Physical activity intensity from ActTrust/GT3X+ counts

The equation

The source study fits sqrt(MET) ~ sqrt(activity_counts) * device_placement via a linear model on indirect-calorimetry data, then reports the fitted coefficients (their Table 2). zeitR doesn’t re-fit that model – it can’t, without breath-by-breath VO2 data – it applies the published coefficients directly:

\[\text{MET} = \left(\beta_0 + \beta_1 \sqrt{\text{activity\_counts}}\right)^2\]

pa_equations() returns this table for all four device x placement combinations the source study validated:

pa_equations()
#> # A tibble: 4 × 7
#>   device placement    b0     b1  cut3  cut6  cut9
#>   <chr>  <chr>     <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1 GT3X+  hip        1.06 0.0199  1132  4853  9468
#> 2 ACTT   hip        1.11 0.0088  5057 23339 46410
#> 3 ACTT   wrist      1.23 0.0081  3761 22368 47203
#> 4 GT3X+  wrist      1.21 0.0127  1698  9503 19787

ACTT is Condor Instruments’ ActTrust device (PIM mode); GT3X+ is ActiGraph’s GT3X+ (vector magnitude). cut3/cut6/cut9 are the published count/min cut-points at the 3, 6, and 9 MET thresholds – provided for reference; the functions below classify on estimated METs directly rather than re-deriving these.

Estimating METs from counts

estimate_ee() applies the equation for a given device and placement:

counts <- c(0, 1000, 5000, 25000, 50000)
estimate_ee(counts, device = "ACTT", placement = "hip")
#> [1] 1.225449 1.919002 2.990319 6.242013 9.454025

Placement matters – the same count value implies different METs at the hip than at the wrist, since wrist movement is a noisier proxy for whole-body energy expenditure:

data.frame(
  counts = counts,
  hip    = estimate_ee(counts, device = "ACTT", placement = "hip"),
  wrist  = estimate_ee(counts, device = "ACTT", placement = "wrist")
)
#>   counts      hip    wrist
#> 1      0 1.225449 1.520289
#> 2   1000 1.919002 2.217551
#> 3   5000 2.990319 3.260757
#> 4  25000 6.242013 6.318801
#> 5  50000 9.454025 9.267245

Classifying PA intensity

classify_pa_intensity() bins any MET value into the four WHO-style intensity bands (light, moderate, vigorous, very vigorous) used throughout the source study:

mets <- estimate_ee(counts, device = "ACTT", placement = "hip")
classify_pa_intensity(mets)
#> [1] light         light         light         vigorous      very_vigorous
#> Levels: light < moderate < vigorous < very_vigorous

classify_pa_counts() combines the two steps – counts straight to an intensity factor – for the common case where you don’t need the intermediate MET values:

classify_pa_counts(counts, device = "ACTT", placement = "hip")
#> [1] light         light         light         vigorous      very_vigorous
#> Levels: light < moderate < vigorous < very_vigorous

Applying it to a real recording

The bundled ActTrust recording used elsewhere in zeitR’s vignettes was collected in PIM/TAT/ZCM mode (see its header), so its activity column is already PIM counts/min – the exact input estimate_ee()/ classify_pa_counts() expect for device = "ACTT". ActTrust is normally worn on the wrist for sleep monitoring, so placement = "wrist" is the appropriate choice here.

FILE <- system.file("extdata", "input1.txt", package = "zeitR")
TZ   <- "America/Sao_Paulo"

result <- run_pipeline_native(FILE, tz = TZ, quiet = TRUE)
#> ℹ Reading 'input1.txt' ...
#> ✔ [input1] Done. 52 main night(s), 0 secondary episode(s).

result$data$pa_intensity <- classify_pa_counts(
  result$data$activity,
  device    = "ACTT",
  placement = "wrist"
)

table(result$data$pa_intensity)
#> 
#>         light      moderate      vigorous very_vigorous 
#>         63111         11985           794           306

Remember what this recording actually represents before over-reading that table: the source equations were fit on a controlled treadmill protocol (walking/running, 3-9 km/h), not free-living daily activity. Applying them to a full multi-day free-living recording – as done here – is exactly the kind of extrapolation flagged in ?pa_equations. Treat the resulting intensity labels as illustrative of the mechanics, not as a validated free-living PA measure.

A quick visual of intensity across the whole recording:

daily <- result$data
daily$date <- as.Date(daily$datetime)
daily <- aggregate(list(n = daily$activity), by = list(
  date = daily$date, pa_intensity = daily$pa_intensity
), FUN = length)

ggplot(daily, aes(x = date, y = n, fill = pa_intensity)) +
  geom_col(width = 1) +
  scale_fill_manual(
    values = c(light = "#D9C8A0", moderate = "#F0A500",
               vigorous = "#C25E2A", very_vigorous = "#2D2640"),
    drop = FALSE
  ) +
  labs(x = NULL, y = "Epochs/day", fill = "PA intensity") +
  theme_minimal(base_size = 12)

Choosing an equation set

equation_set is exposed as an explicit argument on all four functions (default, and currently only, "batista2026") so that a future validation study – covering a different population, device, or free-living protocol – can be added later as an alternative set without changing this API:

# Not yet available -- illustrative only
estimate_ee(counts, device = "ACTT", placement = "wrist",
            equation_set = "some_future_study")

Next steps

• ?pa_equations – the full generalisability discussion: sample characteristics, cross-study cut-point variability, and why the GT3X+ equations here are diagnosable against Sasaki et al. (2011)’s transparent two-regression model but not against Santos-Lozano et al. (2013)’s ANN
• vignette("sleep-analysis") – the sleep half of the 24 h cycle
• ?compute_sleep_metrics, ?compute_cpd_metrics – combine with PA intensity for a fuller rest-activity picture