PhD, Associate Professor M.Kh. Khaupshev¹
PhD, Associate Professor V.M. Musakaev²
PhD, Associate Professor E.B. Yakhutlova²
G.A. Panchenko^2
1Berbekov Kabardino-Balkarian State University , Nalchik
²Kokov Kabardino-Balkarian State Agricultural University, Nalchik

Keywords: health, optimal physical activity, healthy diet, computer model.

Background. It is commonly acknowledged by the research community that individual health is largely (50%) determined by a healthy lifestyle including physical activity and diet as its controllable elements, with the individual tolerance to stressors dependent on their quantity and quality. This means that physical activity and diet may be applied to effectively prevent and correct health disorders, fatigues and diseases; and, hence, the individual physical activity and diet may be modeled on an individual basis for the health control purposes [3, 5, 6].

In this context we would define the optimal physical activity as the controlled-intensity physical activity; and healthy diet as the healthy balance of proteins (17%), fats (13%), hydrocarbons (70%), vitamins, minerals and water, with the energy losses being compensated by inflows of hydrocarbons, fats and proteins in the proportions of 55%, 30% and 15%, respectively. A diet may be considered healthy when it is compliant with the commonly approved health concepts, meets the individual physical activity needs, secures good physical development on a gender-specific basis – and sensitive to the body adaptability logics and individual nutritional needs. A healthy physical activity shall meet the individual need for physical activity, with the healthy diet expected to cover the energy costs and rebuild the body, with such physical activity and healthy diet commonly referred to as the optimal or healthy [1-3]. When designing the student physical activity and diets optimizing computer system, we modeled the ideal physical activity and healthy diet parameters to ensure the optimal health standards for a healthy lifestyle.

Objective of the study was to rate benefits of a student physical activity and diet optimizing computer system.

Methods and structure of the study. Sampled for the system testing experiment were the 1-3-year students (n=653, including 239 males and 414 females) from two universities in the Kabardino-Balkarian Republic, qualified for the academic physical education service and elective sports. First, we registered the sample for the digital questionnaire survey; and second, profiled the actual academic diets versus the ideal healthy diet to form a database for the purposes of the study. We used the physical activity and diet optimizing computer system to compute the energy costs and calorific values of the diets to secure the system being reasonably individualized to the actual needs [2, 4].

Results and discussion. Generally, the energy costs may be classified into the basic metabolism, extra metabolism and digestion. Basic metabolism (10% of the total) covers the everyday individual (age-, gender-, weight- and lifestyle-specific) energy costs as described by the relevant formula: see Table 1.

Table 1. Basic metabolism: quiescent-stage energy cost calculations

Extra metabolism (15-30% of the total) covers the specific energy costs and may be calculated by the basic metabolism rate being multiplied by the physical activity ratio (k) with the following five physical activity classes: (1) very low physical activity (k=1.4), with occasional physical activity cycles and mostly intellectual work; (2) low physical activity (k=1.6), with 1-3 physical activity cycles per week or easy physical work; (3) moderate physical activity (k=1.9) with 3-5 physical activity cycles per week, non-heavy work; (4) high physical activity (k=2.2), with 6 physical activity cycles per week, heavy work; and (5) very high physical activity (k=2.5) with 2 physical activity cycles per day or very heavy work [2, 6].

Controlled physical activity may include exercises varied in intensities and volumes, with the computable workloads and energy costs classified by the work types. Our database included the anthropometric data, physical loads with the energy costs, and HR versus the time-specific workloads. These values were input in the system using the following formula [2, 3, 6, 7]: E = 0,014 х BM х t х (0,12 х HR - 7), with E – energy cost, kcal; BM – body mass, kg; t – practice time, min; and HR – heart rate during the practice, beats per min. Our system also includes the R.I. Kupchinov calorific value calculation procedure with 142 physical activities classified into 23 groups on a gender, body mass, work scope and intensity specific basis [1, 5, 7].

Practical application of the physical activity and healthy diet optimizing model is user-friendly. A student will input his/her actual daily physical activity and diet per week – and the system designs the individual best physical activity and healthy diet model compliant to the relevant norms, computes the energy costs and demands, offers an optimal healthy diet on the whole and on a per-meal basis, computes the proteins, fats and hydrocarbons and their proportions. The database and the system algorithm secure the individual physical activity and healthy diet being computed automatically. The computer system application experience has showed benefits of the physical activity and healthy diet design and management tools – particularly for the academic environments and lifestyles [2, 4, 6].

Based on the pre-experimental tests, the physical activity of the sample was found very low, minimal acceptable and optimal in 70.0% (n=457), 19.6% (n=128) and 10.4% (n=68) of the sample, respectively. Upon completion of the yearly physical activity and diet optimizing computer system testing experiment, the proportions of the very low, minimal acceptable and optimal physical activity improved to 29.8% (n=194), 39.5% (n=258) and 30.7% (n=201), respectively: see Table 2.

