LabStats: 2025

Monday, 9 June 2025

Understanding Anaerobic Threshold (VT2) and VO2 Max in Endurance Training

Introduction: The Science Behind Ventilatory Thresholds

Every endurance athlete, whether a long-distance runner, cyclist, or swimmer, strives to maximize efficiency and delay fatigue. Two key physiological markers—Ventilatory Threshold 1 (VT1) and Ventilatory Threshold 2 (VT2)—play a crucial role in determining an athlete’s metabolic efficiency. These thresholds define how energy is utilized during physical exertion and directly influence overall performance.

VT1 vs. VT2: Defining Energy Transitions

VT1 (Aerobic Threshold): The transition from lipid metabolism (fat-burning) to a mixed metabolism, where both carbohydrates and fats fuel muscle activity.
VT2 (Anaerobic Threshold): The point where the body shifts to a predominantly carbohydrate-based metabolism, leading to rapid lactate accumulation and increased reliance on anaerobic energy pathways.

This physiological transition dictates an athlete's ability to sustain high-intensity efforts, making VT2 a key factor in endurance performance, recovery, and overall fitness.

Factors Influencing VT1 and VT2

Several physiological and external factors influence an individual's ventilatory thresholds:

Age & Sex

Younger athletes generally exhibit higher VT2 values, while aging naturally reduces aerobic capacity.
Men tend to have a higher VO2 Max due to greater lung capacity and muscle mass, but women can achieve similar endurance levels through optimized training.

Body Composition & Health Status

High body fat percentage may reduce VT2 efficiency, as excess weight increases cardiovascular workload.
Health conditions such as cardiovascular disease, respiratory disorders, and metabolic syndromes can significantly impact ventilatory thresholds.

Pharmacological Influences

Medications such as beta-blockers and ACE inhibitors affect heart rate regulation, potentially lowering VT2 due to altered cardiovascular response.

Cardiac Function & Ventricular Parameters

The heart’s ejection fraction and ventricular relaxation capacity dictate oxygen delivery efficiency.
A lower peak heart rate limits maximal cardiac output, directly influencing VO2 Max and VT2.

VO2 Max and VT2: The Connection

VO2 Max is the maximum oxygen uptake during exercise, representing cardiorespiratory efficiency. Since VT2 marks the transition to anaerobic metabolism, it directly correlates with VO2 Max:

Athletes with higher VO2 Max levels can sustain aerobic efforts longer before crossing into VT2.
Increasing VT2 effectively extends endurance, allowing the body to buffer lactate accumulation more efficiently.

Training Strategies to Improve VT2 & VO2 Max

Structured workouts can significantly increase VT2, delay lactate buildup, and optimize VO2 Max. Here’s how:

1. Threshold Training (Lactate Clearance Sessions)

Running or cycling at 95-105% of VT2 intensity improves the body’s ability to metabolize lactate.
Sessions should last 20–40 minutes, mimicking race conditions.

2. High-Intensity Interval Training (HIIT)

Short bursts of maximum effort (~110% VO2 Max) with equal recovery periods.
Enhances anaerobic power while boosting the efficiency of VT2 adaptation.

3. Long Steady-State Workouts

Prolonged efforts at 80-85% VO2 Max strengthen the aerobic base.
Builds endurance while minimizing lactate accumulation.

4. Strength Training for VO2 Max Optimization

Studies show that lower-body strength work (squats, lunges, plyometrics) improves metabolic efficiency.
Stronger muscle fibers require less oxygen, prolonging aerobic capacity.

Monitoring VT2 and VO2 Max with Data Analytics

Coaches and sports scientists track VT2 trends alongside VO2 Max using wearable sensors, lactate testing, and predictive data modeling.

Using R Code for VT2 Estimation

This R function serves as a valuable tool for estimating VT2 based on training data, enabling athletes to: ✅ Analyze trends in ventilatory adaptation ✅ Quantify improvements over multiple training sessions ✅ Adjust pacing strategies for optimal endurance

Final Thoughts: Why VT2 Matters

Understanding VT2 and VO2 Max is fundamental for endurance athletes looking to optimize their training. By integrating targeted workouts, data-driven insights, and physiological testing, athletes can increase their threshold capacity, reduce fatigue, and improve overall performance.

