Load Factor
Recent load with longer-term perspective
CROSSNote uses Load Factor to understand whether your recent physiological load is low, balanced, elevated or unusually high compared with your own recent history.
Instead of reacting only to a single workout or a single day, CROSSNote compares short-term accumulated physiological load with a broader reference window.
In CROSSNote, Load Factor compares your recent 7-day training load with a 40-day chronic load baseline, using EPOC as the underlying measure of physiological training load.
The two time scales behave differently. Recent load responds relatively quickly to new training, while the longer-term baseline changes more gradually as your training pattern evolves.
This gives more perspective than reacting to one isolated workout or spike.
Why the 7 vs 40 day comparison matters
Training load can fluctuate considerably from day to day.
A single intense workout may create a visible spike, but that does not necessarily mean your overall load is excessive. In the same way, several moderate efforts close together may create meaningful accumulated strain even if none of them looks extreme on its own.
By comparing recent load with your longer-term pattern, CROSSNote can better estimate whether your current load is:
below your usual rhythm,
aligned with your normal range,
somewhat elevated,
or significantly above what your body has recently been adapting to.
Importantly, the longer-term reference is not a fixed personal baseline. It evolves over time as your training history changes.
This means that the same absolute amount of training can represent a different Load Factor at different moments, depending on what your body has recently been accustomed to handling.
Reducing noise and saw-tooth effects
Physiological training data can be noisy, and training itself rarely follows a perfectly regular pattern.
CROSSNote therefore does not simply divide the raw load of the last 7 days by the raw load of the previous 40 days.
Daily EPOC values are first protected against unusually large isolated spikes, reducing the influence of extreme outliers. CROSSNote then applies exponential weighting over time.
The recent 7-day load responds relatively quickly to new training, while the 40-day chronic reference changes more slowly. This creates a more stable comparison without removing the effect of genuinely demanding training.
The longer-term reference also considers recent load so that the baseline does not become unrealistically low when training patterns change quickly.
The goal is not to hide real load.
The goal is to avoid overreacting to unstable short-term fluctuations, so Load Factor becomes more useful as a trend signal.
It is not only asking:
What happened yesterday?
It is asking:
How does your recent load compare with what your body has been handling lately?
Looking at Load Factor over time
Load Factor can also be viewed as a historical trend over 28, 90 or 180 days.
These periods are viewing windows, not calculation windows. Selecting a longer period does not change the Load Factor model itself. It simply allows you to observe how the same measure has evolved over a longer period of time.
Every point on the chart is calculated independently for that day. CROSSNote always compares the recent 7-day training load with the 40-day chronic load baseline that existed at that point in time.
This distinction is important.
A Load Factor of 150% three months ago does not compare your training at that time with your baseline today. It means that, on that specific day, your recent 7-day load was elevated relative to the 40-day training history leading up to that day.
The different views therefore answer different questions:
28 days provides a detailed view of recent changes and short-term training patterns.
90 days makes broader training and recovery cycles easier to identify.
180 days provides a longer-term view of how your training load and your capacity to sustain it have evolved over time.
Because the underlying 7 vs 40-day calculation remains identical for every point, these different time ranges can be compared without changing the mathematical meaning of Load Factor.
The longer views are therefore not calculating a “90-day” or “180-day Load Factor.” They are showing 90 or 180 days of historical Load Factor, with each point evaluated against the physiological context that existed at that moment.
How Load Factor affects interpretation
When Load Factor is balanced, CROSSNote may interpret your current state as more stable and aligned with your recent rhythm.
When Load Factor is elevated, CROSSNote becomes more conservative, because your system may still be carrying physiological cost from recent effort.
This can affect how your daily capacity is interpreted, especially after intense training blocks, repeated workouts, poor sleep or periods of accumulated stress.
Load Factor is therefore not intended to describe recovery on its own. It provides training-load context that can be considered alongside other signals such as sleep, HRV, overnight heart rate and recent recovery.
How to read Load Factor
Context, not judgment
Load Factor is not simply “good” or “bad.”
It shows how your recent physiological load compares with what your body has been handling lately.
This is why CROSSNote uses different visual ranges:
Below 80% may suggest a lighter-than-usual load. This can reflect recovery, tapering, reduced demand, travel, illness, or simply a period with less training stimulus.
80–130% is generally the balanced range, where recent load is broadly aligned with your current training rhythm.
Above 130% suggests elevated load. Your body may be carrying more accumulated strain than usual, so recovery needs may be higher.
Above 180% suggests very high load or possible overload risk, especially if combined with poor sleep, low HRV, elevated overnight heart rate, or repeated intense sessions.
The colors in CROSSNote are designed to make these ranges easier to read at a glance.
They do not judge the load as good or bad. They show whether your current load is lower than usual, balanced, elevated or unusually high compared with your own evolving training pattern.