lactater
Tools for Analyzing Lactate Thresholds
Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze
the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP),
and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming.
Beaver WL, Wasserman K, Whipp BJ (1985)
lactater 
The goal of lactater is to provide tools for making it easier to
analyze lactate thresholds.
Installation
You can install the released version of lactater from
CRAN with:
install.packages("lactater")
You can install the development version of lactater from
Github with:
# install.packages("remotes")
remotes::install_github("fmmattioni/lactater")
Demo data
library(lactater)
demo_data
#> step length intensity lactate heart_rate
#> 1 0 0 0 0.93 96
#> 2 1 3 50 0.98 114
#> 3 2 3 75 1.23 134
#> 4 3 3 100 1.88 154
#> 5 4 3 125 2.80 170
#> 6 5 3 150 4.21 182
#> 7 6 3 175 6.66 193
#> 8 7 2 191 8.64 198
Usage
With lactater you can easily estimate lactate thresholds using one or
multiple methods:
results_overall <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = c("Log-log", "OBLA", "Bsln+", "Dmax", "LTP", "LTratio"),
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 17 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 Log-log Log-log 3rd degr… 83.4 1.4 140 <gg>
#> 2 OBLA OBLA 2.0 3rd degr… 105. 2 153 <gg>
#> 3 OBLA OBLA 2.5 3rd degr… 118. 2.5 160 <gg>
#> 4 OBLA OBLA 3.0 3rd degr… 129 3 167 <gg>
#> 5 OBLA OBLA 3.5 3rd degr… 137 3.5 171 <gg>
#> 6 OBLA OBLA 4.0 3rd degr… 145 4 176 <gg>
#> 7 Bsln+ Bsln + 0.5 3rd degr… 82.5 1.43 139 <gg>
#> 8 Bsln+ Bsln + 1.0 3rd degr… 104. 1.93 152 <gg>
#> 9 Bsln+ Bsln + 1.5 3rd degr… 117. 2.43 159 <gg>
#> 10 Dmax Dmax 3rd degr… 132. 3.1 168 <gg>
#> 11 Dmax ModDmax 3rd degr… 140. 3.6 173 <gg>
#> 12 Dmax Exp-Dmax Exponent… 135. 3.3 170 <gg>
#> 13 Dmax Log-Poly-ModDmax 3rd degr… 143 3.8 175 <gg>
#> 14 Dmax Log-Exp-ModDmax Exponent… 146. 4 177 <gg>
#> 15 LTP LTP1 3rd degr… 88.9 1.5 143 <gg>
#> 16 LTP LTP2 3rd degr… 148. 4.1 178 <gg>
#> 17 LTratio LTratio B-Spline… 71.2 1.2 132 <gg>
You can also choose one method:
Log-log
results_loglog <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "Log-log",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 1 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 Log-log Log-log 3rd degree polynom… 83.4 1.4 140 <gg>
OBLA
results_obla <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "OBLA",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 5 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 OBLA OBLA 2.0 3rd degree polyno… 105. 2 153 <gg>
#> 2 OBLA OBLA 2.5 3rd degree polyno… 118. 2.5 160 <gg>
#> 3 OBLA OBLA 3.0 3rd degree polyno… 129 3 167 <gg>
#> 4 OBLA OBLA 3.5 3rd degree polyno… 137 3.5 171 <gg>
#> 5 OBLA OBLA 4.0 3rd degree polyno… 145 4 176 <gg>
Bsln+
results_bsln_plus <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "Bsln+",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 3 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 Bsln+ Bsln + 0.5 3rd degree poly… 82.5 1.43 139 <gg>
#> 2 Bsln+ Bsln + 1.0 3rd degree poly… 104. 1.93 152 <gg>
#> 3 Bsln+ Bsln + 1.5 3rd degree poly… 117. 2.43 159 <gg>
Dmax
results_dmax <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "Dmax",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 5 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 Dmax Dmax 3rd degre… 132. 3.1 168 <gg>
#> 2 Dmax ModDmax 3rd degre… 140. 3.6 173 <gg>
#> 3 Dmax Exp-Dmax Exponenti… 135. 3.3 170 <gg>
#> 4 Dmax Log-Poly-ModDmax 3rd degre… 143 3.8 175 <gg>
#> 5 Dmax Log-Exp-ModDmax Exponenti… 146. 4 177 <gg>
LTP
results_ltp <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "LTP",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 2 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <lis>
#> 1 LTP LTP1 3rd degree polynomi… 88.9 1.5 143 <gg>
#> 2 LTP LTP2 3rd degree polynomi… 148. 4.1 178 <gg>
LTratio
