Training Monotony: Foster Formula vs Bounded Score
Training monotony measures how similar your last seven daily loads are. Compare the Foster formula with Trainingload.ai’s bounded score and training strain.
Training monotony describes how similar the daily loads are across the most recent seven calendar days. Similar days produce a higher value; a clearer mix of high, low, and zero-load days usually produces a lower one.
Trainingload.ai displays a bounded, normalized version—not the classic Foster value. It describes the distribution of load numbers; it does not directly measure fatigue, recovery, overtraining, injury probability, or training quality.
Example values are for interface preview only.
Data and formula
Trainingload.ai includes today and the previous six calendar days. Multiple activities on one day are added together; a day without recorded load contributes zero.
mean_load = sum(loads) / 7
sd_load = sqrt(sum((load − mean_load)²) / 7)
monotony = mean_load / (sd_load + mean_load)If sd_load + mean_load = 0, all seven loads are zero and the product returns monotony = 0.
Training Strain uses the same window:
total_load = sum(loads)
training_strain = total_load × monotony / 0.5Difference from the classic Foster metric
foster_monotony = mean_load / sd_load
foster_strain = total_load × foster_monotonyThe classic value is unbounded and tends toward infinity when every daily load is identical. Trainingload.ai instead uses mean_load / (sd_load + mean_load), which keeps a non-zero week between 0 and 1.
When both mean and standard deviation are positive:
trainingload_monotony = foster_monotony / (1 + foster_monotony)Therefore, a Trainingload.ai value of 0.8 is not comparable to a classic Foster value of 2.0; the two Training Strain formulas also differ.
Reading the value
- Seven zero-load days return 0.
- One loaded day plus six zero days produces about 0.29.
- Seven identical positive-load days produce 1.
- Between those cases, a higher value generally means more similar daily loads.
A low value is not automatically better: one very large load spike plus six rest days is also low-monotony. A high value does not diagnose injury or overtraining; it only says the seven daily load numbers are similar.
The interface bands are <0.6, 0.6–0.8, and >0.8. They are display rules, not validated safety thresholds. The special zero value for an all-rest week does not mean “excellent structure.”
Practical review
Inspect all seven daily loads together with total load. Identify whether the pattern comes from the plan, true rest days, one spike, missing activities, or a threshold/load-source change. Then add sport type, session content, performance, sleep, pain, illness, and subjective fatigue.
References
- Foster (1998): Monitoring training with reference to overtraining syndrome
- Haddad et al. (2017): Review of the session-RPE method
- Jones et al. (2017): Systematic review of training load, fatigue markers, injury, and illness
- Fellrnr: Training Monotony and the bounded modification
FAQ
What is training monotony?
Training monotony is a seven-day similarity score for daily load. Days that look alike raise the value; a mix of hard, easy, and rest days usually lowers it. It describes the numbers, not injury risk.
How is the Foster monotony formula different?
Classic Foster monotony is mean load ÷ standard deviation and has no upper bound. Identical days send it toward infinity. Trainingload.ai uses mean / (sd + mean), so a loaded week stays between 0 and 1. A product value of 0.8 is not a Foster 2.0.
What is training strain?
Training strain multiplies seven-day total load by monotony (in this product, divided by 0.5). High total load plus similar days raises strain. Read it with the seven daily values, not as a standalone red flag.
Is low monotony always better?
No. One huge spike plus six zeros is also low monotony. Use the score to notice a flat week, then check the plan, missing files, and how the sessions felt.
Related pages
Acute:Chronic Workload Ratio (ACWR)
Learn how Trainingload.ai calculates ACWR from same-day Fatigue and Fitness, and why the ratio is a load-review tool rather than an injury prediction.
Aerobic Capacity
Aerobic capacity describes the upper range of oxygen use during hard efforts, commonly represented by VO2 Max and interpreted alongside thresholds, economy, and training load.