Calculate Efficiency Factor (EF)

Efficiency Factor measures how much work you perform per unit of cardiovascular effort. Higher EF indicates better aerobic fitness - you're able to maintain faster pace or higher power at the same heart rate. Tracking EF over time helps monitor aerobic base development and training effectiveness.

EF Metrics:

speed_hr (for running): Speed (m/s) / Average HR
- Higher values = faster speed at same HR = better fitness
power_hr (for cycling): Average Power (watts) / Average HR
- Higher values = more power at same HR = better fitness

What Improves EF?

Aerobic base building (Zone 2 training)
Improved running/cycling economy
Enhanced cardiovascular efficiency
Increased mitochondrial density

Usage

calculate_ef(
  activities_data,
  activity_type = c("Run", "Ride"),
  ef_metric = c("speed_hr", "gap_hr", "power_hr"),
  start_date = NULL,
  end_date = Sys.Date(),
  min_duration_mins = 20,
  min_steady_minutes = 20,
  steady_cv_threshold = 0.08,
  min_hr_coverage = 0.9,
  quality_control = c("off", "flag", "filter"),
  export_dir = NULL
)

Arguments

activities_data

A data frame of activities from load_local_activities(). Must contain columns: date, type, moving_time, distance, average_heartrate, and average_watts (for power_hr metric).

activity_type

Character vector or single string specifying activity type(s) to analyze. Common values: "Run", "Ride", or c("Run", "Ride"). Default: c("Run", "Ride").

ef_metric

Character string specifying the efficiency metric:

"speed_hr": Speed-based efficiency (for running). Formula: speed (m/s) / avg_HR. Units: m/s/bpm (higher = better fitness)
"gap_hr": Grade Adjusted Speed efficiency (for running on hilly terrain). Formula: GAP speed (m/s) / avg_HR. Accounts for elevation changes. Units: m/s/bpm
"power_hr": Power-based efficiency (for cycling). Formula: avg_watts / avg_HR. Units: W/bpm (higher = better fitness) Default: c("speed_hr", "power_hr") (uses first matching metric for activity type). Note: "pace_hr" is accepted as a deprecated alias for "speed_hr" for backward compatibility.

start_date

Optional. Analysis start date (YYYY-MM-DD string, Date, or POSIXct). Defaults to one year before end_date.

end_date

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

min_duration_mins

Numeric. Minimum activity duration in minutes to include in analysis (default: 20). Filters out very short activities that may not represent steady-state aerobic efforts.

min_steady_minutes

Numeric. Minimum duration (minutes) for steady-state segment (default: 20). Activities shorter than this are automatically rejected for EF calculation.

steady_cv_threshold

Numeric. Coefficient of variation threshold for steady-state (default: 0.08 = 8%). Activities with higher variability are rejected as non-steady-state.

min_hr_coverage

Numeric. Minimum HR data coverage threshold (default: 0.9 = 90%). Activities with lower HR coverage are rejected as insufficient data quality.

quality_control

Character. Quality control mode: "off" (no filtering), "flag" (mark issues), or "filter" (exclude flagged data). Default "filter" for scientific rigor.

export_dir

Optional. Path to Strava export directory containing activity files. When provided, enables stream data analysis for more accurate steady-state detection.

Value

A tibble with the following columns:

date: Activity date (Date class)
activity_type: Activity type (character: "Run" or "Ride")
ef_value: Efficiency Factor value (numeric). Higher = better fitness. Units: m/s/bpm for speed_hr, W/bpm for power_hr.
status: Character. "ok" for successful calculation with stream data, "no_streams" for activity-level calculation without stream data, "non_steady" if steady-state criteria not met, "insufficient_data" if data quality issues, "too_short" if below min_steady_minutes, "insufficient_hr_data" if HR coverage below threshold.

Details

Computes Efficiency Factor (EF) for endurance activities, quantifying the relationship between performance output (speed or power) and heart rate. EF is a key indicator of aerobic fitness and training adaptation (Allen et al., 2019).

