Our website is a unique platform where students can share their papers in a matter of giving an example of the work to be done. If you find papers
matching your topic, you may use them only as an example of work. This is 100% legal. You may not submit downloaded papers as your own, that is cheating. Also you
should remember, that this work was alredy submitted once by a student who originally wrote it.
The author of "The Accuracy of Predicting 1RM from nRM using the Brzycki Equation" paper argues that the choice of a predictive equation should be based on valid evidence as their accuracy differs according to the group of muscles whose 1RM is being tested…
Download full paperFile format: .doc, available for editing
Extract of sample "The Accuracy of Predicting 1RM from nRM using the Brzycki Equation"
THE ACCURACY OF PREDICTING 1RM FROM NRM USING THE BRZYCKI EQUATION Introduction There is an increasing demand for resistance training (weight lifting) that has necessitated the need for well-established parameters for prescribing these exercises. The American College of Sports Medicine (ACSM) recommends that resistance trainings be included in the training programs for adults and elderly (ACSM, 2008). Although ACSM has strived to substantiate on these variables, there is still gaps in some evidences while some are contradictory. While the test is being carried out, only one factor such as endurance should be considered at time, and the any method used to do the test should not require any technical competence on the part of the subject. The procedure should be standardized about organization, administration, and environmental conditions. Testing is of crucial importance to trainers as they predict the future performance, indicate weakness and measure improvement of the subject.
Submaximal tests are tests where the subject works below his or her maximum effort and extrapolating determines the maximum capacity. Examples include the Queens College Step test. The disadvantage is that due to the extrapolation made to the unknown peak coupled with experience of massive of inaccuracies (Mackenzie, 1997)
One repetition maximum (1RM) is operationally the heaviest load that can be moved over a specified range of motion, once and with the good form (reference). The test is used to measure the maximum strength of a group of muscles. nRM is the amount that one can lift for n consecutive repetitions. For example, a 10RM is the largest or heaviest weight that one can lift for ten consecutive repetitions (Braith et al., 1993).
Predictive Equations
The standard measure of assessing muscle strength in trainings is the measurement of repetition maximum. nRM is the simplest test method for subjects who lift the weight against gravity. Massive skill is required to measure the repetition maximum without inflicting too much fatigue on the subject whose repetition maximum is being evaluated. Therefore, it requires excellent practice in order to master the skills. Reduction of the chances of occurrence of risk of injury to the subject while performing the 1RM test entail undertaking track changes in muscle strength over a specified period of time. Moreover, it is exclude identification of 1RM of the muscle strength. Several equations are available for conversion, for example, a 10RM to a 1RM although the variable factor between them is the degree of error. Other nRMs are calculated as a percentage of 1RM. The equations may not be entirely accurate, but they are accepted methods of calculating the required RM.
One such equation is the Bricks equation (Bryzicki, 1998, pp.88 – 91). The equation is as follows:
Where x is the number of repetitions performed.
The equation above depicts a relatively linear relationship between the repetition maximal score and the percentage of 1 – RM. Generally, the predicted force of 1 – RM is founded in an approximately 2.5% of the 1 – RM for every increase in the number of maximal repetitions (Heyward, 1991).
The formula has certain limitations, and the most conspicuous is that the method is only valid for predicting a 1RM where the number of repetitions is less than 10. Moreover, prediction accuracy reduces with escalation of the value from 10 upwards.
The validity of the work of Berger (1970) and O’Connor et al. (1989) where non-exercise particular 1RM prediction methods for biceps curl and bench press were compared to the determined 1RM was questioned by Hutchins and Gearhart (2010),. There were twenty-seven male volunteers of age 23.6±3.5 years and who were regular participants in weight lifting. The members participated in resistance trainings on an average of 3.56 ± 1.11 days per week, and the 1RM was determined on both the biceps curl and bench press exercises on the first day of testing. The second testing day involved an experimental trial with 85% of the previously measured 1RM. A weight that was equivalent to 85± 1.3% of the already established 1RM was loaded onto the bar, and the participant instructed to complete one set of repetitions to concentric failure. Equal number of repetitions completed was recorded and used in the Berger 1RM (1970) and O’Conner et al. (1989) equation. The results from O’Conner et al. equation was lower that from the Berger equation. The total equation led to the smallest total error of estimation for both the bench press and biceps curl exercises. The error for Berger equation was 7.2% while that from O’Conner et al. was 5.7%. Both equations underestimate the obtained 1RM (Hutchins &Gearhart, 2010)
Knutzen et al. (1999) studied the validity of six prediction equations that utilizes the repetition-to-fatigue regression formulae. Fifty-one voluntary participants consisting of 21 male and 30 females were put into eight weeks high resistance training with 80% of their 1RM. The participants then took part in 2 experimental sessions that were 5 to 8 days apart. During the first two days, the subjects’ actual and predicted 1RM were taken for underlying eleven machine lifts namely triceps press, biceps curl, bench press, lateral row, supine leg press, flexion, extension, abduction, and adduction coupled with plantar flexion, and dorsiflexion. A three trial protocol was used to reach the maximum weight for each lift.
The subjects were then subjected to the weight they could lift 7-10 times to complete the testing sequence and obtain the predicted 1RM. The data was then entered into the following six equations: Brzycki (1993), Epley (1985), Lander (1985), Mayhew et al. (1995), O’Connor et. al (1989) and Wathan (1994). Correlation analysis between the actual and predicted 1RM depicted a moderate to the high correlation between all the exercises. O’Conner et al. equation consistently predicted the lowest average 1RM amongst all the equations considered in all the exercise (Knutzen, Brilla, & Caine, 1999). It means that the equation is the least applicable when it comes to the prediction of 1RM from a submaximal test.
Although the 1RM protocol has been embraced by the large and continue to be applied, critical questions have been asked concerning its safety. Moreover, people that are not familiar with maximum load bearing activities may incur a lot of damages to their bones, ligaments, and muscles (Braith et al., 1993). The direct evaluation of 1 RM has also been accused of being impractical mostly when dealing with a large number of subjects. This due to the amount of time needed to evaluate the whole group (Brzycki, 1993, pp.88-90; Nascimento et al., 2007, pp.40-42). The prediction models developed have equations that uses submaximal load so as to reduce he risk of maximum strength assessment and their limitations. There was no limitation of soreness of the muscles after indirect prediction of 4RM to 6RM and 7RM to 10RM strength evaluations (Dohoney et al., 2002). According to Chapman et al (1998), the submaximal 1-RM prediction of ninety-eight participants is accomplished within a mean period of two and a half hours as compared to eighteen hours that would have been used to directly determine 1RM.
The aim of this experiment is to examine the accuracy of predicting 1RM from nRM using the Bryzicki equation and relate the findings to clinical practice. Our hypothesis will be that there is no significant difference between the actual and predicted 1RM from nRM of the elbow flexor muscles using Bryzicki equation.
Method
Instrumentation
Blood pressure monitors.
Westminster pulley.
Participants
Eight university students voluntarily took part in the study. The participants were a convenience sample recruited from the MSc Exercise in Rehabilitation module at the School of Health Professions. Prior to conducting the experiment, the participants were informed about the underlying objectives of the study and an informed consent document was signed by them. Moreover, the study was approved by The School of Health Professionals Ethics Panel at the University of Brighton.
Research Design
This experiment was conducted as a within-subject design, in which participants acted as their control. Participants were divided into two groups randomly, where the first group went through testing the 1RM first followed by the nRM and the second group was assigned to perform the testing in the opposite order.
Procedures
In order to test the values of 1RM and nRM for the elbow flexors, the participants were requested to perform a biceps curl using the Westminster pulley system. Before testing, every participant was asked to sit in a chair facing the Westminster pulley while having their dominant elbow rested on the table. To ensure comfort, a soft padding was placed underneath each participant’s elbow. Following that, participants were instructed to hold the handle of the pulley system with no weights added, and flex their elbows to check the available range of motion in order to determine the mid-point of the range. This was done to ensure that when the participant held the handle of the Westminster pulley, it created a 90 ͦ angle with the participants arm. The rope of the pulley was then tightened up so that there was no slack. Each participant was instructed to perform 10 reps of warm-ups using light weights (1kg for males and 0.5 kg for females).
For the 1RM testing session, each participant was asked to attempt a single repetition with a load that represents 90% of what was believed to be their maximum capacity. If an attempt was successful, the amount of weight was increased in correspondence with the participant’s ability to lift. Whilst, if an attempt was unsuccessful, the amount of weight was decreased from the pulley. The participant was given a rest period of 1 minute between each attempt to avoid muscle fatigue. 1RM testing continued until the participant was not able to complete more than a single repetition through the tested ROM. The heaviest load lifted by the participant using proper technique was considered and recorded as the member’s 1RM. The maximum number of lifts in 1RM testing were limited to a maximum of 20 lifts per person.
As for the nRM testing session, each participant was assigned an initial weight that represented 50-75% of their maximum capacity, and were asked to lift that weight until the onset of momentary muscular fatigue. The number of repetition the participants were capable of performing represented the nRM. Furthermore, the equation used to predict the value of 1RM using nRM was developed by Brzycki (1993), which has been mathematically expressed as: [Predicted 1RM = Weight lifted/1.0278 – 0.0278x] where x = the number of reps performed. It is worth noting that 1RM and nRM testing for each participant were separated by one hour to allow for adequate muscle recovery. In addition, to ensure standardization of the procedure, all participants were given the same instructions (Appendix 3).
Data Analysis
All the variables were presented as a mean and standard deviations (mean ± SD). The difference between the actual and predicted 1RM was tested using the paired t test with the statistical significance at p≤0.05. The differences in the values of achieved 1RM and predicted 1RM between males and females were analyzed using an independent t-test. The difference in the data of the actual and predicted 1RM between the trained and untrained participants was also evaluated using an independent t test. All the data was processed in Microsoft Excel and the descriptive statistics expressed as mean and standard deviation.
Results
Table 1 represents the descriptive characterics of the sample (n=8). The mean age of the sample was 31.88 ± 6.38 years. The sample comprised of 4 males and 4 females. The previous training of the sample was recorded and comprised of 3 trained and 5 untrained. 7 of the subjects used the right hand as the dorminant hand while 1 used the left.
Table 1: Descriptive characteristics
The test for normal distribution was tested using Kolmogorov-Simonov test. The test depict that the data was normally distributed as shown in Table 2 below.
Table 2: Normality test for the participants’ data. VAR00001 represents the actual 1RM while VAR00002 represents predicted 1RM.
Figure 1 below represents the actual and predicted 1RM data for each subject. The mean value of the actual 1RM was 10.72 ±4.59 and that of the predicted 1RM was 9.65 ± 4.59. Comparison of the means of the actual and predicted 1RM values using the paired t-test indicated that there was a sifnificant difference between the two means at p
Read
More
Share:
CHECK THESE SAMPLES OF The Accuracy of Predicting 1RM from nRM using the Brzycki Equation
the accuracy of prediction was judged by using a set of goniometer readings which provides the changes in the angles of the upper limb.... The purpose of this study is to understand the feasibility of predicting arm movement trajectories based on features that are extracted from EMG signals, as a result of muscular action, and accelerometer input.... using accelerometer and EMG signals to estimate arm motion
... The major advantage of using TDANN on continuous data is its ability to adapt the network's weights and activation function online by use of back propagation error method....
Ohlson (1980) also developed a bankruptcy prediction model with logit analysis using a number of bankruptcy firms that were traded on NYSE and AMSE during the 1970s.... Begley Joy et al (1997) criticized the estimation models of Altman (1968) and Ohlson (1980) were not performed well by using 1980's data.... Professor Edward Altman invented a model called Z-Scores by applying the multivariate formula to forecast bankruptcy probabilities of the firms over 30 years from 1965-1999....
Lowering costs to acquire and service customers by using technology to automate, manage and analyze processes and data [Lang et.... Supplier Relationship Management (SRM) and Customer Relationship Management (CRM) are two solutions that talk about the same issues but from an opposite perspective, buyer and seller.... Increasing customer satisfaction and retention by integrating information from various channels.... The objectives of SRM strategy must consider a company's specific situation and its needs and expectations from supplier....
The author of the "Predictions of Corporate Failures" paper discusses different models for preventing corporate failures and the study would assess the strengths and weaknesses of those models.... Financial stability is very important for any corporate house.... ... ... ... Any business is very venerable....
he duration in which regression data points were taken determines the accuracy of the regression results.... It is undeniable that linear regression modelling has become one of the most versatile and useful methods of predicting future outcomes based on past events.... using regression analysis for predicting quarterly purchases of building materials may not yield accurate results because of many factors such as the rise of unpredictable situations, the duration in which data points are taken, and the number of variables used for regression analysis....
The predictors serve the purpose of predicting whether the branch is always taken or not taken.... he necessity for branch prediction develops from the utilization of the pipelining technique in modern microarchitectures.... t is important for one to distinguish Dynamic Branch Prediction, from Static Branch Prediction.... Branch prediction is a technique utilized by instruction pre-fetch processors in predicting whether a conditional branch is taken or not taken....
When the money is borrowed from a lender,.... When the money is borrowed from a lender, the lender actually faces risk and associated opportunity cost.... To make up for this opportunity cost he charges interest from the borrower.... The report below discusses two types of mortgages that differ from each other in terms of interest rates....
IntroductionThe art of predicting weather is done by utilizing mathematical models either in the atmosphere and oceans and thus making it possible to predict whether basing on the prevailing weather at a particular moment.... Random Forest was the second Predictive model and was fitted using Rattle for the Sydney New South Wales weather dataset.... This assignment involved the development of a predictive model by making use of data from Canberra weather measurements in predicting the probability of whether it would rain tomorrow....
11 Pages(2750 words)Statistics Project
sponsored ads
Save Your Time for More Important Things
Let us write or edit the lab report on your topic
"The Accuracy of Predicting 1RM from nRM using the Brzycki Equation"
with a personal 20% discount.