Comparing VO2max determined by using the relation between heart rate and accelerometry with submaximal estimated...

Area of Science:

• Exercise Physiology
• Biomedical Engineering
• Wearable Technology

Background:

• Cardiorespiratory physical fitness, measured as maximal oxygen uptake (VO2max), is a key indicator of health.
• Current methods for VO2max estimation often require specialized laboratory equipment and protocols.
• There is a need for accessible and accurate methods for assessing VO2max in diverse settings.

Purpose of the Study:

• To explore parameters for estimating VO2max using heart rate and 3D accelerometer data.
• To develop a model for predicting cardiorespiratory fitness from wearable sensor data.

Main Methods:

• Collected heart rate and 3D accelerometer data from 41 healthy young adults during walking tests.
• Utilized regression analysis with subject characteristics and sensor-derived parameters (slope and intercept of heart rate vs. accelerometer output) to predict VO2max.

Main Results:

• A linear regression model combining slope, intercept, and gender explained 90% of the variance in VO2max (R2 = 0.90).
• The model achieved a standard error of the estimate (SEE) of 2.052 mL O2kg-1min-1.
• These results are comparable to traditional sub-maximal laboratory tests.

Conclusions:

• The integration of heart rate and accelerometer data presents a viable strategy for estimating VO2max.
• This approach shows potential for convenient, real-world assessment of cardiorespiratory fitness.