results_ltratio <- lactate_threshold(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
method = "LTratio",
fit = "3rd degree polynomial",
include_baseline = TRUE,
sport = "cycling",
plot = TRUE
)
#> # A tibble: 1 × 7
#> method_category method fitting intensity lactate heart_rate plot
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <list>
#> 1 LTratio LTratio B-Spline (default) 71.2 1.2 132 <gg>
Lactate curve
In case you would like to retrieve the data for producing your own
plots, you can use the lactate_curve() function:
data_lactate_curve <- lactate_curve(
.data = demo_data,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
fit = "3rd degree polynomial",
include_baseline = FALSE,
sport = "cycling"
)
data_lactate_curve
#> $data
#> # A tibble: 8 × 3
#> intensity lactate heart_rate
#> <int> <dbl> <int>
#> 1 25 0.93 96
#> 2 50 0.98 114
#> 3 75 1.23 134
#> 4 100 1.88 154
#> 5 125 2.8 170
#> 6 150 4.21 182
#> 7 175 6.66 193
#> 8 191 8.64 198
#>
#> $lactate_curve
#> # A tibble: 1,411 × 2
#> intensity lactate
#> <dbl> <dbl>
#> 1 50 0.957
#> 2 50.1 0.959
#> 3 50.2 0.960
#> 4 50.3 0.961
#> 5 50.4 0.963
#> 6 50.5 0.964
#> 7 50.6 0.965
#> 8 50.7 0.966
#> 9 50.8 0.968
#> 10 50.9 0.969
#> # ℹ 1,401 more rows
#>
#> $heart_rate_response
#> # A tibble: 1,411 × 2
#> intensity heart_rate
#> <dbl> <dbl>
#> 1 50 120.
#> 2 50.1 120.
#> 3 50.2 120.
#> 4 50.3 120.
#> 5 50.4 120.
#> 6 50.5 120.
#> 7 50.6 120.
#> 8 50.7 120.
#> 9 50.8 120.
#> 10 50.9 120.
#> # ℹ 1,401 more rows
And then you easily produce plots like the following:
library(ggplot2)
ggplot() +
geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate)) +
geom_point(data = data_lactate_curve$data, aes(intensity, lactate), size = 4) +
theme_light()
ggplot() +
geom_path(data = data_lactate_curve$heart_rate_response, aes(intensity, heart_rate)) +
geom_point(data = data_lactate_curve$data, aes(intensity, heart_rate), size = 4) +
theme_light()
You can also combine the results from the lactate_threshold() function
to plot the results:
ggplot() +
geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate)) +
geom_point(data = data_lactate_curve$data, aes(intensity, lactate), size = 4) +
geom_point(data = results_overall, aes(intensity, lactate, color = method), size = 3) +
theme_light()
You can also compare the lactate curves after a training period, for example:
data_after_training <- tibble::tribble(
~step, ~length, ~intensity, ~lactate, ~heart_rate,
0L, 0L, 0L, 0.93, 88L,
1L, 3L, 50L, 0.98, 108L,
2L, 3L, 75L, 1.12, 126L,
3L, 3L, 100L, 1.6, 138L,
4L, 3L, 125L, 2.1, 156L,
5L, 3L, 150L, 3.75, 169L,
6L, 3L, 175L, 4.98, 182L,
7L, 3L, 200L, 7.78, 190L,
8L, 3L, 225L, 10.12, 199L
)
data_lactate_curve_after_training <- lactate_curve(
.data = data_after_training,
intensity_column = "intensity",
lactate_column = "lactate",
heart_rate_column = "heart_rate",
fit = "3rd degree polynomial",
include_baseline = FALSE,
sport = "cycling"
)
ggplot() +
geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate, color = "before training")) +
geom_point(data = data_lactate_curve$data, aes(intensity, lactate, color = "before training"), size = 4) +
geom_path(data = data_lactate_curve_after_training$lactate_curve, aes(intensity, lactate, color = "after training")) +
geom_point(data = data_lactate_curve_after_training$data, aes(intensity, lactate, color = "after training"), size = 4) +
theme_light() +
labs(color = NULL)
ggplot() +
geom_path(data = data_lactate_curve$heart_rate_response, aes(intensity, heart_rate, color = "before training")) +
geom_point(data = data_lactate_curve$data, aes(intensity, heart_rate, color = "before training"), size = 4) +
geom_path(data = data_lactate_curve_after_training$heart_rate_response, aes(intensity, heart_rate, color = "after training")) +
geom_point(data = data_lactate_curve_after_training$data, aes(intensity, heart_rate, color = "after training"), size = 4) +
theme_light() +
labs(color = NULL)
