Student's self-guided physical training model


Dr.Hab, Professor R.M. Kadyrov¹
Ph.D., Professor A.V. Karavan¹
Ph.D., Professor V.D. Get’man¹
¹ Saint-Petersburg State University of Architecture and Civil Engineering, St. Petersburg

Keywords: self-guided physical training, model, energy expenditures.

Introduction. When instructing students on how to manage their own physical activity, it is very important, that the lessons are formalized, so that the task-oriented approach could be fully applied, which is possible if the content of education is designed as a sequential algorithm and a student gets a clear quantitative outcome upon fulfilling it.

The purpose of the study was to design a students' physical self-training self-management model.

Methods. The research methods used were as follows: detection of energy inputs of target motor actions; evaluation of implicit exercise performance indicators; calculations of regression equations, mean value, mean-square deviation, and mean value error.

Results and discussion. In the first experiment series we tried to teach the test students how to predict training loads relying on their current physical working capacity level. 15 students of Saint-Petersburg State University of Architecture and Civil Engineering took part in the experiment. Preliminary relevance analysis of implicit exercise performance indicators showed that it is reasonable to use response rate and tremor in prediction of training loads. Amount of training load (kcal) can be calculated using the following equation:

Q = 120 + 0,11 х – 1,23 y

Q – total energy input value; х – response rate (msec); y – tremor (number of taps).

It should be noted, that concrete values of implicit indicators are conditioned by the used research equipment. Therefore, limitations in results of the above mentioned equation depend on concrete equipment and methods of experimental data measurement. Note that this equation can be used to calculate general energy input during a specific lesson, consisting of conditioning exercises, weight lifting and gymnastic exercises.

Particularly, we calculated energy input in the following exercises:

– high knees running in place – 160-180 steps per minute;

– alternate trunk bending sideways with 20 kg barbell on shoulders;

– backbends sitting on a bench – 30 times per minute;

– deep lunge scissor jumps, one hand leaning on the wall, another on hips – 40-50 jumps per minute;

– squats with hands clasped behind the head – 24-26 squats per minute;

– alternate sideways leg swings hanging on wall bars, back to wall bars with head turning in the direction of leg swings – 32-34 swings per minute.

Data registered during the experiment are given in Table 1.

Table 1. Energy inputs of physical exercises







(std. u.)


(std. u.)








































Note: RC – respiratory coefficient; KE – caloric equivalent.

Analysis of findings shows that maximum energy inputs are typical for weight lifting exercises. In contrast, exercises with gymnastic bench need minimum energy inputs. Note that suggested battery of exercises is quite challenging.

Dependence between energy inputs and duration of run at different speeds can be expressed by the following equations:

y1 = 0,151 + 5 × 10-3 х

y2 = 0,139 х0,08

y3 = 0,169 х0,06

y4 = 0,170 х0,12

y5 = 0,184 х0,16

у1 ¸ у5 – energy inputs (kcal. kg-1×min-1), for run at 8, 11, 13, 15 and 17 km/h respectively; х – 1¸5 min.

It should be emphasized that double increase of the coefficient at x at speeds 15 and 17 km/h testifies to considerable energy capacity of the exercises [2].

Presented equations as well as quantitative values of exercises' energy inputs served as the theoretical and experimental basis to form training load prediction skills. Content of instructions is geared to form the skills of analyzing and evaluating the results of a formalized training session, as well as criticizing and adjusting the process. An appropriate task to form the following skills among test students given as a result of training was: to be able to compare the results of training session with its goals, to make a conclusion independently, to assign particular aims and tasks considering changes in physical fitness, to criticize the results and adjust conditions of training session.

Thanks to the possibility of calculating quantitative parameters (energy inputs) of a training session on a theoretical level students learn to predict their own motor achievements and make a plan of improvement of their physical fitness. To predict the optimum fitness of trainees, indicators of tremor and sum of recovery pulse rate were used. Linear regression equations are presented below:

4 < y > 8

y = 8 – 1,3 (х – 6),

 6 < x < 9

у – self-rated physical fitness level on a 9-point scale; х – tremor.

4 < y > 9

y = 8 – 0,005 (х – 804)

800 < x < 1600

у – self-rated physical fitness level on a 9-point scale; х – recovery pulse rate up to 80 beats per minute [1].

These equations are used to correlate a subjective rating of physical fitness with implicit indicators of physical working capacity. It should be noted that, self-rating of one’s physical fitness, changing in different training periods within the scale, can maintain the same quantitative values. Therefore, concrete data from the equations predict appropriate physical fitness level in a concrete period of a training session only.

Generally, a variety of exercises used in each training session, as well as different methods of measuring the extent and intensity of workloads assume using an energy inputs value as an overall index.

Students can plan the physical training process relying on calculated equations. Properly instructed students design different training loads, thereby enhancing their consciousness when planning training sessions on their own.

Conclusions. Analysis of the experience of organization of self-governed physical training revealed the unimplemented health-improving aspect of training sessions and poor organization of goals, techniques and methods of training ensuring a healthy lifestyle. We can have a general idea of the technique of planning of the training session by simply summing energy expenditures. In our research we use an individual creative approach to ensure conditions for self-actualization of a trainee, to detect and develop his/her creative abilities and individual style of health and fitness activity.


  1. Ershov, S.A. Fizicheskaya trenirovka prepodavateley voenno-uchebnykh zavedeniy (Physical training of teachers in military educational institutions) / S.A. Ershov. – Yaroslavl: 2000. – 137 P.
  2. Zubakin, V.P. Samoupravlenie fizicheskoy trenirovkoy voennosluzhashchikh (Self-governed physical training of servicemen) / V.P. Zubakin. – St. Petersburg: 2010. – 224 P.

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