Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation

Area of Science:

• Exercise Physiology
• Nutritional Science
• Sports Medicine

Background:

• Limited research exists on purposeful overfeeding in resistance-trained adults.
• Understanding predictors of adaptation is crucial for optimizing training and nutrition strategies.

Purpose of the Study:

• To investigate potential predictors of resistance training (RT) adaptations during a supervised overfeeding protocol.
• To examine changes in body composition, metabolism, and muscular performance.

Main Methods:

• 21 resistance-trained males completed 6 weeks of supervised RT and high-calorie supplementation aiming for ≥0.45 kg/week body mass gain.
• Body composition (4-component model, ultrasonography), resting metabolism, and muscular performance were assessed.
• Physical activity and nutrient intake were monitored; Bayesian regression analyzed predictors of adaptation.

Main Results:

• Significant increases in fat-free mass (4.8%), muscle thickness (elbow flexors: 4.5%, knee extensors: 7.4%), and muscular performance were observed.
• Predictive models showed limited accuracy (Bayes R² 0.18–0.40), indicating poor predictive performance for adaptations.
• Average body mass gain of ~0.55%/week corresponded to 100% fat-free mass gain, but with considerable individual variability.

Conclusions:

• Purposeful overfeeding combined with RT can enhance muscle mass and performance in trained individuals.
• Predicting individual adaptations remains challenging, despite a positive trend with body mass gain rate.
• Further research is needed to elucidate the complex interplay between overfeeding, RT, and adaptation predictors.