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DU KAN AUKA Protocol: How to Test Swimmers with VO2 Master

Written by Alexander Broberg Skeltved, DU KAN AUKA

Many have asked how to test swimmers in the pool with the VO2 Master. We now have the pleasure of presenting a detailed description of our approach.

The portability of the VO2 Master makes it uniquely suitable for measuring oxygen consumption in the field. However, in swimming, we face the challenge of not being able to wear the mask while active. In this article, we will demonstrate how we perform reliable metabolic tests with the VO2 Master on swimmers in a pool.

For a bullet point presentation of the method, scroll all the way to the bottom.

How to Test Swimers: The Set-Up

We invited two national-level swimmers with no prior experience in metabolic testing. The first athlete is a 19-year-old male who specializes in the 800m freestyle (Swimmer 1). The second athlete is also a 19-year-old male, but his main events are the 200 and 400 m freestyle (Swimmer 2).

The Test Protocol

Warm-up:

  • 400m easy
  • 400m around aerobic threshold
  • 400m near anaerobic threshold (with a chance for the swimmers to test the analyzer).
  • 4x50m with a 20m acceleration and 30m easy
  • 200m easy

Main test set:

  • 2×400 at aerobic threshold – Rest 60s
  • 2×400 at anaerobic threshold – Rest 60s
  • 2×200 at Max (instructed to split evenly and aim to reach VO2 max), 3-4 minutes rest, including 100 easy.

Note: The swimmers set their own pace, as they had no prior testing reference. These paces is likely not their true aerobic and anaerobic thresholds.

Instruments

We used the VO2 Master with the “free” protocol and disabled the 15-minute idle timeout. With the accompanying ruler, we determined the correct mask size, which was small in both cases. Calibration was done according to best practices (see our “Validation study of the VO2 Master” ).

To optimize pacing, we equipped the swimmers with the FORM Swim 2 goggles. The in-screen metrics make it optimal for pacing. Additionally, heart rate and technical parameters were discussed post-test with the athletes, though these details are omitted here.

A handheld stopwatch was used to measure the time from the hand touching the wall to the first registered breath, denoted as dt.

Setting the pace

The swimmers were asked to set their pace and given the instruction to pace evenly. During the warm-up, they were given the option to adjust the pace. We then gave them the corresponding 25m pace as that would be easy to follow on the screen of the FORM goggles. An even pace is essential for calculating efficiency at a specific intensity.

Swimmer 1 selected 19, 17, and 16 seconds per 25m across the intensities, while Swimmer 2 chose 19, 17.2, and 16 seconds.

In Figures 1) and 2) we show the splits of swimmers 1 and 2, respectively. If we exclude the first and last lap, which is customary, the pace was even. From past experiences, we would expect a much higher variation in the split times, in particular with athletes who are inexperienced with this type of testing. Most laps were within +/- 0.2 seconds.

For the rest of the calculations, we also excluded the first lap from the calculated averages.

Splits for Swimmer 1 for each 25, collected from the FORM app.
Figure 1: Splits for Swimmer 1 for each 25, collected from the FORM app.
Splits for Swimmer 2 for each 25, collected from the FORM app.
Figure 2: Splits for Swimmer 2 for each 25, collected from the FORM app.

Subjective Comments

Swimmer 1 felt less exhausted after the maximum-effort swims, particularly in the second interval, and noted the second 400m set was more challenging than expected.

Swimmer 2 started slower on the first interval but found a rhythm quickly. He was satisfied with the maximum efforts.

Presenting the Data and Estimating the VO2

Following each interval, we recorded VO2 decline over 40-60 seconds (Figure 3). The measured VO2 data for each swimmer is plotted with a 1-second resolution (each breath as a data point).

a graph where The VO2 data series from each interval, blue from swimmer 1 and red for swimmer 2. To make the data series presentable, the data series of each interval starts at time = 0, 100, 200, ... , 500 seconds, respectively.

Figure 3: The VO2 data series from each interval, blue from swimmer 1 and red for swimmer 2. To make the data series presentable, the data series of each interval starts at time = 0, 100, 200, … , 500 seconds, respectively.

The data series was linearly approximated, represented by VO2 = A – Bt, with the Y-axis as VO2 and the x-axis as time (t). Figures 4 and 5 show each interval’s data series individually with best-fit linear functions.

The key to our method is that each data series can be linearly approximated. A standard linear function is written as y = Bx + A. In this case, the y-axis represents VO2 and the x-axis represents time (t) and because the curve is dropping the constant B is negative. Hence, we write the linear curve as VO2 = A – Bt.

In figures 4 and 5 we show each interval’s data series individually with best-fit linear functions plotted on top. The first data point is located between 3 s and 5 s, which we from earlier called “dt”. The time from the hand touches the wall to we record the first breath.

a graph where VO2 data from swimmer 1, presented with the accompanying best fit linear curve for each interval. The top row corresponds to the aerobic threshold, the second row shows the anaerobic threshold, and the bottom row shows the maximum efforts. The linear approximation is shown in each individual panel.
Figure 4: VO2 data from swimmer 1, presented with the accompanying best fit linear curve for each interval. The top row corresponds to the aerobic threshold, the second row shows the anaerobic threshold, and the bottom row shows the maximum efforts. The linear approximation is shown in each individual panel.
a graph with VO2 data from swimmer 2.
Fig 5: VO2 data from swimmer 2.

Results

a swimmer being tested poolside with the vo2 master portable metabolic analyzer with results being displayed on an ipad

The most important metrics are shown in the table below.

  1. Average pace: Excluding the first lap, shown in the first column.
  2. Estimated VO2 (L/min): By factoring in dt, we are now able to extrapolate VO2 to wall touch (time = 0), represented by constant A in the linear function. The average values are shown in column 2.
  3. Efficiency: This is arguably the most important metric of metabolic testing. For swimmers, we can represent this as VO2 per 100m or calories per 100m, as shown in columns four and five.
a graph showing he average results from both swimmers. The first column is the average pace per 25m, and the second column shows the estimated VO2 in L/min. The third column shows the efficiency measured in liters of O2 per 100m, and finally, the efficiency measured in kcal/100m. 
Table: The average results from both swimmers. The first column is the average pace per 25m, and the second column shows the estimated VO2 in L/min. The third column shows the efficiency measured in liters of O2 per 100m, and finally, the efficiency measured in kcal/100m. 

Short Description

  • Test protocol: Ensure proper warm-up. The main test set should include multiple intervals of sufficient at each focus pace (at least 2).
  • Instruments: VO2 Master for VO2 data, FORM Swim goggles for pacing, stopwatch for wall-to-breath timing.
  • Data Collection: We record the time from when the swimmer’s hand touched the wall to their first breath (“dt”)—critical for pinpointing VO2 data right at wall touch.
  • Analysis Method: VO2 after each interval is plotted over time and linearly fit (VO2 = A – Bt) to estimate initial VO2 values by back-extrapolating to the wall touch moment. It is crucial to add “dt” to the start of the data series.
  • Results: When we have the initial VO2 values at t=0, we estimate the average of each intensity/pace. 

Summary

We have shown a reliable method of using the VO2 Master to do metabolic testing in the pool. This method offers swimmers the opportunity to understand oxygen consumption and energy expenditure.

Why measure VO2 in swimming?

  1. Identify your current efficiency, use it to experiment with technical changes, and find the most important points of improvement.
  2. Track progress over time to evaluate training effects.

Understand your energy expenditure and use it to aid in precise planning of energy intake before, during, and after training.

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