A Muscle Load Feedback Application for Strength Training: A Proof-of-Concept Study

Area of Science:

• Sports Science
• Exercise Physiology
• Human-Computer Interaction

Background:

• Muscle overload injuries are a risk in strength training.
• Personalized feedback on muscle load could help prevent these injuries.
• A novel muscle load feedback application was developed with Gymstory.

Purpose of the Study:

• To evaluate an application providing muscle load feedback for managing muscle load balance and level.
• To assess the impact of the feedback on muscle soreness.
• To understand user experience with the application.

Main Methods:

• Thirty participants were divided into control, partial, and complete feedback groups.
• An automatic exercise tracking system monitored eight workouts.
• Generalized estimation equations and ANOVA analyzed muscle load balance, soreness, and user experience.

Main Results:

• The complete feedback group demonstrated significantly better muscle load balance.
• This group also showed better adherence to suggested exercises and higher user experience scores (Attractiveness, Stimulation, Novelty).
• No significant differences in muscle soreness were observed between groups.

Conclusions:

• Personalized muscle load feedback, including visual maps and exercise suggestions, effectively guides users toward balanced cumulative muscle loads.
• The application shows potential as a training tool to prevent muscle overload in strength training.
• Further research may explore its impact on muscle soreness and long-term injury prevention.