README.md

Process Accelerometer Data from NHANES 2003-2006

Dane Van Domelen vandomed@gmail.com 2020-07-30

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Introduction

The National Health and Nutrition Examination Survey (NHANES) included physical activity monitoring to their 2003-2004 and 2005-2006 study cycles. Altogether, almost 15,000 participants age (\ge) 6 years wore an ActiGraph accelerometer at the hip for 7 consecutive days. The data are freely available to download from the NHANES website. You don’t have to submit a proposal, you don’t have to find a study investigator to ~~leach authorship~~ help you understand the data, none of that. I think the CDC deserves a lot of credit for this setup.

The raw accelerometer data needs to be processed to generate meaningful physical activity varaibles, and indeed a SAS script was provided by the National Cancer Institute for this purpose (National Cancer Institute 2007). But it only calculates a few variables, it isn’t easily modifiable, and it takes a long time to run. So I developed nhanesaccel to process the data in R much faster and with more options.

Install

The package includes the raw data and functions for processing it. It’s way too big for CRAN because of the raw data, so it’s only on GitHub. To install it, you can run:

library("devtools")
install_github("vandomed/nhanesaccel")

If this doesn’t work, you might try installing accelerometry (Van Domelen 2018a) from CRAN first with install.packages("accelerometry"). If you still have problems, e-mail me at vandomed@gmail.com and I can try to help.

Usage

Defaults

The primary function in nhanesaccel is process_nhanes. If you call it without specifying anything, it will process the 2003-2006 data with default data-processing parameters (i.e. my personal preferences).

df <- process_nhanes()
#> Loading NHANES 2003-2004 data... 
#>           0-2
#> Progress: ||
#> 
#> Processing NHANES 2003-2004 data...
#>           0%---------25%---------50%---------75%--------100%
#> Progress: ||||||||||||||||||||||||||||||||||||||||||||||||||
#> 
#> Loading NHANES 2005-2006 data...
#>           0-2
#> Progress: ||
#> 
#> Processing NHANES 2005-2006 data...
#>           0%---------25%---------50%---------75%--------100%
#> Progress: ||||||||||||||||||||||||||||||||||||||||||||||||||
#> 
#> Done.

As you can see from the messages, it processed data from both 2003-2004 and 2005-2006. To see what variables it generated:

names(df)
#>  [1] "seqn"          "valid_days"    "valid_wk_days" "valid_we_days"
#>  [5] "include"       "valid_min"     "counts"        "cpm"          
#>  [9] "steps"         "wtmec2yr_adj"  "wtmec4yr_adj"  "nhanes_wave"

I won’t go through all of them, but seqn is the subject ID number, valid_days is the number of days out of 7 that were deemed suitable for analysis (the subject wore the accelerometer long enough), valid_min is the average number of wear time minutes per day, and counts and cpm are two common indicators of physical activity volume. The wtmec2yr_adj and wtmec4yr_adj variables are the adjusted MEC weights that you’ll want to use for statistical analysis.

Let’s look at the relationship between valid_min and counts in the full dataset.

library("ggplot2")
p <- ggplot(df, aes(x = valid_min, y = counts)) + 
  geom_point() + 
  xlim(0, 1440) + ylim(0, 1750000) + 
  geom_smooth(method = "lm") + 
  labs(title = "Counts vs. Wear Time", x = "Wear time (min/day)", y = "Counts/day") + 
  theme_bw(base_size = 14)
p
#> Warning: Removed 1841 rows containing non-finite values (stat_smooth).
#> Warning: Removed 1841 rows containing missing values (geom_point).

Notice that valid_min ranges from 600 to 1440. The lower bound is due to the default requirement that only days with 600 minutes (10 hours) of wear time be included, and the upper bound, 1440, is the number of minutes in 24 hours. The fact that some subjects average 1440 minutes of wear time means either they didn’t sleep (not likely!) or maybe forgot to remove the device before bed and had small movements while sleeping.

It looks like counts ranged from about 0 all the way up to around 1.5 million, which is very high but plausible considering that roughly 10,000 counts per minute can be achieved during intense physical activity.

As you would expect, participants who wore the device longer tended to accumulate more counts. That’s why we typically adjust for wear time when fitting regression models with accelerometer-derived variables.

Mimicking the NCI’s SAS programs

I figured a lot of users would want to replicate the NCI’s SAS programs, so I wrote process_nhanes to allow for that. Rather than specifying about 30 separate inputs, you can simply use the nci_methods input as a shortcut. To illustrate with just the 2003-2004 data:

df.0304 <- process_nhanes(waves = 1, nci_methods = TRUE)
#> Loading NHANES 2003-2004 data... 
#>           0-2
#> Progress: ||
#> 
#> Processing NHANES 2003-2004 data...
#>           0%---------25%---------50%---------75%--------100%
#> Progress: ||||||||||||||||||||||||||||||||||||||||||||||||||
#> 
#> Done.

