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:

pace_hr (for running): Speed (m/s) / Average HR - Higher values = faster pace 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("pace_hr", "power_hr"),
  start_date = NULL,
  end_date = NULL,
  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:

"pace_hr": Pace-based efficiency (for running). Formula: speed (m/s) / avg_HR. Units: m·s⁻¹·bpm⁻¹ (higher = better fitness)
"power_hr": Power-based efficiency (for cycling). Formula: avg_watts / avg_HR. Units: W·bpm⁻¹ (higher = better fitness)

Default: c("pace_hr", "power_hr") (uses first matching metric for activity type).

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⁻¹·bpm⁻¹ for pace_hr, W·bpm⁻¹ 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 (pace 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:
- pace_hr: (distance / moving_time) / average_heartrate
- power_hr: average_watts / average_heartrate
Return one EF value per activity

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 (pace_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.

Examples

# Example using simulated data
data(athlytics_sample_ef)
print(head(athlytics_sample_ef))
#>         date activity_type ef_value
#> 1 2023-10-28          Ride 0.024177
#> 2 2023-09-25          Ride 0.033798
#> 3 2023-10-25          Ride 0.033060
#> 4 2023-06-03          Ride 0.029016
#> 5 2023-04-22           Run 0.031326
#> 6 2023-06-14          Ride 0.027555

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

# Calculate Pace/HR efficiency factor for Runs
ef_data_run <- calculate_ef(activities_data = activities, 
                             activity_type = "Run", 
                             ef_metric = "pace_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))
} # }