Table 2. Physical activity of the sample: yearly test cycle

Furthermore, we found the weekly energy inputs in the male subgroup 53.7% (n=128) insufficient; 24.3% (n=58) sufficient; and 22.0% (n=53) excessive; and in the female subgroup 59.2% (n=245) insufficient, 21.8% (n-90) sufficient, and 19.0% (n=79) excessive. However, we found 32.5% (n=213 including 78 males and 135 females) willing to improve their physical activity and diets provided that they have enough time for the lifestyle reprogramming. Our computer system helped the sample improve the daily regimens, with the everyday time losses of 2-2.5 hours turned into 12-15 productive hours per week assigned for the physical activity and healthy diet.

The pre-experimental energy costs of the sample were found 6.9% subnormal (versus norm of 4027.3 kcal) in the male subgroup, with 279.8 kcal per day and 1958.6 kcal per week; and 6.9% subnormal (versus norm of 3126.6 kcal) in the female subgroup, with 216.0 kcal per day and 1512.0 kcal per week. Energy values of the actual diets were estimated at 3747.5 kcal and 2910.6 kcal for the male and female subgroups, i.e. insufficient to cover the energy costs. The actual diets were found dominated by 3-4 unhealthy snacks per day, with the following calorific proportions: 23.4% breakfast; 22.7% lunch (if any); often substituted by an afternoon snack (17.7%); and 36.2% supper; with nobody reporting healthy 4 meals a day. Many students reported offsetting shortage of energy inputs due to the unhealthy diets by the weekend overeating at homes. Unhealthy nutrition like that cannot be tolerated for a long time due to their high risks for health.

The new physical activity and diet optimizing computer system helped the sample replenish the energy inputs as follows: by 438.5 kcal a day to 3069.5 kcal a week in the male subgroup (11.7%); and 298.0 kcal a day to 2086.0 kcal a week (10.6%) in the female subgroup. These adjustments made it possible to bridge the weekly gaps and build up the energy inputs to form a basis for the physical activity improvement initiatives: see Table 2.

Table 3. Daily energy costs and inputs of the sample

Therefore, the energy costs and inputs rating tests showed benefits of the physical activity and healthy diet optimizing computer system service – as verified, among other things, by the actual physical activity of the sample tested to significantly (р<0.05) improve for the study period as follows: the optimal physical activity share was tested to increase by 20.3% (n=133); and the healthy diet abiding share by 18.1% (n=118). As a result, these students moved from low-activity subgroup 1 (k=1.4) with malnutrition to moderate-physical activity subgroup 3 (k=1.9) reporting 3-5 physical activity trainings per week and healthy 4 meals a day [2, 6, 7]. The progress shows that students are generally enthusiastic in keeping their physical activity at optimal levels with the healthy nutrition to be fit for the academic studies.

Conclusion. The study data and analyses showed benefits of the students’ physical activity and diets optimizing computer system as verified by the new quality of the physical activity and diets in the test sample. The new system effectively motivated the students to understand and apply the health improvement mechanisms that were unclear to them before, efficiently manage the metabolic processes and energy flows in their bodies, with timely responses to deviations from the health standards. It generally equipped them with the highly useful and customizable toolkit for the own physical activity design and management on a new efficient basis to secure the individual optimal physical activity and healthy diet.

References

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3. Bulich E.G., Muravov I.V. Human Health: Biological Basis of Vital Activity and Physical Activity in its Stimulation. Kiev: Olimpiyskaya lit. publ., 2003. 424 p.
4. Dyuk V.A., Emmanuel V.L. Information Technologies in Biomedical Research. St. Petersburg: Piter publ., 2003. 528 p.
5. Kupchinov R.I. Physical education. Study guide. Minsk: TetraSistems publ., 2006. 352 p.
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Corresponding author: haupshev-m@mail.ru

Abstract

It is commonly acknowledged by the research community that individual health is largely (50%) determined by a healthy lifestyle including physical activity and diet as its controllable elements, with the individual tolerance to stressors dependent on their quantity and quality. This means that physical activity and diet may be applied to effectively prevent and correct health disorders, fatigues and diseases; and, hence, the individual physical activity and diet may be modeled on an individual basis for health control purposes. Objective of the study in this context was to rate benefits of a student physical activity and diets optimizing computer system. Sampled for the system testing experiment were the 1-3-year students (n=653, including 239 males and 414 females) from two republican universities. The pre-experimental tests found the physical activity being minimal, optimal and poor in 19.6% (n=128), 10.4% (n=68) and 70.0 % (n=457) of the sample, respectively; and the diet deficiency was estimated at 279.8 and 216.1 kcal per day for the male and female groups, respectively. The post-experimental tests found the minimal, optimal and poor physical activity in the sample changing to 39.5% (n=258), 30.7% (n=201) and 29.8% (n=194), respectively; and changes to the diets were found to increase the calorific values by 438.5 kcal (11.7%) and 298.0 kcal (10.6%) in the male and female groups, respectively, with the energy deficit fully covered and the surplus energy usable for an extra physical activity. The study data and analyses showed benefits of the students’ physical activity and diets optimizing computer system as verified by the new quality of the physical activity and diets in the test sample.