035)
)
map_nhanes_pa_quantiles(measures, id = "id")
#> id age sex measure value age_category_pa_map
#> 1 P01 25 Female mims 15000 [20,30)
#> 2 P01 25 Female PAXMTSM 15000 [20,30)
#> 3 P01 25 Female total_ac 2773035 [20,30)
#> 4 P01 25 Female total_activity_counts 2773035 [20,30)
#> nhanes_quantile
#> 1 0.5349443
#> 2 0.5349443
#> 3 0.4852706
#> 4 0.4852706
# Select a CDF function directly for a supported measure and stratum.
nhanes_pa_measure_cdf("total_activity_counts", age = 25, sex = "Female")(2773035)
#> [1] 0.4852706By default, quantiles are evaluated against the combined 2011-2012 and 2013-2014 NHANES waves:
map_nhanes_pa_quantiles(study_data, id = "id")
#> id age sex measure value age_category_pa_map nhanes_quantile
#> 1 P01 25 Female mims 15000 [20,30) 0.5349443
#> 2 P02 62 Male ssl_steps 7500 [60,70) 0.3527381
#> 3 P03 84 Female AC 1000000 [80,85) 0.1322205To map against a specific NHANES wave, provide wave:
map_nhanes_pa_quantiles(study_data, id = "id", wave = "2013-2014")
#> id age sex measure value age_category_pa_map nhanes_quantile
#> 1 P01 25 Female mims 15000 [20,30) 0.4943653
#> 2 P02 62 Male ssl_steps 7500 [60,70) 0.3820584
#> 3 P03 84 Female AC 1000000 [80,85) 0.1181001You can also map without sex or age stratification:
map_nhanes_pa_quantiles(study_data, id = "id", sex = NULL)
#> id age sex measure value age_category_pa_map nhanes_quantile
#> 1 P01 25 Female mims 15000 [20,30) 0.5688587
#> 2 P02 62 Male ssl_steps 7500 [60,70) 0.4164160
#> 3 P03 84 Female AC 1000000 [80,85) 0.1408881
map_nhanes_pa_quantiles(study_data, id = "id", age = NULL)
#> id age sex measure value age_category_pa_map nhanes_quantile
#> 1 P01 25 Female mims 15000 Overall 0.53548286
#> 2 P02 62 Male ssl_steps 7500 Overall 0.28321363
#> 3 P03 84 Female AC 1000000 Overall 0.01040967For a single participant-measure value, use
nhanes_pa_quantile():
nhanes_pa_quantile(
value = 15000,
age = 25,
sex = "Female",
measure = "mims"
)
#> [1] 0.5349443If a study already has age categories, pass the column name through
age_category.
This work was supported by NIH P30AG021334 and the Johns Hopkins Older Americans Independence Center (OAIC) Pepper Center grant.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.