ABSTRACT
Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into 'control', 'partial feedback', and 'complete feedback' groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (ß = -18.9; 95% CI [-29.3, -8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload.
ABSTRACT
Although general information is available, specifically detailed information on gym-based fitness-related injuries in the general recreational fitness population is lacking. The aim of our study was to obtain more insight into injuries occurring as a result of gym-based fitness activities. A descriptive online epidemiological study was conducted in November 2020. The survey was distributed by a market research agency to members of their research panel. A total of 494 Dutch fitness participants aged ≥ 18 years (mean 38.9; 59% male) who had sustained a fitness-related injury in the preceding 12 months were included in the study. Most injuries occurred during strength training, individual cardio exercise, yoga/Pilates, cardio exercise in group lessons, and CrossFit. The shoulder, leg, and knee were the most common injured body parts; 73.1% of the injuries occurred during unsupervised gym-based fitness activities, and 46.2% of the injuries occurred during one specific exercise or when using a specific fitness device: running (e.g., on the treadmill) (22.8%); bench press (11.8%); or squats (9.6%). Overuse or overload (n = 119), missteps and sprains (n = 48), or an incorrect posture or movement (n = 43) were most often mentioned as causes of injury. Conclusions: Most self-reported gym-based fitness-related injuries occur during strength training and individual cardio exercise. Special attention should be given to the shoulder during strength training and to the lower extremities during cardio exercise. Injury prevention interventions should be able to be carried out without supervision.
ABSTRACT
Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restrict the athlete's natural movement due to equipment attachment. Therefore, the feasibility and validity of a more applicable method, requiring only a single standard camera for the recordings, combined with a deep-learning model and musculoskeletal model is evaluated in the present study during upper-body strength exercises performed by five athletes. Comparison of muscle forces obtained by the single camera driven model against those obtained from a state-of-the art marker-based driven musculoskeletal model revealed strong to excellent correlations and reasonable RMSD's of 0.4-2.1% of the maximum force (Fmax) for prime movers, and weak to strong correlations with RMSD's of 0.4-0.7% Fmax for stabilizing and secondary muscles. In conclusion, a single camera deep-learning driven model is a feasible method for muscle force analysis in a strength training environment, and first validity results show reasonable accuracies, especially for prime mover muscle forces. However, it is evident that future research should investigate this method for a larger sample size and for multiple exercises.
