We can offer you truly personalized training because our AI algorithm can predict your performance from your data. So we can provide you the best possible training results for your time constraints and tell you what performance you're going to achieve if you stick to the plan.
We deeply care for a scientific and data-driven approach. We know AI Endurance works because not only have we compared it to the most recent peer-reviewed research in exercise physiology, we have shown how our AI optimized training easily outperforms even the best one-size-fits all approach current research has to offer.
Here are a few possible reasons:
1. Garbage in garbage out - is your data clean, i.e. from a well calibrated GPS device or power meter? If not you can exclude time intervals where bad data was taken under 'Data Exclusions' in the Dashboard. A common culprit are saved runs that are actually bike rides. You can usually identify those via spikes in pace in your 'My Past Data' plot in the Dashboard.
2. For cycling, have you been riding with multiple bikes and not all of them are equipped with a power meter? If you record an activity without a power meter but wear your heart rate monitor and preferably have a cadence sensor installed, we estimate your power from those measurements. However, if there are lots of rides without heart rate data the model would be blind for a significant fraction of the actual training. Also if you have/had multiple power meters/smart trainers and those read inconsistently relative to one another that can cause issues.
3. Similarly, if you are not recording all your runs with a GPS device the model is blind to those activities you are not recording.
If you have 'Garmin - Import Data', Suunto or Strava connected, only a few seconds to minutes after you upload the activity. If you have multiple connections, we import your Garmin/Suunto activity first and sync with Strava a few minutes later to check if there is any additional information, such as if your activity was a 'Race' or if the activity name tells us you did an FTP test prior to re-training our machine learning model for each user. We make sure there are no duplicates between Garmin, Suunto and Strava even if you have multiple connections.
If your import data includes HRV, it can take a few minutes for your activity to show up in AI Endurance as the DFA alpha 1 analysis is computationally expensive.
We use your heart rate data to gauge your performance. For instance, if you were able to run 5 min/km or at 200 W for an hour at a significantly lower heart rate than last week, the AI notices this as an improvement in your performance. However, generally heart rate is a much worse predictor for performance than power and GAP.
We calculate your aerobic and anaerobic heart rate thresholds from your DFA alpha 1 data, see this blog post.
For your cycling activities without power, we use heart rate and cadence to estimate your power through a personalized machine learning model.
We track your activity HRV data if you have 'Garmin - Import Data' or 'Suunto - Import Data' enabled and HRV logging enabled on your device. You can easily track your aerobic and anaerobic thresholds from your HRV data via DFA alpha 1. For more information, see also this blog post.
We also use DFA alpha 1 to track your readiness to train, durability and overall fitness improvements. Learn more here.
In order to detect DFA alpha 1 thresholds automatically, AI Endurance has to detect an intensity ramp during your activity as this is the method that is backed by current research on DFA alpha 1 threshold detection. Our algorithm has to be able to identify a time frame that fulfills the following criteria for automatic threshold detection:
1. Steady and smooth increase of intensity, i.e. monotonically decreasing alpha 1 values and monotonically rising pace/power values.
2. Sufficient data points on both sides of the threshold, e.g. alpha 1 < 0.5 and alpha 1 > 0.5 for the anaerobic threshold detection.
3. A not too high ramp speed for increasing the intensity. If you ramp up the intensity too quickly there is a delay in your cardiovascular response relative to your power/pace that would lead to overestimated power/pace thresholds.
You also have the option to download the activity data from the activity page as a csv file and manually perform the ramp detection.
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