Built on Science
Every algorithm, threshold, and formula in MyTrainerOS is backed by peer-reviewed sports science. Here are the studies behind our features.
Nutrition Intelligence
Adaptive TDEE, macro calculation, and nutritional periodization
MacroFactor Validation Study (2023)
Adaptive TDEE estimation from self-reported intake and body weight
Expenditure algorithm achieves ~135 kcal median error after 4 weeks of data, outperforming standard equations.
Mifflin MD, St Jeor ST, et al. (1990)
A new predictive equation for resting energy expenditure in healthy individuals
Mifflin-St Jeor is the most accurate BMR estimation equation for non-obese populations (±10% error).
Morton RW, Murphy KT, et al. (2018)
A systematic review, meta-analysis and meta-regression of protein supplementation
Protein intake of 1.6 g/kg/day maximizes resistance-training-induced gains in muscle mass and strength.
Hector AJ, Phillips SM (2018)
Protein recommendations for weight loss in elite athletes
During caloric restriction, protein needs increase to 2.3–3.1 g/kg FFM/day to preserve lean mass.
Recovery Intelligence
HRV monitoring, readiness scoring, and recovery protocols
Plews DJ, Laursen PB, et al. (2012)
Training adaptation and heart rate variability in elite endurance athletes
lnRMSSD coefficient of variation >10% indicates maladaptation; daily HRV monitoring guides training load.
Plews DJ, Laursen PB, et al. (2013)
Monitoring training with heart-rate variability: How much compliance is needed for valid assessment?
7-day rolling average of lnRMSSD provides reliable baseline; morning measurements preferred.
Walker M (2017)
Why We Sleep: Unlocking the Power of Sleep and Dreams
Sleep <6 hours reduces testosterone, growth hormone, and athletic recovery. 7–9 hours recommended.
Injury Prevention
ACWR monitoring, volume spike detection, and risk prediction
Leckey S, et al. (2025)
Machine learning approaches for sports injury prediction: A scoping review
XGBoost is the top-performing ML model for injury prediction across multiple studies.
Gabbett TJ (2016)
The training—injury prevention paradox
ACWR sweet spot of 0.8–1.3 minimizes injury risk. Spikes >1.5 increase injury odds by 2–4x.
Foster C (1998)
Monitoring training in athletes with reference to overtraining syndrome
Training monotony (mean/SD of daily load) >2.0 with high strain predicts illness and overtraining.
Program Design
E1RM tracking, progressive overload, and plateau detection
Epley B (1985)
Poundage chart
E1RM = Weight × (1 + Reps/30). Standard formula for estimating 1-rep max from submaximal loads.
Brzycki M (1993)
Strength testing—predicting a one-rep max from reps-to-fatigue
E1RM = Weight × 36/(37 - Reps). Most accurate for 3–10 rep range.
Schoenfeld BJ, et al. (2017)
Dose-response relationship between weekly resistance training volume and muscle hypertrophy
10+ weekly sets per muscle group optimal for hypertrophy. Progressive volume increase of 2–5% per mesocycle recommended.
Behavior Change
Habit formation, adherence, and engagement psychology
Lally P, van Jaarsveld CHM, et al. (2010)
How are habits formed: Modelling habit formation in the real world
Average time to automaticity is 66 days. Missing one day has negligible impact on habit formation.
Dai H, Milkman KL, Riis J (2014)
The Fresh Start Effect: Temporal landmarks motivate aspirational behavior
Temporal landmarks (Mondays, month starts, birthdays) increase goal pursuit — the Fresh Start Effect.
Aldosary M, Alrashdan I (2021)
Predicting gym member churn using machine learning
XGBoost model achieved 92.1% accuracy predicting gym member churn from attendance and engagement features.
Burke LE, Wang J, Sevick MA (2011)
Self-monitoring in weight loss: A systematic review of the literature
Self-monitoring of diet and physical activity is the single strongest predictor of successful weight management.
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