Algorithm:

Filter activities by type, date range, and minimum duration
For each activity, calculate:
- speed_hr: (distance / moving_time) / average_heartrate
- power_hr: average_watts / average_heartrate
Return one EF value per activity

Steady-State Detection Method:

When stream data is available (via export_dir), the function applies a rolling coefficient of variation (CV) approach to identify steady-state periods:

Rolling window: A sliding window (default 300 seconds, or 1/4 of data, min 60 s) computes the rolling mean and standard deviation of the output metric (velocity or power).
CV calculation: CV = rolling SD / rolling mean at each time point.
Threshold filtering: Time points where CV < steady_cv_threshold (default 8%) are classified as steady-state.
Minimum duration: At least min_steady_minutes of steady-state data must be present; otherwise the activity is marked as "non_steady".
EF computation: The median EF across all steady-state data points is reported.

This approach follows the principle that valid EF comparisons require quasi-constant output intensity, as outlined by Coyle & González-Alonso (2001), who demonstrated that cardiovascular drift is meaningful only under steady-state exercise conditions. The rolling CV method is a common signal-processing technique for detecting stationarity in physiological time series.

Data Quality Considerations:

Requires heart rate data (activities without HR are excluded)
power_hr requires power meter data (cycling with power)
Best for steady-state endurance efforts (tempo runs, long rides)
Interval workouts may give misleading EF values
Environmental factors (heat, altitude) can affect EF

Interpretation:

Upward trend: Improving aerobic fitness
Stable: Maintenance phase
Downward trend: Possible overtraining, fatigue, or environmental stress
Sudden drop: Check for illness, equipment change, or data quality

Typical EF Ranges (speed_hr for running):

Beginner: 0.01 - 0.015 (m/s per bpm)
Intermediate: 0.015 - 0.020
Advanced: 0.020 - 0.025
Elite: > 0.025

Note: EF values are relative to individual baseline. Focus on personal trends rather than absolute comparisons with other athletes.

References

Allen, H., Coggan, A. R., & McGregor, S. (2019). Training and Racing with a Power Meter (3rd ed.). VeloPress.

Coyle, E. F., & González-Alonso, J. (2001). Cardiovascular drift during prolonged exercise: New perspectives. Exercise and Sport Sciences Reviews, 29(2), 88-92. doi:10.1097/00003677-200104000-00009

Examples

# Example using simulated data
data(sample_ef)
print(head(sample_ef))
#> # A tibble: 6 × 3
#>   date       activity_type ef_value
#>   <date>     <chr>            <dbl>
#> 1 2023-01-01 Run               1.16
#> 2 2023-01-01 Ride              1.86
#> 3 2023-01-04 Run               1.19
#> 4 2023-01-06 Run               1.21
#> 5 2023-01-06 Ride              1.94
#> 6 2023-01-08 Run               1.31

# Runnable example with dummy data:
end <- Sys.Date()
dates <- seq(end - 29, 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)),
  average_watts = rep(200, length(dates)),
  weighted_average_watts = rep(210, length(dates)),
  filename = "",
  stringsAsFactors = FALSE
)

# Calculate EF (Speed/HR)
ef_result <- calculate_ef(
  activities_data = dummy_activities,
  activity_type = "Run",
  ef_metric = "speed_hr",
  end_date = end
)
print(head(ef_result))
#>         date activity_type   ef_value     status
#> 1 2026-01-09           Run 0.01984127 no_streams
#> 2 2026-01-10           Run 0.01984127 no_streams
#> 3 2026-01-11           Run 0.01984127 no_streams
#> 4 2026-01-12           Run 0.01984127 no_streams
#> 5 2026-01-13           Run 0.01984127 no_streams
#> 6 2026-01-14           Run 0.01984127 no_streams

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

# Calculate Speed/HR efficiency factor for Runs
ef_data_run <- calculate_ef(
  activities_data = activities,
  activity_type = "Run",
  ef_metric = "speed_hr"
)
print(tail(ef_data_run))

# Calculate Power/HR efficiency factor for Rides
ef_data_ride <- calculate_ef(
  activities_data = activities,
  activity_type = "Ride",
  ef_metric = "power_hr"
)
print(tail(ef_data_ride))
} # }