Predict performance from a step test — predict

Estimates race times in running and time-trial power in cycling from the thresholds of a step test, with a 95% prediction range. Each estimate comes from an equation fitted on one threshold, in one population, with one test protocol, so it only applies to tests run the same way:

Usage

predict_performance(x, method = NULL, equation = NULL)

# S3 method for class 'lactate_performance'
autoplot(object, ...)

Arguments

x: The result of lactate_threshold().
method: The thresholds to predict from. Defaults to every threshold of x that has an equation.
equation: Your own equation(s) from calibrate_performance(), one or a list of them, used in place of the published ones.
object: The result of predict_performance().
...: Not used.

Value

a lactate_performance tibble with one row per estimate: event, method, threshold, the predicted mean race intensity (speed or power, in the unit of the sport) with its 95% range (intensity_low, intensity_high), for races over a distance the time with its range (time_low, time_high), and the source. Plot it with autoplot(); the article Predicting performance shows it.

Details

Running: 5 km, 10 km, half marathon and marathon from IAT (Roecker et al., 1998; 427 competitive runners, treadmill at 2% grade, 3-min stages, +2 km/h, earlobe hemolyzed whole blood), and the marathon from OBLA 4.0 (Sjödin & Jacobs, 1981; 18 male marathon runners, 4-min stages, fingertip blood). lactater's IAT follows the definition of Roecker et al. (the lowest lactate/speed ratio + 1.5 mmol/L).
Cycling: 45-min time-trial power from LTP2, IAT, ModDmax, OBLA 4.0, Dmax or Log-Poly-ModDmax, fitted with lactater's own thresholds on the open data of Heuberger et al. (2018; 48 well-trained men, 5-min stages, +25 W, venous blood).
Your own equation, from calibrate_performance(): any threshold, sport and protocol, fitted on your own athletes' tests and races. This is the best option when you have the data.

Other thresholds are not offered with the published equations: an equation fitted on one threshold gives wrong numbers for another.

The 95% range is the prediction interval of the regression: the predicted intensity +/- t x the standard error of the estimate x sqrt(1 + 1/n + (threshold - mean)^2 / ((n - 1) * sd^2)). Roecker et al. (1998) reported neither that error nor the mean of the thresholds per distance, so the error is derived from their tables as SD x sqrt(1 - r^2), with the SD of race speed computed from the SD of race time, and the last term is left out.

Expect an individual error of about +/- 10% of race speed in running and +/- 25 W in cycling with the published equations. A warning is given when the steps of the test differ from those of the source study (a different step changes the threshold: Coen et al., 2001; Kuipers et al., 2003), and when a threshold lies outside the range of the source sample. Stage duration, sampling site and treadmill grade are checked too when the test was analyzed with stage_duration, site and grade (see lactate_threshold()); otherwise compare them with the protocol in the printed header. A recent race is a better predictor of the next one than any step test (Vickers & Vertosick, 2016).

References

Coen B, Urhausen A, Kindermann W. Individual anaerobic threshold: methodological aspects of its assessment in running. International Journal of Sports Medicine. 2001;22(1):8-16.

Heuberger JAAC, Gal P, Stuurman FE, de Muinck Keizer WAS, Mejia Miranda Y, Cohen AF. Repeatability and predictive value of lactate threshold concepts in endurance sports. PLOS ONE. 2018;13(11):e0206846.

Kuipers H, Rietjens G, Verstappen F, Schoenmakers H, Hofman G. Effects of stage duration in incremental running tests on physiological variables. International Journal of Sports Medicine. 2003;24(7):486-491.

Roecker K, Schotte O, Niess AM, Horstmann T, Dickhuth HH. Predicting competition performance in long-distance running by means of a treadmill test. Medicine & Science in Sports & Exercise. 1998;30(10):1552-1557.

Sjödin B, Jacobs I. Onset of blood lactate accumulation and marathon running performance. International Journal of Sports Medicine. 1981;2(1):23-26.

Vickers AJ, Vertosick EA. An empirical study of race times in recreational endurance runners. BMC Sports Science, Medicine and Rehabilitation. 2016;8:26.

Examples

runner <- data.frame(
  intensity = c(0, 8, 10, 12, 14, 16, 18),
  lactate = c(1.0, 1.1, 1.2, 1.6, 2.6, 4.6, 8.0)
)
results <- lactate_threshold(runner, intensity, lactate, sport = "running", plot = FALSE)
prediction <- predict_performance(results)
prediction
#> # Predicted performance: running, with 95% prediction ranges
#> # LT2:                   Log-Poly-ModDmax has no equation here; these come from
#> #   IAT and OBLA 4.0
#> # Roecker et al. 1998:   427 competitive runners (88 women); treadmill at 2%
#> #   grade, 3-min stages, +2 km/h, earlobe hemolyzed whole blood; best race
#> #   within 2 months of the test
#> # Sjodin & Jacobs 1981:  18 male marathon runners; treadmill, 4-min stages, +1
#> #   to 2 km/h, fingertip blood; marathon 2 to 3 weeks after the test
#>   event        method threshold time  time_low time_high intensity intensity_low
#>   <chr>        <chr>      <dbl> <chr> <chr>    <chr>         <dbl>         <dbl>
#> 1 5 km         IAT         14.2 17:44 16:05    19:47          16.9          15.2
#> 2 10 km        IAT         14.2 38:26 34:22    43:35          15.6          13.8
#> 3 Half marath… IAT         14.2 1:25… 1:16:24  1:37:58        14.7          12.9
#> 4 Marathon     IAT         14.2 3:08… 2:48:34  3:34:43        13.4          11.8
#> 5 Marathon     OBLA …      15.5 3:04… 2:47:23  3:25:38        13.7          12.3
#> # ℹ 3 more variables: intensity_high <dbl>, extrapolated <lgl>, source <chr>
## the plot is short and wide: see it in the article Predicting performance
if(interactive()) autoplot(prediction)