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Calculate Training Load Exposure (ATL, CTL, ACWR)

Source: R/calculate_exposure.R
calculate_exposure.Rd

Calculates training load metrics like ATL, CTL, and ACWR from local Strava data.

Usage

calculate_exposure(
  activities_data,
  activity_type = c("Run", "Ride", "VirtualRide", "VirtualRun"),
  load_metric = "duration_mins",
  acute_period = 7,
  chronic_period = 42,
  user_ftp = NULL,
  user_max_hr = NULL,
  user_resting_hr = NULL,
  end_date = Sys.Date(),
  missing_load = c("zero", "na"),
  verbose = FALSE
)

Arguments

activities_data

A data frame of activities from load_local_activities(). Must contain columns: date, distance, moving_time, elapsed_time, average_heartrate, average_watts, type, elevation_gain.

activity_type

Type(s) of activities to include (e.g., "Run", "Ride"). Default includes common run/ride types.

load_metric

Method for calculating daily load (e.g., "duration_mins", "distance_km", "tss", "hrss"). Default "duration_mins".

acute_period

Days for the acute load window (e.g., 7).

chronic_period

Days for the chronic load window (e.g., 42). Must be greater than acute_period.

user_ftp

Required if load_metric = "tss". Your Functional Threshold Power.

user_max_hr

Required if load_metric = "hrss". Your maximum heart rate.

user_resting_hr

Required if load_metric = "hrss". Your resting heart rate.

end_date

Optional. Analysis end date (YYYY-MM-DD string or Date). Defaults to today. The analysis period covers the chronic_period days ending on this date.

missing_load

How to treat training days on which the chosen load_metric could not be computed (e.g. HRSS with missing HR, TSS without FTP). "zero" (default) matches the historical behaviour and coalesces these days to 0, conflating them with genuine rest days. "na" keeps them visibly NA; only days with no recorded activity at all are treated as rest (0).

verbose

Logical. If TRUE, prints progress messages. Default FALSE.

Value

A data frame with columns: date, daily_load, atl (Acute Load), ctl (Chronic Load), and acwr (Acute:Chronic Ratio) for the analysis period.

Details

Calculates daily load, ATL, CTL, and ACWR from Strava activities based on the chosen metric and periods.

Provides data for plot_exposure. Requires extra prior data for accurate initial CTL. Requires FTP/HR parameters for TSS/HRSS metrics.

Examples

# Example using simulated data
data(sample_exposure)
print(head(sample_exposure))
#> # A tibble: 6 × 5
#>   date       daily_load   ctl   atl  acwr
#>   <date>          <dbl> <dbl> <dbl> <dbl>
#> 1 2023-01-01        8.8    NA    NA    NA
#> 2 2023-01-02       23.7    NA    NA    NA
#> 3 2023-01-03       20.2    NA    NA    NA
#> 4 2023-01-04       35.1    NA    NA    NA
#> 5 2023-01-05       14.6    NA    NA    NA
#> 6 2023-01-06       31.9    NA    NA    NA

# Runnable example with dummy data:
end <- Sys.Date()
dates <- seq(end - 59, end, by = "day")
dummy_activities <- data.frame(
  date = dates,
  type = "Run",
  moving_time = rep(3600, length(dates)), # 1 hour
  distance = rep(10000, length(dates)), # 10 km
  average_heartrate = rep(140, length(dates)),
  suffer_score = rep(50, length(dates)),
  tss = rep(50, length(dates)),
  stringsAsFactors = FALSE
)

# Calculate Exposure (ATL/CTL)
exposure_result <- calculate_exposure(
  activities_data = dummy_activities,
  activity_type = "Run",
  load_metric = "distance_km",
  acute_period = 7,
  chronic_period = 28,
  end_date = end
)
print(head(exposure_result))
#>         date daily_load atl ctl acwr
#> 1 2026-03-24         10  10  10    1
#> 2 2026-03-25         10  10  10    1
#> 3 2026-03-26         10  10  10    1
#> 4 2026-03-27         10  10  10    1
#> 5 2026-03-28         10  10  10    1
#> 6 2026-03-29         10  10  10    1

if (FALSE) { # \dontrun{
# Example using local Strava export data
activities <- load_local_activities("strava_export_data/activities.csv")

# Calculate training load for Rides using TSS
ride_exposure_tss <- calculate_exposure(
  activities_data = activities,
  activity_type = "Ride",
  load_metric = "tss",
  user_ftp = 280,
  acute_period = 7,
  chronic_period = 28
)
print(head(ride_exposure_tss))

# Calculate training load for Runs using HRSS
run_exposure_hrss <- calculate_exposure(
  activities_data = activities,
  activity_type = "Run",
  load_metric = "hrss",
  user_max_hr = 190,
  user_resting_hr = 50
)
print(tail(run_exposure_hrss))
} # }