Example

Let's assume these data:

Tempo <- matrix(c(46,30,7,50,5,0,12,33),nrow=4,ncol=2,byrow=TRUE)

Dist <- c(7460,1250,1100,2290)

distanza <- 10000

BpM <- c(163,134,153,160)

RunTime(Tempo,Dist,distanza,BpM)

Table 1. Summary of results

Run	Distance (mt)	Velocity (km/h)	Expected Velocity (km/h)	Time (s)	ExpectedTime (10 km)	bpm	Anaerobic Threshold (km/h)
1	7460	9.63	9.46	2790	3806.28	163	9.36
2	1250	9.57	8.45	470	4259.64	134	9.31
3	1100	13.20	11.56	300	3113.47	153	12.83
4	2290	10.95	10.02	753	3592.27	160	10.64

Figures

Figure 1. Running sessions' performances

The scatter plot presents data points illustrating a relationship between velocity (km/h) and distance (m). Observations from the trend suggest:

As distance increases, velocity tends to decrease slightly, indicating a potential endurance effect where pace slows over longer distances.
The initial point shows higher velocity, possibly due to the athlete starting with higher energy levels.
The drop in velocity around mid-range distances could suggest a pacing strategy or fatigue onset.
The final data points stabilize, indicating a consistent pace over longer distances.

The athlete may be adjusting their effort across distances to optimize endurance. Threshold Monitoring: If this dataset correlates with anaerobic threshold, the trend might highlight the optimal endurance balance for sustaining effort efficiently. Training Application: Understanding this pattern can help refine speed maintenance strategies for long-distance runs.

Figure 4. Velocity vs. Distance

The data points indicate a trend of decreasing velocity as the running distance increases. The drop in velocity at 3000m (~11 km/h) suggests possible fatigue onset or pacing strategy adjustments. The gradual decline in velocity might indicate controlled endurance pacing, where the athlete slows down to maintain stamina.

Training Insights: If the anaerobic threshold is being assessed, the change in velocity across distances can help identify the optimal threshold where endurance and performance balance out.

R function: calculate the anaerobic threshold

RunTime <- function(Time,Distance,Distance_reference,bpm) {

## INPUT

#Time: run time measured [min, s]

#Distance: run distance [mt]

#Distance_reference: reference distance [mt] of which we want to evaluate the expected time

#bpm: cardiac frequency (beats per minute)

## OUTPUT

# T2 = tempo corsa atteso su distanza di riferimento (s)

# V2 = velocità attesa su distanza di riferimento (km / h)

# V1 = velocità media singole corse (km / h)

# Vm = velocità media su distanze e tempi rilevati cumulati (km / h)

# FC = Frequenza cardiaca per minuto

# SAN = Soglia Anaerobica stimata

# SAN2 = proiezione Soglia Anaerobica su tempi attesi e distanza di riferimento

# D1 = distanze percorse

# P1 = passo corsa per km [min, s]

# P2 = passo corsa atteso per km [min, s]

T1 <- Time

D1 <- Distance

D2 <- Distance_reference

T = T1[,2] + T1[,1]*60 # run time (seconds)

T2 = T* (D2/D1)^(1.06) # expected time (seconds)

V2 = 3.6 * (D2/T2)

V1 = 3.6 * (D1/T);

p = (1000*T/D1)/60 # run pace per km

P1 = data.frame(floor(p), ((p - floor(p))*60)) # run pace per km [min, s]

p2 = (1000*T2/D2)/60 # expected run pace per km

P2 = data.frame(floor(p2), ((p2 - floor(p2))*60)) # expected run pace per km [min, s]

Vm = 3.6 * (sum(D1)/sum(T));

SAN = 35000/(T*(10000/D1)) #Anaerobic Threshold

SAN2 = 35000/(T2*(10000/D2)) #projection of the Anaerobic Threshold over expected time and reference distance

FC <- bpm

## summary statistics

stats=NULL

stats$V1 = c(mean(V1), sd(V1))

stats$D1 = c(mean(D1), sd(D1))

stats$V2 = c(mean(V2), sd(V2))

stats$T1 = c(mean(T), sd(T))

stats$T2 = c(mean(T2), sd(T2))

stats$SAN = c(mean(SAN), sd(SAN))

stats$SAN2 = c(mean(SAN2), sd(SAN2))

stats$FC = c(mean(FC), sd(FC))

stats$P1 = c(mean(p), sd(p))

stats$P2 = c(mean(p2), sd(p2))

DF <- data.frame(

Run = seq_along(Distance),

Distance = Distance,

Velocity = V1,

Expected_Velocity = V2,

Time = T,

Expected_Time = T2,

bpm = bpm,

Anaerobic_Threshold = SAN

)

##Plots

par(mfrow=c(2,2))

plot(V1,main="Velocity(km / h)",xlab="Run",ylab="km / h",pch=16,type="o",lty=2)