Calculate Acute:Chronic Workload Ratio (ACWR)

This function calculates daily training load and derives acute (short-term) and chronic (long-term) load averages, then computes their ratio (ACWR). The ACWR helps identify periods of rapid training load increases that may elevate injury risk.

Key Concepts:

Acute Load (ATL): Rolling average of recent training (default: 7 days)
Chronic Load (CTL): Rolling average of longer-term training (default: 28 days)
ACWR: Ratio of ATL to CTL (ATL / CTL)
Safe Zone: ACWR between 0.8-1.3 (optimal training stimulus)
Danger Zone: ACWR > 1.5 (increased injury risk)

Usage

calculate_acwr(
  activities_data,
  activity_type = NULL,
  load_metric = "duration_mins",
  acute_period = 7,
  chronic_period = 28,
  start_date = NULL,
  end_date = NULL,
  user_ftp = NULL,
  user_max_hr = NULL,
  user_resting_hr = NULL,
  smoothing_period = 7
)

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

Required character vector. Filter activities by type (e.g., "Run", "Ride"). Must specify to avoid mixing incompatible load metrics.

load_metric

Character string specifying the load calculation method:

"duration_mins": Training duration in minutes (default)
"distance_km": Distance in kilometers
"elapsed_time_mins": Total elapsed time including stops
"tss": Training Stress Score approximation using NP/FTP ratio (requires user_ftp)
"hrss": Heart Rate Stress Score approximation using simplified TRIMP (requires user_max_hr and user_resting_hr)
"elevation_gain_m": Elevation gain in meters

acute_period

Integer. Number of days for the acute load window (default: 7). Represents recent training stimulus. Common values: 3-7 days.

chronic_period

Integer. Number of days for the chronic load window (default: 28). Represents fitness/adaptation level. Must be greater than acute_period. Common values: 21-42 days.

start_date

Optional. Analysis start date (YYYY-MM-DD string, Date, or POSIXct). Defaults to one year before end_date. Earlier data is used for calculating initial chronic load.

end_date

Optional. Analysis end date (YYYY-MM-DD string, Date, or POSIXct). Defaults to current date (Sys.Date()).

user_ftp

Numeric. Your Functional Threshold Power in watts. Required only when load_metric = "tss". Used to normalize power-based training stress.

user_max_hr

Numeric. Your maximum heart rate in bpm. Required only when load_metric = "hrss". Used for heart rate reserve calculations.

user_resting_hr

Numeric. Your resting heart rate in bpm. Required only when load_metric = "hrss". Used for heart rate reserve calculations.

smoothing_period

Integer. Number of days for smoothing the ACWR using a rolling mean (default: 7). Reduces day-to-day noise for clearer trend visualization.

Value

A tibble with the following columns:

date: Date (Date class)
atl: Acute Training Load - rolling average over acute_period days (numeric)
ctl: Chronic Training Load - rolling average over chronic_period days (numeric)
acwr: Raw ACWR value (atl / ctl) (numeric)
acwr_smooth: Smoothed ACWR using smoothing_period rolling mean (numeric)

Details

Computes the Acute:Chronic Workload Ratio (ACWR) from local Strava activity data using rolling average methods. ACWR is a key metric for monitoring training load and injury risk in athletes (Gabbett, 2016; Hulin et al., 2016).

Algorithm:

Daily Aggregation: Sum all activities by date to compute daily load
Complete Time Series: Fill missing days with zero load (critical for ACWR accuracy)
Acute Load (ATL): Rolling mean over acute_period days (default: 7)
Chronic Load (CTL): Rolling mean over chronic_period days (default: 28)
ACWR Calculation: ATL / CTL (set to NA when CTL < 0.01 to avoid division by zero)
Smoothing: Optional rolling mean over smoothing_period days for visualization

Data Requirements: The function automatically fetches additional historical data (chronic_period days before start_date) to ensure accurate chronic load calculations at the analysis start point. Ensure your Strava export contains sufficient historical activities.

Load Metric Implementations:

"tss": Uses normalized power (NP) and FTP to approximate Training Stress Score (TSS). Formula: (duration_sec × NP × IF) / (FTP × 3600) × 100, where IF = NP/FTP (equivalently: hours × IF^2 × 100).
"hrss": HR-based load using heart rate reserve (simplified TRIMP; not TrainingPeaks hrTSS). Formula: duration_sec * (HR - resting_HR) / (max_HR - resting_HR).

Interpretation Guidelines:

ACWR < 0.8: May indicate detraining or insufficient load
ACWR 0.8-1.3: "Sweet spot" - optimal training stimulus with lower injury risk
ACWR 1.3-1.5: Caution zone - monitor for fatigue
ACWR > 1.5: High risk zone - consider load management

Multi-Athlete Studies: For cohort analyses, add an athlete_id column before calculation and use group_by(athlete_id) with group_modify(). See examples below and vignettes for details.

References

Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280.

Hulin, B. T., et al. (2016). The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. British Journal of Sports Medicine, 50(4), 231-236.

Examples

# Example using simulated data (Note: sample data is pre-calculated, shown for demonstration)
data(athlytics_sample_acwr)
print(head(athlytics_sample_acwr))
#> # A tibble: 6 × 5
#>   date         atl   ctl  acwr acwr_smooth
#>   <date>     <dbl> <dbl> <dbl>       <dbl>
#> 1 2023-01-01  63.7  63.7     1          NA
#> 2 2023-01-02  54.4  54.4     1          NA
#> 3 2023-01-03  54.6  54.6     1          NA
#> 4 2023-01-04  55.5  55.5     1          NA
#> 5 2023-01-05  55.8  55.8     1          NA
#> 6 2023-01-06  55.2  55.2     1          NA

if (FALSE) { # \dontrun{
# Example using local Strava export data
# Step 1: Download your Strava data export
# Go to Strava.com > Settings > My Account > Download or Delete Your Account
# You'll receive a ZIP file via email (e.g., export_12345678.zip)

# Step 2: Load activities directly from ZIP (no extraction needed!)
activities <- load_local_activities("export_12345678.zip")

# Or from extracted CSV
activities <- load_local_activities("strava_export_data/activities.csv")

# Step 3: Calculate ACWR for Runs (using distance)
run_acwr <- calculate_acwr(activities_data = activities, 
                           activity_type = "Run",
                           load_metric = "distance_km")
print(tail(run_acwr))

# Calculate ACWR for Rides (using TSS, requires FTP)
ride_acwr_tss <- calculate_acwr(activities_data = activities,
                                activity_type = "Ride",
                                load_metric = "tss", 
                                user_ftp = 280)
print(tail(ride_acwr_tss))

# Plot the results
plot_acwr(run_acwr, highlight_zones = TRUE)

# Multi-athlete cohort analysis
library(dplyr)

# Load data for multiple athletes and add athlete_id
athlete1 <- load_local_activities("athlete1_export.zip") %>%
  mutate(athlete_id = "athlete1")

athlete2 <- load_local_activities("athlete2_export.zip") %>%
  mutate(athlete_id = "athlete2")

# Combine all athletes
cohort_data <- bind_rows(athlete1, athlete2)

# Calculate ACWR for each athlete using group_modify()
cohort_acwr <- cohort_data %>%
  group_by(athlete_id) %>%
  group_modify(~ calculate_acwr(.x, 
                                 activity_type = "Run",
                                 load_metric = "duration_mins")) %>%
  ungroup()

# View results
print(cohort_acwr)
} # }