• Man performing a back squatIntroduction
    Progressive overload is commonly understood to be one of the hallmark requirements for resistance training program design (11). This overload is necessary for muscle and other biomaterial adaptation (11). When self-selecting training loads based on personal choice, some exercisers have been shown to train at only 42–57% of the maximal load they can use for the exercises, this maximal load has been defined as the 1 repetition maximum (1RM) (12). 
    However, training with a minimum of 60–65% of the 1RM is required for strength adaptations (11), and strength is typically trained at loads greater than 85% of the 1RM (1). Thus, many exercisers may be selecting and training with insufficient loads. Exercise load is one of the most important program design variables and good resistance training program design requires a method for load prescription.

    How are Training Loads Prescribed?
    Strength and conditioning coaches should prescribe the training loads. Training loads are often prescribed based on a variety of strategies such as trial and error, subjective assessment, percentage of body weight, and 1RM or multiple RM testing (4,5,6,7,13). While training loads are sometimes determined from testing data, it is impractical and uncommon for the strength and conditioning specialist to test numerous auxiliary exercises (4,5,6,7,13).  
    This raises the question about whether loads can be prescribed using the statistical procedure called regression analysis and prediction equations, without having to test numerous auxiliary exercises or rely on trial and error.  

    In other words, can some multi-joint exercises be tested and used to predict the training loads for a variety of other exercises?    

    Prediction equations have been recommended as a method for load prescription (1) and previous studies have used prediction equations to predict variables such as sprinting times from back squat performance (2) and free weight exercise loads from machine-based resistance training exercise loads (3,14,15). Two recent studies sought to determine if the back squat could be used to predict a variety of other lower body resistance exercise loads (8,10).

    Using Prediction Equations to Prescribe Training Loads
    Studies incorporating NCAA Division I and III athletes revealed that back squat test data can be used to accurately predict the exercise loads for many different lower body resistance training exercises (8,10). One study showed that the back squat could be used to predict training loads for hamstring-based resistance training exercises such as the leg curl, stiff- leg deadlift, single-leg stiff-leg deadlift, and good morning (10) while another study demonstrated that the back squat could be used to predict training loads for resistance training exercises characterized by knee extension, such as the deadlift, lunge, step-up, and leg extension exercise (8).  
    It should be noted that the deadlift can also be thought of as a hamstring-based exercise since it has been found to recruit hamstring muscles as well as any closed kinetic chain exercise previously studied (9). Based on the results of these studies (8,10), the current article provides the prediction equations which can be used via manual calculation or put into a spreadsheet program for convenient determination of lower body resistance training exercise loads from back squat test data. These studies used 6RM loads with all of the exercises. Since some exercises were single-joint, assistance exercises, that precluded the use of 1RM testing for all of the exercises.  
    Recorded 1RM values from the squat can be used to predict 1RM values for the other exercises. The conversion charts can then be used to convert the 1RM values to multiple RM values for each exercise based on the desired repetition scheme used in the program. For example, once estimated 1RM load values are obtained, these load values can be determined for sets other than 1 repetition such as sets of 10 repetitions, using the conversion charts.  
    Table 1 shows all of the prediction equations developed from the two aforementioned studies (8,10). Table 2 shows an example of a 200 lbs (90.90 kg) back squat 6RM load and the resultant 6RM training loads for a variety of lower body exercises based on the prediction equations. 
    Back Squat Testing Table 1
    Table 1. Prediction Equations used to Predict a Variety of Exercise Loads from 6RM Squat Testing Data  
    Back Squat Testing Table 2
    Table 2. Sample Results for Predicting 6RM Training Loads from 200lbs (6RM) Squat Testing Loads

    Other Considerations
    Results of these studies are the most applicable to exercisers who have a similar training age and training status to the NCAA Division I and III athletes who participate in strength and power sports. Previous studies have recommended evaluating differences between genders with respect to strength prediction, suggesting that anatomical differences may affect performance (15).  
    The equations provided above serve as a guideline but may need to be modified slightly for men and women if practitioners note the existence of gender-based differences in the application of these equations.
    Multi-joint exercise testing results from exercises, such as the back squat, can be used to create prediction equations for single joint assistance exercise load prescription. The statistical analysis from these studies demonstrates that the back squat is a good predictor of hamstring and knee extension strength-based exercise loads.  
    Since optimal exercise load is critical for adaptation, strength and conditioning professionals need to prescribe this important variable. Prediction equations provide a practical method for doing so. 
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