Additionally, genetics accounts for a big part of our VO2max. VO2max is highly correlated with body weight, height, age and gender. We can see the data with respect to all other participants of the same sex, on my dataset: For this participant (highlighted in the following figure), the HR while walking went around 115 bpm, while running as slow as 9 km/h hit more 170 bpm, pointing out the poor fitness level. As I mentioned before we can use the HR at predefined intensities as a proxy to fitness level, since higher HR typically indicates lower fitness level. In a dataset I collected during my PhD I had a participant who was very unfit, but also very light. Let's illustrate this issue with an example. Not-normalizing, while correct in principle, hinders interpretability since tables for different weight ranges don't exist. VO2max/kg), however they don't take into account the type of test performed to obtain VO2max, often over-correcting results. VO2max categories are based on normalized units (i.e. Especially when biking, the activity is non-weight bearing, which means the impact of body weight on oxygen uptake is very different compared to weight bearing activities such as running. Again, literature on different normalizations (and allometric coefficients) for activity-specific body weight normalization is inconsistent. VO2max is reported most of the times normalized by body weight, however t he relation between body weight and oxygen uptake is activity dependent. One of the major issues with VO2max is the total lack of agreement on body weight normalizations. How can we measure fitness if the gold standard is affected by such variability? However, such tests are the most commonly performed in research, since they are considered more practical and easier for participants that are not used to do sports (e.g. One of the reasons is that biking tests are often limited by muscle fatigue. And differences can be big, with running VO2max typically being higher. If you do a bike test and a treadmill test, you’ll get two different results. While VO2max is the gold standard, and by definition is the only way to determine fitness level, the exercise protocol performed highly influences results. However, some limitations still apply: the test needs to be re-performed every time that fitness needs to be assessed, still a pre-defined protocol is required. determining the HR during specific activities, was a good step forward in terms of practical applicability, compared to maximal tests. Basically, these tests rely on the inverse relation between fitness and HR, with higher HR typically associated to lower fitness level and viceversa. ![]() Submaxmial tests have been developed already more than 60 years ago to estimate VO2max during specific protocols while monitoring HR at predefined workloads. There are a series of practical limitations to VO2max testing, for example the need for specialized personnel, expensive medical equipment, high motivational demands of the subject, health risks for subjects in non-optimal health conditions (which limits applicability), and so on. VO2max is regarded as the most precise method for determining fitness. I’d love to engage in a deeper conversation on the complexities of determining fitness level, so if you are interested as a user, coach, or expert in the field, feel free to drop me a line.Ĭurrent practice for fitness assessment is direct measurement of oxygen volume during maximal exercise, i.e. I was mainly motivated by the lack of services (methods, apps, or whatever) able to track my fitness level as I keep training and try to improve my personal bests. I will also show some anecdotal evidence of the benefit of using the Fitness Index, based on my data. This post goes into the details of the limitations of current methods used to define fitness and how the Fitness Index overcomes some of these limitations. The indicator is called Fitness Index, and the app I developed around this concept is StayFit. I spent the last few weeks defining a new indicator of cardiovascular endurance and fitness, based on simple parameters that can be acquired with minimal effort using a mobile phone. Additionally, some of the research backing up the assumptions in this post have been recently published in Artificial Intelligence in Medicine, you can find out more here. ![]() ![]() However, I am currently including the Fitness Index as an additional Insight in HRV4Training, please check out HRV4Training if you are interested in physiological data, training, recovery and fitness. UPDATE: StayFit is not available on the Apple Store anymore.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |