PhD, Associate Professor A.M. Trofimov¹
PhD, Associate Professor V.V. Semyannikova^1
1Yelets State Ivan Bunin University, Yelets

Keywords: strength trainings, training workload optimizing, training progress, super-compensation, strength resource, rehabilitative resource, forearm flexor strength.

Background. Strength resource building in muscle fibers is known to be associated with myofibrils growing in volume as manifested by a myofibrillar hypertrophy [1]. It is a common belief nowadays that myofibrils are synthesized in response to metabolites of energy recuperation reactions (possibly creatine) being generated in the sarcoplasm by muscular contractions. The metabolites induce transcription of RNA molecules in the nuclei of muscle cells in the DNA segments that program the myofibrilar protein structures. Myofibrils are actively synthesized for some time after the work is over.

A negative side of the muscular fiber contractions is that the actin and myosin filaments are partially destructed by the acidic sarcoplasm – that is acidified as a result of the energy recuperation chemical reactions initiated by the muscular contractions [3]. The acidification degree is directly dependent on the productivity of oxidative phosphorylation of ADP in mitochondria. It is the destruction of myofibrils that is believed to undermine the strength resource i.e. develop physical fatigue [3].

Activity of the myofibril synthesis upon a training session depends on the amount of work done – i.e. the harder is the work the more active is the synthesis, albeit this correlation is not linear, since the synthesis is known to reach some peak and fall with further work [4]. Upon completion of the rehabilitation period, activity of the myofibril synthesis will fall until fully stops. It should be noted that the more active is the synthesis prior to the rehabilitation period, the longer the synthesis will go – to not only offset large losses of myofibrils in hard training work but also contribute to the super-compensation process. Knowing the startup rate of myofibril synthesis, one can assess the rehabilitative resource accumulated by the working muscular fibers. (Rehabilitative resource may be defined as the amount of protein that may be synthesized after training) [4].

Since the rehabilitative resource growth with training work tends to fall after some peak value, further excessive trainings may reach the point of abrupt regress. Therefore, the training work that generates the rehabilitative resource in the highest excess of the energy costs may be defined as the optimal training work. The higher are the positive and negative deviations from the optimal training work, the lower are the training process benefits. Strength trainings are particularly sensitive to the training work management along the optimal line [4]. The above considerations urged us to undertake an experimental study to find a set of training work optimizing criteria.

Objective of the study was to find an optimal training work management model for the muscular strength resource building trainings.

Methods and structure of the study. We sampled for the training work management model testing expereiment the 1-year male students (n=28 recruited on a voluntary basis) of Yelets State Ivan Bunin University and split them up into 3 groups using the pre-experimental forearm flexor strength rating tests as follows: Group 1: 27.4±4.37kg; Group 2: 27.7±4.34kg; and Group 3: 26.9±4.39kg.

The monthly experiment was timed to the regular Physical Education classes (2 times a week, 8 trainings in total) and designed as follows. The students made a few series of forearm flexing push-offs with shoulders rested on a vertical platform. The forearm flexor strength was tested by a DES-300D deadlift dynamometer prior to and after every training session to rate the forearm flexor fatigue (strength drops). The Group training works were as follow: Group 1 did 3 rounds of 5 reps; Group 2 did 5 rounds of 5 reps; and Group 3 did 7 rounds of 5 reps, with weights in every group rated at 80% of the maximum. The post-experimental tests to rate group forearm flexor strength training progresses were run 3 days upon completion of the last 8^th training session lesson.

Results and discussion. Upon the test data were mathematically processed, we found the group progress averaging 3.45±1.34kg, 5.67±1.51kg and 4.17±1.43kg in Groups 1, 2 and 3, respectively. The Group 2 progress versus Groups 1 and 3 was tested significant by the Student T-criterion; whilst the Group 1 and 3 progresses were found insignificantly different. Therefore, the optimal training work in the experiment was 5 rounds of 5 reps with 80% maximal weight.

The test data were further processed to analyze the post-training forearm flexor fatigue (%) and rate them versus the forearm flexor strength progress data. On the whole, the group forearm flexor fatigue (strength drops) tested to average 5-7%, 6-10% and 8-12% in Group 1, 2 and 3, respectively. We further sampled 10 best individual strength progresses to rate them versus the forearm flexor fatigue data to find the forearm flexor fatigue of 6-7% being correlated with the highest progresses. This finding may mean that the strength training progress depends on the post-training fatigue rather that the training work as such. It is not unusual that the same work may result in different rates of fatigue in different trainees – apparently due to the individual variations in endurance rates of the working muscle groups.

Conclusion. Trainings designed to build up the muscular fibers contraction strength result in growth of the myofibrils volumes with work. The study found the strength training process being the most efficient when the post-training fatigue (strength drop) varies at 6-7%. It is recommended that the strength building training work should be designed and managed on an individualized basis.

References

1. Popov G.I. Biomechanics. Textbook for students of higher educational institutions. M.: Akademiya publ., 2007. 256 p.
2. Smirnov V.M., Dubrovskiy V.I. Physiology of Physical Education and Sports. Textbook for students of higher educational institutions M.: VLADOS-PRESS publ., 2002. 608 p.
3. Kots Ya.M. [ed.] Sport Physiology. Textbook for in-tes physical. culture. M.: Fizkultura i sport publ., 1986. 240 p.
4. Trofimov A.M. From theory of physical activity to sports training methods. Yelets:  Yelets State Ivan Bunin University publ., 2015. 130 p.

Corresponding author: amt59@yandex.ru

Abstract

Objective of the study was to find an optimal training work management model for the muscular strength resource building trainings.

Methods and structure of the study. The optimal training loads were determined during the one-month educational experiment with the involvement of the first-year male students of Yelets State Ivan Bunin University. The sample was made of the students who volunteered to participate in the experiment - a total of 28 subjects, divided into three subgroups based on the rating compiled on the basis of the results of testing of the forearm flexor strength. The rating made it possible to form equivalent groups. The training sessions were conducted twice a week. During the training sessions, the students were asked to perform several series of forearm flexions while setting the shoulders against a vertical platform.

Results and conclusions. The findings confirmed the dependence of the training effect on the amount of training work performed. It was also found that the criterion for assessing the optimal training load is the fatigue rate at the end of work. The optimal fatigue rate was determined. The reason why the same amount of work performed by different people has different training effects is that the muscles being trained have different levels of endurance. In general, the experiment confirmed the importance of implementation of the principle of optimal training load and proved the need for a personalized approach to its definition.

The greatest training effect was detected when the students performed the work resulting in the decreased force of the muscle contraction (fatigue) by 6-7%. The findings indicated the need for a personalized approach to the determination of the training load volumes.