Obviously I don’t fully agree with the NCI’s methods (otherwise they would be the defaults!), but a lot of it is subjective. One thing I don’t like is that they use a really liberal algorithm for non-wear detection, which I suspect tends to misclassify sedentary time as non-wear.

More variables, different settings

You can read about all of the different options in the help file (run ?process_nhanes), but I’ll give one example of adjusting the default parameters. To process the 2003-2004 data with a 45-minute non-wear algorithm, requiring at least 4 valid days of data, and requesting a bunch of variables, you could run:

df2 <- process_nhanes(waves = 1, nonwear_window = 45, valid_days = 4, brevity = 3)
#> Loading NHANES 2003-2004 data... 
#>           0-2
#> Progress: ||
#> 
#> Processing NHANES 2003-2004 data...
#>           0%---------25%---------50%---------75%--------100%
#> Progress: ||||||||||||||||||||||||||||||||||||||||||||||||||
#> 
#> Done.

Per-person vs. per-day data

The default behavior of process_nhanes is to return a per-person file with physical activity variables averaged across all valid days for each subject. If you want a per-day file instead, in long format, you can set return_form = "daily" rather than the default return_form = "averages". For a list containing both datasets, you can set return_form = "both".

Merging other datasets and fitting models

This is a little beyond the scope of the README, but I would suggest my nhanesdata package (Van Domelen 2018b) for accessing the other NHANES datasets and survey (Lumley 2017, 2004) for fitting models while accounting for the survey design of NHANES. You can install nhanesdata with install_github("vandomed/nhanesdata") and survey with install.packages("survey").

Here is a simple example of merging in two datasets (demographics and body measures) to our first df dataset and fitting a model for waist circumference vs. sex and average daily CPM in adults age 18-29.

# Load packages
library("nhanesdata")
library("survey")
library("dplyr")

# Merge in demographics and body measures datasets and recode sex
names(df) <- toupper(names(df))
df <- df %>% 
  dplyr::left_join(full_join(demo_c, demo_d)) %>%
  dplyr::left_join(full_join(bmx_c, bmx_d)) %>%
  dplyr::mutate(sex = factor(RIAGENDR, levels = c(2, 1), labels = c("Female", "Male")))

# Specify survey design object
design <- svydesign(
  data = subset(df, INCLUDE == 1),
  ids = ~SDMVPSU, 
  weights = ~WTMEC4YR_ADJ, 
  strata = ~SDMVSTRA, 
  nest = TRUE
)

# Fit waist circumference vs. sex and average CPM
fit <- svyglm(BMXWAIST ~ sex + CPM, 
              design = subset(design, RIDAGEYR %in% 18: 29 & ! is.na(BMXWAIST)))
summary(fit)$coef
#>                Estimate  Std. Error   t value     Pr(>|t|)
#> (Intercept) 92.02352580 1.133406436 81.191992 8.778405e-35
#> sexMale      4.43712733 0.921831675  4.813381 4.619544e-05
#> CPM         -0.01000123 0.002534363 -3.946250 4.852598e-04

We see that males average 4.44 cm larger waists than females, and CPM (our measure of physical activity volume) is inversely associated with waist circumference, as expected. Note that the slopes for males and females were not statistically different (interaction P = 0.67).

Here is a scatterplot to go along with the fitted model:

p <- ggplot(subset(df, RIDAGEYR %in% 18: 29), 
            aes(x = CPM, y = BMXWAIST, color = sex)) + 
  geom_point() + guides(color = guide_legend(title = "Sex")) + 
  ylim(0, 175) + 
  geom_abline(intercept = fit$coef[1], slope = fit$coef[3], color = "#F8766D") +
  geom_abline(intercept = fit$coef[1] + fit$coef[2], slope = fit$coef[3], color = "#00BFC4") + 
  labs(title = "Waist Circumference vs. Average Daily CPM", 
       x = "CPM", y = "Waist circumference (cm)") + 
  theme_bw(base_size = 14)
p
#> Warning: Removed 522 rows containing missing values (geom_point).

Shiny app

Part of my motivation for developing nhanesaccel was to allow researchers without a SAS license (or knowledge of SAS) to process the data. But what about people who don’t use R either?

I decided to convert process_nhanes into a Shiny app, so that users could just visit a website, select various options within a GUI, and ultimately process the data and export an analysis file.

I’ve written the app, but it isn’t online yet. For now, you have to launch it from R by running:

process_nhanes_app()

References

Lumley, T. 2004. “Analysis of Complex Survey Samples.” *Journal of Statistical Software* 9 (1): 1–19. .
———. 2017. *survey: Analysis of complex survey samples*. .
National Cancer Institute. 2007. “SAS programs for analyzing NHANES 2003-2004 accelerometer data.” .
Van Domelen, Dane R. 2018a. *accelerometry: Functions for processing accelerometer data*.
———. 2018b. *nhanesdata: Data from continuous NHANES (Years 1999-2016)*.


vandomed/nhanesaccel documentation built on Aug. 4, 2020, 5:22 p.m.